diff --git a/.gitattributes b/.gitattributes
index b534f20ca284b79273f54f7c8076e015e2b6bf5f..61523eee6df228ee150a4996691ea28105783d0c 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -10759,3 +10759,70 @@ GEM-3D-ct-text4-newdata-newvae-retrain/logs/full_ct_2d_with_body_mask/inference/
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GEM-3D-ct-text4-newdata-newvae-retrain/misc/overview.jpg filter=lfs diff=lfs merge=lfs -text
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+GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/inference/valid_5_a_2_sample_7.png filter=lfs diff=lfs merge=lfs -text
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+GEM-3D-ct-text4-newdata-newvae-retrain-disease/misc/overview.jpg filter=lfs diff=lfs merge=lfs -text
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/.gitignore b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..4d0b9aa67d09e8f2ba6e64e72a19c78874cd4550
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/.gitignore
@@ -0,0 +1,8 @@
+*.pyc
+*.png
+*.ckpt
+*.zip
+
+logs/
+data/
+infer_model/
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LICENSE b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LICENSE
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/__init__.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..83f733a0e56b0e547b7d6e004a2b3bf6eedeced6
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/__init__.py
@@ -0,0 +1,4 @@
+
+from .models.autoencoder import LeanVAE
+from .models.autoencoder_pl import AutoEncoderEngine
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/__pycache__/__init__.cpython-310.pyc b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/__pycache__/__init__.cpython-310.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..4f66e505a19a6f4e632287ad9f5f2ecb146a5329
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index 0000000000000000000000000000000000000000..60d729867d017cb76cfab4df08cbb5a36842b357
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/__pycache__/__init__.cpython-39.pyc b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/__pycache__/__init__.cpython-39.pyc
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index 0000000000000000000000000000000000000000..6a502ec7c72b1713b322e2833801b3039173eda5
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index 0000000000000000000000000000000000000000..1df8108349509b985289d07ee0df9423be4d90d2
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/__pycache__/data.cpython-39.pyc b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/__pycache__/data.cpython-39.pyc
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index 0000000000000000000000000000000000000000..062096721fbbd51dfd70d2f8b4bd5c14a460ad1f
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/ckpts/LeanVAE-dim16.ckpt b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/ckpts/LeanVAE-dim16.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..1edb223dac2ce1fa35e186ed038f7be19bff827c
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/ckpts/LeanVAE-dim16.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c1d65765e44ced040a43a0bb7084a936e5b3862d21df4a9fd13580508cd1ecb
+size 159199850
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/data.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..03cd3720ab98e9d147bf278c94dd3e19cadc6bc2
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/data.py
@@ -0,0 +1,466 @@
+import os
+import os.path as osp
+import math
+import random
+import argparse
+import numpy as np
+from PIL import Image
+from torch.utils.data import BatchSampler, Dataset, Sampler
+import torch
+import torch.utils.data as data
+import torch.nn.functional as F
+import torch.distributed as dist
+from torchvision.datasets.video_utils import VideoClips
+import pytorch_lightning as pl
+from typing import TypeVar, Optional, Iterator, List
+from collections import Counter, defaultdict
+from decord import VideoReader
+from .utils.video_utils import VideoNorm
+
+try:
+ from torchvision.transforms import InterpolationMode
+
+ def _pil_interp(method):
+ if method == 'bicubic':
+ return InterpolationMode.BICUBIC
+ elif method == 'lanczos':
+ return InterpolationMode.LANCZOS
+ elif method == 'hamming':
+ return InterpolationMode.HAMMING
+ else:
+ # default bilinear, do we want to allow nearest?
+ return InterpolationMode.BILINEAR
+
+
+ import timm.data.transforms as timm_transforms
+
+ timm_transforms._pil_interp = _pil_interp
+except:
+ from timm.data.transforms import _pil_interp
+
+class MultiSizeVideoDataset(data.Dataset):
+ """ A flexible dataset for loading videos of different resolutions stored in a structured format.
+ This dataset reads video file paths from text files, where each file corresponds to a specific resolution (e.g., `256x256`).
+ Returns BCTHW videos in the range [-0.5, 0.5] """
+ def __init__(self, data_list, data_folder=None, sequence_length=17, train=True, sample_rate=1, dynamic_sample=False):
+ """
+ Args:
+ data_list (str): Path to the folder containing text files with video paths.
+ data_folder (Optional[str]): Root folder where videos are stored (if paths in data_list are relative).
+
+ sequence_length: length of extracted video sequences
+ """
+ super().__init__()
+ self.train = train
+ self.data_folder = data_folder
+ self.sequence_length = sequence_length
+ self.dynamic_sample = dynamic_sample
+ self.sample_rate = sample_rate
+
+ lengths = []
+ annotations = []
+ for dir in os.listdir(data_list):
+ file_path = os.path.join(data_list, dir)
+ with open(file_path) as f:
+ annotation = [ann.strip() for ann in f.readlines()]
+ annotations.extend(annotation)
+ lengths.extend([dir] * len(annotation))
+
+ self.annotations = annotations
+ self.lengths = lengths
+
+ self.norm = VideoNorm()
+
+ def __len__(self):
+ return len(self.annotations)
+
+
+ def __getitem__(self, idx):
+
+ video_path = self.annotations[idx] if self.data_folder is None else os.path.join(self.data_folder, self.annotations[idx])
+ try:
+ decord_vr = VideoReader(video_path)
+ total_frames = len(decord_vr)
+ except Exception as e:
+ raise RuntimeError(f"Failed to read video: {video_path}. Error: {e}")
+
+ if self.dynamic_sample:
+ sample_rate = random.randint(1, self.sample_rate)
+ else:
+ sample_rate = self.sample_rate
+
+ required_frames = self.sequence_length * sample_rate
+ if total_frames < self.sequence_length:
+ raise RuntimeError(f"Video {video_path} has only {total_frames} frames, but {self.sequence_length} are required.")
+
+ if total_frames < required_frames:
+ sample_rate = 1
+ required_frames = self.sequence_length
+
+ start_frame_ind = random.randint(0, max(0, total_frames - required_frames))
+ end_frame_ind = min(start_frame_ind + required_frames, total_frames)
+ frame_indice = np.linspace(
+ start_frame_ind, end_frame_ind - 1, self.sequence_length, dtype=int
+ )
+
+ video_data = decord_vr.get_batch(frame_indice).asnumpy()
+ video_data = torch.from_numpy(video_data).float()
+ video_data = video_data.permute(0, 3, 1, 2)
+
+ video = self.norm(video_data).permute(1, 0, 2, 3)
+ return {"video": video}
+
+class MultiFilesBatchVideoSampler(BatchSampler):
+ """A sampler wrapper for grouping videos within same folder into a same batch.
+ Args:
+ sampler (Sampler): Base sampler.
+ dataset (Dataset): Dataset providing data information.
+ batch_size (int): Size of mini-batch.
+ drop_last (bool): If ``True``, the sampler will drop the last batch if
+ its size would be less than ``batch_size``.
+ aspect_ratios (dict): The predefined aspect ratios.
+ """
+ def __init__(self,
+ sampler: Sampler,
+ dataset: Dataset,
+ batch_size: int,
+ train_folder: str = None,
+ drop_last: bool = False
+ ) -> None:
+ if not isinstance(sampler, Sampler):
+ raise TypeError('sampler should be an instance of ``Sampler``, '
+ f'but got {sampler}')
+ if not isinstance(batch_size, int) or batch_size <= 0:
+ raise ValueError('batch_size should be a positive integer value, '
+ f'but got batch_size={batch_size}')
+ self.sampler = sampler
+ self.dataset = dataset
+ self.train_folder = train_folder
+ self.batch_size = batch_size
+ self.drop_last = drop_last
+ self.bucket = {file_name: [] for file_name in os.listdir(self.train_folder)}
+
+ #{file_name: [list(os.listdir(os.path.join(self.train_folder, file_name)))] for file_name in os.listdir(self.train_folder)}
+ self.idx2file = []
+
+
+ def __iter__(self):
+ for idx in self.sampler:
+ file_name = self.idx2file[idx]
+ self.bucket[file_name].append(idx)
+ bucket = self.bucket[file_name]
+ bucket.append(idx)
+ # yield a batch of indices in the same aspect ratio group
+ if len(bucket) == self.batch_size:
+ yield bucket[:]
+ del bucket[:]
+
+
+def group_data_fun(lengths, generator=None):
+ # counter is decrease order
+ counter = Counter(lengths) # counter {'1x256x256': 3, ''} lengths ['1x256x256', '1x256x256', '1x256x256', ...]
+ grouped_indices = defaultdict(list)
+ for idx, item in enumerate(lengths): # group idx to a list
+ grouped_indices[item].append(idx)
+
+ grouped_indices = dict(grouped_indices) # {'1x256x256': [0, 1, 2], ...}
+ sorted_indices = [grouped_indices[item] for (item, _) in sorted(counter.items(), key=lambda x: x[1], reverse=True)]
+
+ # shuffle in each group
+ shuffle_sorted_indices = []
+ for indice in sorted_indices:
+ shuffle_idx = torch.randperm(len(indice), generator=generator).tolist()
+ shuffle_sorted_indices.extend([indice[idx] for idx in shuffle_idx])
+ return shuffle_sorted_indices
+
+def last_group_data_fun(shuffled_megabatches, lengths):
+ # lengths ['1x256x256', '1x256x256', '1x256x256' ...]
+ re_shuffled_megabatches = []
+ # print('shuffled_megabatches', len(shuffled_megabatches))
+ for i_megabatch, megabatch in enumerate(shuffled_megabatches):
+ re_megabatch = []
+ for i_batch, batch in enumerate(megabatch):
+ assert len(batch) != 0
+
+ len_each_batch = [lengths[i] for i in batch] # ['1x256x256', '1x256x256']
+ idx_length_dict = dict([*zip(batch, len_each_batch)]) # {0: '1x256x256', 100: '1x256x256'}
+ count_dict = Counter(len_each_batch) # {'1x256x256': 2} or {'1x256x256': 1, '1x768x256': 1}
+ if len(count_dict) != 1:
+ sorted_by_value = sorted(count_dict.items(), key=lambda item: item[1]) # {'1x256x256': 1, '1x768x256': 1}
+ # import ipdb;ipdb.set_trace()
+ # print(batch, idx_length_dict, count_dict, sorted_by_value)
+ pick_length = sorted_by_value[-1][0] # the highest frequency
+ candidate_batch = [idx for idx, length in idx_length_dict.items() if length == pick_length]
+ random_select_batch = [random.choice(candidate_batch) for i in range(len(len_each_batch) - len(candidate_batch))]
+ # print(batch, idx_length_dict, count_dict, sorted_by_value, pick_length, candidate_batch, random_select_batch)
+ batch = candidate_batch + random_select_batch
+ # print(batch)
+
+ for i in range(1, len(batch)-1):
+ # if not lengths[batch[0]] == lengths[batch[i]]:
+ # print(batch, [lengths[i] for i in batch])
+ # import ipdb;ipdb.set_trace()
+ assert lengths[batch[0]] == lengths[batch[i]]
+ re_megabatch.append(batch)
+ re_shuffled_megabatches.append(re_megabatch)
+
+
+ # for megabatch, re_megabatch in zip(shuffled_megabatches, re_shuffled_megabatches):
+ # for batch, re_batch in zip(megabatch, re_megabatch):
+ # for i, re_i in zip(batch, re_batch):
+ # if i != re_i:
+ # print(i, re_i)
+ return re_shuffled_megabatches
+
+def split_to_even_chunks(megabatch, lengths, world_size, batch_size):
+ """
+ Split a list of indices into `chunks` chunks of roughly equal lengths.
+ """
+ # batch_size=2, world_size=2
+ # [1, 2, 3, 4] -> [[1, 2], [3, 4]]
+ # [1, 2, 3] -> [[1, 2], [3]]
+ # [1, 2] -> [[1], [2]]
+ # [1] -> [[1], []]
+ chunks = [megabatch[i::world_size] for i in range(world_size)]
+
+ pad_chunks = []
+ for idx, chunk in enumerate(chunks):
+ if batch_size != len(chunk):
+ assert batch_size > len(chunk)
+ if len(chunk) != 0: # [[1, 2], [3]] -> [[1, 2], [3, 3]]
+ chunk = chunk + [random.choice(chunk) for _ in range(batch_size - len(chunk))]
+ else:
+ chunk = random.choice(pad_chunks) # [[1], []] -> [[1], [1]]
+ print(chunks[idx], '->', chunk)
+ pad_chunks.append(chunk)
+ return pad_chunks
+
+def get_length_grouped_indices(lengths, batch_size, world_size, gradient_accumulation_size, initial_global_step, generator=None, group_data=False, seed=42):
+ # We need to use torch for the random part as a distributed sampler will set the random seed for torch.
+ if generator is None:
+ generator = torch.Generator().manual_seed(seed) # every rank will generate a fixed order but random index
+ # print('lengths', lengths)
+
+ if group_data:
+ indices = group_data_fun(lengths, generator)
+ else:
+ indices = torch.randperm(len(lengths), generator=generator).tolist()
+
+ megabatch_size = world_size * batch_size
+ megabatches = [indices[i: i + megabatch_size] for i in range(0, len(lengths), megabatch_size)]
+
+ megabatches_len = [[lengths[i] for i in megabatch] for megabatch in megabatches]
+
+ megabatches = [split_to_even_chunks(megabatch, lengths, world_size, batch_size) for megabatch in megabatches]
+
+ split_to_even_chunks_len = [[[lengths[i] for i in batch] for batch in megabatch] for megabatch in megabatches]
+
+ indices_mega = torch.randperm(len(megabatches), generator=generator).tolist()
+ # print(f'rank {accelerator.process_index} seed {seed}, len(megabatches) {len(megabatches)}, indices_mega, {indices_mega[:50]}')
+ shuffled_megabatches = [megabatches[i] for i in indices_mega]
+ shuffled_megabatches_len = [[[lengths[i] for i in batch] for batch in megabatch] for megabatch in shuffled_megabatches]
+ # print(f'\nrank {accelerator.process_index} sorted shuffled_megabatches_len', shuffled_megabatches_len[0], shuffled_megabatches_len[1], shuffled_megabatches_len[-2], shuffled_megabatches_len[-1])
+
+ # import ipdb;ipdb.set_trace()
+ # print('shuffled_megabatches', len(shuffled_megabatches))
+ if group_data:
+ shuffled_megabatches = last_group_data_fun(shuffled_megabatches, lengths)
+ group_shuffled_megabatches_len = [[[lengths[i] for i in batch] for batch in megabatch] for megabatch in shuffled_megabatches]
+ # print(f'\nrank {accelerator.process_index} group_shuffled_megabatches_len', group_shuffled_megabatches_len[0], group_shuffled_megabatches_len[1], group_shuffled_megabatches_len[-2], group_shuffled_megabatches_len[-1])
+
+
+ #initial_global_step = initial_global_step * gradient_accumulation_size #todo
+
+ shuffled_megabatches = shuffled_megabatches[initial_global_step:]
+ #print(f'Skip the data of {initial_global_step} step!')
+
+ return [batch for megabatch in shuffled_megabatches for batch in megabatch]
+
+class LengthGroupedSampler(Sampler):
+ r"""
+ Sampler that samples indices in a way that groups together features of the dataset of roughly the same length while
+ keeping a bit of randomness.
+ """
+
+ def __init__(
+ self,
+ batch_size: int,
+ world_size: int,
+ gradient_accumulation_size: int = 1,
+ initial_global_step: int = 0,
+ lengths: Optional[List[int]] = None,
+ group_data=False,
+ generator=None,
+ rank: Optional[int] = None,
+ seed: int = 0,
+ ):
+ if lengths is None:
+ raise ValueError("Lengths must be provided.")
+
+ self.batch_size = batch_size
+ self.world_size = world_size
+ self.initial_global_step = initial_global_step
+ self.gradient_accumulation_size = gradient_accumulation_size
+ self.lengths = lengths
+ self.group_data = group_data
+ self.generator = generator
+
+ self.rank = rank
+ self.epoch = 0
+
+ self.seed = seed
+
+ megabatch_size = self.batch_size * self.world_size
+ self.num_samples = ((len(lengths) + megabatch_size - 1) // megabatch_size ) * self.batch_size #todo
+ #self.num_samples = self.num_samples #- self.initial_global_step * self.batch_size * self.gradient_accumulation_size
+ # print('self.lengths, self.initial_global_step, self.batch_size, self.world_size, self.gradient_accumulation_size',
+ # len(self.lengths), self.initial_global_step, self.batch_size, self.world_size, self.gradient_accumulation_size)
+
+ def __len__(self):
+ return self.num_samples
+
+ def __iter__(self):
+ g = torch.Generator()
+ g.manual_seed(self.seed + self.epoch)
+ megabatch_indices = get_length_grouped_indices(self.lengths, self.batch_size, self.world_size,
+ self.gradient_accumulation_size, self.initial_global_step,
+ group_data=self.group_data, generator=g)
+
+ # subsample
+ indices = [i for batch in megabatch_indices[self.rank::self.world_size] for i in batch]
+ assert len(indices) == self.num_samples
+
+ return iter(indices)
+
+ def set_epoch(self, epoch: int) -> None:
+ r"""
+ Set the epoch for this sampler.
+
+ When :attr:`shuffle=True`, this ensures all replicas
+ use a different random ordering for each epoch. Otherwise, the next iteration of this
+ sampler will yield the same ordering.
+
+ Args:
+ epoch (int): Epoch number.
+ """
+ self.epoch = epoch
+
+
+class VideoData(pl.LightningDataModule):
+ def __init__(self, args):
+ super().__init__()
+ self.args = args
+
+ def _dataset(self, train):
+ datasets = []
+ for dataset_path, train_list, val_list in zip(self.args.data_path, self.args.train_datalist, self.args.val_datalist):
+
+ dataset = MultiSizeVideoDataset(data_folder=dataset_path, data_list=train_list if train else val_list, sequence_length=self.args.sequence_length,
+ train=train, sample_rate=self.args.sample_rate, dynamic_sample=self.args.dynamic_sample)
+ datasets.append(dataset)
+ return datasets
+
+ def _dataloader(self, train, steps = 0, batch_size = None):
+ dataset = self._dataset(train)
+ if isinstance(self.args.batch_size, int):
+ self.args.batch_size = [self.args.batch_size]
+ self.batch_size = self.args.batch_size if batch_size is None else batch_size
+ assert len(dataset) == len(self.args.batch_size)
+ dataloaders = []
+ for dset, d_batch_size in zip(dataset, self.batch_size):
+ if dist.is_initialized():
+ sampler = LengthGroupedSampler(
+ batch_size=d_batch_size,
+ world_size=dist.get_world_size(),
+ gradient_accumulation_size=1,
+ initial_global_step=steps if train else 0,
+ lengths=dset.lengths,
+ group_data=True,
+ rank = dist.get_rank()
+ )
+ else:
+ sampler = None
+
+ dataloader = data.DataLoader(
+ dset,
+ batch_size=d_batch_size,
+ num_workers=self.args.num_workers if train else 0,
+ pin_memory=False,
+ sampler=sampler,
+ )
+
+ dataloaders.append(dataloader)
+
+ return dataloaders
+
+ def train_dataloader(self):
+ return self._dataloader(True)
+
+ def val_dataloader(self):
+ return self._dataloader(False)[0]
+
+
+ @staticmethod
+ def add_data_specific_args(parent_parser):
+ parser = argparse.ArgumentParser(parents=[parent_parser], add_help=False)
+ parser.add_argument('--data_path', type=str, nargs="+", default=[''])
+ parser.add_argument('--train_datalist', type=str, nargs="+", default=['./video/kinetics-dataset/train/datapath'])
+ parser.add_argument('--val_datalist', type=str, nargs="+", default=['./video/kinetics-dataset/val/datapath'])
+
+ parser.add_argument('--sequence_length', type=int, default=17)
+ parser.add_argument('--sample_rate', type=int, default=1,
+ help='Frame sampling rate')
+ parser.add_argument('--dynamic_sample', action='store_true',
+ help='Enable dynamic sampling rate')
+
+ parser.add_argument('--batch_size', type=int, nargs="+", default=[5])
+ parser.add_argument('--num_workers', type=int, default=8)
+ return parser
+
+if __name__ == "__main__":
+ import os
+ def lines(file_path):
+ with open(file_path, 'r') as file:
+ return sum(1 for line in file)
+ train_folder ='./kinetics-dataset/datapath'
+ lengths_dict = {file_name: lines(os.path.join(train_folder, file_name)) for file_name in os.listdir(train_folder)}
+ lengths = []
+ for k, v in lengths_dict.items():
+ lengths += [k] * min(v, 50) #(v % 7)
+ world_size = 4
+ sampler = []
+ batch_size = 10
+ for rank in range(world_size):
+ sampler.append(LengthGroupedSampler(
+ batch_size=batch_size,
+ world_size=world_size,
+ gradient_accumulation_size=1,
+ initial_global_step=0,
+ lengths=lengths,
+ group_data=True,
+ rank = rank
+ ))
+
+
+ with open('./sampler.txt', 'w') as f:
+ for epoch in range(5):
+ rank_idx = {}
+ bk = []
+ print(f'epoch -------------------------------------- {epoch} ----------------------------------------------------', file=f)
+ for rank in range(world_size):
+ sl = sampler[rank]
+ sl.set_epoch(epoch)
+ for i in iter(sl):
+ bk.append(i)
+ if len(bk) == batch_size:
+ rank_idx.setdefault(f'rank_{rank}', [])
+ rank_idx[f'rank_{rank}'].append(bk)
+ bk = []
+ for num in range(5):
+ print('*'*5 + f'steps {num}' + '*'*5, file=f)
+ for rank, bk in rank_idx.items():
+ print(f'rank {rank}: {bk[num]}', file=f)
+ print([lengths[i] for i in bk[num]], file=f)
+
+
+ exit()
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--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/models/autoencoder.py
@@ -0,0 +1,186 @@
+import argparse
+import torch
+import torch.distributed
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from ..modules import DiagonalGaussianDistribution, Encoder_Arch, Decoder_Arch, ISTA
+from ..utils.patcher_utils import Patcher, UnPatcher
+
+class LeanVAE(nn.Module):
+ def __init__(self, args):
+ super().__init__()
+ self.args = args
+ self.embedding_dim = args.embedding_dim
+
+ self.latent_bottleneck = ISTA(points_num=args.embedding_dim, out_num=args.latent_dim, iter_num=args.ista_iter_num, layer_num=args.ista_layer_num)
+
+ self.dwt = Patcher()
+ self.idwt = UnPatcher()
+
+ self.encoder = Encoder_Arch(l_dim = args.l_dim, h_dim = args.h_dim, sep_num_layer = args.sep_num_layer, fusion_num_layer = args.fusion_num_layer)
+ self.decoder = Decoder_Arch(l_dim = args.l_dim, h_dim = args.h_dim, sep_num_layer = args.sep_num_layer, fusion_num_layer = args.fusion_num_layer)
+
+ self.std_layer = nn.Linear(args.embedding_dim, args.latent_dim)
+
+ self.tile_inference = False
+ self.chunksize_enc = args.chunksize_enc if hasattr(args, 'chunksize_enc') and args.chunksize_enc else 5
+ self.chunksize_dec = args.chunksize_dec if hasattr(args, 'chunksize_dec') and args.chunksize_dec else 5
+ if args.use_tile_inference:
+ self.set_tile_inference(True)
+ else:
+ self.set_tile_inference(False)
+
+ def _set_first_chunk(self, is_first_chunk=True):
+ for module in self.modules():
+ if hasattr(module, 'is_first_chunk'):
+ module.is_first_chunk = is_first_chunk
+
+ def set_tile_inference(self, tile_inference=False):
+ for module in self.modules():
+ if hasattr(module, 'tile_inference'):
+ module.tile_inference = tile_inference
+
+ def _build_chunk_index(self, T = 17, mtype = 'enc'):
+ start_end = []
+ if mtype == 'enc':
+ chunksize = self.chunksize_enc
+ else:
+ chunksize = self.chunksize_dec
+ if T >= chunksize :
+ start_end.append((0, chunksize))
+ start_idx = chunksize
+ else:
+ assert T < chunksize
+
+ for i in range(start_idx, T, chunksize-1):
+ end_idx = min(i + chunksize -1, T)
+ start_end.append((i, end_idx))
+ return start_end
+
+ def encode(self, x):
+ ndim = x.ndim
+ if ndim == 4:
+ x = x.unsqueeze(2)
+ self.set_tile_inference(False)
+
+ if self.tile_inference:
+ z = []
+ chunk_indexs = self._build_chunk_index(T=x.shape[2], mtype='enc')
+ for idx, (start, end) in enumerate(chunk_indexs):
+ if idx == 0:
+ self._set_first_chunk(True)
+ else:
+ self._set_first_chunk(False)
+
+ x_dwt = self.dwt(x[:, :, start:end])
+ p = self.encoder.encode(x=x_dwt)
+ z.append(self.latent_bottleneck.sample(p))
+ z = torch.cat(z, dim = 1)
+ else:
+ x_dwt = self.dwt(x)
+ p = self.encoder.encode(x=x_dwt)
+ z = self.latent_bottleneck.sample(p)
+
+ z = rearrange(z, 'b t h w d -> b d t h w')
+ return z
+
+ def decode(self, z, is_image = False):
+ z = rearrange(z, 'b d t h w -> b t h w d')
+ if is_image:
+ self.set_tile_inference(False)
+ if self.tile_inference:
+ x_recon = []
+ chunk_indexs = self._build_chunk_index(T=z.shape[1], mtype='dec')
+ for idx, (start, end) in enumerate(chunk_indexs):
+ if idx == 0:
+ self._set_first_chunk(True)
+ else:
+ self._set_first_chunk(False)
+ p_rec = self.latent_bottleneck.recon(z[:, start:end])
+ x_dwt_rec = self.decoder.decode(p_rec, is_image=is_image)
+
+ x_recon.append(self.idwt(x=x_dwt_rec))
+ x_recon = torch.cat(x_recon, dim = 2)
+ else:
+ p_rec = self.latent_bottleneck.recon(z)
+ x_dwt_rec = self.decoder.decode(p_rec, is_image=is_image)
+
+ x_recon = self.idwt(x=x_dwt_rec)
+
+ return x_recon
+
+
+
+ @torch.no_grad()
+ def inference(self, x):
+ if x.ndim == 4 :
+ is_image = True
+ else:
+ is_image = False
+ assert x.shape[2] % 4 == 1, f"Expected frame_num % 4 == 1, but got {x.shape[2] % 4}"
+
+ z = self.encode(x)
+ x_recon = self.decode(z, is_image=is_image)
+
+ if is_image:
+ x = x.squeeze(2)
+ return x, x_recon
+
+ def forward(self, x, log_image=False):
+ x_dwt = self.dwt(x)
+ p = self.encoder(x=x_dwt)
+ z_mean = self.latent_bottleneck.sample(p)
+ z_std = self.std_layer(p)
+
+ posterior = DiagonalGaussianDistribution(parameters=(z_mean, z_std))
+ z = posterior.sample()
+ p_rec = self.latent_bottleneck.recon(z)
+
+ x_dwt_rec = self.decoder(p_rec) #b c t h w
+
+
+ x_recon = self.idwt(x=x_dwt_rec)
+
+ if log_image:
+ return x, x_recon
+
+ return x, x_recon, x_dwt, x_dwt_rec, posterior
+
+
+ @classmethod
+ def load_from_checkpoint(cls, ckpt_path, device="cpu", strict=False):
+ """ Load model from checkpoint, initializing args and state_dict """
+ checkpoint = torch.load(ckpt_path, map_location=device)
+
+ if "args" not in checkpoint:
+ raise ValueError("Checkpoint does not contain 'args'. Ensure the checkpoint is saved correctly.")
+
+ args = argparse.Namespace(**checkpoint["args"])
+
+ model = cls(args)
+ if "state_dict" in checkpoint:
+ msg = model.load_state_dict(checkpoint["state_dict"], strict=strict)
+ print(f"Successfully loaded weights from {ckpt_path}, {msg}")
+ return model
+
+ @staticmethod
+ def add_model_specific_args(parent_parser):
+ parser = argparse.ArgumentParser(parents=[parent_parser], add_help=False)
+
+ # Model architecture parameters
+ parser.add_argument("--embedding_dim", type=int, default=512, help="Dimension of the embedding space.")
+ parser.add_argument("--latent_dim", type=int, default=4, help="Dimension of the latent channel.")
+ parser.add_argument("--ista_iter_num", type=int, default=2, help="Number of iterations in ISTA latent bottleneck.")
+ parser.add_argument("--ista_layer_num", type=int, default=2, help="Number of layers in ISTA latent bottleneck.")
+
+ parser.add_argument("--l_dim", type=int, default=128)
+ parser.add_argument("--h_dim", type=int, default=384)
+ parser.add_argument("--sep_num_layer", type=int, default=2, help="Number of separate processing layers in encoder/decoder.")
+ parser.add_argument("--fusion_num_layer", type=int, default=4, help="Number of fusion layers in encoder/decoder.")
+
+ # Tiling inference (for memory-efficient processing)
+ parser.add_argument("--use_tile_inference", action="store_true", help="Enable tiling inference to process video in chunks.")
+ parser.add_argument("--chunksize_enc", type=int, default=9, help="Number of frames per chunk during tiled encoding.")
+ parser.add_argument("--chunksize_dec", type=int, default=5, help="Number of frames per chunk during tiled decoding.")
+ return parser
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/models/autoencoder_pl.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/models/autoencoder_pl.py
new file mode 100644
index 0000000000000000000000000000000000000000..96b58ae98017752e18b096e985e18650b65668be
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/models/autoencoder_pl.py
@@ -0,0 +1,232 @@
+import argparse
+import numpy as np
+from PIL import Image
+import pytorch_lightning as pl
+import torch
+import torch.distributed
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.scheduler.cosine_lr import CosineLRScheduler
+from timm.models.layers import trunc_normal_
+from .autoencoder import LeanVAE
+from ..modules import LPIPS
+from ..utils.gan_loss import AdversarialLoss
+
+class AutoEncoderEngine(pl.LightningModule):
+ def __init__(self, args, data):
+ super().__init__()
+ self.args = args
+ self.video_data = data
+
+ self.autoencoder = LeanVAE(args=args)
+
+ self.automatic_optimization = False
+ self.kl_weight = args.kl_weight
+ self.discriminator_iter_start = args.discriminator_iter_start
+
+ self.perceptual_weight = args.perceptual_weight
+ self.l1_weight = args.l1_weight
+
+ self.automatic_optimization = False
+ self.grad_clip_val = args.grad_clip_val
+
+ if not hasattr(args, "grad_clip_val_disc"):
+ args.grad_clip_val_disc = 1.0
+
+ self.grad_clip_val_disc = args.grad_clip_val_disc
+
+ self.apply(self._init_weights)
+ self.perceptual_model = LPIPS().eval()
+ self.perceptual_model.requires_grad_(False)
+ self.gan_loss = AdversarialLoss(disc_weight=args.disc_weight)
+ self.save_hyperparameters()
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ if m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ if m.weight is not None:
+ nn.init.constant_(m.weight, 1.0)
+
+ elif isinstance(m, nn.Conv3d) or isinstance(m, nn.Conv2d):
+ nn.init.xavier_uniform_(m.weight)
+ nn.init.zeros_(m.bias)
+
+
+ def forward(self, x, optimizer_idx=None, x_recon = None, log_image=False):
+ if log_image:
+ return self.autoencoder(x, log_image)
+
+ if optimizer_idx == 1:
+ discloss = self.gan_loss(inputs=x, reconstructions=x_recon, optimizer_idx=1)
+ self.log("train/discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ return discloss
+
+ elif optimizer_idx == 0:
+ assert x.ndim == 5
+ B, C, T, H, W = x.shape
+ x, x_recon, x_dwt, x_dwt_rec, posterior = self.autoencoder(x)
+ recon_loss = F.l1_loss(x_recon, x)* self.l1_weight
+ kl_loss = posterior.kl()
+ kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] * self.kl_weight
+
+ g_loss = 0.0
+ if self.global_step >= self.discriminator_iter_start:
+ g_loss = self.gan_loss(x, x_recon, optimizer_idx=0)
+ self.log("train/g_loss", g_loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+
+ recon_loss_low = (F.l1_loss(x_dwt_rec[0][:, :3], x_dwt[0][:, :3]) + F.l1_loss(x_dwt_rec[1][:, :3], x_dwt[1][:, :3])) * self.l1_weight * 0.05
+ recon_loss_high = (F.l1_loss(x_dwt_rec[0][:, 3:], x_dwt[0][:, 3:])+ F.l1_loss(x_dwt_rec[1][:, 3:], x_dwt[1][:, 3:])) * self.l1_weight * 0.1
+
+ k = 4
+ valid_start_indices = torch.tensor([x for x in range(T - k + 1) if x % 4 == 1])
+ start_idx = valid_start_indices[torch.randint(0, len(valid_start_indices), (B,))]
+ frame_idx = start_idx.unsqueeze(1) + torch.arange(k)
+ frame_idx = torch.cat((torch.zeros((B, 1), dtype=torch.int), frame_idx), dim=1).to(self.device)
+
+ frame_idx_selected = frame_idx.reshape(-1, 1, k+1, 1, 1).repeat(1, C, 1, H, W)
+ frames = torch.gather(x, 2, frame_idx_selected)
+ frames_recon = torch.gather(x_recon, 2, frame_idx_selected)
+ frames = frames.permute(0, 2, 1, 3, 4).contiguous().view(-1, 3, H, W)
+ frames_recon = frames_recon.permute(0, 2, 1, 3, 4).contiguous().view(-1, 3, H, W)
+ perceptual_loss = self.perceptual_model(frames, frames_recon).mean() * self.perceptual_weight
+
+ self.log("train/recon_loss", recon_loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log("train/kl_loss", kl_loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log("train/recon_loss_low", recon_loss_low, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log("train/recon_loss_high", recon_loss_high, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log("train/perceptual_loss", perceptual_loss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ return perceptual_loss + recon_loss + recon_loss_low + recon_loss_high + kl_loss + g_loss, x_recon
+
+ return perceptual_loss, recon_loss, kl_loss
+
+
+ def training_step(self, batch, batch_idx):
+
+ x = batch[0]['video']
+ cur_global_step = self.global_step
+
+ sch1, sch2 = self.lr_schedulers()
+ opt1, opt2 = self.optimizers()
+
+ cur_global_step = self.global_step
+
+ self.toggle_optimizer(opt1, optimizer_idx=0)
+ loss_generator, x_recon = self.forward(x, optimizer_idx=0)
+ opt1.zero_grad()
+ self.manual_backward(loss_generator)
+ if self.grad_clip_val is not None:
+ self.clip_gradients(opt1, gradient_clip_val=self.grad_clip_val)
+ opt1.step()
+ sch1.step(cur_global_step)
+ self.untoggle_optimizer(optimizer_idx=0)
+
+ if cur_global_step > self.discriminator_iter_start:
+ self.toggle_optimizer(opt2, optimizer_idx=1)
+ loss_discriminator = self.forward(x, optimizer_idx=1, x_recon=x_recon)
+
+ opt2.zero_grad()
+ self.manual_backward(loss_discriminator)
+
+ if self.grad_clip_val_disc is not None:
+ self.clip_gradients(opt2, gradient_clip_val=self.grad_clip_val_disc)
+ opt2.step()
+ sch2.step(cur_global_step)
+ self.untoggle_optimizer(optimizer_idx=1)
+
+
+ def validation_step(self, batch, batch_idx):
+ x = batch['video']
+ perceptual_loss, recon_loss, kl_loss = self.forward(x)
+ self.log('val/recon_loss', recon_loss, prog_bar=True)
+ self.log('val/perceptual_loss', perceptual_loss, prog_bar=True)
+ self.log("val/kl_loss", kl_loss, prog_bar=True)
+
+ def train_dataloader(self):
+ dataloaders = self.video_data._dataloader(train=True)
+ return dataloaders
+
+ def val_dataloader(self):
+ return self.video_data._dataloader(False)[0]
+
+ def configure_optimizers(self):
+ opt_ae = torch.optim.Adam(self.autoencoder.parameters(),
+ lr=self.args.lr, betas=(0.5, 0.9))
+
+ opt_disc = torch.optim.Adam(
+ self.gan_loss.get_trainable_parameters(),
+ lr=self.args.lr_min, betas=(0.5, 0.9))
+
+ lr_min = self.args.lr_min
+ train_iters = self.args.max_steps - self.discriminator_iter_start
+ warmup_steps = self.args.warmup_steps
+ warmup_lr_init = self.args.warmup_lr_init
+
+
+ sch_ae = CosineLRScheduler(
+ opt_ae,
+ lr_min = lr_min,
+ t_initial = train_iters,
+ warmup_lr_init=warmup_lr_init,
+ warmup_t=warmup_steps,
+ cycle_mul = 1.,
+ cycle_limit=1,
+ t_in_epochs=True,
+ )
+
+ sch_disc = CosineLRScheduler(
+ opt_disc,
+ lr_min = lr_min ,
+ t_initial = train_iters,
+ warmup_lr_init=warmup_lr_init,
+ warmup_t= self.args.dis_warmup_steps,
+ cycle_mul = 1.,
+ cycle_limit=1,
+ t_in_epochs=True,
+ )
+
+
+ return [opt_ae, opt_disc], [{"scheduler": sch_ae, "interval": "step"}, {"scheduler": sch_disc, "interval": "step"}]
+
+
+
+ def log_videos(self, batch, **kwargs):
+ log = dict()
+ if isinstance(batch, list):
+ batch = batch[0]
+ x = batch['video']
+ x, x_rec = self(x, log_image=True)
+ log["inputs"] = x
+ log["reconstructions"] = x_rec
+ return log
+
+ @staticmethod
+ def add_model_specific_args(parent_parser):
+ parser = argparse.ArgumentParser(parents=[parent_parser], add_help=False)
+
+ # training configurations
+ parser.add_argument('--lr', type=float, default=5e-5)
+ parser.add_argument('--lr_min', type=float, default=1e-5)
+ parser.add_argument('--warmup_steps', type=int, default=5000)
+ parser.add_argument('--warmup_lr_init', type=float, default=0.)
+ parser.add_argument('--grad_clip_val', type=float, default=1.0)
+ parser.add_argument('--grad_clip_val_disc', type=float, default=1.0)
+
+
+ parser.add_argument('--kl_weight', type=float, default=1e-7)
+ parser.add_argument('--perceptual_weight', type=float, default=4.)
+ parser.add_argument('--l1_weight', type=float, default=4.)
+ parser.add_argument('--disc_weight', type=float, default=0.2)
+
+ # configuration for discriminator
+ parser.add_argument('--dis_warmup_steps', type=int, default=0)
+ parser.add_argument('--discriminator_iter_start', type=int, default=0)
+ parser.add_argument('--dis_lr_multiplier', type=float, default=1.)
+
+ return parser
+
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/__init__.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..72c215a154db4c7fd24fff62fcc897712e5339ed
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/__init__.py
@@ -0,0 +1,3 @@
+from .lpips import LPIPS
+from .backbones import *
+from .vae import DiagonalGaussianDistribution
\ No newline at end of file
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/backbones.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/backbones.py
new file mode 100644
index 0000000000000000000000000000000000000000..606edd41e164046c71da3ba2a9d540fbe9341abc
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/backbones.py
@@ -0,0 +1,402 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from beartype import beartype
+from typing import Tuple
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+import numpy as np
+
+def exists(val):
+ return val is not None
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if callable(d) else d
+
+class PEG3D(nn.Module):
+ def __init__(
+ self,
+ dim
+ ):
+ super().__init__()
+ self.ds_conv = nn.Conv3d(in_channels=dim, out_channels=dim, kernel_size=(3,3,3), groups = dim)
+ self.is_first_chunk = True
+ self.causal_cached = None
+ self.tile_inference = False
+
+ def forward(self, x):
+ x = rearrange(x, 'b t h w d -> b d t h w')
+ if self.tile_inference:
+ if self.is_first_chunk:
+ x = F.pad(x, (1, 1, 1, 1, 2, 0), value=0.)
+ else:
+ x = F.pad(x, (1, 1, 1, 1, 0, 0), value=0.)
+ x = torch.concatenate((self.causal_cached, x), dim=2)
+
+ self.causal_cached = x[:, :, -2:].clone()
+ else:
+ x = F.pad(x, (1, 1, 1, 1, 2, 0), value=0.)
+ x = self.ds_conv(x.contiguous())
+ x = rearrange(x, 'b d t h w -> b t h w d')
+ return x
+
+
+class GEGLU(nn.Module):
+ def forward(self, x):
+ x, gate = x.chunk(2, dim=-1)
+ return F.gelu(gate) * x
+
+
+def ffd(dim, mult=4, dropout=0.):
+ inner_dim = int(mult * (2 / 3) * dim)
+ return nn.Sequential(
+ nn.LayerNorm(dim),
+ nn.Linear(dim, inner_dim * 2, bias=False),
+ GEGLU(),
+ nn.Dropout(dropout),
+ nn.Linear(inner_dim, dim, bias=False)
+ )
+
+
+class NAF(nn.Module):
+ def __init__(self,
+ num_layer,
+ dim,
+ ):
+ super(NAF, self).__init__()
+ self.num_layer = num_layer
+ self.dconv_layer = nn.Sequential()
+ self.ffd_layer = nn.Sequential()
+ for _ in range(num_layer):
+ self.ffd_layer.append(ffd(dim, 4))
+ self.dconv_layer.append(PEG3D(dim))
+
+ def forward(self, x):
+ for i in range(self.num_layer):
+ x = self.dconv_layer[i](x)
+ x = self.ffd_layer[i](x)
+ return x
+
+
+class ResNAF(nn.Module):
+ def __init__(self,
+ num_layer,
+ dim,
+ ):
+ super(ResNAF, self).__init__()
+ self.num_layer = num_layer
+ self.dconv_layer = nn.Sequential()
+ self.ffd_layer = nn.Sequential()
+ for _ in range(num_layer):
+ self.ffd_layer.append(ffd(dim, 4))
+ self.dconv_layer.append(PEG3D(dim))
+
+ def forward(self, x):
+ for i in range(self.num_layer):
+ x = x + self.dconv_layer[i](x)
+ x = x + self.ffd_layer[i](x)
+ return x
+
+
+class Encoder_Arch(nn.Module):
+ def __init__(self,
+ l_dim = 128,
+ h_dim = 384,
+ sep_num_layer = 2,
+ fusion_num_layer = 4,
+ patch_size = (2,4,4),
+ in_channel = 3
+ ):
+ super(Encoder_Arch, self).__init__()
+
+ self.is_first_chunk = True
+ self.tile_inference = False
+
+ self.in_channel = in_channel
+
+ self._build_linear_patch(in_channel=in_channel, out_channel_low=l_dim, out_channel_high=h_dim, pt=patch_size[0], ph=patch_size[1], pw=patch_size[2])
+
+ self.low_layer = ResNAF(num_layer=sep_num_layer, dim=l_dim)
+ self.high_layer = ResNAF(num_layer=sep_num_layer, dim=h_dim)
+ self.fusion_layer = ResNAF(num_layer=fusion_num_layer, dim=l_dim + h_dim)
+
+ def _build_linear_patch(self, in_channel = 3, out_channel_low = 128, out_channel_high = 384, pt = 2, ph = 4, pw = 4):
+ patch_config = {
+ 'video_low': (pt, ph, pw),
+ 'video_high': (pt, ph, pw),
+ 'image_low': (1, ph, pw),
+ 'image_high': (1, ph, pw)
+ }
+
+ for name, (t, h, w) in patch_config.items():
+ if 'low' in name:
+ in_dim = in_channel * t * h * w
+ out_dim = out_channel_low
+ else:
+ out_dim = out_channel_high
+ in_dim = in_channel * t * h * w * 7 if 'video' in name else in_channel * t * h * w * 3
+ proj = nn.Sequential(
+ Rearrange(f'b c (nt {t}) (nh {h}) (nw {w}) -> b nt nh nw (c {t} {h} {w})' if 'video' in name else f'b c (nh {h}) (nw {w}) -> b 1 nh nw (c {h} {w})'),
+ nn.Linear(in_dim, out_dim)
+ )
+ self.add_module(f"{name}_proj", proj)
+
+
+ def _linear_patch(self, x, proj_type):
+ low_comp, high_comp = x[:, :self.in_channel], x[:, self.in_channel:]
+ return getattr(self, f"{proj_type}_low_proj")(low_comp), getattr(self, f"{proj_type}_high_proj")(high_comp)
+
+ def forward(self, x):
+ xi, xv = x
+ xi_low, xi_high = self._linear_patch(xi, 'image')
+ xv_low, xv_high = self._linear_patch(xv, 'video')
+
+ low_x = torch.cat([xi_low, xv_low], dim=1)
+ high_x = torch.cat([xi_high, xv_high], dim=1)
+
+ high_x = self.high_layer(high_x)
+ low_x = self.low_layer(low_x)
+ x = torch.cat([low_x, high_x], dim=-1)
+ x = self.fusion_layer(x)
+ return x
+
+
+
+ def encode(self, x):
+ xi, xv = x
+ if xi is not None and xv is not None:
+ xi_low, xi_high = self._linear_patch(xi, 'image')
+ xv_low, xv_high = self._linear_patch(xv, 'video')
+
+ low_x = torch.cat([xi_low, xv_low], dim=1)
+ high_x = torch.cat([xi_high, xv_high], dim=1)
+ elif xi is not None:
+ low_x, high_x = self._linear_patch(xi, 'image')
+ elif xv is not None:
+ low_x, high_x = self._linear_patch(xv, 'video')
+
+ high_x = self.high_layer(high_x)
+ low_x = self.low_layer(low_x)
+ x = torch.cat([low_x, high_x], dim=-1)
+ x = self.fusion_layer(x)
+ return x
+
+
+
+class Encoder_Arch(nn.Module):
+ def __init__(self,
+ l_dim = 128,
+ h_dim = 384,
+ sep_num_layer = 2,
+ fusion_num_layer = 4,
+ patch_size = (2,4,4),
+ in_channel = 3
+ ):
+ super(Encoder_Arch, self).__init__()
+
+ self.is_first_chunk = True
+ self.tile_inference = False
+
+ self.in_channel = in_channel
+
+ self._build_linear_patch(in_channel=in_channel, out_channel_low=l_dim, out_channel_high=h_dim, pt=patch_size[0], ph=patch_size[1], pw=patch_size[2])
+
+ self.low_layer = ResNAF(num_layer=sep_num_layer, dim=l_dim)
+ self.high_layer = ResNAF(num_layer=sep_num_layer, dim=h_dim)
+ self.fusion_layer = ResNAF(num_layer=fusion_num_layer, dim=l_dim + h_dim)
+
+ def _build_linear_patch(self, in_channel = 3, out_channel_low = 128, out_channel_high = 384, pt = 2, ph = 4, pw = 4):
+ patch_config = {
+ 'video_low': (pt, ph, pw),
+ 'video_high': (pt, ph, pw),
+ 'image_low': (1, ph, pw),
+ 'image_high': (1, ph, pw)
+ }
+
+ for name, (t, h, w) in patch_config.items():
+ if 'low' in name:
+ in_dim = in_channel * t * h * w
+ out_dim = out_channel_low
+ else:
+ out_dim = out_channel_high
+ in_dim = in_channel * t * h * w * 7 if 'video' in name else in_channel * t * h * w * 3
+ proj = nn.Sequential(
+ Rearrange('b c (nt pt) (nh ph) (nw pw) -> b nt nh nw (c pt ph pw)', pt=t, ph=h, pw=w),
+ nn.Linear(in_dim, out_dim)
+ )
+ self.add_module(f"{name}_proj", proj)
+
+
+ def _linear_patch(self, x, proj_type):
+ low_comp, high_comp = x[:, :self.in_channel], x[:, self.in_channel:]
+ return getattr(self, f"{proj_type}_low_proj")(low_comp), getattr(self, f"{proj_type}_high_proj")(high_comp)
+
+ def _feature_transform(self, low_x, high_x):
+ low_x = self.low_layer(low_x)
+ high_x = self.high_layer(high_x)
+ x = torch.cat([low_x, high_x], dim=-1)
+ x = self.fusion_layer(x)
+ return x
+
+ def forward(self, x):
+ xi, xv = x
+ xi_low, xi_high = self._linear_patch(x=xi, proj_type='image')
+ xv_low, xv_high = self._linear_patch(x=xv, proj_type='video')
+
+ low_x = torch.cat([xi_low, xv_low], dim=1)
+ high_x = torch.cat([xi_high, xv_high], dim=1)
+
+ return self._feature_transform(low_x=low_x, high_x=high_x)
+
+
+
+ def encode(self, x):
+ xi, xv = x
+ if xi is not None and xv is not None:
+ xi_low, xi_high = self._linear_patch(x=xi, proj_type='image')
+ xv_low, xv_high = self._linear_patch(x=xv, proj_type='video')
+
+ low_x = torch.cat([xi_low, xv_low], dim=1)
+ high_x = torch.cat([xi_high, xv_high], dim=1)
+ elif xi is not None:
+ low_x, high_x = self._linear_patch(x=xi, proj_type='image')
+ elif xv is not None:
+ low_x, high_x = self._linear_patch(x=xv, proj_type='video')
+
+ return self._feature_transform(low_x=low_x, high_x=high_x)
+
+
+
+class Decoder_Arch(nn.Module):
+ def __init__(self,
+ l_dim = 128,
+ h_dim = 384,
+ sep_num_layer = 2,
+ fusion_num_layer = 4,
+ patch_size = (2,4,4),
+ in_channel = 3
+ ):
+ super(Decoder_Arch, self).__init__()
+
+ self.l_dim = l_dim
+ self.is_first_chunk = True
+ self.tile_inference = False
+
+ self._build_linear_unpatch(in_channel=in_channel, out_channel_low=l_dim, out_channel_high=h_dim, pt=patch_size[0], ph=patch_size[1], pw=patch_size[2])
+
+ self.low_layer = ResNAF(num_layer=sep_num_layer, dim=l_dim)
+ self.high_layer = ResNAF(num_layer=sep_num_layer, dim=h_dim)
+ self.fusion_layer = ResNAF(num_layer=fusion_num_layer, dim=l_dim + h_dim)
+
+
+ def _build_linear_unpatch(self, in_channel = 3, out_channel_low = 128, out_channel_high = 384, pt = 2, ph = 4, pw = 4):
+ patch_config = {
+ 'video_low': (pt, ph, pw),
+ 'video_high': (pt, ph, pw),
+ 'image_low': (1, ph, pw),
+ 'image_high': (1, ph, pw)
+ }
+
+ for name, (t, h, w) in patch_config.items():
+ if 'low' in name:
+ out_dim = in_channel * t * h * w
+ in_dim = out_channel_low
+ else:
+ in_dim = out_channel_high
+ out_dim = in_channel * t * h * w * 7 if 'video' in name else in_channel * t * h * w * 3
+ proj = nn.Sequential(
+ nn.Linear(in_dim, out_dim),
+ Rearrange('b nt nh nw (c pt ph pw) -> b c (nt pt) (nh ph) (nw pw)', pt=t, ph=h, pw=w),
+ )
+ self.add_module(f"{name}_proj", proj)
+
+ def _linear_unpatch(self, x, proj_type):
+ low_comp, high_comp = getattr(self, f"{proj_type}_low_proj")(x[0]), getattr(self, f"{proj_type}_high_proj")(x[1])
+ return torch.cat([low_comp, high_comp], dim=1)
+
+ def _feature_transform(self, x):
+ x = self.fusion_layer(x)
+ low_x = self.low_layer(x[:,:,:,:,:self.l_dim])
+ high_x = self.high_layer(x[:,:,:,:,self.l_dim:])
+
+ return low_x, high_x
+
+
+ def decode(self, x, is_image = False):
+ low_x, high_x = self._feature_transform(x)
+
+ if is_image:
+ xi = self._linear_unpatch(x=(low_x, high_x), proj_type='image')
+ return (xi, None)
+
+ else:
+ if self.tile_inference and not self.is_first_chunk:
+ xv = self._linear_unpatch(x=(low_x, high_x), proj_type='video')
+ return (None, xv)
+ else:
+ xi = self._linear_unpatch(x=(low_x[:, :1], high_x[:, :1]), proj_type='image')
+ xv = self._linear_unpatch(x=(low_x[:, 1:], high_x[:, 1:]), proj_type='video')
+ return (xi, xv)
+
+ def forward(self, x):
+ low_x, high_x = self._feature_transform(x)
+ xi = self._linear_unpatch(x=(low_x[:, :1], high_x[:, :1]), proj_type='image')
+ xv = self._linear_unpatch(x=(low_x[:, 1:], high_x[:, 1:]), proj_type='video')
+ return (xi, xv)
+
+class ISTA(nn.Module):
+ def __init__(self,
+ points_num = 512,
+ out_num = 4,
+ iter_num = 2,
+ layer_num = 2,
+ ):
+ super(ISTA, self).__init__()
+ phi_init = np.random.normal(0.0, (1 / points_num) ** 0.5, size=(out_num, points_num))
+ self.phi = nn.Parameter(torch.from_numpy(phi_init).float(), requires_grad=True)
+ self.Q = nn.Parameter(torch.from_numpy(np.transpose(phi_init)).float(), requires_grad=True)
+ self.iter_num = iter_num
+ self.forward_l = nn.ModuleList()
+ self.backward_l = nn.ModuleList()
+
+ for _ in range(self.iter_num):
+ self.forward_l.append(NAF(num_layer=layer_num, dim=points_num))
+ self.backward_l.append(NAF(num_layer=layer_num, dim=points_num))
+
+ self.weights = nn.ParameterList()
+ self.etas = nn.ParameterList()
+ self.threshold = nn.ParameterList()
+
+ for _ in range(self.iter_num):
+ self.threshold.append(nn.Parameter(torch.Tensor([0.01]), requires_grad=True))
+ self.weights.append(nn.Parameter(torch.tensor(1.), requires_grad=True))
+
+ def sample(self, x):
+ b, t, h, w, d = x.shape
+ y = x.view(-1, d) @ self.phi.T
+ return y.view(b, t, h, w, -1)
+
+ def recon(self, y):
+ b, t, h, w, c = y.shape
+ y = y.reshape(-1, c)
+ recon = torch.mm(y, self.Q.t())
+ _, d = recon.shape
+ for i in range(self.iter_num):
+ recon_r = recon - self.weights[i] * torch.mm((torch.mm(recon, self.phi.t()) - y), self.phi)
+ recon = recon_r.reshape(b, t, h, w, -1)
+ recon = self.forward_l[i](recon)
+ recon = torch.mul(torch.sign(recon), F.relu(torch.abs(recon) - self.threshold[i]))
+
+ recon = self.backward_l[i](recon).view(-1, d)
+ recon = recon_r + recon
+ return recon.view(b, t, h, w, -1)
+
+
+ def forward(self, x):
+ y = self.sample(x)
+ recon = self.recon(y)
+ return recon
+
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/cache/vgg.pth b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/cache/vgg.pth
new file mode 100644
index 0000000000000000000000000000000000000000..f57dcf5cc764d61c8a460365847fb2137ff0a62d
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/cache/vgg.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a78928a0af1e5f0fcb1f3b9e8f8c3a2a5a3de244d830ad5c1feddc79b8432868
+size 7289
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/discriminator.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/discriminator.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5b3ee43db5b456649578cc8554cd6993ec3152e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/discriminator.py
@@ -0,0 +1,130 @@
+from typing import Any, Union
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+import functools
+
+class ActNorm(nn.Module):
+ def __init__(self, num_features, logdet=False, affine=True, allow_reverse_init=False):
+ assert affine
+ super().__init__()
+ self.logdet = logdet
+ self.loc = nn.Parameter(torch.zeros(1, num_features, 1, 1))
+ self.scale = nn.Parameter(torch.ones(1, num_features, 1, 1))
+ self.allow_reverse_init = allow_reverse_init
+
+ self.register_buffer("initialized", torch.tensor(0, dtype=torch.uint8))
+
+ def initialize(self, input):
+ with torch.no_grad():
+ flatten = input.permute(1, 0, 2, 3).contiguous().view(input.shape[1], -1)
+ mean = flatten.mean(1).unsqueeze(1).unsqueeze(2).unsqueeze(3).permute(1, 0, 2, 3)
+ std = flatten.std(1).unsqueeze(1).unsqueeze(2).unsqueeze(3).permute(1, 0, 2, 3)
+
+ self.loc.data.copy_(-mean)
+ self.scale.data.copy_(1 / (std + 1e-6))
+
+ def forward(self, input, reverse=False):
+ if reverse:
+ return self.reverse(input)
+ if len(input.shape) == 2:
+ input = input[:, :, None, None]
+ squeeze = True
+ else:
+ squeeze = False
+
+ _, _, height, width = input.shape
+
+ if self.training and self.initialized.item() == 0:
+ self.initialize(input)
+ self.initialized.fill_(1)
+
+ h = self.scale * (input + self.loc)
+
+ if squeeze:
+ h = h.squeeze(-1).squeeze(-1)
+
+ if self.logdet:
+ log_abs = torch.log(torch.abs(self.scale))
+ logdet = height * width * torch.sum(log_abs)
+ logdet = logdet * torch.ones(input.shape[0]).to(input)
+ return h, logdet
+
+ return h
+
+ def reverse(self, output):
+ if self.training and self.initialized.item() == 0:
+ if not self.allow_reverse_init:
+ raise RuntimeError(
+ "Initializing ActNorm in reverse direction is "
+ "disabled by default. Use allow_reverse_init=True to enable."
+ )
+ else:
+ self.initialize(output)
+ self.initialized.fill_(1)
+
+ if len(output.shape) == 2:
+ output = output[:, :, None, None]
+ squeeze = True
+ else:
+ squeeze = False
+
+ h = output / self.scale - self.loc
+
+ if squeeze:
+ h = h.squeeze(-1).squeeze(-1)
+ return h
+
+
+
+class NLayerDiscriminator(nn.Module):
+ """Defines a PatchGAN discriminator as in Pix2Pix."""
+ # https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/networks.py
+ def __init__(self, input_nc=3, ndf=64, n_layers=3, use_actnorm=False):
+ """Construct a PatchGAN discriminator
+ Parameters:
+ input_nc (int) -- the number of channels in input images
+ ndf (int) -- the number of filters in the last conv layer
+ n_layers (int) -- the number of conv layers in the discriminator
+ """
+ super(NLayerDiscriminator, self).__init__()
+ if not use_actnorm:
+ norm_layer = nn.BatchNorm2d
+ else:
+ norm_layer = ActNorm
+ if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
+ use_bias = norm_layer.func != nn.BatchNorm2d
+ else:
+ use_bias = norm_layer != nn.BatchNorm2d
+
+ kw = 4
+ padw = 1
+ sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
+ nf_mult = 1
+ nf_mult_prev = 1
+ for n in range(1, n_layers): # gradually increase the number of filters
+ nf_mult_prev = nf_mult
+ nf_mult = min(2**n, 8)
+ sequence += [
+ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
+ norm_layer(ndf * nf_mult),
+ nn.LeakyReLU(0.2, True),
+ ]
+
+ nf_mult_prev = nf_mult
+ nf_mult = min(2**n_layers, 8)
+ sequence += [
+ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
+ norm_layer(ndf * nf_mult),
+ nn.LeakyReLU(0.2, True),
+ ]
+
+ sequence += [
+ nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
+ ] # output 1 channel prediction map
+ self.main = nn.Sequential(*sequence)
+
+ def forward(self, input):
+ """Standard forward."""
+ return self.main(input)
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/lpips.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/lpips.py
new file mode 100644
index 0000000000000000000000000000000000000000..c63164f1804b37aa9e9a6739efb0b96138d4fb1b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/lpips.py
@@ -0,0 +1,230 @@
+"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
+
+import os, hashlib
+import requests
+from tqdm import tqdm
+
+import torch
+import torch.nn as nn
+from torchvision import models
+from collections import namedtuple
+import torchvision
+URL_MAP = {
+ "vgg_lpips": "https://heibox.uni-heidelberg.de/f/607503859c864bc1b30b/?dl=1"
+}
+
+CKPT_MAP = {
+ "vgg_lpips": "vgg.pth"
+}
+
+MD5_MAP = {
+ "vgg_lpips": "d507d7349b931f0638a25a48a722f98a"
+}
+
+def download(url, local_path, chunk_size=1024):
+ os.makedirs(os.path.split(local_path)[0], exist_ok=True)
+ with requests.get(url, stream=True) as r:
+ total_size = int(r.headers.get("content-length", 0))
+ with tqdm(total=total_size, unit="B", unit_scale=True) as pbar:
+ with open(local_path, "wb") as f:
+ for data in r.iter_content(chunk_size=chunk_size):
+ if data:
+ f.write(data)
+ pbar.update(chunk_size)
+
+
+def md5_hash(path):
+ with open(path, "rb") as f:
+ content = f.read()
+ return hashlib.md5(content).hexdigest()
+
+
+def get_ckpt_path(name, root, check=False):
+ assert name in URL_MAP
+ path = os.path.join(root, CKPT_MAP[name])
+ if not os.path.exists(path) or (check and not md5_hash(path) == MD5_MAP[name]):
+ print("Downloading {} model from {} to {}".format(name, URL_MAP[name], path))
+ download(URL_MAP[name], path)
+ md5 = md5_hash(path)
+ assert md5 == MD5_MAP[name], md5
+ return path
+
+
+class LPIPS(nn.Module):
+ # Learned perceptual metric
+ def __init__(self, use_dropout=True):
+ super().__init__()
+ self.scaling_layer = ScalingLayer()
+ self.chns = [64, 128, 256, 512, 512] # vg16 features
+ self.net = vgg16(pretrained=True, requires_grad=False)
+ self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
+ self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
+ self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
+ self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
+ self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
+ self.load_from_pretrained()
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def load_from_pretrained(self, name="vgg_lpips"):
+ ckpt = get_ckpt_path(name, os.path.join(os.path.dirname(os.path.abspath(__file__)), "cache"))
+ self.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
+ print("loaded pretrained LPIPS loss from {}".format(ckpt))
+
+ @classmethod
+ def from_pretrained(cls, name="vgg_lpips"):
+ if name is not "vgg_lpips":
+ raise NotImplementedError
+ model = cls()
+ ckpt = get_ckpt_path(name, os.path.join(os.path.dirname(os.path.abspath(__file__)), "cache"))
+ r = model.load_state_dict(torch.load(ckpt, map_location=torch.device("cpu")), strict=False)
+ print(r)
+ return model
+
+ def forward(self, input, target):
+ in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
+ outs0, outs1 = self.net(in0_input), self.net(in1_input)
+ feats0, feats1, diffs = {}, {}, {}
+ lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
+ for kk in range(len(self.chns)):
+ feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])
+ diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
+
+ res = [spatial_average(lins[kk].model(diffs[kk]), keepdim=True) for kk in range(len(self.chns))]
+ val = res[0]
+ for l in range(1, len(self.chns)):
+ # print(res[l].shape)
+ val += res[l]
+
+ return val
+
+
+
+
+
+class ScalingLayer(nn.Module):
+ def __init__(self):
+ super(ScalingLayer, self).__init__()
+ self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])
+ self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])
+
+ def forward(self, inp):
+ return (inp - self.shift) / self.scale
+
+
+class NetLinLayer(nn.Module):
+ """ A single linear layer which does a 1x1 conv """
+ def __init__(self, chn_in, chn_out=1, use_dropout=False):
+ super(NetLinLayer, self).__init__()
+ layers = [nn.Dropout(), ] if (use_dropout) else []
+ layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]
+ self.model = nn.Sequential(*layers)
+
+
+class vgg16(torch.nn.Module):
+ def __init__(self, requires_grad=False, pretrained=True):
+ super(vgg16, self).__init__()
+ vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
+ self.slice1 = torch.nn.Sequential()
+ self.slice2 = torch.nn.Sequential()
+ self.slice3 = torch.nn.Sequential()
+ self.slice4 = torch.nn.Sequential()
+ self.slice5 = torch.nn.Sequential()
+ self.N_slices = 5
+ for x in range(4):
+ self.slice1.add_module(str(x), vgg_pretrained_features[x])
+ for x in range(4, 9):
+ self.slice2.add_module(str(x), vgg_pretrained_features[x])
+ for x in range(9, 16):
+ self.slice3.add_module(str(x), vgg_pretrained_features[x])
+ for x in range(16, 23):
+ self.slice4.add_module(str(x), vgg_pretrained_features[x])
+ for x in range(23, 30):
+ self.slice5.add_module(str(x), vgg_pretrained_features[x])
+ if not requires_grad:
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, X):
+ h = self.slice1(X)
+ h_relu1_2 = h
+ h = self.slice2(h)
+ h_relu2_2 = h
+ h = self.slice3(h)
+ h_relu3_3 = h
+ h = self.slice4(h)
+ h_relu4_3 = h
+ h = self.slice5(h)
+ h_relu5_3 = h
+ vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
+ out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+ return out
+
+
+def normalize_tensor(x,eps=1e-10):
+ norm_factor = torch.sqrt(torch.sum(x**2,dim=1,keepdim=True))
+ return x/(norm_factor+eps)
+
+
+def spatial_average(x, keepdim=True):
+ return x.mean([2,3],keepdim=keepdim)
+
+
+class ResNetLPIPS(nn.Module):
+ # Learned perceptual metric
+ def __init__(self, use_dropout=True):
+ super().__init__()
+ net, _ = clip.load(device='cpu', name='RN50')
+ self.net = net.visual
+ self.net.attnpool = nn.Identity()
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, input, target):
+
+ outs0, outs1 = self.net(input), self.net(target)
+ #feats0, feats1= normalize_tensor(outs0), normalize_tensor(outs1)
+ diffs = ((outs0 - outs1) ** 2 ) #(feats0 - feats1) ** 2
+
+ return diffs
+
+
+class MeanShift(nn.Conv2d):
+ def __init__(self, data_mean, data_std, data_range=1, norm=True):
+ c = len(data_mean)
+ super(MeanShift, self).__init__(c, c, kernel_size=1)
+ std = torch.Tensor(data_std)
+ self.weight.data = torch.eye(c).view(c, c, 1, 1)
+ if norm:
+ self.weight.data.div_(std.view(c, 1, 1, 1))
+ self.bias.data = -1 * data_range * torch.Tensor(data_mean)
+ self.bias.data.div_(std)
+ else:
+ self.weight.data.mul_(std.view(c, 1, 1, 1))
+ self.bias.data = data_range * torch.Tensor(data_mean)
+ self.requires_grad = False
+
+class VGGPerceptualLoss(torch.nn.Module):
+ def __init__(self, rank):
+ super(VGGPerceptualLoss, self).__init__()
+ blocks = []
+ pretrained = True
+ self.vgg_pretrained_features = models.vgg19(pretrained=pretrained).features
+ self.normalize = MeanShift([0.485, 0.456, 0.406], [0.229, 0.224, 0.225], norm=True).to(rank)
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, Y, X, indices=None):
+ X = self.normalize(X)
+ Y = self.normalize(Y)
+ indices = [2, 7, 12, 21, 30]
+ weights = [1.0/2.6, 1.0/4.8, 1.0/3.7, 1.0/5.6, 10/1.5]
+ k = 0
+ loss = 0
+ for i in range(indices[-1]):
+ X = self.vgg_pretrained_features[i](X)
+ Y = self.vgg_pretrained_features[i](Y)
+ if (i+1) in indices:
+ loss += weights[k] * (X - Y.detach()).abs().mean() * 0.1
+ k += 1
+ return loss
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/vae.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/vae.py
new file mode 100644
index 0000000000000000000000000000000000000000..7acc228efac8a8b318fff172d0cefc8fe686a93a
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/modules/vae.py
@@ -0,0 +1,73 @@
+import torch
+import numpy as np
+
+class DiagonalGaussianDistribution(object):
+ def __init__(self, parameters, deterministic=False):
+ self.parameters = parameters
+ self.mean, self.logvar = parameters #torch.chunk(parameters, 2, dim=1)
+ self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+ self.deterministic = deterministic
+ self.std = torch.exp(0.5 * self.logvar)
+ self.var = torch.exp(self.logvar)
+ if self.deterministic:
+ self.var = self.std = torch.zeros_like(self.mean).to(device=self.mean.device)
+
+ def sample(self):
+ x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.mean.device)
+ return x
+
+ def kl(self, other=None):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ else:
+ if other is None:
+ return 0.5 * torch.sum(torch.pow(self.mean, 2)
+ + self.var - 1.0 - self.logvar,
+ dim=[1, 2, 3])
+ else:
+ return 0.5 * torch.sum(
+ torch.pow(self.mean - other.mean, 2) / other.var
+ + self.var / other.var - 1.0 - self.logvar + other.logvar,
+ dim=[1, 2, 3])
+
+ def nll(self, sample, dims=[1,2,3]):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ logtwopi = np.log(2.0 * np.pi)
+ return 0.5 * torch.sum(
+ logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+ dim=dims)
+
+ def mode(self):
+ return self.mean
+
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+ """
+ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+ Compute the KL divergence between two gaussians.
+ Shapes are automatically broadcasted, so batches can be compared to
+ scalars, among other use cases.
+ """
+ tensor = None
+ for obj in (mean1, logvar1, mean2, logvar2):
+ if isinstance(obj, torch.Tensor):
+ tensor = obj
+ break
+ assert tensor is not None, "at least one argument must be a Tensor"
+
+ # Force variances to be Tensors. Broadcasting helps convert scalars to
+ # Tensors, but it does not work for torch.exp().
+ logvar1, logvar2 = [
+ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+ for x in (logvar1, logvar2)
+ ]
+
+ return 0.5 * (
+ -1.0
+ + logvar2
+ - logvar1
+ + torch.exp(logvar1 - logvar2)
+ + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+ )
\ No newline at end of file
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/callbacks.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/callbacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e7d4fa62a683e079a3e5b41f3644bd836ee9f19
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/callbacks.py
@@ -0,0 +1,120 @@
+import os
+import numpy as np
+from PIL import Image
+
+import torch
+import torchvision
+from pytorch_lightning.callbacks import Callback
+from pytorch_lightning.utilities.distributed import rank_zero_only
+
+import random
+from .utils import save_video_grid
+
+
+
+class VideoLogger(Callback):
+ def __init__(self, batch_frequency, max_videos, clamp=True, increase_log_steps=True):
+ super().__init__()
+ self.batch_freq = batch_frequency
+ self.max_videos = max_videos
+ self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
+ if not increase_log_steps:
+ self.log_steps = [self.batch_freq]
+ self.clamp = clamp
+
+
+ @rank_zero_only
+ def log_local(self, save_dir, split, videos,
+ global_step, current_epoch, batch_idx):
+ root = os.path.join(save_dir, "videos", split)
+ for k in videos:
+ grid = videos[k] + 0.5
+ filename = "gs-{:06}_e-{:06}_b-{:06}_{}.mp4".format(
+ global_step,
+ current_epoch,
+ batch_idx,
+ k)
+ path = os.path.join(root, filename)
+ os.makedirs(os.path.split(path)[0], exist_ok=True)
+ save_video_grid(grid, path)
+
+ def log_vid(self, pl_module, batch, batch_idx, split="train"):
+ # print(batch_idx, self.batch_freq, self.check_frequency(batch_idx) and hasattr(pl_module, "log_videos") and callable(pl_module.log_videos) and self.max_videos > 0)
+ if (self.check_frequency(batch_idx) and # batch_idx % self.batch_freq == 0
+ hasattr(pl_module, "log_videos") and
+ callable(pl_module.log_videos) and
+ self.max_videos > 0):
+ # print(batch_idx, self.batch_freq, self.check_frequency(batch_idx))
+ logger = type(pl_module.logger)
+
+ is_train = pl_module.training
+ if is_train:
+ pl_module.eval()
+
+ with torch.no_grad():
+ videos = pl_module.log_videos(batch, split=split, batch_idx=batch_idx)
+
+ for k in videos:
+ N = min(videos[k].shape[0], self.max_videos)
+ videos[k] = videos[k][:N]
+ if isinstance(videos[k], torch.Tensor):
+ videos[k] = videos[k].detach().cpu()
+ if self.clamp:
+ videos[k] = torch.clamp(videos[k], -0.5, 0.5)
+
+ self.log_local(pl_module.logger.save_dir, split, videos,
+ pl_module.global_step, pl_module.current_epoch, batch_idx)
+
+ if is_train:
+ pl_module.train()
+
+ def check_frequency(self, batch_idx):
+ if (batch_idx % self.batch_freq) == 0 or (batch_idx in self.log_steps):
+ try:
+ self.log_steps.pop(0)
+ except IndexError:
+ pass
+ return True
+ return False
+
+ def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+ if batch[0]['video'].ndim == 4:
+ return
+ self.log_vid(pl_module, batch, batch_idx, split="train")
+
+ def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+ self.log_vid(pl_module, batch, batch_idx, split="val")
+
+
+class DatasetCallback(Callback):
+ def __init__(self, initial_batch_size, new_batch_size, step_threshold):
+ self.initial_batch_size = initial_batch_size
+ self.new_batch_size = new_batch_size
+ self.step_threshold = step_threshold
+
+ def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx):
+ if trainer.global_step == self.step_threshold:
+ # 更新 DataLoader 的 batch_size
+ trainer.train_dataloader = trainer.video_data._dataloader(train=True, batch_size=self.new_batch_size) # self.new_batch_size
+ print(f'Batch size changed to {self.new_batch_size} at step {self.step_threshold}')
+ # def __init__(self):
+ # self.seqlen_list = [17, 21, 17, 25, 21, 29, 33, 17, 21, 17]
+
+# #临时取消
+
+# def on_batch_start(self, trainer, pl_module):
+# seq_len = random.randint(0, 9)
+# trainer.train_dataloader.dataset.datasets[0].sequence_length = self.seqlen_list[seq_len]
+
+# def on_train_batch_start(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+# seq_len = batch_idx % 10
+# trainer.train_dataloader.dataset.datasets[dataloader_idx].sequence_length = self.seqlen_list[seq_len]
+# return
+# #为啥上面那个没报错 下边那个报错说是list呢？？
+# def on_epoch_end(self, trainer, pl_module):
+# with open('tmp_shape2.txt', 'a') as f:
+# print(trainer.current_epoch, file=f )
+# #trainer.train_dataloader[0].sampler.set_epoch(trainer.current_epoch)
+# return
+
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/gan_loss.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/gan_loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..471c1f56ec17164ef871239460b8b600c4146425
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/gan_loss.py
@@ -0,0 +1,147 @@
+from typing import Any, Union
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+import functools
+from ..modules.discriminator import NLayerDiscriminator
+
+def hinge_d_loss(logits_real, logits_fake):
+ loss_real = torch.mean(F.relu(1.0 - logits_real))
+ loss_fake = torch.mean(F.relu(1.0 + logits_fake))
+ d_loss = 0.5 * (loss_real + loss_fake)
+ return d_loss
+
+
+def vanilla_d_loss(logits_real, logits_fake):
+ d_loss = 0.5 * (torch.mean(F.softplus(-logits_real)) + torch.mean(F.softplus(logits_fake)))
+ return d_loss
+
+
+def adopt_weight(weight, global_step, threshold=0, value=0.0):
+ if global_step < threshold:
+ weight = value
+ return weight
+
+
+def _sigmoid_cross_entropy_with_logits(labels, logits):
+ """
+ non-saturating loss
+ """
+ zeros = torch.zeros_like(logits, dtype=logits.dtype)
+ condition = logits >= zeros
+ relu_logits = torch.where(condition, logits, zeros)
+ neg_abs_logits = torch.where(condition, -logits, logits)
+ return relu_logits - logits * labels + torch.log1p(torch.exp(neg_abs_logits))
+
+
+def non_saturate_gen_loss(logits_fake):
+ """
+ logits_fake: [B 1 H W]
+ """
+ B = logits_fake.shape[0]
+ logits_fake = logits_fake.reshape(B, -1)
+ logits_fake = torch.mean(logits_fake, dim=-1)
+ gen_loss = torch.mean(_sigmoid_cross_entropy_with_logits(labels=torch.ones_like(logits_fake), logits=logits_fake))
+ return gen_loss
+
+
+def lecam_reg(real_pred, fake_pred, lecam_ema):
+ reg = torch.mean(F.relu(real_pred - lecam_ema.logits_fake_ema).pow(2)) + torch.mean(
+ F.relu(lecam_ema.logits_real_ema - fake_pred).pow(2)
+ )
+ return reg
+
+
+class LeCAM_EMA(object):
+ # https://github.com/TencentARC/SEED-Voken/blob/main/src/Open_MAGVIT2/modules/losses/vqperceptual.py
+ def __init__(self, init=0.0, decay=0.999):
+ self.logits_real_ema = init
+ self.logits_fake_ema = init
+ self.decay = decay
+
+ def update(self, logits_real, logits_fake):
+ self.logits_real_ema = self.logits_real_ema * self.decay + torch.mean(logits_real).item() * (1 - self.decay)
+ self.logits_fake_ema = self.logits_fake_ema * self.decay + torch.mean(logits_fake).item() * (1 - self.decay)
+
+def weights_init(m):
+ classname = m.__class__.__name__
+ if classname.find("Conv") != -1:
+ nn.init.normal_(m.weight.data, 0.0, 0.02)
+ elif classname.find("BatchNorm") != -1:
+ nn.init.normal_(m.weight.data, 1.0, 0.02)
+ nn.init.constant_(m.bias.data, 0)
+
+class AdversarialLoss(nn.Module):
+ def __init__(
+ self,
+ disc_num_layers: int = 3,
+ disc_in_channels: int = 3,
+ disc_weight: float = 0.2,
+ lecam_loss_weight: float = 0.005,
+ disc_loss: str = "hinge",
+ dims: int = 3,
+ gen_loss_cross_entropy: bool = True,
+ ):
+ super().__init__()
+ self.dims = dims
+ assert disc_loss in ["hinge", "vanilla"]
+ self.discriminator = NLayerDiscriminator(
+ input_nc=disc_in_channels, n_layers=disc_num_layers, use_actnorm=False
+ ).apply(weights_init)
+
+ self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+ self.discriminator_weight = disc_weight
+
+ self.gen_loss_cross_entropy = gen_loss_cross_entropy
+ self.lecam_loss_weight = lecam_loss_weight
+ if self.lecam_loss_weight > 0:
+ self.lecam_ema = LeCAM_EMA()
+
+ def get_trainable_parameters(self) -> Any:
+ return self.discriminator.parameters()
+
+ def forward(
+ self,
+ inputs,
+ reconstructions,
+ optimizer_idx,
+ ):
+
+ if optimizer_idx == 0:
+ if self.dims > 2:
+ inputs, reconstructions = map(
+ lambda x: rearrange(x, "b c t h w -> (b t) c h w"),
+ (inputs, reconstructions),
+ )
+
+ # generator update
+ logits_fake = self.discriminator(reconstructions)
+
+ if not self.gen_loss_cross_entropy:
+ g_loss = -torch.mean(logits_fake)
+ else:
+ g_loss = non_saturate_gen_loss(logits_fake)
+
+ g_loss = self.discriminator_weight * g_loss
+ return g_loss
+
+
+ if optimizer_idx == 1:
+ inputs, reconstructions = map(
+ lambda x: rearrange(x, "b c t h w -> (b t) c h w"),
+ (inputs, reconstructions),
+ )
+
+ logits_real = self.discriminator(inputs.contiguous().detach())
+ logits_fake = self.discriminator(reconstructions.contiguous().detach())
+
+ non_saturate_d_loss = self.disc_loss(logits_real, logits_fake)
+
+ if self.lecam_loss_weight > 0:
+ self.lecam_ema.update(logits_real, logits_fake)
+ lecam_loss = lecam_reg(logits_real, logits_fake, self.lecam_ema)
+ d_loss = lecam_loss * self.lecam_loss_weight + non_saturate_d_loss
+ else:
+ d_loss = non_saturate_d_loss
+ return d_loss
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/patcher_utils.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/patcher_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..acefc5dd7001eaefac5be490b2d5037270d8338a
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/patcher_utils.py
@@ -0,0 +1,246 @@
+import os
+import random
+import shutil
+import sys
+from datetime import datetime
+import math
+import numpy as np
+import pywt
+import torch
+from torch.nn import Module
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn as nn
+
+_PERSISTENT = False
+
+
+class Patcher(torch.nn.Module):
+ def __init__(self, rescale = True):
+ super().__init__()
+ self.register_buffer(
+ "wavelets", torch.tensor([0.7071067811865476, 0.7071067811865476]), persistent=_PERSISTENT
+ )
+ self.register_buffer(
+ "_arange",
+ torch.arange(2),
+ persistent=_PERSISTENT,
+ )
+
+ self.rescale = rescale
+ for param in self.parameters():
+ param.requires_grad = False
+
+
+
+ def _2ddwt(self, x, mode="reflect", rescale=False):
+ dtype = x.dtype
+ h = self.wavelets
+ x = x.squeeze(2)
+
+ n = h.shape[0]
+ g = x.shape[1]
+ hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1)
+ hh = (h * ((-1) ** self._arange)).reshape(1, 1, -1).repeat(g, 1, 1)
+ hh = hh.to(dtype=dtype)
+ hl = hl.to(dtype=dtype)
+
+ x = F.pad(x, pad=(n - 2, n - 1, n - 2, n - 1), mode=mode).to(dtype)
+ xl = F.conv2d(x, hl.unsqueeze(2), groups=g, stride=(1, 2))
+ xh = F.conv2d(x, hh.unsqueeze(2), groups=g, stride=(1, 2))
+ xll = F.conv2d(xl, hl.unsqueeze(3), groups=g, stride=(2, 1))
+ xlh = F.conv2d(xl, hh.unsqueeze(3), groups=g, stride=(2, 1))
+ xhl = F.conv2d(xh, hl.unsqueeze(3), groups=g, stride=(2, 1))
+ xhh = F.conv2d(xh, hh.unsqueeze(3), groups=g, stride=(2, 1))
+
+ out = torch.cat([xll, xlh, xhl, xhh], dim=1)
+ if rescale:
+ out = out * 2
+ return out.unsqueeze(2)
+
+ def _3ddwt(self, x, mode="reflect", rescale=False):
+ dtype = x.dtype
+ h = self.wavelets
+
+ n = h.shape[0]
+ g = x.shape[1]
+ hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1)
+ hh = (h * ((-1) ** self._arange)).reshape(1, 1, -1).repeat(g, 1, 1)
+ hh = hh.to(dtype=dtype)
+ hl = hl.to(dtype=dtype)
+
+ # Handles temporal axis.
+ x = F.pad(
+ x, pad=(max(0, n - 2), n - 1, n - 2, n - 1, n - 2, n - 1), mode=mode
+ ).to(dtype)
+ xl = F.conv3d(x, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1))
+ xh = F.conv3d(x, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1))
+
+ # Handles spatial axes.
+ xll = F.conv3d(xl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+ xlh = F.conv3d(xl, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+ xhl = F.conv3d(xh, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+ xhh = F.conv3d(xh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+
+ xlll = F.conv3d(xll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+ xllh = F.conv3d(xll, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+ xlhl = F.conv3d(xlh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+ xlhh = F.conv3d(xlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+ xhll = F.conv3d(xhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+ xhlh = F.conv3d(xhl, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+ xhhl = F.conv3d(xhh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+ xhhh = F.conv3d(xhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+
+ out = torch.cat([xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh], dim=1)
+ if rescale:
+ out = out * (2 * torch.sqrt(torch.tensor(2.0)))
+ return out
+
+ def forward(self, x):
+ if x.shape[2] > 1:
+ if x.shape[2] % 2 == 1:
+ xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2)
+ xi = self._2ddwt(xi, rescale=self.rescale)
+ xv = self._3ddwt(xv, rescale=self.rescale)
+ return xi, xv
+ else:
+ xv = self._3ddwt(x, rescale=self.rescale)
+ return None, xv
+
+ return (self._2ddwt(x, rescale=self.rescale), None)
+
+
+class UnPatcher(torch.nn.Module):
+
+ def __init__(self, rescale = True):
+ super().__init__()
+ self.register_buffer(
+ "wavelets", torch.tensor([0.7071067811865476, 0.7071067811865476]), persistent=_PERSISTENT
+ )
+ self.register_buffer(
+ "_arange",
+ torch.arange(2),
+ persistent=_PERSISTENT,
+ )
+ self.rescale = rescale
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, x):
+ xi, xv = x
+ if xi is not None and xv is not None:
+ xi = self._2didwt(xi, rescale=self.rescale)
+ xv = self._3didwt(xv, rescale=self.rescale)
+ return torch.cat([xi.unsqueeze(2), xv], dim=2)
+ elif xv is None and xi is not None:
+ return self._2didwt(xi, rescale=self.rescale)
+ elif xv is not None and xi is None:
+ return self._3didwt(xv, rescale=self.rescale)
+
+ def _2didwt(self, x, mode="reflect", rescale=False):
+ dtype = x.dtype
+ h = self.wavelets
+ n = h.shape[0]
+ x = x.squeeze(2)
+
+ g = x.shape[1] // 4
+ hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1])
+ hh = (h * ((-1) ** self._arange)).reshape(1, 1, -1).repeat(g, 1, 1)
+ hh = hh.to(dtype=dtype)
+ hl = hl.to(dtype=dtype)
+
+ xll, xlh, xhl, xhh = torch.chunk(x.to(dtype), 4, dim=1)
+
+ # Inverse transform.
+ yl = torch.nn.functional.conv_transpose2d(
+ xll, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+ )
+ yl += torch.nn.functional.conv_transpose2d(
+ xlh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+ )
+ yh = torch.nn.functional.conv_transpose2d(
+ xhl, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+ )
+ yh += torch.nn.functional.conv_transpose2d(
+ xhh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+ )
+ y = torch.nn.functional.conv_transpose2d(
+ yl, hl.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2)
+ )
+ y += torch.nn.functional.conv_transpose2d(
+ yh, hh.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2)
+ )
+
+ if rescale:
+ y = y / 2
+ return y
+
+ def _3didwt(self, x, mode="reflect", rescale=False):
+ dtype = x.dtype
+ h = self.wavelets
+ n = h.shape[0]
+
+ g = x.shape[1] // 8 # split into 8 spatio-temporal filtered tesnors.
+ hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1])
+ hh = (h * ((-1) ** self._arange)).reshape(1, 1, -1).repeat(g, 1, 1)
+ hl = hl.to(dtype=dtype)
+ hh = hh.to(dtype=dtype)
+
+ xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh = torch.chunk(x, 8, dim=1)
+
+ # Height height transposed convolutions.
+ xll = F.conv_transpose3d(
+ xlll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+ )
+ xll += F.conv_transpose3d(
+ xllh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+ )
+
+ xlh = F.conv_transpose3d(
+ xlhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+ )
+ xlh += F.conv_transpose3d(
+ xlhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+ )
+
+ xhl = F.conv_transpose3d(
+ xhll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+ )
+ xhl += F.conv_transpose3d(
+ xhlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+ )
+
+ xhh = F.conv_transpose3d(
+ xhhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+ )
+ xhh += F.conv_transpose3d(
+ xhhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+ )
+
+ # Handles width transposed convolutions.
+ xl = F.conv_transpose3d(
+ xll, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+ )
+ xl += F.conv_transpose3d(
+ xlh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+ )
+ xh = F.conv_transpose3d(
+ xhl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+ )
+ xh += F.conv_transpose3d(
+ xhh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+ )
+
+ # Handles time axis transposed convolutions.
+ x = F.conv_transpose3d(
+ xl, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)
+ )
+ x += F.conv_transpose3d(
+ xh, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)
+ )
+
+ if rescale:
+ x = x / (2 * torch.sqrt(torch.tensor(2.0)))
+ return x
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/utils.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..647af2a3d1a6e0507fcefd89ebf6dd3e9d8edb8b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/utils.py
@@ -0,0 +1,171 @@
+import torch
+import imageio
+
+import math
+import numpy as np
+import skvideo.io
+
+import sys
+import pdb as pdb_original
+
+class ForkedPdb(pdb_original.Pdb):
+ """A Pdb subclass that may be used
+ from a forked multiprocessing child
+
+ """
+ def interaction(self, *args, **kwargs):
+ _stdin = sys.stdin
+ try:
+ sys.stdin = open('/dev/stdin')
+ pdb_original.Pdb.interaction(self, *args, **kwargs)
+ finally:
+ sys.stdin = _stdin
+
+
+
+# Shifts src_tf dim to dest dim
+# i.e. shift_dim(x, 1, -1) would be (b, c, t, h, w) -> (b, t, h, w, c)
+def shift_dim(x, src_dim=-1, dest_dim=-1, make_contiguous=True):
+ n_dims = len(x.shape)
+ if src_dim < 0:
+ src_dim = n_dims + src_dim
+ if dest_dim < 0:
+ dest_dim = n_dims + dest_dim
+
+ assert 0 <= src_dim < n_dims and 0 <= dest_dim < n_dims
+
+ dims = list(range(n_dims))
+ del dims[src_dim]
+
+ permutation = []
+ ctr = 0
+ for i in range(n_dims):
+ if i == dest_dim:
+ permutation.append(src_dim)
+ else:
+ permutation.append(dims[ctr])
+ ctr += 1
+ x = x.permute(permutation)
+ if make_contiguous:
+ x = x.contiguous()
+ return x
+
+
+# reshapes tensor start from dim i (inclusive)
+# to dim j (exclusive) to the desired shape
+# e.g. if x.shape = (b, thw, c) then
+# view_range(x, 1, 2, (t, h, w)) returns
+# x of shape (b, t, h, w, c)
+def view_range(x, i, j, shape):
+ shape = tuple(shape)
+
+ n_dims = len(x.shape)
+ if i < 0:
+ i = n_dims + i
+
+ if j is None:
+ j = n_dims
+ elif j < 0:
+ j = n_dims + j
+
+ assert 0 <= i < j <= n_dims
+
+ x_shape = x.shape
+ target_shape = x_shape[:i] + shape + x_shape[j:]
+ return x.view(target_shape)
+
+
+def accuracy(output, target, topk=(1,)):
+ """Computes the accuracy over the k top predictions for the specified values of k"""
+ with torch.no_grad():
+ maxk = max(topk)
+ batch_size = target.size(0)
+
+ _, pred = output.topk(maxk, 1, True, True)
+ pred = pred.t()
+ correct = pred.eq(target.reshape(1, -1).expand_as(pred))
+
+ res = []
+ for k in topk:
+ correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
+ res.append(correct_k.mul_(100.0 / batch_size))
+ return res
+
+
+def tensor_slice(x, begin, size):
+ assert all([b >= 0 for b in begin])
+ size = [l - b if s == -1 else s
+ for s, b, l in zip(size, begin, x.shape)]
+ assert all([s >= 0 for s in size])
+
+ slices = [slice(b, b + s) for b, s in zip(begin, size)]
+ return x[slices]
+
+
+def adopt_weight(global_step, threshold=0, value=0.):
+ weight = 1
+ if global_step < threshold:
+ weight = value
+ return weight
+
+
+def save_video_grid(video, fname, nrow=None, fps=3):
+ b, c, t, h, w = video.shape
+ video = video.permute(0, 2, 3, 4, 1).contiguous()
+
+ video = (video.detach().cpu().numpy() * 255).astype('uint8')
+ if nrow is None:
+ nrow = math.ceil(math.sqrt(b))
+ ncol = math.ceil(b / nrow)
+ padding = 0 #临时修改
+ video_grid = np.zeros((t, (padding + h) * nrow + padding,
+ (padding + w) * ncol + padding, c), dtype='uint8')
+ # print(video_grid.shape)
+ for i in range(b):
+ r = i // ncol
+ c = i % ncol
+ start_r = (padding + h) * r
+ start_c = (padding + w) * c
+ video_grid[:, start_r:start_r + h, start_c:start_c + w] = video[i]
+ video = []
+ for i in range(t):
+ video.append(video_grid[i])
+ imageio.mimsave(fname, video, fps=fps)
+ # skvideo.io.vwrite(fname, video_grid, inputdict={'-r': '5'})
+ # print('saved videos to', fname)
+
+
+def comp_getattr(args, attr_name, default=None):
+ if hasattr(args, attr_name):
+ return getattr(args, attr_name)
+ else:
+ return default
+
+
+def visualize_tensors(t, name=None, nest=0):
+ if name is not None:
+ print(name, "current nest: ", nest)
+ print("type: ", type(t))
+ if 'dict' in str(type(t)):
+ print(t.keys())
+ for k in t.keys():
+ if t[k] is None:
+ print(k, "None")
+ else:
+ if 'Tensor' in str(type(t[k])):
+ print(k, t[k].shape)
+ elif 'dict' in str(type(t[k])):
+ print(k, 'dict')
+ visualize_tensors(t[k], name, nest + 1)
+ elif 'list' in str(type(t[k])):
+ print(k, len(t[k]))
+ visualize_tensors(t[k], name, nest + 1)
+ elif 'list' in str(type(t)):
+ print("list length: ", len(t))
+ for t2 in t:
+ visualize_tensors(t2, name, nest + 1)
+ elif 'Tensor' in str(type(t)):
+ print(t.shape)
+ else:
+ print(t)
+ return ""
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/video_utils.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/video_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2153e91852e00d3e4f3536e5a8403497ba999a6f
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/LeanVAE/utils/video_utils.py
@@ -0,0 +1,824 @@
+from email.policy import default
+
+import numbers
+import random
+import re
+from enum import Enum
+
+import numpy as np
+from PIL import Image
+import torch
+import torchvision as tv
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+from decord import VideoReader
+
+from torch.nn.functional import interpolate as img_tensor_resize
+from torch.nn.functional import pad as img_tensor_pad
+from torch.nn.modules.utils import _quadruple
+from torchvision.transforms.functional import pad as img_pad
+from torchvision.transforms.functional import resize as img_resize
+class LMDB_Image:
+ def __init__(self, image, id):
+ self.channels = image.shape[2]
+ self.size = image.shape[:2]
+ self.image = image.tobytes()
+ self.id = id
+
+ def get_image(self):
+ image = np.frombuffer(self.image, dtype=np.uint8)
+ return image.reshape(*self.size, self.channels)
+
+# save each video as a class with byte data, which can be decoded from lmdb database.
+class LMDB_VIDEO:
+ def __init__(self, image, id):
+ self.size = image.shape
+ self.image = image.tobytes()
+ self.id = id
+
+ def get_image(self):
+ image = np.frombuffer(self.image, dtype=np.uint8)
+ return image.reshape(*self.size)
+_pil_interpolation_to_str = {
+ Image.NEAREST: "PIL.Image.NEAREST",
+ Image.BILINEAR: "PIL.Image.BILINEAR",
+ Image.BICUBIC: "PIL.Image.BICUBIC",
+ Image.LANCZOS: "PIL.Image.LANCZOS",
+ Image.HAMMING: "PIL.Image.HAMMING",
+ Image.BOX: "PIL.Image.BOX",
+}
+
+
+class VideoNorm(object):
+ """Apply Normalization to Image Pixels on GPU"""
+
+ def __init__(
+ self,
+ mean=[0.5, 0.5, 0.5],
+ std=[1.0, 1.0, 1.0],
+ #mean=[0.48145466, 0.4578275, 0.40821073],
+ #std=[0.26862954, 0.26130258, 0.27577711],
+ ):
+ # self.mean = torch.tensor(mean).cuda().view(1, 3, 1, 1)
+ # self.std = torch.tensor(std).cuda().view(1, 3, 1, 1)
+ self.mean = torch.tensor(mean).view(1, 3, 1, 1)
+ self.std = torch.tensor(std).view(1, 3, 1, 1)
+
+ def __call__(self, img):
+ """
+ Args:
+ img: float image tensors, (N, 3, H, W)
+ Returns:
+ img: normalized float image tensors
+ """
+ if torch.max(img) > 1 and self.mean.max() <= 1:
+ img.div_(255.0)
+ re = img.sub_(self.mean).div_(self.std)
+ return re
+
+
+
+
+class VideoResizeSquare(object):
+ def __init__(self, out_size, interpolation="nearest"):
+ assert isinstance(out_size, int)
+ self.out_size = out_size
+ self.interpolation = interpolation
+
+ def __call__(self, video):
+ """
+ Args:
+ img (torch.tensor): video to be scaled.
+
+ Returns:
+ torch.tensor: Rescaled video.
+ """
+ if isinstance(video, torch.Tensor):
+ if len(video.shape) == 4:
+ t, h, w, c = video.shape
+ assert (
+ c == 3
+ ), "Expecting 3-channel color video, got video of shape {}".format(
+ video.shape
+ )
+ else:
+ raise RuntimeError(
+ "Expecting 4-dimensional tensor of shape (b,t,h,w), got {}".format(
+ video.shape
+ )
+ )
+
+ # t, h, w, c -> t, c, h, w
+ video = video.permute(0, 3, 1, 2)
+ short_side = h if h < w else w
+ resized_video = img_tensor_resize(
+ video,
+ size=((self.out_size, self.out_size)),
+ mode=self.interpolation,
+ )
+
+ # t, c, h, w -> t, h, w, c
+ return resized_video.permute(0, 2, 3, 1)
+
+ else:
+ # in other data class, the order of shape might be different.
+ raise NotImplementedError(
+ "Support only torch.Tensor as input, got {}".format(type(video))
+ )
+
+ def __repr__(self):
+ return self.__class__.__name__ + "(size={0}, interpolation={1})".format(
+ self.out_size, self.interpolation
+ )
+
+
+
+def load_video_from_path_tvio(
+ video_path,
+ frm_sampling_strategy,
+ height=None,
+ width=None,
+ fps=-1,
+ num_frm=None,
+):
+ video = tv.io.read_video(rf"{video_path}", pts_unit="sec")
+ if not height or not width:
+ sampled_frms = np.array(video[0])
+ else:
+ # T, H, W, C
+ sampled_frms_tensor = video[0]
+ # expected: t, c, h, w
+ resize_func = VideoResizeSquare(out_size=height)
+ sampled_frms_tensor = resize_func(sampled_frms_tensor)
+ sampled_frms = np.array(sampled_frms_tensor)
+
+ specified_num_frm = num_frm
+ default_fps = video[2]["video_fps"]
+ vlen = sampled_frms.shape[0]
+
+ if fps != -1:
+ # resample the video to the specified fps
+ duration = vlen / default_fps
+ num_frames_to_sample = int(duration * fps)
+ resample_indices = np.linspace(
+ 0, vlen - 1, num_frames_to_sample
+ ).astype(int)
+
+ # print(default_fps, fps, resample_indices)
+ sampled_frms = sampled_frms[resample_indices]
+ default_fps = fps
+
+ vlen = sampled_frms.shape[0]
+ if num_frm is None:
+ num_frm = vlen
+
+ num_frm = min(num_frm, vlen)
+
+ if frm_sampling_strategy == "uniform":
+ frame_indices = np.linspace(0, vlen - 1, num_frm).astype(int)
+
+ elif frm_sampling_strategy == "rand":
+ # frame_indices = sorted(random.sample(range(vlen), num_frm))
+ rand_start = random.randint(0, vlen - num_frm)
+ frame_indices = np.array(range(rand_start, rand_start + num_frm)).astype(int)
+
+ elif frm_sampling_strategy == "center":
+ center = vlen // 2
+ if num_frm % 2 ==0:
+ frame_indices = np.array(range(center - num_frm // 2, center + num_frm // 2)).astype(int)
+ else:
+ frame_indices = np.array(range(center - num_frm // 2, center + num_frm // 2 + 1)).astype(int)
+
+ elif frm_sampling_strategy == "all":
+ frame_indices = np.arange(0, vlen).astype(int)
+
+ else:
+ raise NotImplementedError(
+ "Invalid sampling strategy {} ".format(frm_sampling_strategy)
+ )
+
+ raw_sample_frms = sampled_frms[
+ frame_indices
+ ]
+
+ if specified_num_frm is None:
+ masks = np.ones(len(raw_sample_frms), dtype=np.uint8)
+
+ # pad the video if the number of frames is less than specified
+ elif len(raw_sample_frms) < specified_num_frm:
+ prev_length = len(raw_sample_frms)
+ zeros = np.zeros(
+ (specified_num_frm - prev_length, height, width, 3),
+ dtype=np.uint8,
+ )
+ raw_sample_frms = np.concatenate((raw_sample_frms, zeros), axis=0)
+ masks = np.zeros(specified_num_frm, dtype=np.uint8)
+ masks[:prev_length] = 1
+
+ else:
+ masks = np.ones(specified_num_frm, dtype=np.uint8)
+
+
+ return raw_sample_frms, masks
+
+
+def load_video_from_path_decord(
+ video_path,
+ frm_sampling_strategy,
+ height=None,
+ width=None,
+ start_time=None,
+ end_time=None,
+ fps=-1,
+ num_frm=None,
+):
+ #return np.zeros((num_frm, 256, 256, 3), dtype=np.uint8), np.ones(num_frm, dtype=np.uint8)
+ specified_num_frm = num_frm
+
+ if not height or not width:
+ vr = VideoReader(rf"{video_path}")
+ else:
+ vr = VideoReader(video_path, width=width, height=height)
+
+ default_fps = vr.get_avg_fps()
+ if default_fps <= fps:
+ fps = -1
+
+ if fps != -1:
+ # resample the video to the specified fps
+ duration = len(vr) / default_fps
+ num_frames_to_sample = int(duration * fps)
+ resample_indices = np.linspace(
+ 0, len(vr) - 1, num_frames_to_sample
+ ).astype(int)
+
+ # print(default_fps, fps, resample_indices)
+ sampled_frms = vr.get_batch(resample_indices).asnumpy().astype(np.uint8)
+ default_fps = fps
+
+
+ else:
+ sampled_frms = vr.get_batch(np.arange(0, len(vr), 1, dtype=int)).asnumpy().astype(np.uint8)
+
+ vlen = sampled_frms.shape[0]
+
+ if num_frm is None:
+ num_frm = vlen
+
+ num_frm = min(num_frm, vlen)
+
+ if start_time or end_time:
+ assert (
+ fps > 0
+ ), "must provide video fps if specifying start and end time."
+ start_idx = min(int(start_time * fps), vlen)
+ end_idx = min(int(end_time * fps), vlen)
+
+ else:
+ start_idx, end_idx = 0, vlen
+
+ if frm_sampling_strategy == "uniform":
+ frame_indices = np.linspace(0, vlen - 1, num_frm).astype(int)
+
+ elif frm_sampling_strategy == "nlvl_uniform":
+ frame_indices = np.arange(
+ start_idx, end_idx, vlen / num_frm
+ ).astype(int)
+
+ elif frm_sampling_strategy == "nlvl_rand":
+ frame_indices = np.arange(
+ start_idx, end_idx, vlen / num_frm
+ ).astype(int)
+
+ strides = [
+ frame_indices[i] - frame_indices[i - 1]
+ for i in range(1, len(frame_indices))
+ ] + [vlen - frame_indices[-1]]
+ pertube = np.array(
+ [np.random.randint(0, stride) for stride in strides]
+ )
+
+ frame_indices = frame_indices + pertube
+
+ elif frm_sampling_strategy == "rand":
+ # frame_indices = sorted(random.sample(range(vlen), num_frm))
+ rand_start = random.randint(0, vlen - num_frm)
+ frame_indices = np.array(range(rand_start, rand_start + num_frm)).astype(int)
+
+ elif frm_sampling_strategy == "center":
+ center = vlen // 2
+ if num_frm % 2 ==0:
+ frame_indices = np.array(range(center - num_frm // 2, center + num_frm // 2)).astype(int)
+ else:
+ frame_indices = np.array(range(center - num_frm // 2, center + num_frm // 2 + 1)).astype(int)
+
+ elif frm_sampling_strategy == "headtail":
+ frame_indices_head = sorted(
+ random.sample(range(vlen // 2), num_frm // 2)
+ )
+ frame_indices_tail = sorted(
+ random.sample(range(vlen // 2, vlen), num_frm // 2)
+ )
+ frame_indices = frame_indices_head + frame_indices_tail
+
+ elif frm_sampling_strategy == "all":
+ frame_indices = np.arange(0, vlen).astype(int)
+
+
+ elif frm_sampling_strategy == "rand_sep":
+ sep = random.choice([1,2,3,4,5,6,7,8])
+ while (sep * num_frm) > vlen:
+ sep = random.choice([1,2,3,4,5,6,7,8])
+ rand_start = random.randint(0, vlen - sep * num_frm)
+ frame_indices = np.array(range(rand_start, rand_start + sep * num_frm, sep)).astype(int)
+
+ else:
+ raise NotImplementedError(
+ "Invalid sampling strategy {} ".format(frm_sampling_strategy)
+ )
+
+ raw_sample_frms = sampled_frms[
+ frame_indices
+ ]
+
+ if specified_num_frm is None:
+ masks = np.ones(len(raw_sample_frms), dtype=np.uint8)
+
+ # pad the video if the number of frames is less than specified
+ elif len(raw_sample_frms) < specified_num_frm:
+ prev_length = len(raw_sample_frms)
+ zeros = np.zeros(
+ (specified_num_frm - prev_length, height, width, 3),
+ dtype=np.uint8,
+ )
+ raw_sample_frms = np.concatenate((raw_sample_frms, zeros), axis=0)
+ masks = np.zeros(specified_num_frm, dtype=np.uint8)
+ masks[:prev_length] = 1
+
+ else:
+ masks = np.ones(specified_num_frm, dtype=np.uint8)
+
+ return raw_sample_frms, masks
+
+
+def load_video_from_path_lmdb(
+ sampled_frms,
+ frm_sampling_strategy,
+ height=None,
+ width=None,
+ start_time=None,
+ end_time=None,
+ fps=-1,
+ num_frm=None,
+):
+ #return np.zeros((num_frm, 256, 256, 3), dtype=np.uint8), np.ones(num_frm, dtype=np.uint8)
+ specified_num_frm = num_frm
+
+ vlen = sampled_frms.shape[0]
+
+ if num_frm is None:
+ num_frm = vlen
+
+ num_frm = min(num_frm, vlen)
+
+ if start_time or end_time:
+ assert (
+ fps > 0
+ ), "must provide video fps if specifying start and end time."
+ start_idx = min(int(start_time * fps), vlen)
+ end_idx = min(int(end_time * fps), vlen)
+
+ else:
+ start_idx, end_idx = 0, vlen
+
+ if frm_sampling_strategy == "uniform":
+ frame_indices = np.linspace(0, vlen - 1, num_frm).astype(int)
+
+ elif frm_sampling_strategy == "nlvl_uniform":
+ frame_indices = np.arange(
+ start_idx, end_idx, vlen / num_frm
+ ).astype(int)
+
+ elif frm_sampling_strategy == "nlvl_rand":
+ frame_indices = np.arange(
+ start_idx, end_idx, vlen / num_frm
+ ).astype(int)
+
+ strides = [
+ frame_indices[i] - frame_indices[i - 1]
+ for i in range(1, len(frame_indices))
+ ] + [vlen - frame_indices[-1]]
+ pertube = np.array(
+ [np.random.randint(0, stride) for stride in strides]
+ )
+
+ frame_indices = frame_indices + pertube
+
+ elif frm_sampling_strategy == "rand":
+ # frame_indices = sorted(random.sample(range(vlen), num_frm))
+ rand_start = random.randint(0, vlen - num_frm)
+ frame_indices = np.array(range(rand_start, rand_start + num_frm)).astype(int)
+
+ elif frm_sampling_strategy == "center":
+ center = vlen // 2
+ if num_frm % 2 ==0:
+ frame_indices = np.array(range(center - num_frm // 2, center + num_frm // 2)).astype(int)
+ else:
+ frame_indices = np.array(range(center - num_frm // 2, center + num_frm // 2 + 1)).astype(int)
+
+ elif frm_sampling_strategy == "headtail":
+ frame_indices_head = sorted(
+ random.sample(range(vlen // 2), num_frm // 2)
+ )
+ frame_indices_tail = sorted(
+ random.sample(range(vlen // 2, vlen), num_frm // 2)
+ )
+ frame_indices = frame_indices_head + frame_indices_tail
+
+ elif frm_sampling_strategy == "all":
+ frame_indices = np.arange(0, vlen).astype(int)
+
+
+ elif frm_sampling_strategy == "rand_sep":
+ sep = random.choice([1,2,3,4,5,6,7,8])
+ while (sep * num_frm) > vlen:
+ sep = random.choice([1,2,3,4,5,6,7,8])
+ rand_start = random.randint(0, vlen - sep * num_frm)
+ frame_indices = np.array(range(rand_start, rand_start + sep * num_frm, sep)).astype(int)
+
+ else:
+ raise NotImplementedError(
+ "Invalid sampling strategy {} ".format(frm_sampling_strategy)
+ )
+
+ raw_sample_frms = sampled_frms[
+ frame_indices
+ ]
+
+ if specified_num_frm is None:
+ masks = np.ones(len(raw_sample_frms), dtype=np.uint8)
+
+ # pad the video if the number of frames is less than specified
+ elif len(raw_sample_frms) < specified_num_frm:
+ prev_length = len(raw_sample_frms)
+ zeros = np.zeros(
+ (specified_num_frm - prev_length, height, width, 3),
+ dtype=np.uint8,
+ )
+ raw_sample_frms = np.concatenate((raw_sample_frms, zeros), axis=0)
+ masks = np.zeros(specified_num_frm, dtype=np.uint8)
+ masks[:prev_length] = 1
+
+ else:
+ masks = np.ones(specified_num_frm, dtype=np.uint8)
+
+ return raw_sample_frms, masks
+
+def image_to_tensor(image: np.ndarray, keepdim: bool = True) -> torch.Tensor:
+ """Converts a numpy image to a PyTorch 4d tensor image.
+ Args:
+ image (numpy.ndarray): image of the form :math:`(H, W, C)`, :math:`(H, W)` or
+ :math:`(B, H, W, C)`.
+ keepdim (bool): If ``False`` unsqueeze the input image to match the shape
+ :math:`(B, H, W, C)`. Default: ``True``
+ Returns:
+ torch.Tensor: tensor of the form :math:`(B, C, H, W)` if keepdim is ``False``,
+ :math:`(C, H, W)` otherwise.
+ """
+ if not isinstance(image, (np.ndarray,)):
+ raise TypeError(
+ "Input type must be a numpy.ndarray. Got {}".format(type(image))
+ )
+
+ if len(image.shape) > 4 or len(image.shape) < 2:
+ raise ValueError(
+ "Input size must be a two, three or four dimensional array"
+ )
+
+ input_shape = image.shape
+ tensor: torch.Tensor = torch.from_numpy(image)
+
+ if len(input_shape) == 2:
+ # (H, W) -> (1, H, W)
+ tensor = tensor.unsqueeze(0)
+ elif len(input_shape) == 3:
+ # (H, W, C) -> (C, H, W)
+ tensor = tensor.permute(2, 0, 1)
+ elif len(input_shape) == 4:
+ # (B, H, W, C) -> (B, C, H, W)
+ tensor = tensor.permute(0, 3, 1, 2)
+ keepdim = True # no need to unsqueeze
+ else:
+ raise ValueError(
+ "Cannot process image with shape {}".format(input_shape)
+ )
+
+ return tensor.unsqueeze(0) if not keepdim else tensor
+
+
+def get_padding(image, max_w, max_h, pad_all=False):
+ # keep the images to upper-left corner
+ if isinstance(image, torch.Tensor):
+ h, w = image.shape[-2:]
+ else:
+ w, h = image.size
+ h_padding, v_padding = max_w - w, max_h - h
+ if pad_all:
+ h_padding /= 2
+ v_padding /= 2
+ l_pad = h_padding if h_padding % 1 == 0 else h_padding + 0.5
+ t_pad = v_padding if v_padding % 1 == 0 else v_padding + 0.5
+ r_pad = h_padding if h_padding % 1 == 0 else h_padding - 0.5
+ b_pad = v_padding if v_padding % 1 == 0 else v_padding - 0.5
+ else:
+ l_pad, t_pad = 0, 0
+ r_pad, b_pad = h_padding, v_padding
+ if isinstance(image, torch.Tensor):
+ padding = (int(l_pad), int(r_pad), int(t_pad), int(b_pad))
+ else:
+ padding = (int(l_pad), int(t_pad), int(r_pad), int(b_pad))
+ return padding
+
+
+class ImagePad(object):
+ def __init__(self, max_w, max_h, fill=0, padding_mode="constant"):
+ assert isinstance(fill, (numbers.Number, str, tuple))
+ assert padding_mode in ["constant", "edge", "reflect", "symmetric"]
+ self.max_w = max_w
+ self.max_h = max_h
+ self.fill = fill
+ self.padding_mode = padding_mode
+
+ def __call__(self, img):
+ """
+ Args:
+ img (PIL Image): Image to be padded.
+
+ Returns:
+ PIL Image: Padded image.
+ """
+ if isinstance(img, torch.Tensor):
+ paddings = _quadruple(get_padding(img, self.max_w, self.max_h))
+ return img_tensor_pad(img, paddings, self.padding_mode, self.fill)
+ return img_pad(
+ img,
+ get_padding(img, self.max_w, self.max_h),
+ self.fill,
+ self.padding_mode,
+ )
+
+ def __repr__(self):
+ return (
+ self.__class__.__name__
+ + "(padding={0}, fill={1}, padding_mode={2})".format(
+ self.fill, self.padding_mode
+ )
+ )
+
+
+def get_resize_size(image, max_size):
+ """
+ Args:
+ image: PIL Image or torch.tensor
+ max_size:
+
+ Returns:
+
+ Note the height/width order difference
+ >>> pil_img = Image.open("raw_img_tensor.jpg")
+ >>> pil_img.size
+ (640, 480) # (width, height)
+ >>> np_img = np.array(pil_img)
+ >>> np_img.shape
+ (480, 640, 3) # (height, width, 3)
+ """
+ # note the order of height and width for different inputs
+ if isinstance(image, torch.Tensor):
+ # width, height = image.shape[-2:]
+ height, width = image.shape[-2:]
+ else:
+ width, height = image.size
+
+ if height >= width:
+ ratio = width * 1.0 / height
+ new_height = max_size
+ new_width = new_height * ratio
+ else:
+ ratio = height * 1.0 / width
+ new_width = max_size
+ new_height = new_width * ratio
+ size = (int(new_height), int(new_width))
+ return size
+
+
+class VideoRandomSquareCrop(object):
+ def __init__(self, crop_size, p=0.5):
+ assert isinstance(crop_size, int)
+ self.crop_size = crop_size
+ self.p = p
+
+ def __call__(self, video):
+ """
+ Args:
+ img (torch.tensor): video to be cropped.
+
+ Returns:
+ torch.tensor: cropped video.
+ """
+ if isinstance(video, torch.Tensor):
+ if len(video.shape) == 4:
+ b, t, h, w = video.shape
+ else:
+ raise RuntimeError(
+ "Expecting 4-dimensional tensor of shape (b,t,h,w), got {}".format(
+ video.shape
+ )
+ )
+
+ if random.uniform(0, 1) < self.p:
+ video = torch.flip(video, (3,))
+
+ x = random.randint(0, h - self.crop_size)
+ y = random.randint(0, w - self.crop_size)
+
+ return video[:, :, x : x + self.crop_size, y : y + self.crop_size]
+
+ else:
+ if random.uniform(0, 1) < self.p:
+ video = np.ascontiguousarray(np.flip(video, (2,)))
+ t, h, w, c = video.shape
+ x = random.randint(0, h - self.crop_size)
+ y = random.randint(0, w - self.crop_size)
+
+ return video[:, x : x + self.crop_size, y : y + self.crop_size, :]
+
+
+class ImageResize(object):
+ """Resize the input image (torch.tensor) to the given size.
+
+ Args:
+ max_size (int): Desired output size. If size is a sequence like
+ (h, w), output size will be matched to this. If size is an int,
+ smaller edge of the image will be matched to this number.
+ i.e, if height > width, then image will be rescaled to
+ (size * height / width, size)
+ interpolation (int, optional): Desired interpolation. Default is
+ ``PIL.Image.BILINEAR``
+ """
+
+ def __init__(self, max_size, interpolation=Image.BILINEAR):
+ assert isinstance(max_size, int)
+ self.max_size = max_size
+ self.interpolation = interpolation
+
+ def __call__(self, img):
+ """
+ Args:
+ img (torch.tensor): Image to be scaled.
+
+ Returns:
+ torch.tensor: Rescaled image.
+ """
+ if isinstance(img, torch.Tensor):
+ assert isinstance(self.interpolation, str)
+ return img_tensor_resize(
+ img,
+ size=get_resize_size(img, self.max_size),
+ mode=self.interpolation,
+ align_corners=False,
+ )
+ return img_resize(
+ img, get_resize_size(img, self.max_size), self.interpolation
+ )
+
+ def __repr__(self):
+ interpolate_str = _pil_interpolation_to_str[self.interpolation]
+ return self.__class__.__name__ + "(size={0}, interpolation={1})".format(
+ self.size, interpolate_str
+ )
+
+
+def get_imagenet_transform(min_size=600, max_size=1000):
+ """parameters from https://github.com/pytorch/examples/blob/master/imagenet/main.py
+ This simply crop the center square from the image
+ """
+ if min_size != 600:
+ import warnings
+
+ warnings.warn(
+ f"Warning: min_size is not used in image transform, "
+ f"setting min_size will have no effect."
+ )
+ return transforms.Compose(
+ [
+ ImageResize(
+ max_size, Image.BILINEAR
+ ), # longer side will be resized to 1000
+ ImagePad(max_size, max_size), # pad to 1000 * 1000
+ ]
+ )
+
+
+class ImageNorm(object):
+ """Apply Normalization to Image Pixels on GPU"""
+
+ def __init__(self, mean, std):
+ self.mean = torch.tensor(mean).cuda().view(1, 1, 3, 1, 1)
+ self.std = torch.tensor(std).cuda().view(1, 1, 3, 1, 1)
+ # assert max(std) <= 1 and min(std) >= 0\
+ # or max(mean) <= 1 and min(mean) >= 0,\
+ # "Please provide mean or std within range [0, 1]"
+
+ def __call__(self, img):
+ """
+ Args:
+ img: float image tensors, (B, N, 3, H, W)
+
+ Returns:
+ img: normalized float image tensors
+ """
+ if torch.max(img) > 1 and self.mean.max() <= 1:
+ img.div_(255.0)
+ return img.sub_(self.mean).div_(self.std)
+
+
+def chunk_list(examples, chunk_size=2, pad_to_divisible=True):
+ """
+ Args:
+ examples: iterable, examples grouped by image/video
+ chunk_size: int, number of examples in each chunk.
+ pad_to_divisible: bool, pad the examples to be divisible by chunk_size.
+ >>> test_examples = [3, 4, 5, 6, 7]
+ >>> chunk_list(test_examples, chunk_size=2, pad_to_divisible=True)
+ [[3, 4], [5, 6], [7, 7]] # the lst element has some randomness
+ >>> chunk_list(test_examples, chunk_size=2, pad_to_divisible=False)
+ [[3, 4], [5, 6], [7]]
+ """
+ n_examples = len(examples)
+ remainder = n_examples % chunk_size
+ if pad_to_divisible and remainder > 0:
+ n_pad = chunk_size - remainder
+ pad = random.choices(examples, k=n_pad) # with replacement
+ examples = examples + pad
+ n_examples = len(examples)
+ remainder = 0
+ chunked_examples = []
+ n_chunks = int(n_examples / chunk_size)
+ n_chunks = n_chunks + 1 if remainder > 0 else n_chunks
+ for i in range(n_chunks):
+ chunked_examples.append(examples[i * chunk_size : (i + 1) * chunk_size])
+ return chunked_examples
+
+
+# def repeat_tensor_rows(raw_tensor, row_repeats):
+# """ repeat raw_tensor[i] row_repeats[i] times.
+# Args:
+# raw_tensor: (B, *)
+# row_repeats: list(int), len(row_repeats) == len(raw_tensor)
+# """
+# assert len(raw_tensor) == len(raw_tensor), "Has to be the same length"
+# if sum(row_repeats) == len(row_repeats):
+# return raw_tensor
+# else:
+# indices = torch.LongTensor(
+# flat_list_of_lists([[i] * r for i, r in enumerate(row_repeats)])
+# ).to(raw_tensor.device)
+# return raw_tensor.index_select(0, indices)
+
+
+def pre_caption(caption, max_words=50):
+ caption = re.sub(
+ r"([.!\"()*#:;~])",
+ " ",
+ caption.lower(),
+ )
+ caption = re.sub(
+ r"\s{2,}",
+ " ",
+ caption,
+ )
+ caption = caption.rstrip("\n")
+ caption = caption.strip(" ")
+
+ # truncate caption
+ caption_words = caption.split(" ")
+ if len(caption_words) > max_words:
+ caption = " ".join(caption_words[:max_words])
+
+ return caption
+
+
+
+
+class InterpolationMode(Enum):
+ """Interpolation modes
+ Available interpolation methods are ``nearest``, ``bilinear``, ``bicubic``, ``box``, ``hamming``, and ``lanczos``.
+ """
+
+ NEAREST = "nearest"
+ BILINEAR = "bilinear"
+ BICUBIC = "bicubic"
+ # For PIL compatibility
+ BOX = "box"
+ HAMMING = "hamming"
+ LANCZOS = "lanczos"
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/README.md b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b2db10e5c830399479fe0caeaf15b403690d648f
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/README.md
@@ -0,0 +1,85 @@
+# Generative Enhancement for 3D Medical Images
+
+This is the official code for https://arxiv.org/abs/2403.12852.
+
+
+
+## Installation
+
+Clone this repository and install packages:
+```
+git clone https://github.com/HKU-MedAI/GEM-3D.git
+conda env create --file environment.yml
+conda activate gem
+```
+
+## Preparation
+
+1. The preprocessed datasets can be found [here](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/ltzhu99_connect_hku_hk/ES0_s1XN3_BDhQn6W3cPvPgB-LnX9SJqUcBQ3dA8g-jqZA?e=3qiz5v). (For BraTS, we use the version from [Medical Segmentation Decathlon](http://medicaldecathlon.com/#tasks) and only use the FLAIR modality. For abdomen dataset, we crop the outlier regions where contain too many empty slices. Note that we need the gt segmentation for benchmark, and we mannually split the training splits. The datasets are preprocessed with [nnUNet](https://github.com/MIC-DKFZ/nnUNet).)
+
+2. Download [KL-f8 AE](https://ommer-lab.com/files/latent-diffusion/kl-f8.zip) from [LDM](https://github.com/CompVis/latent-diffusion).
+
+
+3. You can download our pretrained models [here](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/ltzhu99_connect_hku_hk/EdGegBBAYVZMnhLhAvs46OUBRDPVmYBI6IqX20K3OWGTQA?e=McrYWl).
+
+## Training
+```
+# stage 1, 150k iters
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py --base configs/latent-diffusion/brain_stage1.yaml -t --gpus 0,1,2,3,4,5,6,7,
+
+# Edit the path-to-stage1-ckpt keyword in the stage 2 yaml
+vi configs/latent-diffusion/brain_stage2.yaml
+
+# stage 2, 50k iters
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py --base configs/latent-diffusion/brain_stage2.yaml -t --gpus 0,1,2,3,4,5,6,7,
+```
+```
+# baseline (a modified version of Make-A-Volume[MICCAI 2023]): use the yaml with 'base' and train the models in two stages, the same as the commands above
+```
+```
+# position conditioned slice generation model (one-stage), 150k iters
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py --base configs/latent-diffusion/brain_slice.yaml -t --gpus 0,1,2,3,4,5,6,7,
+```
+Note that the training costs more than 30G for each card, requiring GPUs like V100 and A100.
+
+And the training can also be conducted with fewer cards.
+
+## Inference
+
+Prepare the models in the directory `infer_model/`.
+```
+# postfix:
+# {ic,icma,ig,igma} correspond to the 4 settings in the paper (Tab.1)
+# test means that data comes from the test split (Tab.2)
+# base corresponds to the baseline
+# slice corresponds to position conditioned slice generation model
+python inference_{dataset}_{postfix}.py
+
+## e.g.,
+python inference_brain_ic.py
+```
+The inference costs GPU memory within 20G.
+
+## Citation
+
+If you find our work useful, please kindly cite as:
+```
+@article{zhu2024generative,
+ title={Generative Enhancement for 3D Medical Images},
+ author={Zhu, Lingting and Codella, Noel and Chen, Dongdong and Jin, Zhenchao and Yuan, Lu and Yu, Lequan},
+ journal={arXiv preprint arXiv:2403.12852},
+ year={2024}
+}
+
+@inproceedings{zhu2023make,
+ title={Make-a-volume: Leveraging latent diffusion models for cross-modality 3d brain mri synthesis},
+ author={Zhu, Lingting and Xue, Zeyue and Jin, Zhenchao and Liu, Xian and He, Jingzhen and Liu, Ziwei and Yu, Lequan},
+ booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
+ pages={592--601},
+ year={2023},
+ organization={Springer}
+}
+```
+
+## Acknowledgement
+The codebase is developed based on [SD](https://github.com/CompVis/stable-diffusion) (Rombach et al.).
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/config.sh b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/config.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b6ddfa2e6a80a81a414757217d4001ed1e4c79db
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/config.sh
@@ -0,0 +1,34 @@
+# stage 1, 150k iters
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py --base configs/latent-diffusion/brain_stage1.yaml -t --gpus 0,1,2,3,4,5,6,7,
+
+# Edit the path-to-stage1-ckpt keyword in the stage 2 yaml
+vi configs/latent-diffusion/brain_stage2.yaml
+
+# stage 2, 50k iters
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python main.py --base configs/latent-diffusion/brain_stage2.yaml -t --gpus 0,1,2,3,4,5,6,7,
+
+
+
+
+CUDA_VISIBLE_DEVICES=0 python main.py --base configs/latent-diffusion/brain_stage1.yaml -t --gpus 0,
+
+CUDA_VISIBLE_DEVICES=0 python main.py --base configs/latent-diffusion/brain_slice.yaml -t --gpus 0,
+
+CUDA_VISIBLE_DEVICES=0 python main.py --base configs/latent-diffusion/abdomen_3d.yaml -t --gpus 0,
+
+CUDA_VISIBLE_DEVICES=0 python main.py --base configs/latent-diffusion/brain_slice_atten.yaml -t --gpus 0,
+
+CUDA_VISIBLE_DEVICES=0 python main.py --base configs/latent-diffusion/brain_slice_atten2.yaml -t --gpus 0,
+
+CUDA_VISIBLE_DEVICES=0 python main.py --base configs/latent-diffusion/brain_slice_atten3.yaml -t --gpus 0,
+
+CUDA_VISIBLE_DEVICES=0 python main.py --base configs/latent-diffusion/brain_slice_atten4.yaml -t --gpus 0,
+
+CUDA_VISIBLE_DEVICES=0 python main.py --base configs/latent-diffusion/brain_slice_atten5.yaml -t --gpus 0,
+
+
+CUDA_VISIBLE_DEVICES=0 python main.py --base configs/latent-diffusion/full_ct_2d_with_body_mask.yaml -t --gpus 0, --resume ./logs/full_ct_2d_with_body_mask
+
+CUDA_VISIBLE_DEVICES=0 python main.py --base configs/latent-diffusion/full_ct_3d_with_body_mask_finetune.yaml -t --gpus 0, --resume ./logs/full_ct_3d_with_body_mask_finetune2
+
+pip install beartype PyWavelets
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..56952ba5c1861d34258b413f45dc69cc4c530eeb
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-09-13T02-58-56_brain_slice/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e7de493c0863442030a795d1184fa9727f1d8cba
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-10-30T18-29-30_brain_slice_13class_new_randomclass_alldata/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_13class_randomclass.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset103_AbdomenAtlas2.0_base'
+ data_repeat: 4
+ validation:
+ target: ldm.data.volume_dataset_13class_randomclass.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset103_AbdomenAtlas2.0_base'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..88c5c6da6a5b9b8d62ad8dd80ca9b98965ff1e93
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata2.yaml
@@ -0,0 +1,94 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-10-31T18-08-08_brain_slice_13class_new_randomclass_alldata2/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_13class_randomclass.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset103_AbdomenAtlas2.0_base'
+ data_repeat: 4
+ data_file: 'data_split/splits_final_all.json'
+ validation:
+ target: ldm.data.volume_dataset_13class_randomclass.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset103_AbdomenAtlas2.0_base'
+ data_file: 'data_split/splits_final_all.json'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata3.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata3.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..15236399e4f11d469c47b99bf7c9931ab995584b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata3.yaml
@@ -0,0 +1,94 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-11-04T07-42-05_brain_slice_13class_new_randomclass_alldata3/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_13class_randomclass.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset104_AbdomenAtlas2.0_1102'
+ data_repeat: 4
+ data_file: 'data_split/splits_final_all2.json'
+ validation:
+ target: ldm.data.volume_dataset_13class_randomclass.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset104_AbdomenAtlas2.0_1102'
+ data_file: 'data_split/splits_final_all2.json'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata4.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata4.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..04fc5db5434b658303f65569d44e0b911b29d63e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_13class_randomclass_alldata4.yaml
@@ -0,0 +1,94 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-11-06T12-22-22_brain_slice_13class_new_randomclass_alldata4/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_13class_randomclass.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset105_AbdomenAtlas2.0_1106'
+ data_repeat: 4
+ data_file: 'data_split/splits_final_all3.json'
+ validation:
+ target: ldm.data.volume_dataset_13class_randomclass.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset105_AbdomenAtlas2.0_1106'
+ data_file: 'data_split/splits_final_all3.json'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_28class_randomclass.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_28class_randomclass.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8ad8ed99a640b36e035ecef611336963e505aa65
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_28class_randomclass.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-10-13T09-17-24_brain_slice_28class_new_randomclass/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_28class_randomclass.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset101_AbdomenAtlasInternal'
+ data_repeat: 4
+ validation:
+ target: ldm.data.volume_dataset_28class_randomclass.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset101_AbdomenAtlasInternal'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_28class_randomclass2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_28class_randomclass2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..aed19612e02b38d31704969a5c14515dae8d459d
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_28class_randomclass2.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-10-19T04-42-07_brain_slice_28class_new_randomclass/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_28class_randomclass.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset101_AbdomenAtlasInternal'
+ data_repeat: 4
+ validation:
+ target: ldm.data.volume_dataset_28class_randomclass.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset101_AbdomenAtlasInternal'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_28class_randomclass3.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_28class_randomclass3.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e60a5c4a29a67d0b43f867a1791e0b43e8e3fc2d
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_28class_randomclass3.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-10-17T08-03-29_brain_slice_28class_new_randomclass/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_28class_randomclass.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset101_AbdomenAtlasInternal'
+ data_repeat: 4
+ validation:
+ target: ldm.data.volume_dataset_28class_randomclass.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset101_AbdomenAtlasInternal'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_atten3.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_atten3.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2675ecb7c09de350866fc702f5b5b1b05d8c0d6c
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_atten3.yaml
@@ -0,0 +1,93 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: hybrid
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-10-11T04-22-28_brain_slice_atten3/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 128
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 4096
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 2
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_atten5.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_atten5.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1a8939f57246c6f8f6972f119a430cddc7783a2e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_atten5.yaml
@@ -0,0 +1,93 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: hybrid
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-10-11T04-32-33_brain_slice_atten5/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 5
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_kidney.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_kidney.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2328f6286a6c20ca6576fc3e8b3abd95b6e7c7aa
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_kidney.yaml
@@ -0,0 +1,96 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-10-31T09-36-59_brain_slice_kidney/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset018_AbdomenAtlas2.0_base_kidney'
+ planner: 'nnUNetPlans_3d_fullres'
+ data_file: 'data_split/kidney/splits_final.json'
+ data_repeat: 4
+ validation:
+ target: ldm.data.volume_dataset.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset018_AbdomenAtlas2.0_base_kidney'
+ planner: 'nnUNetPlans_3d_fullres'
+ data_file: 'data_split/kidney/splits_final.json'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_pancreas.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_pancreas.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..949617ab3329326a1f02091c320f574413742652
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_3d_pancreas.yaml
@@ -0,0 +1,96 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: masked_volume
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: /storage/chenqi/code/GEM-3D-ct/logs/2024-10-31T09-43-34_brain_slice_pancreas/checkpoints/last.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset017_AbdomenAtlas2.0_base_pancreas'
+ planner: 'nnUNetPlans_3d_fullres'
+ data_file: 'data_split/pancreas/splits_final.json'
+ data_repeat: 4
+ validation:
+ target: ldm.data.volume_dataset.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset017_AbdomenAtlas2.0_base_pancreas'
+ planner: 'nnUNetPlans_3d_fullres'
+ data_file: 'data_split/pancreas/splits_final.json'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_base_stage1.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_base_stage1.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..37cfe5164f06d92ff0949e62be7c7d80f5f465a3
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_base_stage1.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 8
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: 'models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_base.volume_train
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset200_Abdomen'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset_base.volume_val
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset200_Abdomen'
+
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 20000
+ max_images: 8
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_base_stage2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_base_stage2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e200c08d09a4d6664d0a910299cef4b52677c7c7
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_base_stage2.yaml
@@ -0,0 +1,93 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: path-to-stage1-ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 8
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: 'models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_base.volume_train
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset200_Abdomen'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset_base.volume_val
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset200_Abdomen'
+
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 20000
+ max_images: 8
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_slice.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_slice.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4c3360c0cda853d0b0d3b53c1d6bb8b73e8dc8e5
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_slice.yaml
@@ -0,0 +1,94 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: pos_id
+ cond_stage_trainable: false
+ conditioning_key: crossattn
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 4
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 128
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: 'models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config:
+ target: ldm.modules.encoders.modules.PositionalEmbedder
+ params:
+ dim: 128
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 5
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset200_Abdomen'
+ data_repeat: 300
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset200_Abdomen'
+
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 20000
+ max_images: 8
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_stage1.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_stage1.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..645ae86af5143000dae304f9ac4a76d033a999b3
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_stage1.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: ref_and_volume_seg
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 12
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: 'models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset.volume_train
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset200_Abdomen'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset.volume_val
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset200_Abdomen'
+
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 20000
+ max_images: 8
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_stage2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_stage2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6132ebfca21c0161b2fedad95f66608a3f3d0b51
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/abdomen_stage2.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: ref_and_volume_seg
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: path-to-stage1-ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 12
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: 'models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset.volume_train
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset200_Abdomen'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset.volume_val
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset200_Abdomen'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 20000
+ max_images: 8
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_base_stage1.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_base_stage1.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6b707fb63b865776dacd93eb2af8c8b67a92f75c
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_base_stage1.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 8
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: 'models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_base.volume_train
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset202_Brain'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset_base.volume_val
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset202_Brain'
+
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 20000
+ max_images: 8
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_base_stage2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_base_stage2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c22e3cae54b1c2b63ddd5e18f7129feb8c54bb3a
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_base_stage2.yaml
@@ -0,0 +1,93 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: path-to-stage1-ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 8
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: 'models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset_base.volume_train
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset202_Brain'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset_base.volume_val
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset202_Brain'
+
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 20000
+ max_images: 8
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..db27b89a55e936f1428e183e92bcbb971b77deb0
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 200 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..87b95745018d70ac2dc149d94f302b0ed54c29c2
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset103_AbdomenAtlas2.0_base'
+ data_repeat: 4 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset103_AbdomenAtlas2.0_base'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a87b5034aa78be063f904eff089a6edca0fbb610
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset103_AbdomenAtlas2.0_base'
+ data_repeat: 4 # 1000 100
+ data_file: 'data_split/splits_final_new.json'
+ validation:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset103_AbdomenAtlas2.0_base'
+ data_repeat: 80 # 1000 100
+ data_file: 'data_split/splits_final_new.json'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cea75aeaed628f090b2b13bfb1b452f7d33dd196
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata2.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset103_AbdomenAtlas2.0_base'
+ data_repeat: 4 # 1000 100
+ data_file: 'data_split/splits_final_all.json'
+ validation:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset103_AbdomenAtlas2.0_base'
+ data_repeat: 10 # 1000 100
+ data_file: 'data_split/splits_final_all.json'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata3.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata3.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ba7b7d038c08dee9c96533848b77ecba2facc29c
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata3.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset104_AbdomenAtlas2.0_1102'
+ data_repeat: 4 # 1000 100
+ data_file: 'data_split/splits_final_all2.json'
+ validation:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset104_AbdomenAtlas2.0_1102'
+ data_repeat: 10 # 1000 100
+ data_file: 'data_split/splits_final_all2.json'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata4.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata4.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e8236f13d1c8a0c3f02778fedf6944b13b99b51e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata4.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset105_AbdomenAtlas2.0_1106'
+ data_repeat: 4 # 1000 100
+ data_file: 'data_split/splits_final_all3.json'
+ validation:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset105_AbdomenAtlas2.0_1106'
+ data_repeat: 10 # 1000 100
+ data_file: 'data_split/splits_final_all3.json'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata_kl4.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata_kl4.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2f2a9a78fa498ad5bbbea7fb8d7ada95812564e1
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_13class_new_randomclass_alldata_kl4.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 128
+ channels: 7
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 128
+ in_channels: 7 # 4
+ out_channels: 3 # 4
+ model_channels: 192
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f4/model.ckpt'
+ embed_dim: 3
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 3
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 16 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass_kl4.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset104_AbdomenAtlas2.0_1102'
+ data_repeat: 4 # 1000 100
+ data_file: 'data_split/splits_final_all2.json'
+ validation:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass_kl4.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset104_AbdomenAtlas2.0_1102'
+ data_repeat: 1 # 1000 100
+ data_file: 'data_split/splits_final_all2.json'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 24
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
+ # max_epochs: 400
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f764ddc774430556a7492a417e5688f69a9485e0
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset_abdomenatlas_resize.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset100_AbdomenAtlasX'
+ data_repeat: 4 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset_abdomenatlas_resize.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset100_AbdomenAtlasX'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class_new.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class_new.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0784ba552c5e3c3e9737cc69b736cff142329eac
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class_new.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset_abdomenatlas_28class.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset101_AbdomenAtlasInternal'
+ data_repeat: 4 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset_abdomenatlas_28class.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset101_AbdomenAtlasInternal'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class_new_randomclass.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class_new_randomclass.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b1386468645c74fb00cd6e2499f8b4128fe77c50
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class_new_randomclass.yaml
@@ -0,0 +1,98 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 8
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 8 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/sd/qichen/full_ct_gen/latent-diffusion/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ # cond_stage_config: __is_first_stage__
+
+ cond_stage_config:
+ target: ldm.modules.encoders.modules.BERTEmbedder
+ params:
+ n_embed: 1280
+ n_layer: 32
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 2 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000 # 100
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class_new_randomclass_2k.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class_new_randomclass_2k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b724f5a02400388737a6d5975216c16b4b3576b4
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_28class_new_randomclass_2k.yaml
@@ -0,0 +1,91 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset101_AbdomenAtlasInternal'
+ data_repeat: 4 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset_abdomenatlas_28class_randomclass.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset101_AbdomenAtlasInternal'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
+ max_epochs: 200
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1e7b20b53fa354c7bf821b0c697368fb1f02ed10
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 4 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 200 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3a84d911b03025a442926e16167d77602ddcc52b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten2.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 4 # 4
+ out_channels: 4 # 4
+ model_channels: 128
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 5
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 56 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 200 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 56
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
+ max_epochs: 200
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten3.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten3.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b654f88a0b2b96874b7d32e9643d8fc8ae7c1a2b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten3.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: hybrid # crossattn
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 128
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 5
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 56 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 200 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 56
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
+ max_epochs: 200
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten4.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten4.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..af302905d3c984295b6b777c4bb38213e23723f5
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten4.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 4 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 5
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 48 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 200 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 56
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
+ max_epochs: 200
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten5.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten5.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ae38ec78fd29f3f80d48e86b681e9fc1d0b7d1ff
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_atten5.yaml
@@ -0,0 +1,92 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: hybrid # crossattn
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 5
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 48 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 200 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100
+ max_images: 56
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
+ max_epochs: 200
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_ch384.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_ch384.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..01ed38b74795928fe5a438c21f15edb33c3a6689
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_ch384.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 384
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 200 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_ch512.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_ch512.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b8ac17442464075e48f803040701e1bb4a0a2dff
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_ch512.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 512
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 32 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 200 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
+ data_repeat: 80 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_kidney.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_kidney.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6898365658ff7f724b73ba01bc274d44ff24150d
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_kidney.yaml
@@ -0,0 +1,94 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 48 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset018_AbdomenAtlas2.0_base_kidney'
+ planner: 'nnUNetPlans_3d_fullres'
+ data_file: 'data_split/kidney/splits_final.json'
+ data_repeat: 20 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset018_AbdomenAtlas2.0_base_kidney'
+ planner: 'nnUNetPlans_3d_fullres'
+ data_file: 'data_split/kidney/splits_final.json'
+ data_repeat: 20 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_pancreas.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_pancreas.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8673c4b8f22f107824ed008044bb59a3e2f31f82
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_slice_pancreas.yaml
@@ -0,0 +1,94 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: slice_data
+ cond_stage_key: masked_slice
+ cond_stage_trainable: false
+ conditioning_key: concat # crossattn
+ image_size: 64
+ channels: 9
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 9 # 4
+ out_channels: 4 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: '/storage/chenqi/code/latent-diffusion-main/models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 48 # 32
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.slice_dataset.slice_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset017_AbdomenAtlas2.0_base_pancreas'
+ planner: 'nnUNetPlans_3d_fullres'
+ data_file: 'data_split/pancreas/splits_final.json'
+ data_repeat: 50 # 1000 100
+ validation:
+ target: ldm.data.slice_dataset.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset017_AbdomenAtlas2.0_base_pancreas'
+ planner: 'nnUNetPlans_3d_fullres'
+ data_file: 'data_split/pancreas/splits_final.json'
+ data_repeat: 20 # 1000 100
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 500
+ max_images: 32
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_stage1.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_stage1.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a3ab2756587418343eef51b37e1d70f01577a216
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_stage1.yaml
@@ -0,0 +1,90 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: ref_and_volume_seg
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 12
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: 'models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset.volume_train
+ params:
+ data_root: '/storage/chenqi/data/gem_3d_data/nnUNet_preprocessed/'
+ data_name: 'Dataset202_Brain'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset.volume_val
+ params:
+ data_root: '/storage/chenqi/data/gem_3d_data/nnUNet_preprocessed/'
+ data_name: 'Dataset202_Brain'
+
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 20000
+ max_images: 8
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_stage2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_stage2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c90408472e62f1a13e009e02e99ba7f07dac72e6
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/brain_stage2.yaml
@@ -0,0 +1,93 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: ref_and_volume_seg
+ cond_stage_trainable: false
+ conditioning_key: concat
+ image_size: 64
+ channels: 4
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ ckpt_path: path-to-stage1-ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 12
+ out_channels: 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: false
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ ckpt_path: 'models/first_stage_models/kl-f8/model.ckpt'
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+ cond_stage_config: __is_first_stage__
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ train:
+ target: ldm.data.volume_dataset.volume_train
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset202_Brain'
+ data_repeat: 100
+ validation:
+ target: ldm.data.volume_dataset.volume_val
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset202_Brain'
+
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 20000
+ max_images: 8
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b4038ffac8caf74db49fdb7dc87ad0dbd03c4745
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask.yaml
@@ -0,0 +1,67 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32 # 4
+ out_channels: 16 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000 # 100
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2ffca8f631a80af53ebca26c877095e483dfd949
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask2.yaml
@@ -0,0 +1,67 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.5
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32 # 4
+ out_channels: 16 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000 # 100
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask3.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask3.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f57bb7c6736731ab039bb1264b1f4549f2da43b7
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask3.yaml
@@ -0,0 +1,67 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.51
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32 # 4
+ out_channels: 16 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000 # 100
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask4.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask4.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b4038ffac8caf74db49fdb7dc87ad0dbd03c4745
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_2d_with_body_mask4.yaml
@@ -0,0 +1,67 @@
+model:
+ base_learning_rate: 5.0e-7
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32 # 4
+ out_channels: 16 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8 # 32
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000 # 100
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e8f1d72ffebfb9cbe5689decbe05b018f53cd8db
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask.yaml
@@ -0,0 +1,70 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: False
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae-retrain/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000999.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32 # 4
+ out_channels: 16 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e8f1d72ffebfb9cbe5689decbe05b018f53cd8db
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask2.yaml
@@ -0,0 +1,70 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: False
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae-retrain/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000999.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32 # 4
+ out_channels: 16 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask2_finetune.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask2_finetune.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..033260c6cfa4181cbd87dc28c252aa8b0e2090ef
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask2_finetune.yaml
@@ -0,0 +1,71 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: False
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae/logs/full_ct_2d_with_body_mask4/checkpoints/epoch=000833.ckpt
+ # ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae/logs/full_ct_2d_with_body_mask/checkpoints/base.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32 # 4
+ out_channels: 16 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask2_finetune2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask2_finetune2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..621c875b2faff63b17e93a097d1f41b7b0b249e9
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask2_finetune2.yaml
@@ -0,0 +1,70 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: False
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae/logs/full_ct_2d_with_body_mask4/checkpoints/epoch=000833.ckpt
+ load_only_unet: True
+ fix_t: False
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32 # 4
+ out_channels: 16 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask_finetune.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask_finetune.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..14b225d0b231cd329c57257b5b441338773e7631
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask_finetune.yaml
@@ -0,0 +1,70 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.0
+ use_ema: False
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae/logs/full_ct_2d_with_body_mask/checkpoints/base.ckpt
+ load_only_unet: True
+ fix_t: True
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32 # 4
+ out_channels: 16 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask_finetune2.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask_finetune2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..37017b47fe7ee6ef9c2e9f0efe53fe98bf8adb09
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/full_ct_3d_with_body_mask_finetune2.yaml
@@ -0,0 +1,70 @@
+model:
+ base_learning_rate: 5.0e-6
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ # cond_stage_trainable: false
+ conditioning_key: crossattn # crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.0
+ use_ema: False
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae/logs/full_ct_2d_with_body_mask/checkpoints/base.ckpt
+ load_only_unet: True
+ fix_t: False
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32 # 4
+ out_channels: 16 # 4
+ model_channels: 224
+ attention_resolutions: [ 8, 4, 2 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 3, 4 ]
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: True
+ legacy: False
+
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv'
+
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ csv_file: '/sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv'
+ labels: '/sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv'
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: False
+
+ trainer:
+ benchmark: True
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3919299787898eaa15bf2594bc571d4ac998de98
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen.yaml
@@ -0,0 +1,11 @@
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ validation:
+ target: ldm.data.slice_dataset_infer.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen3d.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen3d.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f87d78f163e381bcd8ddcd03ad061a5e8c19c5c7
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen3d.yaml
@@ -0,0 +1,12 @@
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ validation:
+ # target: ldm.data.volume_dataset_infer.volume_val
+ target: ldm.data.volume_dataset_infer.volume_train
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen_28class_randomclass.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen_28class_randomclass.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b1fda1e8074104293585b7fb56c408b640997a90
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen_28class_randomclass.yaml
@@ -0,0 +1,11 @@
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ validation:
+ target: ldm.data.slice_dataset_infer_28class_randomclass.slice_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen_test.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen_test.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..99590484c0d32f7260e344f2245da263aa6284a3
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_abdomen_test.yaml
@@ -0,0 +1,11 @@
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ validation:
+ target: ldm.data.volume_dataset.volume_test
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset201_Abdomen_test'
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_brain.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_brain.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1e52b9de4d46b2d3d50575996a06db5f1a80ef6f
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_brain.yaml
@@ -0,0 +1,11 @@
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ validation:
+ target: ldm.data.volume_dataset.volume_test
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset202_Brain'
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_brain_test.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_brain_test.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..80bcabf1bd9acfe663f6b4bb835ea575891d37ab
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/inference_brain_test.yaml
@@ -0,0 +1,11 @@
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ validation:
+ target: ldm.data.volume_dataset.volume_test
+ params:
+ data_root: './data/nnUNet_preprocessed/'
+ data_name: 'Dataset203_Brain_test'
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/mask_generation.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/mask_generation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..09ed691b3a1209780a1d5644e9b1e3e00ad8908e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/configs/latent-diffusion/mask_generation.yaml
@@ -0,0 +1,11 @@
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 1
+ num_workers: 5
+ wrap: false
+ validation:
+ target: ldm.data.mask_generation.volume_val
+ params:
+ data_root: '/storage/chenqi/data/our_data/nnUNet_preprocessed/'
+ data_name: 'Dataset010_MSD_Liver'
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split.py
new file mode 100644
index 0000000000000000000000000000000000000000..93e6e36f375bf381c00ef6dd8eadcd2138f27c7f
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split.py
@@ -0,0 +1,43 @@
+import random
+import os
+
+# 设定文件路径
+train_file = 'train.txt' # 训练集输出文件
+eval_file = 'eval.txt' # 验证集输出文件
+test_file = 'test.txt' # 测试集输出文件
+
+# 读取数据
+data = sorted(os.listdir('/storage/chenqi/data/BraTS_2019_Data_Training/All'))
+
+# 随机打乱数据
+random.shuffle(data)
+
+# 计算各个数据集的大小
+train_size = 290
+eval_size = 8
+test_size = 37
+
+# 划分数据集
+train_data = data[:train_size]
+eval_data = data[train_size:train_size + eval_size]
+test_data = data[train_size + eval_size:]
+
+# 保存到txt文件
+with open(train_file, 'w') as file:
+ for i in train_data:
+ file.write(i)
+ file.write('\n')
+
+with open(eval_file, 'w') as file:
+ for i in eval_data:
+ file.write(i)
+ file.write('\n')
+
+
+with open(test_file, 'w') as file:
+ for i in test_data:
+ file.write(i)
+ file.write('\n')
+
+
+print(f"数据集已划分完成，并分别保存为: {train_file}, {eval_file}, {test_file}")
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/colon_json.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/colon_json.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cab94d85965ff5a548586f5a98641e39d48417a
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/colon_json.py
@@ -0,0 +1,32 @@
+import os
+from batchgenerators.utilities.file_and_folder_operations import join, load_json, isfile, save_json, maybe_mkdir_p
+import csv
+
+with open('/storage/chenqi/X_data/annotatedtumor_txt/colon_tumor_cases.txt', 'r') as f:
+ all_files=f.readlines()
+all_files = [os.path.basename(i.split(' ')[0]).split('.')[0] for i in all_files]
+all_files = [i.split('\n')[0] for i in all_files]
+
+with open('/storage/chenqi/code/DiffTumor/STEP3.SegmentationModel/cross_eval/colon_tumor_data_fold/real_tumor_val_1.txt', 'r') as f:
+ val_liver=f.readlines()
+val_liver = [os.path.basename(i.split(' ')[0]).split('.')[0] for i in val_liver]
+
+cases_info = [*csv.DictReader(open('final_mapping_qichen.csv'))]
+
+val_bdmap=[]
+for i in cases_info:
+ if i['source_dataset'] == 'MSD-Colon':
+ for j in val_liver:
+ if j == i['source_id']:
+ val_bdmap.append(i['AbdomenAtlas_id'])
+
+
+breakpoint()
+write_content = []
+train_liver = list(set(all_files) - set(val_bdmap))
+write_content.append({"train":train_liver, "val":val_bdmap})
+
+# breakpoint()
+
+splits_file = 'splits_final_colon_bdmap.json'
+save_json(write_content, splits_file)
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/eso_json.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/eso_json.py
new file mode 100644
index 0000000000000000000000000000000000000000..6843ae6df17c1d32f9eb3c846882ded8f8f5d2d5
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/eso_json.py
@@ -0,0 +1,18 @@
+import os
+from batchgenerators.utilities.file_and_folder_operations import join, load_json, isfile, save_json, maybe_mkdir_p
+import csv
+
+
+all_files = os.listdir('/storage/chenqi/X_data/esophagus_BodyMapPro')
+val_files = load_json('/storage/chenqi/code/GEM-3D-ct/data_split/splits_final_eso_bdmap.json')[0]['val']
+
+
+write_content = []
+train_liver = list(set(all_files) - set(val_files))
+# breakpoint()
+write_content.append({"train":train_liver, "val":val_files})
+
+# breakpoint()
+
+splits_file = 'splits_eso.json'
+save_json(write_content, splits_file)
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/final_mapping_qichen.csv b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/final_mapping_qichen.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9f74411ae7b5e01687f94bf9ea29fd937b9213b8
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/final_mapping_qichen.csv
@@ -0,0 +1,5196 @@
+AbdomenAtlas_id,source_id,source_dataset,liver,pancreas,kidney
+BDMAP_00000001,PETCT_404f8c732f,autoPET,unknown,unknown,unknown
+BDMAP_00000002,PANCREAS_0039,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000003,s0543,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000004,colon_195,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000005,TCIAColon_0256_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000006,PETCT_63464433c8,autoPET,unknown,unknown,unknown
+BDMAP_00000007,hepaticvessel_447,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000008,case_00280,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000009,word_0086,WORD,unknown,unknown,unknown
+BDMAP_00000010,PETCT_f637b5930b,autoPET,unknown,unknown,unknown
+BDMAP_00000011,PETCT_49479d6e64,autoPET,unknown,unknown,unknown
+BDMAP_00000012,TCIAColon_0300_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000013,hepaticvessel_200,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000014,s0904,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000015,hepaticvessel_406,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000016,LDCT-L277_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00000017,FLARE23_Ts_0084_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000018,s1089,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000019,TCIAColon_0233_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00000020,img0034,BTCV,healthy,healthy,healthy
+BDMAP_00000021,s0369,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000022,TCIAColon_0249_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000023,case_00228,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000024,pancreas_476,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000025,hepaticvessel_343,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000026,NIH-LYMPH-ABD-041_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00000027,colon_188,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000028,Case_01035_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000029,hepaticvessel_334,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000030,colon_139,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000031,PETCT_b53ba7c6bf,autoPET,unknown,unknown,unknown
+BDMAP_00000032,PETCT_5d553bf6b4,autoPET,unknown,unknown,unknown
+BDMAP_00000033,PETCT_ba81e4b04b,autoPET,unknown,unknown,unknown
+BDMAP_00000034,case_00268,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000035,hepaticvessel_325,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000036,case_00401,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000037,hepaticvessel_036,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000038,hepaticvessel_199,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000039,case_00066,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000040,TCIAColon_0262_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000041,PETCT_f5c2c09846,autoPET,unknown,unknown,unknown
+BDMAP_00000042,s0250,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000043,case_00298,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000044,case_00512,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000045,colon_128,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000046,CPTAC-PDA-C3N-02010_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000047,Case_00056_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000048,TCIAColon_0188_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000049,Case_00535_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000050,amos_0059,AMOS,unknown,unknown,unknown
+BDMAP_00000051,s1374,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000052,pancreas_385,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000053,PETCT_2ce074c2ea,autoPET,unknown,unknown,unknown
+BDMAP_00000054,CPTAC-PDA-C3N-03000_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000055,pancreas_014,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000056,spleen_33,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00000057,TCIAColon_0166_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000058,LDCT-L193_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00000059,case_00286,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000060,TCIAColon_0232_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000061,s1348,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000062,case_00017,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000063,TCIAColon_0161_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000064,liver_148,MSD-Liver,unknown,unknown,unknown
+BDMAP_00000065,LDCT-L219_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00000066,case_00116,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000067,TCIAColon_0082_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000068,Case_00799_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000069,colon_012,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000070,s0429,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000071,amos_0044,AMOS,unknown,unknown,unknown
+BDMAP_00000072,PETCT_5de3ac617a,autoPET,unknown,unknown,unknown
+BDMAP_00000073,PETCT_ded50b1e68,autoPET,unknown,unknown,unknown
+BDMAP_00000074,lung_016,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000075,hepaticvessel_005,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000076,amos_0159,AMOS,unknown,unknown,unknown
+BDMAP_00000077,PETCT_6170317f2e,autoPET,unknown,unknown,unknown
+BDMAP_00000078,lung_003,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000079,s0896,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000080,TCIAColon_0286_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000081,PETCT_2a78eed085,autoPET,unknown,unknown,unknown
+BDMAP_00000082,colon_150,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000083,PETCT_61348439bf,autoPET,unknown,unknown,unknown
+BDMAP_00000084,liver_59,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000085,PETCT_b2f82ed4b9,autoPET,unknown,unknown,unknown
+BDMAP_00000086,TCGA-BLCA-4Z-AA86_0_3,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00000087,pancreas_346,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000088,PETCT_d3dac0d1cd,autoPET,unknown,unknown,unknown
+BDMAP_00000089,amos_0038,AMOS,unknown,unknown,unknown
+BDMAP_00000090,case_00147,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000091,liver_38,LiTS,healthy,unknown,unknown
+BDMAP_00000092,FLARE23_Ts_0016_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000093,pancreas_348,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000094,hepaticvessel_431,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000095,Case_00773_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000096,colon_084,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000097,TCIAColon_0260_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000098,PETCT_90ea6a6aaf,autoPET,unknown,unknown,unknown
+BDMAP_00000099,TCIAColon_0154_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000100,liver_71,LiTS,unhealthy,unknown,unknown
+BDMAP_00000101,liver_13,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000102,case_00272,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000103,TCIAColon_0102_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000104,case_00130,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000105,hepaticvessel_186,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000106,CPTAC-PDA-C3N-03670_0_7,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000107,TCIAColon_0195_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00000108,s0342,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000109,img0040,BTCV,healthy,healthy,healthy
+BDMAP_00000110,PANCREAS_0021,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000111,lung_086,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000112,hepaticvessel_172,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000113,liver_113,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000114,TCIAColon_0122_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000115,hepaticvessel_354,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000116,CPTAC-PDA-C3N-03670_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000117,case_00188,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000118,volume-20,CT-ORG,unknown,unknown,unknown
+BDMAP_00000119,PETCT_6604b228c6,autoPET,unknown,unknown,unknown
+BDMAP_00000120,PETCT_d6a491e16d,autoPET,unknown,unknown,unknown
+BDMAP_00000121,hepaticvessel_169,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000122,case_00473,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000123,CPTAC-PDA-C3N-01167_0_6,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000124,Case_00002_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000125,pancreas_468,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000126,s0090,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000127,s0899,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000128,case_00270,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000129,case_00264,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000130,PETCT_0f44cec2e6,autoPET,unknown,unknown,unknown
+BDMAP_00000131,pancreas_121,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000132,colon_069,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000133,PETCT_442a09f90e,autoPET,unknown,unknown,unknown
+BDMAP_00000134,hepaticvessel_195,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000135,TCGA-STAD-A8DU_0_2,TCGA-STAD,unknown,unknown,unknown
+BDMAP_00000136,s0606,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000137,case_00129,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000138,colon_166,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000139,case_00233,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000140,liver_119,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000141,img0064,BTCV,healthy,healthy,healthy
+BDMAP_00000142,s0477,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000143,hepaticvessel_424,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000144,TCIAColon_0242_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000145,TCIAColon_0240_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000146,PETCT_ca47fe5e7d,autoPET,unknown,unknown,unknown
+BDMAP_00000147,PETCT_8eb3998417,autoPET,unknown,unknown,unknown
+BDMAP_00000148,s0428,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000149,case_00460,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000150,s1405,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000151,amos_0288,AMOS,unknown,unknown,unknown
+BDMAP_00000152,colon_051,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000153,hepaticvessel_194,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000154,pancreas_255,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000155,hepaticvessel_167,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000156,TCIAColon_0207_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000157,pancreas_306,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000158,TCIAColon_0055_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000159,case_00243,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000160,amos_0231,AMOS,unknown,unknown,unknown
+BDMAP_00000161,case_00579,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000162,case_00205,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000163,PETCT_13f476b02b,autoPET,unknown,unknown,unknown
+BDMAP_00000164,TCIAColon_0116_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000165,s0143,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000166,lung_081,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000167,case_00292,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000168,TCIAColon_0065_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000169,TCIAColon_0206_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000170,TCIAColon_0020_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00000171,hepaticvessel_027,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000172,hepaticvessel_398,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000173,TCIAColon_0001_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000174,s0257,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000175,PETCT_7a3a27371a,autoPET,unknown,unknown,unknown
+BDMAP_00000176,case_00529,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000177,PETCT_d3f13dff4b,autoPET,unknown,unknown,unknown
+BDMAP_00000178,Case_00675_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000179,TCIAColon_0132_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000180,TCIAColon_0036_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000181,TCIAColon_0047_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000182,CPTAC-PDA-C3N-03430_0_13,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000183,TCIAColon_0134_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000184,s0657,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000185,FLARE23_Ts_0097_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000186,volume-48,CT-ORG,unknown,unknown,unknown
+BDMAP_00000187,hepaticvessel_355,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000188,NIH-LYMPH-MED-016_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00000189,hepaticvessel_332,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000190,colon_133,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000191,TCIAColon_0046_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000192,pancreas_199,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000193,TCIAColon_0006_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000194,PETCT_80f21e1ed0,autoPET,unknown,unknown,unknown
+BDMAP_00000195,amos_0214,AMOS,unknown,unknown,unknown
+BDMAP_00000196,PETCT_335a00191d,autoPET,unknown,unknown,unknown
+BDMAP_00000197,pancreas_409,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000198,PANCREAS_0044,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000199,s1069,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000200,word_0139,WORD,unknown,unknown,unknown
+BDMAP_00000201,hepaticvessel_222,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000202,TCIAColon_0120_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000203,Case_00708_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000204,hepaticvessel_134,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000205,liver_14,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000206,s1221,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000207,PETCT_b62864161e,autoPET,unknown,unknown,unknown
+BDMAP_00000208,5_image,CHAOS,healthy,healthy,healthy
+BDMAP_00000209,Case_00900_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000210,hepaticvessel_012,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000211,Case_00791_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000212,PETCT_a8a5e96821,autoPET,unknown,unknown,unknown
+BDMAP_00000213,PETCT_05d5a79faf,autoPET,unknown,unknown,unknown
+BDMAP_00000214,word_0029,WORD,unknown,unknown,unknown
+BDMAP_00000215,FLARE23_Ts_0036_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000216,hepaticvessel_303,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000217,s0182,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000218,TCIAColon_0063_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000219,case_00523,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000220,img0009,BTCV,healthy,healthy,healthy
+BDMAP_00000221,FLARE23_Ts_0064_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000222,s0394,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000223,colon_200,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000224,Case_00762_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000225,pancreas_370,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000226,colon_156,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000227,s0344,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000228,Case_00029_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000229,case_00070,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000230,TCIAColon_0256_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000231,PETCT_07fec0d985,autoPET,unknown,unknown,unknown
+BDMAP_00000232,liver_81,LiTS,unhealthy,unknown,unknown
+BDMAP_00000233,case_00472,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000234,PETCT_24cb79a92b,autoPET,unknown,unknown,unknown
+BDMAP_00000235,lung_054,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000236,pancreas_301,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000237,PETCT_5e0bc6cdda,autoPET,unknown,unknown,unknown
+BDMAP_00000238,TCIAColon_0154_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000239,hepaticvessel_032,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000240,case_00412,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000241,case_00566,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000242,PETCT_2946d59a94,autoPET,unknown,unknown,unknown
+BDMAP_00000243,pancreas_241,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000244,case_00299,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000245,case_00400,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000246,PANCREAS_0040,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000247,liver_77,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000248,TCIAColon_0225_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000249,case_00293,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000250,CPTAC-UCEC-C3L-01732_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00000251,pancreas_494,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000252,word_0123,WORD,unknown,unknown,unknown
+BDMAP_00000253,PETCT_e00c98b415,autoPET,unknown,unknown,unknown
+BDMAP_00000254,amos_0348,AMOS,unknown,unknown,unknown
+BDMAP_00000255,hepaticvessel_071,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000256,FLARE23_Ts_0014_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000257,amos_0115,AMOS,unknown,unknown,unknown
+BDMAP_00000258,hepaticvessel_189,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000259,case_00584,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000260,PETCT_d6de1252d9,autoPET,unknown,unknown,unknown
+BDMAP_00000261,FLARE23_Ts_0080_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000262,NIH-LYMPH-MED-003_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00000263,case_00174,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000264,colon_092,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000265,PETCT_aa27cb9156,autoPET,unknown,unknown,unknown
+BDMAP_00000266,PETCT_c3cc732d95,autoPET,unknown,unknown,unknown
+BDMAP_00000267,s1337,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000268,hepaticvessel_418,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000269,PETCT_072833774e,autoPET,unknown,unknown,unknown
+BDMAP_00000270,hepaticvessel_139,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000271,CPTAC-PDA-C3N-02997_0_5,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000272,amos_0152,AMOS,unknown,unknown,unknown
+BDMAP_00000273,liver_40,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000274,PETCT_49f3d297b0,autoPET,unknown,unknown,unknown
+BDMAP_00000275,s1210,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000276,PETCT_baacf43b4f,autoPET,unknown,unknown,unknown
+BDMAP_00000277,PETCT_f60ea3abc5,autoPET,unknown,unknown,unknown
+BDMAP_00000278,hepaticvessel_378,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000279,case_00054,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000280,Case_00853_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000281,PETCT_0223010e46,autoPET,unknown,unknown,unknown
+BDMAP_00000282,TCIAColon_0139_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00000283,volume-111,CT-ORG,unknown,unknown,unknown
+BDMAP_00000284,Case_00910_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000285,case_00487,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000286,PETCT_4c670a5445,autoPET,unknown,unknown,unknown
+BDMAP_00000287,Case_00951_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000288,s0620,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000289,TCIAColon_0021_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000290,CPTAC-PDA-C3N-03430_1_8,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000291,amos_0103,AMOS,unknown,unknown,unknown
+BDMAP_00000292,volume-86,CT-ORG,unknown,unknown,unknown
+BDMAP_00000293,CPTAC-CCRCC-C3N-00312_0_4,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00000294,PETCT_69ee62b035,autoPET,unknown,unknown,unknown
+BDMAP_00000295,TCIAColon_0060_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00000296,TCIAColon_0119_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000297,case_00432,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000298,s0721,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000299,hepaticvessel_037,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000300,PETCT_f47e31ceb5,autoPET,unknown,unknown,unknown
+BDMAP_00000301,PETCT_47f4460050,autoPET,unknown,unknown,unknown
+BDMAP_00000302,s0330,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000303,TCIAColon_0114_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000304,case_00112,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000305,PETCT_cf20ad1656,autoPET,unknown,unknown,unknown
+BDMAP_00000306,liver_191,MSD-Liver,unknown,unknown,unknown
+BDMAP_00000307,TCIAColon_0158_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000308,s0752,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000309,FLARE23_Ts_0090_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000310,hepaticvessel_181,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000311,PETCT_6d62e15c29,autoPET,unknown,unknown,unknown
+BDMAP_00000312,word_0073,WORD,unknown,unknown,unknown
+BDMAP_00000313,hepaticvessel_300,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000314,amos_0404,AMOS,unknown,unknown,unknown
+BDMAP_00000315,s0243,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000316,hepaticvessel_326,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000317,PETCT_f4813007a0,autoPET,unknown,unknown,unknown
+BDMAP_00000318,PETCT_080922db5a,autoPET,unknown,unknown,unknown
+BDMAP_00000319,case_00230,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000320,case_00461,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000321,case_00109,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000322,hepaticvessel_457,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000323,TCIAColon_0241_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000324,pancreas_355,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000325,hepaticvessel_175,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000326,amos_0332,AMOS,unknown,unknown,unknown
+BDMAP_00000327,TCGA-OV-61-1901_1_1,TCGA-OV,unknown,unknown,unknown
+BDMAP_00000328,pancreas_432,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000329,case_00481,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000330,PETCT_e344879c2e,autoPET,unknown,unknown,unknown
+BDMAP_00000331,case_00182,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000332,pancreas_104,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000333,PETCT_f5cff2e59d,autoPET,unknown,unknown,unknown
+BDMAP_00000334,PETCT_f21755a99b,autoPET,unknown,unknown,unknown
+BDMAP_00000335,s1368,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000336,case_00235,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000337,TCIAColon_0077_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000338,pancreas_091,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000339,TCIAColon_0084_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00000340,PETCT_dd887591a9,autoPET,unknown,unknown,unknown
+BDMAP_00000341,s0663,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000342,amos_0064,AMOS,unknown,unknown,unknown
+BDMAP_00000343,PETCT_b1219c408b,autoPET,unknown,unknown,unknown
+BDMAP_00000344,TCIAColon_0079_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000345,colon_103,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000346,FLARE23_Ts_0047_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000347,liver_95,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000348,PETCT_1f6b6b0548,autoPET,unknown,unknown,unknown
+BDMAP_00000349,s0884,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000350,Case_00766_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000351,PANCREAS_0048,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000352,s0206,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000353,case_00297,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000354,TCIAColon_0146_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000355,case_00141,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000356,case_00218,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000357,PETCT_274688d0a8,autoPET,unknown,unknown,unknown
+BDMAP_00000358,PETCT_277fc3c67c,autoPET,unknown,unknown,unknown
+BDMAP_00000359,case_00051,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000360,CPTAC-PDA-C3N-01383_0_5,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000361,s0293,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000362,liver_6,LiTS,unhealthy,unknown,unknown
+BDMAP_00000363,NIH-LYMPH-ABD-077_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00000364,case_00548,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000365,PETCT_72ee9ec897,autoPET,unknown,unknown,unknown
+BDMAP_00000366,case_00197,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000367,TCIAColon_0192_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00000368,case_00115,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000369,Case_00809_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000370,PETCT_816732638d,autoPET,unknown,unknown,unknown
+BDMAP_00000371,s1183,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000372,word_0066,WORD,unknown,unknown,unknown
+BDMAP_00000373,colon_141,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000374,PETCT_956520090b,autoPET,unknown,unknown,unknown
+BDMAP_00000375,case_00065,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000376,PETCT_2b8064fbe2,autoPET,unknown,unknown,unknown
+BDMAP_00000377,hepaticvessel_081,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000378,TCIAColon_0082_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000379,PETCT_dadcd69ba9,autoPET,unknown,unknown,unknown
+BDMAP_00000380,hepaticvessel_114,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000381,lung_083,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000382,CPTAC-CCRCC-C3L-01954_3_5,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00000383,colon_110,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000384,amos_0376,AMOS,unknown,unknown,unknown
+BDMAP_00000385,word_0140,WORD,unknown,unknown,unknown
+BDMAP_00000386,26_image,CHAOS,healthy,healthy,healthy
+BDMAP_00000387,hepaticvessel_347,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000388,pancreas_312,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000389,PETCT_2716c9bfff,autoPET,unknown,unknown,unknown
+BDMAP_00000390,s0607,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000391,CPTAC-PDA-C3N-01715_0_6,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000392,s0709,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000393,TCIAColon_0280_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000394,FLARE23_Ts_0037_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000395,Case_00206_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000396,FLARE23_Ts_0043_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000397,TCIAColon_0040_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00000398,PETCT_b509ace2a2,autoPET,unknown,unknown,unknown
+BDMAP_00000399,Case_01050_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000400,liver_100,LiTS,unhealthy,unknown,unknown
+BDMAP_00000401,TCIAColon_0153_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000402,NIH-LYMPH-ABD-082_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00000403,PETCT_4095c12e2d,autoPET,unknown,unknown,unknown
+BDMAP_00000404,TCIAColon_0295_0_6,TCIAColon,unknown,unknown,unknown
+BDMAP_00000405,FLARE23_Ts_0001_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000406,PETCT_5ac4fd9091,autoPET,unknown,unknown,unknown
+BDMAP_00000407,TCIAColon_0267_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000408,volume-103,CT-ORG,unknown,unknown,unknown
+BDMAP_00000409,hepaticvessel_220,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000410,Case_00413_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000411,pancreas_069,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000412,TCIAColon_0158_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000413,liver_39,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000414,case_00037,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000415,hepaticvessel_285,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000416,pancreas_209,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000417,PETCT_4f7a8f41c0,autoPET,unknown,unknown,unknown
+BDMAP_00000418,Case_00795_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000419,TCIAColon_0226_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000420,amos_0135,AMOS,unknown,unknown,unknown
+BDMAP_00000421,pancreas_428,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000422,FLARE23_Ts_0022_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000423,word_0138,WORD,unknown,unknown,unknown
+BDMAP_00000424,pancreas_482,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000425,TCIAColon_0055_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000426,s0903,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000427,pancreas_320,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000428,case_00278,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000429,case_00530,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000430,pancreas_106,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000431,pancreas_377,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000432,case_00149,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000433,Case_00576_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000434,case_00541,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000435,case_00463,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000436,hepaticvessel_193,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000437,liver_126,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000438,pancreas_096,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000439,case_00145,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000440,hepaticvessel_439,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000441,pancreas_189,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000442,Case_00756_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000443,amos_0362,AMOS,unknown,unknown,unknown
+BDMAP_00000444,hepaticvessel_247,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000445,CPTAC-CCRCC-C3L-00799_0_2,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00000446,PETCT_a4cb97ffc7,autoPET,unknown,unknown,unknown
+BDMAP_00000447,pancreas_379,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000448,s0667,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000449,case_00135,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000450,hepaticvessel_460,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000451,PETCT_d51664a9e4,autoPET,unknown,unknown,unknown
+BDMAP_00000452,case_00062,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000453,hepaticvessel_312,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000454,PETCT_5bd311a5e9,autoPET,unknown,unknown,unknown
+BDMAP_00000455,TCIAColon_0296_0_6,TCIAColon,unknown,unknown,unknown
+BDMAP_00000456,s1314,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000457,hepaticvessel_070,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000458,case_00096,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000459,PETCT_01682f60c3,autoPET,unknown,unknown,unknown
+BDMAP_00000460,pancreas_139,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000461,lung_075,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000462,pancreas_211,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000463,Case_01012_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000464,hepaticvessel_141,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000465,case_00199,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000466,hepaticvessel_083,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000467,s0252,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000468,word_0113,WORD,unknown,unknown,unknown
+BDMAP_00000469,case_00082,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000470,pancreas_088,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000471,PETCT_6c23f2686d,autoPET,unknown,unknown,unknown
+BDMAP_00000472,Case_00921_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000473,lung_033,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000474,word_0038,WORD,unknown,unknown,unknown
+BDMAP_00000475,PETCT_e2309b8f92,autoPET,unknown,unknown,unknown
+BDMAP_00000476,hepaticvessel_238,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000477,PETCT_d4a08e4bde,autoPET,unknown,unknown,unknown
+BDMAP_00000478,amos_0390,AMOS,unknown,unknown,unknown
+BDMAP_00000479,amos_0078,AMOS,unknown,unknown,unknown
+BDMAP_00000480,liver_69,LiTS,unhealthy,unknown,unknown
+BDMAP_00000481,case_00221,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000482,PETCT_2f587d00b1,autoPET,unknown,unknown,unknown
+BDMAP_00000483,word_0100,WORD,unknown,unknown,unknown
+BDMAP_00000484,amos_0350,AMOS,unknown,unknown,unknown
+BDMAP_00000485,amos_0058,AMOS,unknown,unknown,unknown
+BDMAP_00000486,PETCT_be0d3c767e,autoPET,unknown,unknown,unknown
+BDMAP_00000487,case_00081,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000488,TCIAColon_0124_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000489,Case_00968_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000490,pancreas_463,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000491,Case_00054_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000492,TCIAColon_0079_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000493,pancreas_141,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000494,colon_049,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000495,img0023,BTCV,healthy,healthy,healthy
+BDMAP_00000496,19_image,CHAOS,healthy,healthy,healthy
+BDMAP_00000497,PETCT_fde79b6aa9,autoPET,unknown,unknown,unknown
+BDMAP_00000498,CPTAC-PDA-C3N-01166_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000499,PETCT_91cfa804b0,autoPET,unknown,unknown,unknown
+BDMAP_00000500,hepaticvessel_450,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000501,TCIAColon_0111_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000502,TCIAColon_0180_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000503,Case_00849_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000504,Case_00050_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000505,case_00081,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000506,TCGA-BLCA-4Z-AA80_0_2,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00000507,s1028,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000508,Case_00729_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000509,TCIAColon_0174_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00000510,TCIAColon_0184_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000511,case_00060,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000512,CPTAC-PDA-C3N-03670_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000513,hepaticvessel_023,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000514,hepaticvessel_111,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000515,TCIAColon_0203_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000516,case_00073,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000517,PETCT_c9f1b9980f,autoPET,unknown,unknown,unknown
+BDMAP_00000518,case_00249,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000519,lung_057,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000520,PETCT_8c8cd2699c,autoPET,unknown,unknown,unknown
+BDMAP_00000521,pancreas_452,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000522,PETCT_7c96c4626d,autoPET,unknown,unknown,unknown
+BDMAP_00000523,amos_0004,AMOS,unknown,unknown,unknown
+BDMAP_00000524,PETCT_e252be4334,autoPET,unknown,unknown,unknown
+BDMAP_00000525,word_0102,WORD,unknown,unknown,unknown
+BDMAP_00000526,TCIAColon_0110_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000527,s1334,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000528,PETCT_8b714a64e7,autoPET,unknown,unknown,unknown
+BDMAP_00000529,PETCT_581fa95eb0,autoPET,unknown,unknown,unknown
+BDMAP_00000530,s0771,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000531,amos_0143,AMOS,unknown,unknown,unknown
+BDMAP_00000532,case_00520,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000533,CPTAC-PDA-C3N-02997_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000534,hepaticvessel_063,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000535,s0545,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000536,PETCT_d3dac0d1cd,autoPET,unknown,unknown,unknown
+BDMAP_00000537,TCIAColon_0083_1_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000538,PETCT_0223010e46,autoPET,unknown,unknown,unknown
+BDMAP_00000539,TCIAColon_0115_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000540,case_00125,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000541,pancreas_393,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000542,pancreas_111,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000543,Case_00187_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000544,PETCT_1253499c80,autoPET,unknown,unknown,unknown
+BDMAP_00000545,s0183,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000546,word_0083,WORD,unknown,unknown,unknown
+BDMAP_00000547,colon_111,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000548,amos_0097,AMOS,unknown,unknown,unknown
+BDMAP_00000549,hepaticvessel_442,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000550,PETCT_1019ae8551,autoPET,unknown,unknown,unknown
+BDMAP_00000551,amos_0405,AMOS,unknown,unknown,unknown
+BDMAP_00000552,colon_008,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000553,TCIAColon_0275_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000554,lung_005,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000555,case_00145,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000556,volume-109,CT-ORG,unknown,unknown,unknown
+BDMAP_00000557,PETCT_d5103c8fbf,autoPET,unknown,unknown,unknown
+BDMAP_00000558,pancreas_311,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000559,CPTAC-UCEC-C3N-00743_0_4,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00000560,s0115,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000561,CPTAC-PDA-C3N-01167_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000562,case_00058,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000563,PETCT_7ed037687a,autoPET,unknown,unknown,unknown
+BDMAP_00000564,PETCT_6c660140ca,autoPET,unknown,unknown,unknown
+BDMAP_00000565,PETCT_ae96f738c0,autoPET,unknown,unknown,unknown
+BDMAP_00000566,s1404,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000567,case_00072,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000568,colon_213,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000569,case_00485,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000570,LDCT-L248_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00000571,case_00583,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000572,liver_39,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000573,s0566,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000574,case_00101,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000575,PETCT_416d3b9b78,autoPET,unknown,unknown,unknown
+BDMAP_00000576,TCIAColon_0155_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000577,14_image,CHAOS,healthy,healthy,healthy
+BDMAP_00000578,s1187,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000579,CPTAC-CCRCC-C3L-01462_0_9,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00000580,img0037,BTCV,healthy,healthy,healthy
+BDMAP_00000581,PETCT_ec6b934720,autoPET,unknown,unknown,unknown
+BDMAP_00000582,case_00414,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000583,volume-56,CT-ORG,unknown,unknown,unknown
+BDMAP_00000584,spleen_18,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00000585,s0589,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000586,Case_00852_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000587,case_00033,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000588,TCIAColon_0015_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000589,pancreas_147,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000590,pancreas_205,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000591,Case_00689_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000592,hepaticvessel_101,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000593,TCIAColon_0228_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000594,s0656,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000595,PETCT_0b98dbe00d,autoPET,unknown,unknown,unknown
+BDMAP_00000596,PETCT_8838c53a63,autoPET,unknown,unknown,unknown
+BDMAP_00000597,amos_0181,AMOS,unknown,unknown,unknown
+BDMAP_00000598,PANCREAS_0027,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000599,Case_00065_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000600,PETCT_912a25778f,autoPET,unknown,unknown,unknown
+BDMAP_00000601,FLARE23_Ts_0033_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000602,Case_00061_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000603,hepaticvessel_177,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000604,case_00436,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000605,TCIAColon_0097_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000606,TCIAColon_0085_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000607,liver_56,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000608,case_00031,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000609,s0171,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000610,s0011,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000611,TCGA-BLCA-4Z-AA7O_0_12,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00000612,TCIAColon_0281_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000613,PETCT_7a77b26403,autoPET,unknown,unknown,unknown
+BDMAP_00000614,pancreas_037,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000615,liver_42,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000616,case_00454,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000617,s1389,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000618,pancreas_254,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000619,PETCT_b3e923029c,autoPET,unknown,unknown,unknown
+BDMAP_00000620,word_0035,WORD,unknown,unknown,unknown
+BDMAP_00000621,hepaticvessel_394,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000622,TCIAColon_0026_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000623,pancreas_012,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000624,pancreas_123,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000625,s0329,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000626,case_00410,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000627,TCIAColon_0196_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000628,s1363,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000629,word_0104,WORD,unknown,unknown,unknown
+BDMAP_00000630,TCIAColon_0189_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000631,PETCT_cd9bdca46b,autoPET,unknown,unknown,unknown
+BDMAP_00000632,PETCT_ee97822c60,autoPET,unknown,unknown,unknown
+BDMAP_00000633,pancreas_263,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000634,PETCT_7e5729ea40,autoPET,unknown,unknown,unknown
+BDMAP_00000635,PETCT_323cc5aff8,autoPET,unknown,unknown,unknown
+BDMAP_00000636,PETCT_9c36b318e8,autoPET,unknown,unknown,unknown
+BDMAP_00000637,TCIAColon_0220_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000638,TCIAColon_0123_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000639,s1384,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000640,PETCT_a4a66c4fa7,autoPET,unknown,unknown,unknown
+BDMAP_00000641,Case_00783_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000642,liver_75,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000643,amos_0010,AMOS,unknown,unknown,unknown
+BDMAP_00000644,Case_01000_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000645,word_0047,WORD,unknown,unknown,unknown
+BDMAP_00000646,case_00136,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000647,s0731,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000648,hepaticvessel_058,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000649,case_00221,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000650,TCIAColon_0266_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00000651,hepaticvessel_321,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000652,PETCT_cc5c58c82d,autoPET,unknown,unknown,unknown
+BDMAP_00000653,case_00452,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000654,colon_017,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000655,PETCT_7255ccbcf4,autoPET,unknown,unknown,unknown
+BDMAP_00000656,case_00226,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000657,hepaticvessel_146,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000658,word_0117,WORD,unknown,unknown,unknown
+BDMAP_00000659,img0077,BTCV,healthy,healthy,healthy
+BDMAP_00000660,case_00443,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000661,PETCT_92c5c944a5,autoPET,unknown,unknown,unknown
+BDMAP_00000662,case_00587,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000663,Case_00964_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000664,s0484,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000665,PETCT_9aa97cf103,autoPET,unknown,unknown,unknown
+BDMAP_00000666,word_0090,WORD,unknown,unknown,unknown
+BDMAP_00000667,case_00011,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000668,PETCT_dd975744e7,autoPET,unknown,unknown,unknown
+BDMAP_00000669,TCIAColon_0186_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000670,FLARE23_Ts_0015_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000671,liver_45,LiTS,unhealthy,unknown,unknown
+BDMAP_00000672,PETCT_e79291780d,autoPET,unknown,unknown,unknown
+BDMAP_00000673,PANCREAS_0002,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000674,TCIAColon_0024_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000675,TCIAColon_0235_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000676,pancreas_256,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000677,pancreas_036,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000678,word_0018,WORD,unknown,unknown,unknown
+BDMAP_00000679,pancreas_092,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000680,Case_01058_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000681,CPTAC-CCRCC-C3L-00792_0_2,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00000682,PANCREAS_0017,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000683,pancreas_016,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000684,s0924,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000685,21_image,CHAOS,healthy,healthy,healthy
+BDMAP_00000686,PETCT_94962fe878,autoPET,unknown,unknown,unknown
+BDMAP_00000687,case_00122,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000688,s0392,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000689,TCIAColon_0073_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000690,colon_142,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000691,PETCT_41f4d41517,autoPET,unknown,unknown,unknown
+BDMAP_00000692,pancreas_293,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000693,pancreas_038,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000694,amos_0147,AMOS,unknown,unknown,unknown
+BDMAP_00000695,hepaticvessel_207,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000696,pancreas_309,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000697,PETCT_94cc0dac49,autoPET,unknown,unknown,unknown
+BDMAP_00000698,case_00555,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000699,PETCT_1f2a4f4280,autoPET,unknown,unknown,unknown
+BDMAP_00000700,amos_0166,AMOS,unknown,unknown,unknown
+BDMAP_00000701,pancreas_326,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000702,TCIAColon_0096_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000703,hepaticvessel_445,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000704,PETCT_cc1698663a,autoPET,unknown,unknown,unknown
+BDMAP_00000705,PETCT_0b57b247b6,autoPET,unknown,unknown,unknown
+BDMAP_00000706,case_00149,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000707,hepaticvessel_381,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000708,s0894,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000709,liver_124,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000710,case_00524,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000711,PETCT_fbd907a179,autoPET,unknown,unknown,unknown
+BDMAP_00000712,hepaticvessel_359,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000713,case_00177,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000714,pancreas_361,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000715,pancreas_328,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000716,colon_185,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000717,colon_013,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000718,PETCT_3a40a26443,autoPET,unknown,unknown,unknown
+BDMAP_00000719,word_0002,WORD,unknown,unknown,unknown
+BDMAP_00000720,TCIAColon_0014_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000721,spleen_52,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00000722,s0194,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000723,PETCT_46ee4ddd68,autoPET,unknown,unknown,unknown
+BDMAP_00000724,case_00058,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000725,case_00072,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000726,s0699,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000727,s0698,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000728,Case_00812_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000729,amos_0279,AMOS,unknown,unknown,unknown
+BDMAP_00000730,TCIAColon_0009_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000731,lung_045,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000732,PETCT_dc6174cb5d,autoPET,unknown,unknown,unknown
+BDMAP_00000733,FLARE23_Ts_0010_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000734,s0500,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000735,pancreas_185,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000736,PETCT_a19ef8e0d2,autoPET,unknown,unknown,unknown
+BDMAP_00000737,liver_141,MSD-Liver,unknown,unknown,unknown
+BDMAP_00000738,TCIAColon_0199_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000739,TCIAColon_0296_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00000740,FLARE23_Ts_0045_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000741,PETCT_21e4ffcb52,autoPET,unknown,unknown,unknown
+BDMAP_00000742,CPTAC-PDA-C3N-02697_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000743,s0314,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000744,FLARE23_Ts_0042_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000745,liver_86,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000746,PETCT_d626611daf,autoPET,unknown,unknown,unknown
+BDMAP_00000747,PETCT_db3bac356a,autoPET,unknown,unknown,unknown
+BDMAP_00000748,Case_00397_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000749,pancreas_238,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000750,TCIAColon_0216_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000751,case_00187,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000752,amos_0361,AMOS,unknown,unknown,unknown
+BDMAP_00000753,PETCT_f4668d2bdc,autoPET,unknown,unknown,unknown
+BDMAP_00000754,NIH-LYMPH-ABD-073_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00000755,TCIAColon_0297_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000756,case_00228,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000757,28_image,CHAOS,healthy,healthy,healthy
+BDMAP_00000758,CPTAC-CCRCC-C3L-00815_0_3,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00000759,PETCT_1cb2d26a19,autoPET,unknown,unknown,unknown
+BDMAP_00000760,Case_00969_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000761,TCIAColon_0113_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000762,lung_026,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000763,s1264,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000764,PANCREAS_0028,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000765,Case_00886_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000766,amos_0239,AMOS,unknown,unknown,unknown
+BDMAP_00000767,PETCT_38733c001e,autoPET,unknown,unknown,unknown
+BDMAP_00000768,hepaticvessel_042,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000769,PETCT_2122b88719,autoPET,unknown,unknown,unknown
+BDMAP_00000770,Case_01062_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000771,pancreas_392,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000772,PETCT_ac700ab4db,autoPET,unknown,unknown,unknown
+BDMAP_00000773,case_00285,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000774,pancreas_419,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000775,PETCT_960c5f4262,autoPET,unknown,unknown,unknown
+BDMAP_00000776,s0016,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000777,lung_004,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000778,liver_75,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000779,hepaticvessel_379,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000780,PETCT_a87dda9193,autoPET,unknown,unknown,unknown
+BDMAP_00000781,PETCT_d3208ff062,autoPET,unknown,unknown,unknown
+BDMAP_00000782,TCIAColon_0148_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000783,CPTAC-PDA-C3N-01714_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000784,TCIAColon_0153_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000785,colon_147,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000786,TCIAColon_0283_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000787,NIH-LYMPH-ABD-080_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00000788,case_00159,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000789,pancreas_442,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000790,CPTAC-UCEC-C3N-01003_0_2,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00000791,TCIAColon_0190_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000792,PETCT_01140d52d8,autoPET,unknown,unknown,unknown
+BDMAP_00000793,TCIAColon_0015_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000794,case_00025,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000795,Case_00949_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000796,s1153,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000797,TCIAColon_0248_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000798,TCIAColon_0095_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000799,PETCT_07574bfa00,autoPET,unknown,unknown,unknown
+BDMAP_00000800,s0147,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000801,TCIAColon_0050_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000802,amos_0083,AMOS,unknown,unknown,unknown
+BDMAP_00000803,TCIAColon_0066_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000804,colon_101,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000805,case_00466,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000806,word_0075,WORD,unknown,unknown,unknown
+BDMAP_00000807,CPTAC-CCRCC-C3N-00491_0_7,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00000808,s1366,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000809,pancreas_347,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000810,case_00277,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000811,PETCT_1fe6e48293,autoPET,unknown,unknown,unknown
+BDMAP_00000812,case_00059,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000813,hepaticvessel_341,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000814,PETCT_97320b0b58,autoPET,unknown,unknown,unknown
+BDMAP_00000815,PETCT_e1a5b05186,autoPET,unknown,unknown,unknown
+BDMAP_00000816,colon_204,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000817,PETCT_47cd731006,autoPET,unknown,unknown,unknown
+BDMAP_00000818,word_0132,WORD,unknown,unknown,unknown
+BDMAP_00000819,Case_00671_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000820,pancreas_474,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000821,img0028,BTCV,healthy,healthy,healthy
+BDMAP_00000822,amos_0410,AMOS,unknown,unknown,unknown
+BDMAP_00000823,spleen_42,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00000824,PETCT_7958ce0f7c,autoPET,unknown,unknown,unknown
+BDMAP_00000825,TCIAColon_0120_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000826,case_00240,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000827,liver_90,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000828,colon_202,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000829,pancreas_317,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000830,PETCT_0f4ee9e078,autoPET,unknown,unknown,unknown
+BDMAP_00000831,liver_97,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000832,hepaticvessel_304,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000833,case_00015,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000834,case_00036,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000835,s0358,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000836,case_00296,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000837,case_00242,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000838,word_0028,WORD,unknown,unknown,unknown
+BDMAP_00000839,case_00471,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000840,PETCT_dae86b88e2,autoPET,unknown,unknown,unknown
+BDMAP_00000841,FLARE23_Ts_0008_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000842,s0502,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000843,case_00220,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000844,PETCT_a933c9af60,autoPET,unknown,unknown,unknown
+BDMAP_00000845,amos_0133,AMOS,unknown,unknown,unknown
+BDMAP_00000846,PANCREAS_0033,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000847,PETCT_5c57a98a43,autoPET,unknown,unknown,unknown
+BDMAP_00000848,PETCT_e4e5baf901,autoPET,unknown,unknown,unknown
+BDMAP_00000849,TCGA-OV-61-2003_0_3,TCGA-OV,unknown,unknown,unknown
+BDMAP_00000850,TCIAColon_0245_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000851,colon_100,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000852,PETCT_8ec13728df,autoPET,unknown,unknown,unknown
+BDMAP_00000853,Case_00449_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000854,TCIAColon_0140_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000855,pancreas_336,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000856,hepaticvessel_317,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000857,colon_123,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000858,PETCT_c58b637e17,autoPET,unknown,unknown,unknown
+BDMAP_00000859,pancreas_243,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000860,TCIAColon_0032_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000861,22_image,CHAOS,healthy,healthy,healthy
+BDMAP_00000862,CPTAC-UCEC-C3N-00866_0_3,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00000863,TCIAColon_0069_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000864,TCIAColon_0149_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00000865,amos_0391,AMOS,unknown,unknown,unknown
+BDMAP_00000866,hepaticvessel_136,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000867,case_00265,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000868,PETCT_d64d6a87c8,autoPET,unknown,unknown,unknown
+BDMAP_00000869,TCIAColon_0084_0_6,TCIAColon,unknown,unknown,unknown
+BDMAP_00000870,s0299,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000871,case_00210,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000872,lung_095,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000873,colon_149,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000874,colon_024,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000875,word_0119,WORD,unknown,unknown,unknown
+BDMAP_00000876,TCIAColon_0022_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000877,hepaticvessel_019,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000878,PANCREAS_0006,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000879,pancreas_455,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000880,PETCT_ce629e2993,autoPET,unknown,unknown,unknown
+BDMAP_00000881,colon_187,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000882,pancreas_343,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000883,case_00178,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000884,word_0116,WORD,unknown,unknown,unknown
+BDMAP_00000885,CPTAC-UCEC-C3N-01008_0_9,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00000886,s1403,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000887,case_00087,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000888,TCIAColon_0113_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000889,case_00428,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000890,lung_006,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000891,Case_00684_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000892,Case_00060_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000893,s0859,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000894,hepaticvessel_165,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000895,pancreas_426,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000896,PETCT_f24f3ce1da,autoPET,unknown,unknown,unknown
+BDMAP_00000897,hepaticvessel_094,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000898,s0059,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000899,NIH-LYMPH-ABD-072_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00000900,hepaticvessel_059,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000901,Case_00903_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000902,case_00288,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000903,PETCT_9c9a347388,autoPET,unknown,unknown,unknown
+BDMAP_00000904,TCIAColon_0024_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00000905,TCIAColon_0123_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000906,hepaticvessel_435,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000907,liver_121,LiTS,unhealthy,unknown,unknown
+BDMAP_00000908,CPTAC-CCRCC-C3N-00380_0_2,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00000909,s0108,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000910,TCIAColon_0200_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000911,hepaticvessel_393,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00000912,FLARE23_Ts_0009_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000913,colon_219,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000914,liver_176,MSD-Liver,unknown,unknown,unknown
+BDMAP_00000915,hepaticvessel_362,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000916,case_00172,KiTS19,unknown,unknown,unhealthy
+BDMAP_00000917,NIH-LYMPH-ABD-059_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00000918,liver_43,LiTS,unhealthy,unknown,unknown
+BDMAP_00000919,PETCT_824c6a0014,autoPET,unknown,unknown,unknown
+BDMAP_00000920,PETCT_44cfacfd6e,autoPET,unknown,unknown,unknown
+BDMAP_00000921,liver_42,LiTS,unhealthy,unknown,unknown
+BDMAP_00000922,PETCT_6fe855c937,autoPET,unknown,unknown,unknown
+BDMAP_00000923,case_00417,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000924,hepaticvessel_227,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000925,PETCT_3c3050f75e,autoPET,unknown,unknown,unknown
+BDMAP_00000926,case_00155,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000927,FLARE23_Ts_0099_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000928,PANCREAS_0018,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00000929,colon_174,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000930,CPTAC-PDA-C3N-03430_1_2,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00000931,CPTAC-CCRCC-C3N-00491_0_9,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00000932,TCIAColon_0127_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00000933,PETCT_bd7603e9df,autoPET,unknown,unknown,unknown
+BDMAP_00000934,TCIAColon_0092_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000935,case_00484,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000936,case_00269,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000937,colon_087,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000938,pancreas_223,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00000939,colon_099,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000940,pancreas_236,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000941,liver_127,LiTS,unhealthy,unknown,unknown
+BDMAP_00000942,colon_032,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000943,hepaticvessel_428,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000944,TCIAColon_0227_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000945,TCIAColon_0182_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000946,hepaticvessel_410,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000947,hepaticvessel_088,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000948,case_00211,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000949,lung_049,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000950,PETCT_aaf2c3aa12,autoPET,unknown,unknown,unknown
+BDMAP_00000951,CPTAC-UCEC-C3N-01008_1_12,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00000952,word_0049,WORD,unknown,unknown,unknown
+BDMAP_00000953,Case_00250_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000954,TCIAColon_0019_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000955,lung_071,MSD-Lung,unknown,unknown,unknown
+BDMAP_00000956,pancreas_117,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000957,Case_00220_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000958,s0649,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000959,hepaticvessel_217,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000960,amos_0014,AMOS,unknown,unknown,unknown
+BDMAP_00000961,Case_00746_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000962,word_0085,WORD,unknown,unknown,unknown
+BDMAP_00000963,PETCT_d3d3b7d4ff,autoPET,unknown,unknown,unknown
+BDMAP_00000964,s1044,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000965,liver_79,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000966,TCIAColon_0211_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00000967,LDCT-L209_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00000968,case_00246,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000969,s0139,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000970,s0412,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000971,liver_109,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00000972,pancreas_040,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000973,Case_00876_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00000974,hepaticvessel_001,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00000975,amos_0077,AMOS,unknown,unknown,unknown
+BDMAP_00000976,TCGA-BLCA-DK-A1AA_0_1,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00000977,case_00551,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000978,PETCT_e2834e6f5c,autoPET,unknown,unknown,unknown
+BDMAP_00000979,PETCT_3f3df20439,autoPET,unknown,unknown,unknown
+BDMAP_00000980,case_00507,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000981,hepaticvessel_105,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000982,case_00193,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000983,PETCT_9f206193d3,autoPET,unknown,unknown,unknown
+BDMAP_00000984,PETCT_770ee48d09,autoPET,unknown,unknown,unknown
+BDMAP_00000985,FLARE23_Ts_0013_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00000986,TCIAColon_0248_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00000987,case_00249,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000988,colon_018,MSD-Colon,unknown,unknown,unknown
+BDMAP_00000989,case_00171,KiTS21,unknown,unknown,unhealthy
+BDMAP_00000990,PETCT_389c968170,autoPET,unknown,unknown,unknown
+BDMAP_00000991,s0193,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000992,PETCT_5a58935b68,autoPET,unknown,unknown,unknown
+BDMAP_00000993,pancreas_304,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00000994,hepaticvessel_339,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00000995,s1123,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000996,s1296,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000997,s1273,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00000998,case_00528,KiTS23,unknown,unknown,unhealthy
+BDMAP_00000999,s0476,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001000,PETCT_43c9c71b31,autoPET,unknown,unknown,unknown
+BDMAP_00001001,case_00531,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001002,PANCREAS_0026,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00001003,hepaticvessel_131,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001004,LDCT-L232_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00001005,liver_188,MSD-Liver,unknown,unknown,unknown
+BDMAP_00001006,TCIAColon_0133_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001007,s0625,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001008,PETCT_f0a1a38a3b,autoPET,unknown,unknown,unknown
+BDMAP_00001009,TCGA-OV-61-1998_1_1,TCGA-OV,unknown,unknown,unknown
+BDMAP_00001010,liver_70,LiTS,unhealthy,unknown,unknown
+BDMAP_00001011,word_0130,WORD,unknown,unknown,unknown
+BDMAP_00001012,hepaticvessel_033,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001013,PETCT_e5d50c5569,autoPET,unknown,unknown,unknown
+BDMAP_00001014,TCIAColon_0160_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001015,colon_118,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001016,hepaticvessel_161,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001017,PETCT_ac11b344b6,autoPET,unknown,unknown,unknown
+BDMAP_00001018,liver_112,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001019,PETCT_846c1af245,autoPET,unknown,unknown,unknown
+BDMAP_00001020,case_00020,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001021,colon_153,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001022,TCIAColon_0274_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001023,Case_00230_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001024,colon_212,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001025,liver_108,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001026,FLARE23_Ts_0032_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001027,case_00022,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001028,PETCT_b5923963ac,autoPET,unknown,unknown,unknown
+BDMAP_00001029,s0760,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001030,pancreas_164,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001031,hepaticvessel_327,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001032,colon_183,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001033,Case_00184_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001034,PETCT_aea7906fd2,autoPET,unknown,unknown,unknown
+BDMAP_00001035,case_00408,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001036,amos_0001,AMOS,unknown,unknown,unknown
+BDMAP_00001037,Case_00450_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001038,pancreas_443,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001039,img0079,BTCV,healthy,healthy,healthy
+BDMAP_00001040,pancreas_218,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001041,FLARE23_Ts_0040_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001042,PETCT_d4b2ff9721,autoPET,unknown,unknown,unknown
+BDMAP_00001043,CPTAC-PDA-C3N-01383_0_6,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00001044,TCIAColon_0258_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00001045,case_00516,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001046,TCIAColon_0011_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001047,s1174,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001048,PETCT_83e4c5b578,autoPET,unknown,unknown,unknown
+BDMAP_00001049,s0991,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001050,case_00254,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001051,PETCT_48a34ccdf0,autoPET,unknown,unknown,unknown
+BDMAP_00001052,TCIAColon_0044_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00001053,amos_0199,AMOS,unknown,unknown,unknown
+BDMAP_00001054,s1373,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001055,liver_116,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001056,PETCT_d4112bc7ba,autoPET,unknown,unknown,unknown
+BDMAP_00001057,pancreas_058,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001058,s1294,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001059,case_00490,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001060,PETCT_607c1b38e3,autoPET,unknown,unknown,unknown
+BDMAP_00001061,25_image,CHAOS,healthy,healthy,healthy
+BDMAP_00001062,PETCT_d8d9e52cd5,autoPET,unknown,unknown,unknown
+BDMAP_00001063,PETCT_55d55902c6,autoPET,unknown,unknown,unknown
+BDMAP_00001064,CPTAC-PDA-C3N-03430_0_7,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00001065,hepaticvessel_333,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001066,TCIAColon_0250_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001067,pancreas_296,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001068,case_00010,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001069,pancreas_017,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001070,amos_0317,AMOS,unknown,unknown,unknown
+BDMAP_00001071,FLARE23_Ts_0098_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001072,Case_00525_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001073,s0937,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001074,img0030,BTCV,healthy,healthy,healthy
+BDMAP_00001075,case_00115,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001076,word_0013,WORD,unknown,unknown,unknown
+BDMAP_00001077,case_00451,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001078,colon_007,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001079,volume-116,CT-ORG,unknown,unknown,unknown
+BDMAP_00001080,PETCT_5109748328,autoPET,unknown,unknown,unknown
+BDMAP_00001081,TCIAColon_0136_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001082,s1082,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001083,PETCT_a0c4dc1ef1,autoPET,unknown,unknown,unknown
+BDMAP_00001084,TCIAColon_0132_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001085,NIH-LYMPH-ABD-065_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00001086,Case_00726_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001087,TCIAColon_0138_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001088,lung_022,MSD-Lung,unknown,unknown,unknown
+BDMAP_00001089,case_00161,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001090,PETCT_0af7ffe12a,autoPET,unknown,unknown,unknown
+BDMAP_00001091,TCIAColon_0048_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001092,pancreas_127,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001093,liver_81,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001094,PETCT_8f2878df50,autoPET,unknown,unknown,unknown
+BDMAP_00001095,colon_214,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001096,pancreas_284,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001097,hepaticvessel_180,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001098,PETCT_54977a7e76,autoPET,unknown,unknown,unknown
+BDMAP_00001099,Case_00804_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001100,amos_0282,AMOS,unknown,unknown,unknown
+BDMAP_00001101,word_0134,WORD,unknown,unknown,unknown
+BDMAP_00001102,colon_119,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001103,Case_00926_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001104,case_00447,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001105,volume-73,CT-ORG,unknown,unknown,unknown
+BDMAP_00001106,PETCT_13d0984c93,autoPET,unknown,unknown,unknown
+BDMAP_00001107,case_00247,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001108,TCIAColon_0006_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001109,case_00429,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001110,TCIAColon_0244_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001111,spleen_29,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00001112,CPTAC-PDA-C3N-03670_0_5,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00001113,Case_00811_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001114,liver_43,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001115,Case_00304_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001116,hepaticvessel_329,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001117,FLARE23_Ts_0083_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001118,pancreas_118,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001119,case_00212,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001120,PETCT_ea42c88cc7,autoPET,unknown,unknown,unknown
+BDMAP_00001121,liver_186,MSD-Liver,unknown,unknown,unknown
+BDMAP_00001122,liver_51,LiTS,unhealthy,unknown,unknown
+BDMAP_00001123,s0089,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001124,PETCT_8aa48809ea,autoPET,unknown,unknown,unknown
+BDMAP_00001125,pancreas_418,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001126,case_00107,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001127,PETCT_f01e83894d,autoPET,unknown,unknown,unknown
+BDMAP_00001128,case_00238,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001129,PETCT_73597f33fe,autoPET,unknown,unknown,unknown
+BDMAP_00001130,case_00094,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001131,hepaticvessel_287,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001132,TCIAColon_0173_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001133,volume-137,CT-ORG,unknown,unknown,unknown
+BDMAP_00001134,hepaticvessel_009,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001135,TCIAColon_0054_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001136,hepaticvessel_313,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001137,s1235,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001138,case_00095,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001139,TCIAColon_0296_0_5,TCIAColon,unknown,unknown,unknown
+BDMAP_00001140,PETCT_9a83943958,autoPET,unknown,unknown,unknown
+BDMAP_00001141,hepaticvessel_388,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001142,PETCT_173b804eda,autoPET,unknown,unknown,unknown
+BDMAP_00001143,liver_201,MSD-Liver,unknown,unknown,unknown
+BDMAP_00001144,s0747,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001145,Case_00680_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001146,lung_027,MSD-Lung,unknown,unknown,unknown
+BDMAP_00001147,PETCT_a471649d35,autoPET,unknown,unknown,unknown
+BDMAP_00001148,case_00425,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001149,hepaticvessel_130,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001150,TCIAColon_0281_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00001151,amos_0336,AMOS,unknown,unknown,unknown
+BDMAP_00001152,colon_023,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001153,s0042,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001154,hepaticvessel_151,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001155,pancreas_332,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001156,TCIAColon_0231_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001157,pancreas_068,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001158,TCGA-OV-OY-A56Q_0_1,TCGA-OV,unknown,unknown,unknown
+BDMAP_00001159,TCIAColon_0287_0_7,TCIAColon,unknown,unknown,unknown
+BDMAP_00001160,TCIAColon_0049_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001161,case_00102,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001162,PETCT_979f9c3dba,autoPET,unknown,unknown,unknown
+BDMAP_00001163,PETCT_474d7af918,autoPET,unknown,unknown,unknown
+BDMAP_00001164,pancreas_341,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001165,volume-92,CT-ORG,unknown,unknown,unknown
+BDMAP_00001166,hepaticvessel_400,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001167,hepaticvessel_256,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001168,FLARE23_Ts_0038_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001169,case_00424,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001170,hepaticvessel_203,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001171,liver_21,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001172,hepaticvessel_224,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001173,TCIAColon_0111_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001174,NIH-LYMPH-MED-006_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00001175,pancreas_235,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001176,LDCT-L266_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00001177,volume-91,CT-ORG,unknown,unknown,unknown
+BDMAP_00001178,TCIAColon_0170_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001179,s0196,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001180,TCIAColon_0091_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001181,amos_0118,AMOS,unknown,unknown,unknown
+BDMAP_00001182,PETCT_d8e3aa97a0,autoPET,unknown,unknown,unknown
+BDMAP_00001183,case_00545,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001184,case_00239,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001185,liver_99,LiTS,unhealthy,unknown,unknown
+BDMAP_00001186,hepaticvessel_328,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001187,PETCT_f8314eb3f7,autoPET,unknown,unknown,unknown
+BDMAP_00001188,Case_00743_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001189,s0370,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001190,hepaticvessel_229,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001191,lung_029,MSD-Lung,unknown,unknown,unknown
+BDMAP_00001192,CPTAC-CCRCC-C3L-00610_1_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001193,CPTAC-CCRCC-C3L-01954_0_5,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001194,TCIAColon_0215_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001195,PETCT_108c1763d4,autoPET,unknown,unknown,unknown
+BDMAP_00001196,amos_0069,AMOS,unknown,unknown,unknown
+BDMAP_00001197,pancreas_349,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001198,liver_10,LiTS,unhealthy,unknown,unknown
+BDMAP_00001199,PETCT_2f2ee78c89,autoPET,unknown,unknown,unknown
+BDMAP_00001200,liver_72,LiTS,unhealthy,unknown,unknown
+BDMAP_00001201,CPTAC-PDA-C3N-01167_0_15,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00001202,PETCT_3481507253,autoPET,unknown,unknown,unknown
+BDMAP_00001203,s0762,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001204,pancreas_240,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001205,pancreas_197,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001206,TCIAColon_0237_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001207,TCIAColon_0287_0_6,TCIAColon,unknown,unknown,unknown
+BDMAP_00001208,FLARE23_Ts_0028_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001209,colon_140,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001210,s1257,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001211,hepaticvessel_389,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001212,colon_098,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001213,img0062,BTCV,healthy,healthy,healthy
+BDMAP_00001214,pancreas_270,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001215,colon_072,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001216,word_0125,WORD,unknown,unknown,unknown
+BDMAP_00001217,amos_0198,AMOS,unknown,unknown,unknown
+BDMAP_00001218,case_00542,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001219,hepaticvessel_423,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001220,lung_065,MSD-Lung,unknown,unknown,unknown
+BDMAP_00001221,PETCT_8af3757198,autoPET,unknown,unknown,unknown
+BDMAP_00001222,PETCT_510fb36781,autoPET,unknown,unknown,unknown
+BDMAP_00001223,case_00139,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001224,CPTAC-CCRCC-C3L-01352_0_2,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001225,case_00438,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001226,Case_00980_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001227,PETCT_4011ffe8ae,autoPET,unknown,unknown,unknown
+BDMAP_00001228,hepaticvessel_120,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001229,word_0062,WORD,unknown,unknown,unknown
+BDMAP_00001230,case_00538,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001231,amos_0027,AMOS,unknown,unknown,unknown
+BDMAP_00001232,TCIAColon_0259_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001233,PETCT_5ef99b0b6b,autoPET,unknown,unknown,unknown
+BDMAP_00001234,Case_00819_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001235,Case_00190_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001236,liver_130,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001237,colon_078,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001238,case_00444,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001239,Case_00941_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001240,TCIAColon_0128_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001241,word_0031,WORD,unknown,unknown,unknown
+BDMAP_00001242,case_00209,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001243,PETCT_25707f94a2,autoPET,unknown,unknown,unknown
+BDMAP_00001244,TCIAColon_0056_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001245,word_0032,WORD,unknown,unknown,unknown
+BDMAP_00001246,case_00413,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001247,pancreas_230,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001248,s0623,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001249,hepaticvessel_319,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001250,colon_190,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001251,case_00511,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001252,CPTAC-CCRCC-C3L-01459_0_4,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001253,amos_0049,AMOS,unknown,unknown,unknown
+BDMAP_00001254,CPTAC-UCEC-C3N-01171_0_12,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00001255,case_00076,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001256,case_00522,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001257,Case_00749_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001258,case_00179,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001259,s1127,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001260,amos_0019,AMOS,unknown,unknown,unknown
+BDMAP_00001261,case_00421,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001262,PETCT_2b29531d8c,autoPET,unknown,unknown,unknown
+BDMAP_00001263,word_0067,WORD,unknown,unknown,unknown
+BDMAP_00001264,PETCT_faf4a6dbaa,autoPET,unknown,unknown,unknown
+BDMAP_00001265,case_00503,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001266,TCIAColon_0151_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001267,TCIAColon_0232_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001268,case_00106,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001269,TCIAColon_0117_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001270,liver_106,LiTS,healthy,unknown,unknown
+BDMAP_00001271,Case_01054_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001272,PETCT_1f65acff65,autoPET,unknown,unknown,unknown
+BDMAP_00001273,case_00453,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001274,TCIAColon_0237_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001275,case_00271,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001276,s0078,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001277,TCIAColon_0290_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001278,hepaticvessel_290,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001279,Case_00044_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001280,PETCT_548213edf7,autoPET,unknown,unknown,unknown
+BDMAP_00001281,case_00105,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001282,hepaticvessel_430,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001283,case_00195,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001284,s1371,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001285,amos_0294,AMOS,unknown,unknown,unknown
+BDMAP_00001286,case_00431,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001287,PETCT_802f19931c,autoPET,unknown,unknown,unknown
+BDMAP_00001288,pancreas_253,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001289,case_00046,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001290,TCIAColon_0059_0_5,TCIAColon,unknown,unknown,unknown
+BDMAP_00001291,TCIAColon_0054_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001292,s1045,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001293,CPTAC-UCEC-C3N-00734_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00001294,PETCT_38a374d01a,autoPET,unknown,unknown,unknown
+BDMAP_00001295,s0137,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001296,case_00294,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001297,TCIAColon_0193_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001298,word_0070,WORD,unknown,unknown,unknown
+BDMAP_00001299,PETCT_c094a24c03,autoPET,unknown,unknown,unknown
+BDMAP_00001300,s0015,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001301,CPTAC-PDA-C3N-02997_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00001302,PETCT_58f276897e,autoPET,unknown,unknown,unknown
+BDMAP_00001303,s0052,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001304,hepaticvessel_102,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001305,case_00547,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001306,spleen_40,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00001307,PETCT_5001354a69,autoPET,unknown,unknown,unknown
+BDMAP_00001308,s0168,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001309,liver_112,LiTS,unhealthy,unknown,unknown
+BDMAP_00001310,Case_00547_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001311,TCIAColon_0142_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001312,TCIAColon_0254_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001313,PETCT_44b08f570e,autoPET,unknown,unknown,unknown
+BDMAP_00001314,PETCT_7c1e6175e0,autoPET,unknown,unknown,unknown
+BDMAP_00001315,TCGA-SARC-A5V2_0_1,TCGA-SARC,unknown,unknown,unknown
+BDMAP_00001316,liver_28,LiTS,unhealthy,unknown,unknown
+BDMAP_00001317,TCIAColon_0084_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001318,PETCT_437d2c96fe,autoPET,unknown,unknown,unknown
+BDMAP_00001319,PETCT_e68c4577d8,autoPET,unknown,unknown,unknown
+BDMAP_00001320,s0320,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001321,s0546,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001322,PETCT_4be28f9cd7,autoPET,unknown,unknown,unknown
+BDMAP_00001323,hepaticvessel_096,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001324,case_00559,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001325,case_00248,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001326,TCIAColon_0286_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00001327,PETCT_43323b7d42,autoPET,unknown,unknown,unknown
+BDMAP_00001328,CPTAC-CCRCC-C3L-00815_0_5,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001329,hepaticvessel_190,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001330,s1230,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001331,pancreas_297,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001332,hepaticvessel_262,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001333,liver_102,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001334,TCIAColon_0193_0_5,TCIAColon,unknown,unknown,unknown
+BDMAP_00001335,PETCT_7ef5343426,autoPET,unknown,unknown,unknown
+BDMAP_00001336,s0508,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001337,TCIAColon_0276_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001338,colon_010,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001339,Case_00041_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001340,NIH-LYMPH-ABD-020_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00001341,hepaticvessel_039,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001342,TCIAColon_0130_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001343,liver_104,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001344,PETCT_34d3efa936,autoPET,unknown,unknown,unknown
+BDMAP_00001345,s0416,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001346,hepaticvessel_022,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001347,s0639,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001348,PANCREAS_0031,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00001349,PETCT_fcdbe15200,autoPET,unknown,unknown,unknown
+BDMAP_00001350,s1212,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001351,PETCT_19b68a666b,autoPET,unknown,unknown,unknown
+BDMAP_00001352,case_00108,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001353,18_image,CHAOS,healthy,healthy,healthy
+BDMAP_00001354,s0461,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001355,TCIAColon_0167_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001356,s0485,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001357,PETCT_9860ffe82d,autoPET,unknown,unknown,unknown
+BDMAP_00001358,hepaticvessel_310,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001359,case_00138,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001360,CPTAC-PDA-C3N-03426_0_7,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00001361,pancreas_158,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001362,liver_74,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001363,Case_01044_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001364,hepaticvessel_259,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001365,amos_0161,AMOS,unknown,unknown,unknown
+BDMAP_00001366,PETCT_75d5e740f0,autoPET,unknown,unknown,unknown
+BDMAP_00001367,PETCT_2a41141775,autoPET,unknown,unknown,unknown
+BDMAP_00001368,pancreas_213,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001369,s1161,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001370,case_00262,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001371,TCIAColon_0117_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001372,TCIAColon_0261_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001373,PETCT_5255c79083,autoPET,unknown,unknown,unknown
+BDMAP_00001374,s0571,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001375,PETCT_4b688f46b0,autoPET,unknown,unknown,unknown
+BDMAP_00001376,case_00211,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001377,s1285,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001378,PETCT_e52eca67d7,autoPET,unknown,unknown,unknown
+BDMAP_00001379,liver_0,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001380,case_00240,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001381,Case_00786_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001382,hepaticvessel_416,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001383,case_00000,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001384,Case_01041_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001385,PETCT_176bdc5388,autoPET,unknown,unknown,unknown
+BDMAP_00001386,PETCT_4076ea9a15,autoPET,unknown,unknown,unknown
+BDMAP_00001387,s0328,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001388,TCIAColon_0072_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001389,liver_138,MSD-Liver,unknown,unknown,unknown
+BDMAP_00001390,PETCT_7d97f7a102,autoPET,unknown,unknown,unknown
+BDMAP_00001391,hepaticvessel_350,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001392,TCIAColon_0038_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001393,TCIAColon_0089_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001394,hepaticvessel_029,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001395,hepaticvessel_397,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001396,case_00565,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001397,liver_47,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001398,TCIAColon_0291_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001399,s1173,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001400,amos_0141,AMOS,unknown,unknown,unknown
+BDMAP_00001401,TCIAColon_0102_0_5,TCIAColon,unknown,unknown,unknown
+BDMAP_00001402,FLARE23_Ts_0024_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001403,Case_00950_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001404,PETCT_bf178a41b2,autoPET,unknown,unknown,unknown
+BDMAP_00001405,TCIAColon_0042_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001406,PETCT_d4d3d7dfc1,autoPET,unknown,unknown,unknown
+BDMAP_00001407,PETCT_bd52fdf529,autoPET,unknown,unknown,unknown
+BDMAP_00001408,PETCT_3c94a00f90,autoPET,unknown,unknown,unknown
+BDMAP_00001409,hepaticvessel_441,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001410,case_00089,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001411,PETCT_2f7200f771,autoPET,unknown,unknown,unknown
+BDMAP_00001412,pancreas_437,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001413,case_00096,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001414,pancreas_405,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001415,s0692,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001416,PETCT_99e5728030,autoPET,unknown,unknown,unknown
+BDMAP_00001417,img0025,BTCV,healthy,healthy,healthy
+BDMAP_00001418,s0511,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001419,pancreas_413,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001420,case_00480,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001421,PETCT_05bed31780,autoPET,unknown,unknown,unknown
+BDMAP_00001422,case_00004,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001423,s0675,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001424,s0390,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001425,PETCT_e9feb1135e,autoPET,unknown,unknown,unknown
+BDMAP_00001426,colon_115,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001427,hepaticvessel_147,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001428,CPTAC-UCEC-C3N-00866_1_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00001429,TCGA-BLCA-DK-A1A7_0_3,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00001430,TCIAColon_0009_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001431,PETCT_05d8640f52,autoPET,unknown,unknown,unknown
+BDMAP_00001432,FLARE23_Ts_0005_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001433,spleen_50,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00001434,case_00134,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001435,Case_00240_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001436,spleen_11,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00001437,hepaticvessel_168,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001438,colon_031,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001439,PETCT_5fa9d9a820,autoPET,unknown,unknown,unknown
+BDMAP_00001440,pancreas_222,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001441,pancreas_308,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001442,amos_0392,AMOS,unknown,unknown,unknown
+BDMAP_00001443,LDCT-L220_0_0,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00001444,case_00150,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001445,colon_206,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001446,pancreas_420,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001447,img0036,BTCV,healthy,healthy,healthy
+BDMAP_00001448,TCIAColon_0277_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001449,amos_0079,AMOS,unknown,unknown,unknown
+BDMAP_00001450,Case_00947_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001451,hepaticvessel_024,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001452,PETCT_f1594a7d8a,autoPET,unknown,unknown,unknown
+BDMAP_00001453,hepaticvessel_368,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001454,s0720,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001455,PETCT_5c1baad5d8,autoPET,unknown,unknown,unknown
+BDMAP_00001456,case_00077,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001457,PETCT_23ed525e82,autoPET,unknown,unknown,unknown
+BDMAP_00001458,CPTAC-UCEC-C3L-01744_0_3,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00001459,s0441,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001460,PETCT_424e9b79c5,autoPET,unknown,unknown,unknown
+BDMAP_00001461,pancreas_310,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001462,colon_048,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001463,PETCT_b41bc7c1e5,autoPET,unknown,unknown,unknown
+BDMAP_00001464,pancreas_258,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001465,case_00150,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001466,Case_00922_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001467,lung_059,MSD-Lung,unknown,unknown,unknown
+BDMAP_00001468,s1382,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001469,PETCT_5e8b013935,autoPET,unknown,unknown,unknown
+BDMAP_00001470,TCIAColon_0175_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001471,liver_78,LiTS,unhealthy,unknown,unknown
+BDMAP_00001472,s0213,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001473,CPTAC-PDA-C3N-01907_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00001474,case_00259,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001475,case_00287,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001476,pancreas_087,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001477,TCIAColon_0177_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001478,Case_00831_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001479,FLARE23_Ts_0086_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001480,PETCT_42e9f16c09,autoPET,unknown,unknown,unknown
+BDMAP_00001481,colon_076,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001482,hepaticvessel_386,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001483,case_00563,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001484,lung_078,MSD-Lung,unknown,unknown,unknown
+BDMAP_00001485,TCIAColon_0228_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001486,PETCT_b510436d83,autoPET,unknown,unknown,unknown
+BDMAP_00001487,case_00266,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001488,volume-83,CT-ORG,unknown,unknown,unknown
+BDMAP_00001489,NIH-LYMPH-ABD-048_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00001490,PETCT_ebaa30ba2e,autoPET,unknown,unknown,unknown
+BDMAP_00001491,pancreas_237,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001492,s0031,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001493,FLARE23_Ts_0062_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001494,s1319,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001495,word_0128,WORD,unknown,unknown,unknown
+BDMAP_00001496,case_00162,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001497,TCIAColon_0209_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001498,img0063,BTCV,healthy,healthy,healthy
+BDMAP_00001499,TCIAColon_0183_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001500,CPTAC-CCRCC-C3L-01465_0_5,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001501,Case_01032_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001502,liver_91,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001503,word_0115,WORD,unknown,unknown,unknown
+BDMAP_00001504,pancreas_010,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001505,TCIAColon_0187_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001506,hepaticvessel_270,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001507,hepaticvessel_025,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001508,Case_00019_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001509,colon_021,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001510,hepaticvessel_100,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001511,case_00462,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001512,Case_00223_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001513,PETCT_2202a936e0,autoPET,unknown,unknown,unknown
+BDMAP_00001514,colon_122,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001515,PETCT_e69152b6b4,autoPET,unknown,unknown,unknown
+BDMAP_00001516,case_00052,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001517,case_00403,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001518,case_00184,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001519,PETCT_bc80d06f97,autoPET,unknown,unknown,unknown
+BDMAP_00001520,case_00177,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001521,pancreas_276,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001522,case_00257,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001523,pancreas_126,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001524,NIH-LYMPH-ABD-066_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00001525,TCGA-BLCA-4Z-AA7M_0_4,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00001526,Case_01040_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001527,PETCT_de118d7ab9,autoPET,unknown,unknown,unknown
+BDMAP_00001528,NIH-LYMPH-MED-013_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00001529,TCIAColon_0260_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00001530,case_00164,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001531,TCIAColon_0233_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001532,PETCT_5c55b3087d,autoPET,unknown,unknown,unknown
+BDMAP_00001533,case_00208,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001534,TCIAColon_0051_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001535,colon_105,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001536,PETCT_55819a54c2,autoPET,unknown,unknown,unknown
+BDMAP_00001537,PETCT_2140db0ca5,autoPET,unknown,unknown,unknown
+BDMAP_00001538,liver_137,MSD-Liver,unknown,unknown,unknown
+BDMAP_00001539,liver_57,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001540,FLARE23_Ts_0050_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001541,TCIAColon_0001_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001542,PETCT_2e97a9e5c2,autoPET,unknown,unknown,unknown
+BDMAP_00001543,PETCT_b899150306,autoPET,unknown,unknown,unknown
+BDMAP_00001544,PETCT_1fcfa34d10,autoPET,unknown,unknown,unknown
+BDMAP_00001545,pancreas_331,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001546,s0039,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001547,Case_01057_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001548,PETCT_bf7652bccc,autoPET,unknown,unknown,unknown
+BDMAP_00001549,case_00558,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001550,Case_00802_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001551,PETCT_41fadf6520,autoPET,unknown,unknown,unknown
+BDMAP_00001552,PETCT_595975bc57,autoPET,unknown,unknown,unknown
+BDMAP_00001553,PETCT_2a6f4f0753,autoPET,unknown,unknown,unknown
+BDMAP_00001554,TCIAColon_0229_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001555,s0332,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001556,amos_0036,AMOS,unknown,unknown,unknown
+BDMAP_00001557,case_00264,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001558,hepaticvessel_437,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001559,TCIAColon_0156_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001560,hepaticvessel_178,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001561,amos_0023,AMOS,unknown,unknown,unknown
+BDMAP_00001562,case_00561,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001563,case_00292,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001564,pancreas_203,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001565,pancreas_159,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001566,PETCT_1553bd8c8f,autoPET,unknown,unknown,unknown
+BDMAP_00001567,Case_00960_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001568,CPTAC-CCRCC-C3N-00244_0_6,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001569,s0648,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001570,s1085,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001571,PETCT_43647ff727,autoPET,unknown,unknown,unknown
+BDMAP_00001572,TCIAColon_0190_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001573,TCIAColon_0243_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001574,PETCT_5b0aa4e971,autoPET,unknown,unknown,unknown
+BDMAP_00001575,PETCT_75210090cd,autoPET,unknown,unknown,unknown
+BDMAP_00001576,pancreas_171,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001577,hepaticvessel_278,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001578,FLARE23_Ts_0020_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001579,s1058,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001580,PETCT_44b95650c3,autoPET,unknown,unknown,unknown
+BDMAP_00001581,Case_00742_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001582,s0054,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001583,hepaticvessel_271,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001584,pancreas_004,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001585,TCIAColon_0096_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001586,liver_152,MSD-Liver,unknown,unknown,unknown
+BDMAP_00001587,PETCT_9775598501,autoPET,unknown,unknown,unknown
+BDMAP_00001588,Case_00078_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001589,hepaticvessel_054,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001590,pancreas_224,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001591,PETCT_ae3074834a,autoPET,unknown,unknown,unknown
+BDMAP_00001592,TCIAColon_0266_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001593,hepaticvessel_425,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001594,lung_010,MSD-Lung,unknown,unknown,unknown
+BDMAP_00001595,hepaticvessel_267,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001596,PETCT_7b9d625330,autoPET,unknown,unknown,unknown
+BDMAP_00001597,pancreas_125,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001598,colon_029,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001599,s1287,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001600,colon_209,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001601,NIH-LYMPH-ABD-074_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00001602,case_00407,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001603,PETCT_53ccb9efbb,autoPET,unknown,unknown,unknown
+BDMAP_00001604,TCIAColon_0269_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001605,pancreas_099,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001606,s0135,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001607,case_00450,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001608,s0080,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001609,TCIAColon_0131_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001610,hepaticvessel_061,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001611,word_0046,WORD,unknown,unknown,unknown
+BDMAP_00001612,case_00186,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001613,PETCT_b3d4773f85,autoPET,unknown,unknown,unknown
+BDMAP_00001614,PETCT_1472967bef,autoPET,unknown,unknown,unknown
+BDMAP_00001615,PETCT_0168f65af8,autoPET,unknown,unknown,unknown
+BDMAP_00001616,Case_00939_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001617,pancreas_364,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001618,pancreas_109,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001619,volume-53,CT-ORG,unknown,unknown,unknown
+BDMAP_00001620,case_00255,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001621,TCIAColon_0150_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001622,PETCT_2745fb1adb,autoPET,unknown,unknown,unknown
+BDMAP_00001623,TCIAColon_0295_0_7,TCIAColon,unknown,unknown,unknown
+BDMAP_00001624,case_00220,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001625,hepaticvessel_348,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001626,s0070,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001627,amos_0009,AMOS,unknown,unknown,unknown
+BDMAP_00001628,pancreas_029,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001629,PETCT_e90a287d09,autoPET,unknown,unknown,unknown
+BDMAP_00001630,PETCT_234f8427c0,autoPET,unknown,unknown,unknown
+BDMAP_00001631,hepaticvessel_090,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001632,pancreas_083,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001633,FLARE23_Ts_0087_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001634,spleen_31,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00001635,case_00441,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001636,case_00526,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001637,Case_00954_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001638,PETCT_9afd440677,autoPET,unknown,unknown,unknown
+BDMAP_00001639,PETCT_ca47fe5e7d,autoPET,unknown,unknown,unknown
+BDMAP_00001640,amos_0153,AMOS,unknown,unknown,unknown
+BDMAP_00001641,Case_00664_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001642,TCIAColon_0221_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001643,lung_001,MSD-Lung,unknown,unknown,unknown
+BDMAP_00001644,FLARE23_Ts_0065_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001645,hepaticvessel_357,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001646,pancreas_269,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001647,case_00586,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001648,TCIAColon_0044_1_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00001649,pancreas_119,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001650,s0253,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001651,hepaticvessel_197,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001652,PETCT_27ad42f8a9,autoPET,unknown,unknown,unknown
+BDMAP_00001653,TCIAColon_0106_0_5,TCIAColon,unknown,unknown,unknown
+BDMAP_00001654,CPTAC-CCRCC-C3L-01954_0_8,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001655,amos_0089,AMOS,unknown,unknown,unknown
+BDMAP_00001656,case_00492,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001657,hepaticvessel_254,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001658,liver_192,MSD-Liver,unknown,unknown,unknown
+BDMAP_00001659,s0961,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001660,TCIAColon_0250_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001661,PETCT_d098458d29,autoPET,unknown,unknown,unknown
+BDMAP_00001662,PANCREAS_0007,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00001663,PETCT_9d6699f215,autoPET,unknown,unknown,unknown
+BDMAP_00001664,case_00055,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001665,TCIAColon_0080_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001666,PETCT_62405d117c,autoPET,unknown,unknown,unknown
+BDMAP_00001667,s0010,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001668,pancreas_063,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001669,FLARE23_Ts_0006_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001670,CPTAC-UCEC-C3N-01008_1_11,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00001671,hepaticvessel_171,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001672,hepaticvessel_231,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001673,Case_00764_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001674,Case_00911_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001675,hepaticvessel_144,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001676,case_00187,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001677,Case_00793_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001678,PETCT_f9c4d4f9ab,autoPET,unknown,unknown,unknown
+BDMAP_00001679,case_00233,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001680,img0061,BTCV,healthy,healthy,healthy
+BDMAP_00001681,word_0091,WORD,unknown,unknown,unknown
+BDMAP_00001682,PETCT_a90955352e,autoPET,unknown,unknown,unknown
+BDMAP_00001683,TCIAColon_0043_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00001684,FLARE23_Ts_0046_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001685,hepaticvessel_002,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001686,TCIAColon_0294_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001687,s0726,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001688,case_00080,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001689,PETCT_265c26907a,autoPET,unknown,unknown,unknown
+BDMAP_00001690,Case_00403_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001691,colon_003,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001692,liver_24,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001693,TCIAColon_0273_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001694,s0260,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001695,PETCT_5d994c3f44,autoPET,unknown,unknown,unknown
+BDMAP_00001696,TCIAColon_0033_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001697,liver_3,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001698,TCIAColon_0041_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001699,liver_96,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001700,amos_0109,AMOS,unknown,unknown,unknown
+BDMAP_00001701,pancreas_215,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001702,TCIAColon_0072_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001703,CPTAC-UCEC-C3L-00938_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00001704,pancreas_226,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001705,case_00151,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001706,PETCT_c32e1daf9b,autoPET,unknown,unknown,unknown
+BDMAP_00001707,case_00416,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001708,CPTAC-UCEC-C3L-00962_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00001709,hepaticvessel_211,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001710,case_00093,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001711,case_00554,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001712,pancreas_327,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001713,PETCT_674c49778c,autoPET,unknown,unknown,unknown
+BDMAP_00001714,s0266,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001715,colon_037,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001716,pancreas_478,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001717,s1130,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001718,PETCT_8b14e6a819,autoPET,unknown,unknown,unknown
+BDMAP_00001719,TCIAColon_0012_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001720,PETCT_ea0fd89f0f,autoPET,unknown,unknown,unknown
+BDMAP_00001721,hepaticvessel_383,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001722,s0075,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001723,Case_01003_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001724,TCIAColon_0114_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001725,PETCT_0dbf2c2731,autoPET,unknown,unknown,unknown
+BDMAP_00001726,img0078,BTCV,healthy,healthy,healthy
+BDMAP_00001727,PETCT_f15b868db6,autoPET,unknown,unknown,unknown
+BDMAP_00001728,TCIAColon_0267_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001729,TCIAColon_0016_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001730,TCIAColon_0087_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001731,PETCT_8bebb676bf,autoPET,unknown,unknown,unknown
+BDMAP_00001732,colon_022,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001733,TCIAColon_0092_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001734,s0423,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001735,case_00057,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001736,pancreas_334,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001737,colon_145,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001738,s0739,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001739,Case_00059_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001740,amos_0193,AMOS,unknown,unknown,unknown
+BDMAP_00001741,hepaticvessel_401,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001742,PETCT_5420205a5f,autoPET,unknown,unknown,unknown
+BDMAP_00001743,TCIAColon_0122_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00001744,spleen_8,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00001745,s0046,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001746,pancreas_103,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001747,case_00489,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001748,pancreas_108,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001749,PETCT_d8a92bafe3,autoPET,unknown,unknown,unknown
+BDMAP_00001750,Case_01017_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001751,TCIAColon_0206_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001752,case_00291,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001753,liver_9,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001754,pancreas_374,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001755,s0327,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001756,TCIAColon_0071_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001757,case_00120,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001758,TCIAColon_0157_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001759,amos_0272,AMOS,unknown,unknown,unknown
+BDMAP_00001760,Case_00882_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001761,hepaticvessel_365,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001762,PETCT_5d6bf1e75f,autoPET,unknown,unknown,unknown
+BDMAP_00001763,TCIAColon_0192_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001764,amos_0374,AMOS,unknown,unknown,unknown
+BDMAP_00001765,PETCT_548bd05667,autoPET,unknown,unknown,unknown
+BDMAP_00001766,liver_107,LiTS,unhealthy,unknown,unknown
+BDMAP_00001767,TCIAColon_0058_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001768,case_00496,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001769,colon_074,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001770,PETCT_9f7c68f5ca,autoPET,unknown,unknown,unknown
+BDMAP_00001771,amos_0017,AMOS,unknown,unknown,unknown
+BDMAP_00001772,case_00020,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001773,s0549,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001774,PANCREAS_0011,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00001775,amos_0249,AMOS,unknown,unknown,unknown
+BDMAP_00001776,word_0053,WORD,unknown,unknown,unknown
+BDMAP_00001777,TCIAColon_0292_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001778,TCIAColon_0083_1_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00001779,hepaticvessel_443,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001780,PETCT_30e2b83b74,autoPET,unknown,unknown,unknown
+BDMAP_00001781,PETCT_791ec15924,autoPET,unknown,unknown,unknown
+BDMAP_00001782,case_00509,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001783,PETCT_c787334539,autoPET,unknown,unknown,unknown
+BDMAP_00001784,case_00552,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001785,colon_102,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001786,liver_29,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001787,word_0036,WORD,unknown,unknown,unknown
+BDMAP_00001788,s0237,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001789,PETCT_ab1fa26e5e,autoPET,unknown,unknown,unknown
+BDMAP_00001790,s0091,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001791,case_00123,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001792,TCIAColon_0018_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001793,PETCT_e61c9da258,autoPET,unknown,unknown,unknown
+BDMAP_00001794,case_00519,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001795,PETCT_742a9413db,autoPET,unknown,unknown,unknown
+BDMAP_00001796,PETCT_39159c05c2,autoPET,unknown,unknown,unknown
+BDMAP_00001797,s0602,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001798,FLARE23_Ts_0076_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001799,PETCT_9a66a81ad1,autoPET,unknown,unknown,unknown
+BDMAP_00001800,PETCT_a21200dcba,autoPET,unknown,unknown,unknown
+BDMAP_00001801,hepaticvessel_122,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001802,liver_53,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001803,hepaticvessel_280,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001804,pancreas_198,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001805,word_0106,WORD,unknown,unknown,unknown
+BDMAP_00001806,pancreas_375,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001807,case_00569,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001808,CPTAC-UCEC-C3N-00520_0_8,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00001809,colon_168,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001810,liver_106,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001811,hepaticvessel_436,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001812,Case_00841_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001813,liver_66,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001814,spleen_15,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00001815,s0553,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001816,pancreas_483,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001817,case_00007,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001818,hepaticvessel_338,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001819,TCIAColon_0247_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001820,PANCREAS_0041,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00001821,spleen_3,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00001822,PETCT_af119148fe,autoPET,unknown,unknown,unknown
+BDMAP_00001823,pancreas_187,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001824,hepaticvessel_196,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001825,spleen_62,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00001826,case_00142,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001827,hepaticvessel_241,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001828,pancreas_325,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001829,hepaticvessel_107,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001830,hepaticvessel_373,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001831,case_00023,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001832,CPTAC-CCRCC-C3L-01459_1_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001833,PETCT_b1de3d4248,autoPET,unknown,unknown,unknown
+BDMAP_00001834,liver_92,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001835,colon_176,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001836,pancreas_315,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001837,s0471,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001838,s0912,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001839,Case_00738_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001840,PETCT_6d6a193655,autoPET,unknown,unknown,unknown
+BDMAP_00001841,FLARE23_Ts_0007_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001842,pancreas_316,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001843,TCIAColon_0088_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001844,CPTAC-CCRCC-C3N-00313_0_7,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001845,pancreas_388,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001846,word_0010,WORD,unknown,unknown,unknown
+BDMAP_00001847,s1189,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001848,pancreas_144,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001849,hepaticvessel_385,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001850,colon_179,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001851,case_00290,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001852,Case_00724_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001853,case_00201,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001854,colon_132,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001855,amos_0054,AMOS,unknown,unknown,unknown
+BDMAP_00001856,PETCT_790246c76c,autoPET,unknown,unknown,unknown
+BDMAP_00001857,PETCT_7f2a604e03,autoPET,unknown,unknown,unknown
+BDMAP_00001858,PETCT_06a46414eb,autoPET,unknown,unknown,unknown
+BDMAP_00001859,hepaticvessel_244,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001860,PETCT_20a607649d,autoPET,unknown,unknown,unknown
+BDMAP_00001861,hepaticvessel_369,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001862,pancreas_046,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001863,case_00478,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001864,Case_01043_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001865,colon_005,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001866,pancreas_408,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001867,case_00201,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001868,case_00499,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001869,CPTAC-CCRCC-C3N-00194_0_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001870,Case_00948_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001871,PANCREAS_0019,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00001872,s0286,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001873,PETCT_e04b7fa860,autoPET,unknown,unknown,unknown
+BDMAP_00001874,TCIAColon_0160_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001875,colon_027,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001876,hepaticvessel_191,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001877,CPTAC-CCRCC-C3N-00492_0_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001878,case_00263,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001879,hepaticvessel_091,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001880,PETCT_963a71819a,autoPET,unknown,unknown,unknown
+BDMAP_00001881,TCIAColon_0129_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001882,PETCT_cab58c1fee,autoPET,unknown,unknown,unknown
+BDMAP_00001883,TCIAColon_0105_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001884,hepaticvessel_322,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001885,volume-105,CT-ORG,unknown,unknown,unknown
+BDMAP_00001886,amos_0105,AMOS,unknown,unknown,unknown
+BDMAP_00001887,29_image,CHAOS,healthy,healthy,healthy
+BDMAP_00001888,PETCT_ec581d49ef,autoPET,unknown,unknown,unknown
+BDMAP_00001889,PETCT_e09a20d8fc,autoPET,unknown,unknown,unknown
+BDMAP_00001890,PETCT_d69c3fceba,autoPET,unknown,unknown,unknown
+BDMAP_00001891,case_00505,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001892,case_00152,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001893,PETCT_ce0ffef966,autoPET,unknown,unknown,unknown
+BDMAP_00001894,FLARE23_Ts_0055_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001895,s0069,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001896,Case_00251_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001897,PETCT_d4f3375362,autoPET,unknown,unknown,unknown
+BDMAP_00001898,pancreas_329,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001899,pancreas_430,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001900,PETCT_ad99263bec,autoPET,unknown,unknown,unknown
+BDMAP_00001901,amos_0235,AMOS,unknown,unknown,unknown
+BDMAP_00001902,lung_023,MSD-Lung,unknown,unknown,unknown
+BDMAP_00001903,PETCT_9b9d67b3ff,autoPET,unknown,unknown,unknown
+BDMAP_00001904,hepaticvessel_289,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001905,pancreas_398,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001906,case_00002,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001907,liver_5,LiTS,unhealthy,unknown,unknown
+BDMAP_00001908,case_00532,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001909,hepaticvessel_396,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001910,Case_00690_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001911,case_00026,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001912,pancreas_365,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001913,case_00262,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001914,s0612,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001915,word_0136,WORD,unknown,unknown,unknown
+BDMAP_00001916,pancreas_484,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001917,colon_095,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001918,case_00197,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001919,case_00241,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001920,PETCT_3cf68da275,autoPET,unknown,unknown,unknown
+BDMAP_00001921,pancreas_160,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00001922,colon_070,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001923,PETCT_605369e88d,autoPET,unknown,unknown,unknown
+BDMAP_00001924,colon_131,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001925,PETCT_80b8393c44,autoPET,unknown,unknown,unknown
+BDMAP_00001926,PETCT_30287f520f,autoPET,unknown,unknown,unknown
+BDMAP_00001927,FLARE23_Ts_0031_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001928,hepaticvessel_370,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001929,PETCT_b4e5c1047c,autoPET,unknown,unknown,unknown
+BDMAP_00001930,hepaticvessel_258,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001931,amos_0021,AMOS,unknown,unknown,unknown
+BDMAP_00001932,hepaticvessel_026,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001933,FLARE23_Ts_0081_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001934,s0548,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001935,TCIAColon_0061_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001936,liver_163,MSD-Liver,unknown,unknown,unknown
+BDMAP_00001937,8_image,CHAOS,healthy,healthy,healthy
+BDMAP_00001938,PETCT_c596a50286,autoPET,unknown,unknown,unknown
+BDMAP_00001939,CPTAC-UCEC-C3N-01171_0_11,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00001940,PETCT_d0086487f4,autoPET,unknown,unknown,unknown
+BDMAP_00001941,case_00074,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001942,PANCREAS_0013,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00001943,PANCREAS_0034,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00001944,Case_00052_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001945,case_00012,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001946,FLARE23_Ts_0027_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00001947,Case_00024_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001948,TCIAColon_0027_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001949,PETCT_af73b85f2c,autoPET,unknown,unknown,unknown
+BDMAP_00001950,hepaticvessel_015,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00001951,PETCT_345b11778a,autoPET,unknown,unknown,unknown
+BDMAP_00001952,PETCT_8e905366fe,autoPET,unknown,unknown,unknown
+BDMAP_00001953,PETCT_5951afb86e,autoPET,unknown,unknown,unknown
+BDMAP_00001954,TCIAColon_0030_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001955,TCIAColon_0161_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001956,PETCT_a37da9e85d,autoPET,unknown,unknown,unknown
+BDMAP_00001957,liver_126,LiTS,unhealthy,unknown,unknown
+BDMAP_00001958,colon_191,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001959,PETCT_e0fdc6212a,autoPET,unknown,unknown,unknown
+BDMAP_00001960,TCIAColon_0090_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001961,Case_00393_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001962,liver_22,LiTS,unhealthy,unknown,unknown
+BDMAP_00001963,NIH-LYMPH-ABD-045_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00001964,PETCT_95efdaba3c,autoPET,unknown,unknown,unknown
+BDMAP_00001965,s0751,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001966,liver_16,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001967,amos_0138,AMOS,unknown,unknown,unknown
+BDMAP_00001968,colon_158,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001969,TCIAColon_0003_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001970,PETCT_ca58410fad,autoPET,unknown,unknown,unknown
+BDMAP_00001971,amos_0006,AMOS,unknown,unknown,unknown
+BDMAP_00001972,hepaticvessel_268,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00001973,TCIAColon_0282_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001974,s0349,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001975,PETCT_b1aa7ce13e,autoPET,unknown,unknown,unknown
+BDMAP_00001976,TCIAColon_0236_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001977,case_00086,KiTS21,unknown,unknown,unhealthy
+BDMAP_00001978,PETCT_245182006a,autoPET,unknown,unknown,unknown
+BDMAP_00001979,CPTAC-CCRCC-C3L-01352_0_10,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00001980,Case_00448_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00001981,liver_76,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00001982,colon_046,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001983,PETCT_556b56485a,autoPET,unknown,unknown,unknown
+BDMAP_00001984,s1070,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001985,colon_164,MSD-Colon,unknown,unknown,unknown
+BDMAP_00001986,s1336,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00001987,case_00114,KiTS19,unknown,unknown,unhealthy
+BDMAP_00001988,hepaticvessel_240,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00001989,amos_0088,AMOS,unknown,unknown,unknown
+BDMAP_00001990,TCIAColon_0068_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00001991,TCIAColon_0045_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00001992,TCIAColon_0020_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00001993,pancreas_439,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00001994,PETCT_4785066413,autoPET,unknown,unknown,unknown
+BDMAP_00001995,case_00557,KiTS23,unknown,unknown,unhealthy
+BDMAP_00001996,PETCT_c1c9e78e0e,autoPET,unknown,unknown,unknown
+BDMAP_00001997,PETCT_b327726c24,autoPET,unknown,unknown,unknown
+BDMAP_00001998,PETCT_36d8219e3f,autoPET,unknown,unknown,unknown
+BDMAP_00001999,hepaticvessel_010,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002000,hepaticvessel_273,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002001,TCGA-STAD-A8DU_0_3,TCGA-STAD,unknown,unknown,unknown
+BDMAP_00002002,hepaticvessel_215,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002003,hepaticvessel_223,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002004,Case_00697_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002005,PETCT_cdd237e9b3,autoPET,unknown,unknown,unknown
+BDMAP_00002006,PETCT_11e258cc1f,autoPET,unknown,unknown,unknown
+BDMAP_00002007,TCIAColon_0021_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00002008,hepaticvessel_133,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002009,amos_0084,AMOS,unknown,unknown,unknown
+BDMAP_00002010,s0308,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002011,TCIAColon_0068_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002012,FLARE23_Ts_0023_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002013,s0613,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002014,amos_0071,AMOS,unknown,unknown,unknown
+BDMAP_00002015,TCIAColon_0207_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002016,TCIAColon_0244_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002017,case_00064,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002018,PETCT_f068e22258,autoPET,unknown,unknown,unknown
+BDMAP_00002019,img0007,BTCV,healthy,healthy,healthy
+BDMAP_00002020,Case_00465_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002021,pancreas_077,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002022,case_00083,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002023,amos_0264,AMOS,unknown,unknown,unknown
+BDMAP_00002024,word_0030,WORD,unknown,unknown,unknown
+BDMAP_00002025,hepaticvessel_121,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002026,pancreas_177,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002027,CPTAC-CCRCC-C3L-01958_0_5,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002028,s0550,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002029,pancreas_261,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002030,s0560,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002031,s0749,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002032,s0617,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002033,PETCT_a82f03863a,autoPET,unknown,unknown,unknown
+BDMAP_00002034,PETCT_f0e1b89b41,autoPET,unknown,unknown,unknown
+BDMAP_00002035,case_00099,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002036,Case_00878_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002037,Case_00825_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002038,colon_085,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002039,colon_126,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002040,PETCT_62ced197ca,autoPET,unknown,unknown,unknown
+BDMAP_00002041,case_00427,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002042,hepaticvessel_157,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002043,PETCT_b57e1c1f5f,autoPET,unknown,unknown,unknown
+BDMAP_00002044,TCIAColon_0012_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002045,hepaticvessel_454,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002046,amos_0060,AMOS,unknown,unknown,unknown
+BDMAP_00002047,hepaticvessel_051,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002048,lung_064,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002049,PETCT_685d7c09b5,autoPET,unknown,unknown,unknown
+BDMAP_00002050,CPTAC-CCRCC-C3N-00437_0_3,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002051,PETCT_a29cae0316,autoPET,unknown,unknown,unknown
+BDMAP_00002052,Case_00970_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002053,hepaticvessel_119,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002054,s0324,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002055,liver_38,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002056,s0945,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002057,case_00455,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002058,s0635,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002059,PANCREAS_0003,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00002060,case_00217,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002061,amos_0104,AMOS,unknown,unknown,unknown
+BDMAP_00002062,TCIAColon_0037_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002063,TCIAColon_0180_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002064,s0228,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002065,case_00500,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002066,Case_00932_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002067,case_00105,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002068,case_00030,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002069,PETCT_6f46454a8d,autoPET,unknown,unknown,unknown
+BDMAP_00002070,s0940,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002071,PETCT_d6b2218bf3,autoPET,unknown,unknown,unknown
+BDMAP_00002072,hepaticvessel_364,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002073,hepaticvessel_422,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002074,TCIAColon_0124_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002075,case_00442,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002076,pancreas_376,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002077,amos_0195,AMOS,unknown,unknown,unknown
+BDMAP_00002078,TCIAColon_0011_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002079,hepaticvessel_216,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002080,volume-97,CT-ORG,unknown,unknown,unknown
+BDMAP_00002081,TCIAColon_0236_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002082,pancreas_447,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002083,Case_00545_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002084,volume-124,CT-ORG,unknown,unknown,unknown
+BDMAP_00002085,case_00564,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002086,PETCT_b258dfa7c2,autoPET,unknown,unknown,unknown
+BDMAP_00002087,case_00080,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002088,pancreas_057,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002089,spleen_53,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00002090,PETCT_e664932bbc,autoPET,unknown,unknown,unknown
+BDMAP_00002091,case_00207,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002092,TCIAColon_0121_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002093,s0765,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002094,PETCT_d40a16781a,autoPET,unknown,unknown,unknown
+BDMAP_00002095,PETCT_64c0cba177,autoPET,unknown,unknown,unknown
+BDMAP_00002096,PETCT_b4247b8ecd,autoPET,unknown,unknown,unknown
+BDMAP_00002097,TCIAColon_0050_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002098,amos_0400,AMOS,unknown,unknown,unknown
+BDMAP_00002099,case_00406,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002100,s0513,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002101,PETCT_8a52353a72,autoPET,unknown,unknown,unknown
+BDMAP_00002102,PETCT_62011ff15b,autoPET,unknown,unknown,unknown
+BDMAP_00002103,colon_001,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002104,NIH-LYMPH-ABD-013_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002105,PETCT_1b1bdfc35b,autoPET,unknown,unknown,unknown
+BDMAP_00002106,PETCT_380f71df1e,autoPET,unknown,unknown,unknown
+BDMAP_00002107,PETCT_61d5bc58fc,autoPET,unknown,unknown,unknown
+BDMAP_00002108,PETCT_9da159b835,autoPET,unknown,unknown,unknown
+BDMAP_00002109,img0001,BTCV,healthy,healthy,healthy
+BDMAP_00002110,amos_0383,AMOS,unknown,unknown,unknown
+BDMAP_00002111,PETCT_d472033d3b,autoPET,unknown,unknown,unknown
+BDMAP_00002112,PETCT_d951eeb735,autoPET,unknown,unknown,unknown
+BDMAP_00002113,TCIAColon_0149_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002114,case_00040,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002115,TCGA-OV-61-1906_0_1,TCGA-OV,unknown,unknown,unknown
+BDMAP_00002116,PETCT_c898e2abcb,autoPET,unknown,unknown,unknown
+BDMAP_00002117,liver_36,LiTS,unhealthy,unknown,unknown
+BDMAP_00002118,TCIAColon_0191_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002119,colon_189,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002120,pancreas_075,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002121,hepaticvessel_455,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002122,case_00286,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002123,volume-133,CT-ORG,unknown,unknown,unknown
+BDMAP_00002124,TCIAColon_0230_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002125,TCIAColon_0016_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002126,pancreas_273,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002127,hepaticvessel_153,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002128,PETCT_5a274a0f26,autoPET,unknown,unknown,unknown
+BDMAP_00002129,FLARE23_Ts_0004_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002130,PETCT_bc80d06f97,autoPET,unknown,unknown,unknown
+BDMAP_00002131,PETCT_a86b2dc6ce,autoPET,unknown,unknown,unknown
+BDMAP_00002132,CPTAC-UCEC-C3N-00743_0_3,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00002133,case_00198,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002134,Case_01036_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002135,amos_0066,AMOS,unknown,unknown,unknown
+BDMAP_00002136,TCGA-BLCA-4Z-AA7O_0_2,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00002137,TCIAColon_0067_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002138,PETCT_527c5afc5d,autoPET,unknown,unknown,unknown
+BDMAP_00002139,hepaticvessel_263,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002140,word_0061,WORD,unknown,unknown,unknown
+BDMAP_00002141,CPTAC-UCEC-C3L-02399_0_3,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00002142,s0088,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002143,PETCT_75046e0c12,autoPET,unknown,unknown,unknown
+BDMAP_00002144,hepaticvessel_092,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002145,TCIAColon_0288_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002146,PETCT_d206b891ee,autoPET,unknown,unknown,unknown
+BDMAP_00002147,liver_114,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002148,Case_00252_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002149,lung_046,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002150,s0963,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002151,TCIAColon_0214_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002152,colon_114,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002153,hepaticvessel_198,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002154,PETCT_ea051a3e6c,autoPET,unknown,unknown,unknown
+BDMAP_00002155,word_0096,WORD,unknown,unknown,unknown
+BDMAP_00002156,TCIAColon_0102_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002157,TCIAColon_0067_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002158,liver_37,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002159,Case_00943_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002160,TCIAColon_0223_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002161,s0158,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002162,amos_0016,AMOS,unknown,unknown,unknown
+BDMAP_00002163,case_00143,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002164,case_00426,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002165,hepaticvessel_266,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002166,case_00222,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002167,liver_11,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002168,hepaticvessel_016,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002169,TCIAColon_0063_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002170,FLARE23_Ts_0074_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002171,pancreas_095,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002172,liver_37,LiTS,unhealthy,unknown,unknown
+BDMAP_00002173,case_00411,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002174,s0145,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002175,PETCT_4a255db7bd,autoPET,unknown,unknown,unknown
+BDMAP_00002176,PETCT_f7067b7bbb,autoPET,unknown,unknown,unknown
+BDMAP_00002177,s0898,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002178,PETCT_7785c10e91,autoPET,unknown,unknown,unknown
+BDMAP_00002179,PETCT_63dd9503eb,autoPET,unknown,unknown,unknown
+BDMAP_00002180,word_0003,WORD,unknown,unknown,unknown
+BDMAP_00002181,case_00100,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002182,PETCT_7978e9fe62,autoPET,unknown,unknown,unknown
+BDMAP_00002183,CPTAC-PDA-C3N-01714_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002184,case_00575,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002185,colon_203,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002186,word_0094,WORD,unknown,unknown,unknown
+BDMAP_00002187,TCIAColon_0162_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002188,pancreas_204,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002189,hepaticvessel_004,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002190,hepaticvessel_113,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002191,PETCT_6aea5c3a03,autoPET,unknown,unknown,unknown
+BDMAP_00002192,PETCT_aee7d68f86,autoPET,unknown,unknown,unknown
+BDMAP_00002193,colon_198,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002194,amos_0225,AMOS,unknown,unknown,unknown
+BDMAP_00002195,TCIAColon_0100_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00002196,s0519,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002197,word_0005,WORD,unknown,unknown,unknown
+BDMAP_00002198,volume-117,CT-ORG,unknown,unknown,unknown
+BDMAP_00002199,colon_120,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002200,PETCT_35c9c85a96,autoPET,unknown,unknown,unknown
+BDMAP_00002201,PETCT_9ad7dffae6,autoPET,unknown,unknown,unknown
+BDMAP_00002202,TCIAColon_0086_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002203,TCIAColon_0205_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002204,PETCT_d32cf972af,autoPET,unknown,unknown,unknown
+BDMAP_00002205,TCIAColon_0066_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002206,s1063,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002207,amos_0177,AMOS,unknown,unknown,unknown
+BDMAP_00002208,s0086,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002209,lung_014,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002210,PETCT_3cd49210eb,autoPET,unknown,unknown,unknown
+BDMAP_00002211,case_00195,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002212,PANCREAS_0024,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00002213,pancreas_457,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002214,liver_128,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002215,TCIAColon_0163_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002216,pancreas_120,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002217,amos_0086,AMOS,unknown,unknown,unknown
+BDMAP_00002218,PETCT_1a3d4e63ee,autoPET,unknown,unknown,unknown
+BDMAP_00002219,TCIAColon_0285_0_5,TCIAColon,unknown,unknown,unknown
+BDMAP_00002220,spleen_39,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00002221,PETCT_1bb48bfb40,autoPET,unknown,unknown,unknown
+BDMAP_00002222,s0119,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002223,colon_184,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002224,PETCT_9b53627d23,autoPET,unknown,unknown,unknown
+BDMAP_00002225,Case_00998_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002226,case_00041,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002227,pancreas_227,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002228,hepaticvessel_060,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002229,colon_116,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002230,colon_104,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002231,lung_066,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002232,pancreas_052,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002233,pancreas_247,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002234,amos_0224,AMOS,unknown,unknown,unknown
+BDMAP_00002235,PETCT_5ac322455f,autoPET,unknown,unknown,unknown
+BDMAP_00002236,PANCREAS_0042,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00002237,pancreas_183,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002238,hepaticvessel_176,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002239,TCIAColon_0141_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002240,TCIAColon_0162_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002241,PETCT_62b114e09b,autoPET,unknown,unknown,unknown
+BDMAP_00002242,case_00067,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002243,hepaticvessel_205,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002244,pancreas_366,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002245,s0341,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002246,PETCT_9f491f1481,autoPET,unknown,unknown,unknown
+BDMAP_00002247,CPTAC-PDA-C3N-01714_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002248,hepaticvessel_432,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002249,s1099,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002250,pancreas_421,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002251,case_00144,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002252,liver_54,LiTS,unhealthy,unknown,unknown
+BDMAP_00002253,colon_015,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002254,TCIAColon_0118_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002255,hepaticvessel_184,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002256,Case_00988_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002257,PETCT_983a76fd43,autoPET,unknown,unknown,unknown
+BDMAP_00002258,volume-110,CT-ORG,unknown,unknown,unknown
+BDMAP_00002259,pancreas_461,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002260,case_00194,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002261,PETCT_0cda25453b,autoPET,unknown,unknown,unknown
+BDMAP_00002262,PETCT_34aa521b46,autoPET,unknown,unknown,unknown
+BDMAP_00002263,Case_00524_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002264,PETCT_9e864bb710,autoPET,unknown,unknown,unknown
+BDMAP_00002265,case_00176,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002266,liver_146,MSD-Liver,unknown,unknown,unknown
+BDMAP_00002267,liver_7,LiTS,unhealthy,unknown,unknown
+BDMAP_00002268,pancreas_338,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002269,s0028,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002270,TCIAColon_0184_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002271,liver_68,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002272,PETCT_7948aa0e26,autoPET,unknown,unknown,unknown
+BDMAP_00002273,case_00283,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002274,s1291,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002275,liver_89,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002276,colon_063,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002277,TCIAColon_0198_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002278,pancreas_045,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002279,PETCT_39eca178a1,autoPET,unknown,unknown,unknown
+BDMAP_00002280,s0157,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002281,CPTAC-PDA-C3N-03430_1_5,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002282,liver_77,LiTS,unhealthy,unknown,unknown
+BDMAP_00002283,liver_44,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002284,NIH-LYMPH-MED-012_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002285,Case_00884_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002286,PETCT_9a2c6e618a,autoPET,unknown,unknown,unknown
+BDMAP_00002287,colon_079,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002288,case_00521,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002289,liver_122,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002290,PETCT_fb014a1ea0,autoPET,unknown,unknown,unknown
+BDMAP_00002291,TCIAColon_0285_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00002292,PETCT_b663adb148,autoPET,unknown,unknown,unknown
+BDMAP_00002293,TCIAColon_0195_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002294,TCIAColon_0229_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002295,pancreas_372,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002296,TCIAColon_0179_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002297,s0694,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002298,pancreas_100,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002299,s0836,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002300,FLARE23_Ts_0088_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002301,NIH-LYMPH-ABD-063_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002302,case_00202,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002303,s1387,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002304,pancreas_354,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002305,colon_040,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002306,CPTAC-PDA-C3N-02998_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002307,PETCT_456d14846b,autoPET,unknown,unknown,unknown
+BDMAP_00002308,s1100,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002309,pancreas_299,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002310,case_00227,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002311,pancreas_133,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002312,TCGA-OV-61-1907_2_1,TCGA-OV,unknown,unknown,unknown
+BDMAP_00002313,pancreas_131,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002314,TCIAColon_0027_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002315,NIH-LYMPH-ABD-081_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002316,s0743,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002317,hepaticvessel_098,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002318,colon_077,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002319,case_00497,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002320,pancreas_381,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002321,Case_00458_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002322,CPTAC-CCRCC-C3N-00310_0_5,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002323,s1390,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002324,PETCT_98c6af8b90,autoPET,unknown,unknown,unknown
+BDMAP_00002325,TCIAColon_0025_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002326,case_00479,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002327,TCIAColon_0062_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002328,pancreas_387,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002329,FLARE23_Ts_0017_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002330,NIH-LYMPH-MED-009_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002331,Case_00433_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002332,pancreas_130,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002333,case_00501,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002334,word_0015,WORD,unknown,unknown,unknown
+BDMAP_00002335,PETCT_41472f5ce9,autoPET,unknown,unknown,unknown
+BDMAP_00002336,liver_156,MSD-Liver,unknown,unknown,unknown
+BDMAP_00002337,s0239,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002338,Case_01022_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002339,s1238,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002340,TCGA-BLCA-DK-A1A7_0_1,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00002341,colon_082,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002342,volume-102,CT-ORG,unknown,unknown,unknown
+BDMAP_00002343,pancreas_174,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002344,PETCT_b2244b5591,autoPET,unknown,unknown,unknown
+BDMAP_00002345,CPTAC-PDA-C3N-03426_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002346,PETCT_55ca11402a,autoPET,unknown,unknown,unknown
+BDMAP_00002347,case_00256,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002348,pancreas_397,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002349,liver_28,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002350,TCIAColon_0028_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002351,PETCT_f1ca0f7c4c,autoPET,unknown,unknown,unknown
+BDMAP_00002352,lung_053,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002353,PETCT_93bea242d1,autoPET,unknown,unknown,unknown
+BDMAP_00002354,case_00491,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002355,Case_00202_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002356,colon_121,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002357,amos_0099,AMOS,unknown,unknown,unknown
+BDMAP_00002358,Case_00965_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002359,liver_8,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002360,PETCT_4848bebb10,autoPET,unknown,unknown,unknown
+BDMAP_00002361,case_00202,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002362,pancreas_265,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002363,case_00128,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002364,NIH-LYMPH-ABD-071_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002365,amos_0281,AMOS,unknown,unknown,unknown
+BDMAP_00002366,s1369,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002367,img0008,BTCV,healthy,healthy,healthy
+BDMAP_00002368,PETCT_2b60c8135a,autoPET,unknown,unknown,unknown
+BDMAP_00002369,PETCT_4760c0139a,autoPET,unknown,unknown,unknown
+BDMAP_00002370,CPTAC-UCEC-C3N-01171_0_7,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00002371,hepaticvessel_078,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002372,amos_0045,AMOS,unknown,unknown,unknown
+BDMAP_00002373,liver_41,LiTS,unhealthy,unknown,unknown
+BDMAP_00002374,word_0148,WORD,unknown,unknown,unknown
+BDMAP_00002375,PETCT_17d334cb6c,autoPET,unknown,unknown,unknown
+BDMAP_00002376,Case_00709_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002377,s0677,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002378,PETCT_ad7c6a4a58,autoPET,unknown,unknown,unknown
+BDMAP_00002379,PETCT_d46f0109f8,autoPET,unknown,unknown,unknown
+BDMAP_00002380,amos_0175,AMOS,unknown,unknown,unknown
+BDMAP_00002381,volume-104,CT-ORG,unknown,unknown,unknown
+BDMAP_00002382,CPTAC-PDA-C3N-01715_0_5,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002383,case_00090,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002384,Case_00285_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002385,s0124,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002386,Case_00015_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002387,pancreas_358,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002388,TCIAColon_0053_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002389,NIH-LYMPH-ABD-006_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002390,FLARE23_Ts_0002_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002391,s0307,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002392,Case_00293_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002393,PETCT_ddbb3c69f0,autoPET,unknown,unknown,unknown
+BDMAP_00002394,Case_00887_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002395,pancreas_039,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002396,case_00175,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002397,PETCT_80ccbdadf9,autoPET,unknown,unknown,unknown
+BDMAP_00002398,TCIAColon_0219_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002399,PETCT_7d8aa1e94d,autoPET,unknown,unknown,unknown
+BDMAP_00002400,hepaticvessel_366,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002401,colon_112,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002402,pancreas_228,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002403,case_00254,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002404,case_00214,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002405,CPTAC-PDA-C3N-01167_0_8,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002406,s0109,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002407,case_00576,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002408,PETCT_14929994cf,autoPET,unknown,unknown,unknown
+BDMAP_00002409,CPTAC-PDA-C3N-03670_0_2,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002410,case_00078,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002411,pancreas_339,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002412,23_image,CHAOS,healthy,healthy,healthy
+BDMAP_00002413,hepaticvessel_245,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002414,NIH-LYMPH-ABD-014_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002415,hepaticvessel_340,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002416,PETCT_4fb1817df3,autoPET,unknown,unknown,unknown
+BDMAP_00002417,case_00017,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002418,hepaticvessel_272,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002419,liver_33,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002420,hepaticvessel_448,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002421,case_00172,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002422,case_00409,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002423,TCIAColon_0004_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002424,liver_200,MSD-Liver,unknown,unknown,unknown
+BDMAP_00002425,hepaticvessel_048,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002426,case_00216,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002427,NIH-LYMPH-ABD-044_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002428,TCIAColon_0018_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002429,TCIAColon_0269_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002430,NIH-LYMPH-ABD-055_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002431,NIH-LYMPH-MED-005_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002432,LDCT-L277_0_0,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00002433,case_00209,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002434,case_00107,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002435,s0777,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002436,liver_132,MSD-Liver,unknown,unknown,unknown
+BDMAP_00002437,case_00027,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002438,Case_00212_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002439,PETCT_465176d213,autoPET,unknown,unknown,unknown
+BDMAP_00002440,case_00014,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002441,hepaticvessel_236,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002442,CPTAC-CCRCC-C3N-00435_0_2,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002443,s1007,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002444,Case_00936_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002445,s0319,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002446,case_00179,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002447,TCIAColon_0201_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002448,amos_0337,AMOS,unknown,unknown,unknown
+BDMAP_00002449,PETCT_cd50f3fec4,autoPET,unknown,unknown,unknown
+BDMAP_00002450,s0507,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002451,colon_134,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002452,PETCT_cb240e6f0f,autoPET,unknown,unknown,unknown
+BDMAP_00002453,case_00016,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002454,s1340,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002455,Case_01055_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002456,PETCT_64909533c5,autoPET,unknown,unknown,unknown
+BDMAP_00002457,pancreas_441,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002458,colon_215,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002459,PETCT_b92d7f441e,autoPET,unknown,unknown,unknown
+BDMAP_00002460,PETCT_0117d7f11f,autoPET,unknown,unknown,unknown
+BDMAP_00002461,TCIAColon_0268_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002462,hepaticvessel_128,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002463,liver_19,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002464,PETCT_7abe274c10,autoPET,unknown,unknown,unknown
+BDMAP_00002465,colon_086,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002466,hepaticvessel_212,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002467,amos_0129,AMOS,unknown,unknown,unknown
+BDMAP_00002468,PETCT_90d668ed29,autoPET,unknown,unknown,unknown
+BDMAP_00002469,Case_00183_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002470,TCGA-BLCA-DK-A3IN_0_2,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00002471,case_00225,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002472,case_00437,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002473,lung_043,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002474,case_00032,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002475,pancreas_266,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002476,case_00275,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002477,CPTAC-PDA-C3N-01382_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002478,hepaticvessel_066,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002479,liver_90,LiTS,unhealthy,unknown,unknown
+BDMAP_00002480,CPTAC-UCEC-C3N-00866_2_3,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00002481,case_00070,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002482,s0227,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002483,PETCT_c5a143a604,autoPET,unknown,unknown,unknown
+BDMAP_00002484,case_00261,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002485,liver_2,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002486,hepaticvessel_020,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002487,case_00191,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002488,PETCT_8b40e356dc,autoPET,unknown,unknown,unknown
+BDMAP_00002489,s0365,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002490,s1249,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002491,PETCT_591b3025ff,autoPET,unknown,unknown,unknown
+BDMAP_00002492,case_00124,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002493,hepaticvessel_095,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002494,TCIAColon_0230_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00002495,liver_105,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002496,case_00284,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002497,hepaticvessel_164,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002498,case_00281,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002499,TCIAColon_0022_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002500,TCGA-OV-61-1738_1_1,TCGA-OV,unknown,unknown,unknown
+BDMAP_00002501,case_00038,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002502,PETCT_2d9638360e,autoPET,unknown,unknown,unknown
+BDMAP_00002503,pancreas_383,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002504,case_00056,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002505,CPTAC-CCRCC-C3N-00491_0_4,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002506,s0992,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002507,PETCT_4cb875dc0b,autoPET,unknown,unknown,unknown
+BDMAP_00002508,Case_00427_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002509,case_00190,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002510,TCIAColon_0102_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00002511,case_00238,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002512,PETCT_8311aeddb9,autoPET,unknown,unknown,unknown
+BDMAP_00002513,PETCT_bb564e29d2,autoPET,unknown,unknown,unknown
+BDMAP_00002514,pancreas_462,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002515,lung_044,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002516,TCIAColon_0019_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002517,s1209,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002518,case_00041,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002519,hepaticvessel_292,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002520,TCIAColon_0047_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002521,s0224,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002522,FLARE23_Ts_0089_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002523,pancreas_399,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002524,case_00245,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002525,PETCT_2dc17aaeaf,autoPET,unknown,unknown,unknown
+BDMAP_00002526,TCIAColon_0181_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002527,hepaticvessel_079,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002528,hepaticvessel_076,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002529,liver_125,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002530,colon_180,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002531,CPTAC-CCRCC-C3L-01459_1_5,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002532,s0746,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002533,Case_00835_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002534,spleen_59,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00002535,s1208,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002536,amos_0185,AMOS,unknown,unknown,unknown
+BDMAP_00002537,CPTAC-CCRCC-C3L-01954_3_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002538,PETCT_448225c237,autoPET,unknown,unknown,unknown
+BDMAP_00002539,s1008,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002540,volume-85,CT-ORG,unknown,unknown,unknown
+BDMAP_00002541,colon_083,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002542,Case_00728_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002543,LDCT-L237_0_0,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00002544,amos_0299,AMOS,unknown,unknown,unknown
+BDMAP_00002545,case_00119,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002546,TCGA-BLCA-4Z-AA81_0_3,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00002547,lung_055,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002548,s0651,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002549,hepaticvessel_017,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002550,PETCT_321bba14bc,autoPET,unknown,unknown,unknown
+BDMAP_00002551,TCIAColon_0299_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00002552,case_00093,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002553,PETCT_a627fc68a1,autoPET,unknown,unknown,unknown
+BDMAP_00002554,amos_0408,AMOS,unknown,unknown,unknown
+BDMAP_00002555,word_0109,WORD,unknown,unknown,unknown
+BDMAP_00002556,TCIAColon_0078_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002557,s0616,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002558,Case_00254_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002559,Case_00992_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002560,Case_00678_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002561,amos_0341,AMOS,unknown,unknown,unknown
+BDMAP_00002562,case_00185,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002563,Case_00917_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002564,Case_00975_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002565,s1354,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002566,liver_142,MSD-Liver,unknown,unknown,unknown
+BDMAP_00002567,PETCT_b6fc20942c,autoPET,unknown,unknown,unknown
+BDMAP_00002568,TCIAColon_0211_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002569,PANCREAS_0014,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00002570,case_00256,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002571,case_00297,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002572,spleen_55,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00002573,FLARE23_Ts_0058_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002574,case_00013,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002575,Case_00857_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002576,s0733,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002577,TCIAColon_0034_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002578,s1031,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002579,hepaticvessel_089,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002580,pancreas_298,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002581,Case_00863_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002582,case_00420,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002583,liver_99,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002584,PETCT_0cda25453b,autoPET,unknown,unknown,unknown
+BDMAP_00002585,pancreas_496,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002586,s1276,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002587,FLARE23_Ts_0011_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002588,PETCT_0e2034240b,autoPET,unknown,unknown,unknown
+BDMAP_00002589,NIH-LYMPH-MED-014_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002590,hepaticvessel_201,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002591,TCIAColon_0071_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002592,case_00459,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002593,PETCT_68ef307665,autoPET,unknown,unknown,unknown
+BDMAP_00002594,pancreas_220,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002595,PETCT_7147385005,autoPET,unknown,unknown,unknown
+BDMAP_00002596,CPTAC-CCRCC-C3L-02202_0_2,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002597,s0211,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002598,colon_138,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002599,hepaticvessel_159,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002600,word_0056,WORD,unknown,unknown,unknown
+BDMAP_00002601,Case_00961_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002602,TCIAColon_0129_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002603,pancreas_078,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002604,spleen_63,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00002605,PETCT_61348439bf,autoPET,unknown,unknown,unknown
+BDMAP_00002606,Case_00769_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002607,TCIAColon_0278_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002608,PETCT_a9d7a14ba1,autoPET,unknown,unknown,unknown
+BDMAP_00002609,case_00546,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002610,PETCT_06d55e8295,autoPET,unknown,unknown,unknown
+BDMAP_00002611,PETCT_32aa845af1,autoPET,unknown,unknown,unknown
+BDMAP_00002612,colon_196,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002613,Case_00768_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002614,PETCT_ede28cc3c2,autoPET,unknown,unknown,unknown
+BDMAP_00002615,TCIAColon_0272_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002616,pancreas_369,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002617,CPTAC-PDA-C3N-02697_0_5,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002618,PETCT_8e02f36295,autoPET,unknown,unknown,unknown
+BDMAP_00002619,pancreas_404,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002620,PETCT_e37d5b9bc2,autoPET,unknown,unknown,unknown
+BDMAP_00002621,LDCT-L248_0_0,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00002622,PETCT_997d4ef9a7,autoPET,unknown,unknown,unknown
+BDMAP_00002623,PETCT_f8de0cde56,autoPET,unknown,unknown,unknown
+BDMAP_00002624,hepaticvessel_230,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002625,pancreas_307,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002626,case_00092,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002627,PETCT_741f6130ed,autoPET,unknown,unknown,unknown
+BDMAP_00002628,s0790,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002629,FLARE23_Ts_0091_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002630,PETCT_b7c1533a39,autoPET,unknown,unknown,unknown
+BDMAP_00002631,case_00005,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002632,PETCT_4404466919,autoPET,unknown,unknown,unknown
+BDMAP_00002633,Case_00979_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002634,TCIAColon_0185_0_5,TCIAColon,unknown,unknown,unknown
+BDMAP_00002635,PETCT_dd8f9f217c,autoPET,unknown,unknown,unknown
+BDMAP_00002636,volume-70,CT-ORG,unknown,unknown,unknown
+BDMAP_00002637,Case_00805_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002638,case_00139,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002639,img0033,BTCV,healthy,healthy,healthy
+BDMAP_00002640,PETCT_9a2bfe901f,autoPET,unknown,unknown,unknown
+BDMAP_00002641,lung_058,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002642,CPTAC-PDA-C3N-03430_0_10,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002643,LDCT-L273_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00002644,TCIAColon_0109_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002645,TCIAColon_0224_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002646,PETCT_9f6e8b1b43,autoPET,unknown,unknown,unknown
+BDMAP_00002647,pancreas_451,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002648,case_00104,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002649,Case_00830_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002650,PANCREAS_0047,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00002651,hepaticvessel_018,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002652,TCIAColon_0074_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002653,case_00515,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002654,colon_096,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002655,case_00106,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002656,case_00445,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002657,pancreas_340,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002658,s0082,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002659,PETCT_69c90b6820,autoPET,unknown,unknown,unknown
+BDMAP_00002660,amos_0402,AMOS,unknown,unknown,unknown
+BDMAP_00002661,case_00467,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002662,PETCT_27d69a8466,autoPET,unknown,unknown,unknown
+BDMAP_00002663,case_00241,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002664,s0030,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002665,case_00092,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002666,TCIAColon_0223_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002667,hepaticvessel_104,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002668,word_0007,WORD,unknown,unknown,unknown
+BDMAP_00002669,FLARE23_Ts_0085_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002670,PETCT_b621742469,autoPET,unknown,unknown,unknown
+BDMAP_00002671,s0880,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002672,LDCT-L203_0_0,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00002673,case_00025,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002674,PETCT_fa45f610c4,autoPET,unknown,unknown,unknown
+BDMAP_00002675,Case_00009_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002676,TCIAColon_0107_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002677,PETCT_c227131152,autoPET,unknown,unknown,unknown
+BDMAP_00002678,TCIAColon_0265_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002679,PETCT_4f6ff86453,autoPET,unknown,unknown,unknown
+BDMAP_00002680,LDCT-L212_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00002681,s1297,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002682,word_0040,WORD,unknown,unknown,unknown
+BDMAP_00002683,CPTAC-UCEC-C3N-00866_1_3,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00002684,s1164,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002685,PETCT_fe705ea1cc,autoPET,unknown,unknown,unknown
+BDMAP_00002686,pancreas_464,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002687,PETCT_ad7cd4a9d2,autoPET,unknown,unknown,unknown
+BDMAP_00002688,pancreas_244,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002689,case_00131,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002690,pancreas_064,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002691,hepaticvessel_142,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002692,PETCT_3bce0eb7aa,autoPET,unknown,unknown,unknown
+BDMAP_00002693,PETCT_53a0610615,autoPET,unknown,unknown,unknown
+BDMAP_00002694,Case_00017_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002695,case_00165,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002696,case_00166,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002697,PETCT_f6295a93a6,autoPET,unknown,unknown,unknown
+BDMAP_00002698,hepaticvessel_188,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002699,liver_52,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002700,PETCT_d31a5688a2,autoPET,unknown,unknown,unknown
+BDMAP_00002701,s0050,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002702,s0690,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002703,s0763,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002704,colon_066,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002705,hepaticvessel_255,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002706,hepaticvessel_125,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002707,case_00295,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002708,case_00049,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002709,CPTAC-PDA-C3N-01167_0_9,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002710,case_00013,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002711,PETCT_d103e57f0e,autoPET,unknown,unknown,unknown
+BDMAP_00002712,liver_119,LiTS,healthy,unknown,unknown
+BDMAP_00002713,FLARE23_Ts_0092_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002714,TCIAColon_0213_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002715,PETCT_e0a7ccecad,autoPET,unknown,unknown,unknown
+BDMAP_00002716,FLARE23_Ts_0061_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002717,liver_98,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002718,CPTAC-PDA-C3N-02697_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002719,liver_123,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002720,case_00002,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002721,TCIAColon_0038_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002722,TCIAColon_0172_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00002723,TCGA-BLCA-4Z-AA7W_0_5,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00002724,CPTAC-CCRCC-C3L-00815_0_6,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002725,hepaticvessel_221,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002726,s0669,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002727,colon_060,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002728,case_00208,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002729,TCIAColon_0204_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002730,colon_181,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002731,hepaticvessel_420,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002732,colon_170,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002733,word_0147,WORD,unknown,unknown,unknown
+BDMAP_00002734,amos_0186,AMOS,unknown,unknown,unknown
+BDMAP_00002735,s0536,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002736,s0950,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002737,pancreas_345,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002738,s0923,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002739,colon_193,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002740,amos_0102,AMOS,unknown,unknown,unknown
+BDMAP_00002741,hepaticvessel_279,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002742,case_00079,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002743,lung_093,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002744,case_00009,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002745,TCIAColon_0170_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002746,case_00464,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002747,colon_182,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002748,liver_65,LiTS,unhealthy,unknown,unknown
+BDMAP_00002749,hepaticvessel_417,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002750,LDCT-L229_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00002751,case_00051,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002752,PETCT_65346b03b8,autoPET,unknown,unknown,unknown
+BDMAP_00002753,PANCREAS_0046,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00002754,Case_00192_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002755,Case_00761_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002756,PETCT_1956667fce,autoPET,unknown,unknown,unknown
+BDMAP_00002757,s1289,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002758,pancreas_018,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002759,s0591,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002760,case_00236,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002761,PETCT_dac5cd2a4d,autoPET,unknown,unknown,unknown
+BDMAP_00002762,Case_00217_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002763,PANCREAS_0038,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00002764,spleen_58,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00002765,s0447,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002766,s0467,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002767,hepaticvessel_137,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002768,PETCT_adc49adb3d,autoPET,unknown,unknown,unknown
+BDMAP_00002769,PETCT_eeeda112bb,autoPET,unknown,unknown,unknown
+BDMAP_00002770,PETCT_15f4b7254f,autoPET,unknown,unknown,unknown
+BDMAP_00002771,colon_146,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002772,TCIAColon_0246_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002773,PETCT_f7a9267a69,autoPET,unknown,unknown,unknown
+BDMAP_00002774,pancreas_475,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002775,case_00068,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002776,case_00036,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002777,s0915,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002778,PETCT_e6b8b963b2,autoPET,unknown,unknown,unknown
+BDMAP_00002779,2_image,CHAOS,healthy,healthy,healthy
+BDMAP_00002780,PETCT_7cfd708a53,autoPET,unknown,unknown,unknown
+BDMAP_00002781,PETCT_ef9d41b836,autoPET,unknown,unknown,unknown
+BDMAP_00002782,s0077,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002783,CPTAC-PDA-C3N-03430_1_10,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002784,TCIAColon_0115_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002785,PETCT_04ab5c61c9,autoPET,unknown,unknown,unknown
+BDMAP_00002786,hepaticvessel_115,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002787,PETCT_372746cc99,autoPET,unknown,unknown,unknown
+BDMAP_00002788,case_00006,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002789,PETCT_987c8a1160,autoPET,unknown,unknown,unknown
+BDMAP_00002790,s1140,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002791,liver_23,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002792,TCIAColon_0099_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00002793,pancreas_267,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002794,s1046,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002795,PETCT_62b114e09b,autoPET,unknown,unknown,unknown
+BDMAP_00002796,TCIAColon_0110_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002797,volume-122,CT-ORG,unknown,unknown,unknown
+BDMAP_00002798,case_00089,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002799,PETCT_2791a0b3c2,autoPET,unknown,unknown,unknown
+BDMAP_00002800,word_0063,WORD,unknown,unknown,unknown
+BDMAP_00002801,PETCT_9837205b34,autoPET,unknown,unknown,unknown
+BDMAP_00002802,liver_170,MSD-Liver,unknown,unknown,unknown
+BDMAP_00002803,case_00140,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002804,case_00108,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002805,liver_80,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002806,hepaticvessel_187,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002807,case_00405,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002808,CPTAC-CCRCC-C3N-00437_0_5,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002809,s1159,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002810,pancreas_073,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002811,PETCT_390129c0d2,autoPET,unknown,unknown,unknown
+BDMAP_00002812,PETCT_d674a028b1,autoPET,unknown,unknown,unknown
+BDMAP_00002813,PETCT_37472e737f,autoPET,unknown,unknown,unknown
+BDMAP_00002814,PETCT_4cc808d16f,autoPET,unknown,unknown,unknown
+BDMAP_00002815,pancreas_201,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002816,hepaticvessel_065,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002817,TCIAColon_0053_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002818,PETCT_f9e0c504af,autoPET,unknown,unknown,unknown
+BDMAP_00002819,TCIAColon_0270_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002820,word_0112,WORD,unknown,unknown,unknown
+BDMAP_00002821,pancreas_003,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002822,pancreas_423,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002823,PETCT_8c5d99b459,autoPET,unknown,unknown,unknown
+BDMAP_00002824,s0593,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002825,amos_0052,AMOS,unknown,unknown,unknown
+BDMAP_00002826,case_00153,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002827,TCIAColon_0268_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002828,colon_011,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002829,pancreas_286,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002830,PETCT_96e7a3ab28,autoPET,unknown,unknown,unknown
+BDMAP_00002831,hepaticvessel_375,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002832,PETCT_3f2ee0274d,autoPET,unknown,unknown,unknown
+BDMAP_00002833,case_00052,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002834,case_00091,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002835,PETCT_f650c87621,autoPET,unknown,unknown,unknown
+BDMAP_00002836,PETCT_223f70f0b0,autoPET,unknown,unknown,unknown
+BDMAP_00002837,word_0142,WORD,unknown,unknown,unknown
+BDMAP_00002838,s1391,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002839,TCIAColon_0141_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002840,case_00065,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002841,liver_17,LiTS,unhealthy,unknown,unknown
+BDMAP_00002842,TCIAColon_0272_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002843,PETCT_37952b7ffb,autoPET,unknown,unknown,unknown
+BDMAP_00002844,pancreas_110,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002845,colon_137,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002846,colon_030,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002847,PETCT_7faf36a152,autoPET,unknown,unknown,unknown
+BDMAP_00002848,TCIAColon_0299_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002849,liver_101,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002850,lung_038,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002851,volume-128,CT-ORG,unknown,unknown,unknown
+BDMAP_00002852,s1233,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002853,s0107,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002854,case_00043,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002855,pancreas_259,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002856,liver_67,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002857,FLARE23_Ts_0095_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00002858,PETCT_84e7715ed9,autoPET,unknown,unknown,unknown
+BDMAP_00002859,amos_0378,AMOS,unknown,unknown,unknown
+BDMAP_00002860,TCIAColon_0070_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002861,s0371,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002862,NIH-LYMPH-ABD-043_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002863,pancreas_330,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002864,case_00100,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002865,TCIAColon_0029_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002866,Case_00862_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002867,PETCT_3b26172779,autoPET,unknown,unknown,unknown
+BDMAP_00002868,hepaticvessel_109,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00002869,word_0045,WORD,unknown,unknown,unknown
+BDMAP_00002870,case_00008,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002871,case_00285,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002872,PETCT_bef465b4ad,autoPET,unknown,unknown,unknown
+BDMAP_00002873,case_00110,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002874,NIH-LYMPH-ABD-011_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002875,colon_201,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002876,s0615,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002877,PETCT_ff1451316e,autoPET,unknown,unknown,unknown
+BDMAP_00002878,PETCT_c799ccc2f6,autoPET,unknown,unknown,unknown
+BDMAP_00002879,PETCT_8b73608326,autoPET,unknown,unknown,unknown
+BDMAP_00002880,s1061,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002881,PETCT_b6a1ee33ef,autoPET,unknown,unknown,unknown
+BDMAP_00002882,PETCT_3049d4f1a4,autoPET,unknown,unknown,unknown
+BDMAP_00002883,TCGA-OV-61-1998_1_2,TCGA-OV,unknown,unknown,unknown
+BDMAP_00002884,case_00033,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002885,pancreas_195,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002886,CPTAC-PDA-C3N-02998_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002887,PETCT_08212d7f6c,autoPET,unknown,unknown,unknown
+BDMAP_00002888,Case_00193_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002889,case_00186,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002890,16_image,CHAOS,healthy,healthy,healthy
+BDMAP_00002891,s0804,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002892,spleen_45,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00002893,case_00141,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002894,TCIAColon_0234_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002895,PETCT_193dea6ac7,autoPET,unknown,unknown,unknown
+BDMAP_00002896,pancreas_231,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002897,lung_096,MSD-Lung,unknown,unknown,unknown
+BDMAP_00002898,Case_00797_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002899,pancreas_137,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002900,PETCT_1a8bd52f71,autoPET,unknown,unknown,unknown
+BDMAP_00002901,PETCT_bd7c02faa9,autoPET,unknown,unknown,unknown
+BDMAP_00002902,case_00470,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002903,PETCT_12025abab5,autoPET,unknown,unknown,unknown
+BDMAP_00002904,TCIAColon_0008_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002905,Case_00978_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002906,case_00223,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002907,volume-139,CT-ORG,unknown,unknown,unknown
+BDMAP_00002908,TCIAColon_0262_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002909,liver_93,LiTS,unhealthy,unknown,unknown
+BDMAP_00002910,s1022,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002911,s1024,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002912,s0842,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002913,PETCT_9902caa7ec,autoPET,unknown,unknown,unknown
+BDMAP_00002914,TCGA-BLCA-DK-A1A5_0_2,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00002915,TCIAColon_0173_0_5,TCIAColon,unknown,unknown,unknown
+BDMAP_00002916,TCIAColon_0086_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002917,pancreas_440,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00002918,liver_35,LiTS,unhealthy,unknown,unknown
+BDMAP_00002919,TCIAColon_0217_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002920,case_00181,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002921,CPTAC-UCEC-C3L-01246_0_4,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00002922,hepaticvessel_008,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002923,TCIAColon_0075_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002924,colon_050,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002925,PETCT_d6a48a629d,autoPET,unknown,unknown,unknown
+BDMAP_00002926,s0133,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002927,s0662,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002928,case_00213,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002929,hepaticvessel_082,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002930,case_00237,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002931,case_00031,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002932,img0075,BTCV,healthy,healthy,healthy
+BDMAP_00002933,case_00204,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002934,PETCT_c38b168f1a,autoPET,unknown,unknown,unknown
+BDMAP_00002935,TCIAColon_0205_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002936,case_00476,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002937,amos_0349,AMOS,unknown,unknown,unknown
+BDMAP_00002938,NIH-LYMPH-ABD-007_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00002939,hepaticvessel_219,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002940,case_00049,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002941,hepaticvessel_053,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002942,case_00494,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002943,CPTAC-UCEC-C3L-02399_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00002944,pancreas_070,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002945,pancreas_225,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002946,amos_0048,AMOS,unknown,unknown,unknown
+BDMAP_00002947,pancreas_391,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00002948,case_00260,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002949,PETCT_6b20c7eabb,autoPET,unknown,unknown,unknown
+BDMAP_00002950,TCGA-OV-OY-A56P_2_1,TCGA-OV,unknown,unknown,unknown
+BDMAP_00002951,PETCT_06e7c24059,autoPET,unknown,unknown,unknown
+BDMAP_00002952,hepaticvessel_103,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002953,case_00573,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002954,TCIAColon_0007_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002955,liver_61,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00002956,PETCT_40a125468a,autoPET,unknown,unknown,unknown
+BDMAP_00002957,PETCT_ae4dcc5dd3,autoPET,unknown,unknown,unknown
+BDMAP_00002958,s0778,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002959,case_00124,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002960,hepaticvessel_013,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002961,TCIAColon_0031_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002962,hepaticvessel_291,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00002963,TCIAColon_0278_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002964,s0076,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002965,s0626,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002966,CPTAC-CCRCC-C3L-01465_1_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002967,Case_00055_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00002968,spleen_16,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00002969,s0166,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002970,hepaticvessel_067,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002971,PETCT_1b199d094d,autoPET,unknown,unknown,unknown
+BDMAP_00002972,TCIAColon_0169_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00002973,TCIAColon_0216_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002974,s0732,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002975,TCIAColon_0135_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002976,amos_0162,AMOS,unknown,unknown,unknown
+BDMAP_00002977,CPTAC-CCRCC-C3N-00492_0_2,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00002978,s0240,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002979,CPTAC-PDA-C3N-01382_0_7,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002980,TCIAColon_0261_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00002981,case_00580,KiTS23,unknown,unknown,unhealthy
+BDMAP_00002982,TCIAColon_0186_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002983,s0592,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002984,hepaticvessel_314,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002985,s0495,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002986,colon_217,MSD-Colon,unknown,unknown,unknown
+BDMAP_00002987,word_0001,WORD,unknown,unknown,unknown
+BDMAP_00002988,s0556,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002989,hepaticvessel_402,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00002990,case_00276,KiTS21,unknown,unknown,unhealthy
+BDMAP_00002991,case_00271,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002992,case_00298,KiTS19,unknown,unknown,unhealthy
+BDMAP_00002993,PETCT_09ee00bdc6,autoPET,unknown,unknown,unknown
+BDMAP_00002994,s0473,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00002995,TCIAColon_0101_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00002996,TCIAColon_0052_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002997,CPTAC-PDA-C3N-01383_0_2,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00002998,TCIAColon_0065_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00002999,NIH-LYMPH-ABD-008_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003000,hepaticvessel_129,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003001,TCIAColon_0171_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003002,liver_114,LiTS,healthy,unknown,unknown
+BDMAP_00003003,volume-55,CT-ORG,unknown,unknown,unknown
+BDMAP_00003004,hepaticvessel_208,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003005,PETCT_2dac5ef654,autoPET,unknown,unknown,unknown
+BDMAP_00003006,amos_0098,AMOS,unknown,unknown,unknown
+BDMAP_00003007,PETCT_8de6953d23,autoPET,unknown,unknown,unknown
+BDMAP_00003008,spleen_6,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003009,TCIAColon_0013_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003010,s0458,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003011,Case_01023_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003012,s0189,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003013,PANCREAS_0012,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00003014,PETCT_d5e2aa7feb,autoPET,unknown,unknown,unknown
+BDMAP_00003015,hepaticvessel_034,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003016,FLARE23_Ts_0067_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003017,pancreas_280,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003018,PETCT_d1c8141930,autoPET,unknown,unknown,unknown
+BDMAP_00003019,case_00123,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003020,TCIAColon_0042_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00003021,hepaticvessel_411,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003022,PETCT_bb45bfcffd,autoPET,unknown,unknown,unknown
+BDMAP_00003023,CPTAC-UCEC-C3N-00520_0_6,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00003024,FLARE23_Ts_0057_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003025,PETCT_544676de40,autoPET,unknown,unknown,unknown
+BDMAP_00003026,TCIAColon_0097_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003027,word_0068,WORD,unknown,unknown,unknown
+BDMAP_00003028,hepaticvessel_003,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003029,Case_00893_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003030,TCIAColon_0046_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003031,colon_009,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003032,FLARE23_Ts_0035_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003033,PANCREAS_0030,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00003034,case_00128,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003035,CPTAC-UCEC-C3N-00866_2_4,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00003036,pancreas_416,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003037,word_0114,WORD,unknown,unknown,unknown
+BDMAP_00003038,PETCT_41bd54f97a,autoPET,unknown,unknown,unknown
+BDMAP_00003039,Case_00693_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003040,PETCT_c58477f3b4,autoPET,unknown,unknown,unknown
+BDMAP_00003041,amos_0024,AMOS,unknown,unknown,unknown
+BDMAP_00003042,PETCT_0143bab87a,autoPET,unknown,unknown,unknown
+BDMAP_00003043,hepaticvessel_233,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003044,Case_00962_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003045,amos_0113,AMOS,unknown,unknown,unknown
+BDMAP_00003046,PETCT_1291700093,autoPET,unknown,unknown,unknown
+BDMAP_00003047,TCIAColon_0255_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003048,TCIAColon_0078_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003049,Case_00679_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003050,PETCT_cf3725edf0,autoPET,unknown,unknown,unknown
+BDMAP_00003051,PETCT_f1501b1f45,autoPET,unknown,unknown,unknown
+BDMAP_00003052,liver_107,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003053,s1143,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003054,volume-64,CT-ORG,unknown,unknown,unknown
+BDMAP_00003055,PETCT_562294be56,autoPET,unknown,unknown,unknown
+BDMAP_00003056,PETCT_a7b323b3fd,autoPET,unknown,unknown,unknown
+BDMAP_00003057,TCIAColon_0031_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003058,case_00084,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003059,FLARE23_Ts_0073_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003060,Case_00920_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003061,PETCT_eda00de6b0,autoPET,unknown,unknown,unknown
+BDMAP_00003062,CPTAC-CCRCC-C3N-00244_0_3,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00003063,case_00293,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003064,hepaticvessel_371,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003065,TCIAColon_0033_1_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003066,s0789,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003067,CPTAC-UCEC-C3N-00743_0_2,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00003068,s1322,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003069,s1152,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003070,case_00274,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003071,word_0022,WORD,unknown,unknown,unknown
+BDMAP_00003072,amos_0126,AMOS,unknown,unknown,unknown
+BDMAP_00003073,case_00126,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003074,liver_127,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003075,TCIAColon_0212_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003076,s0603,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003077,TCIAColon_0026_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003078,hepaticvessel_124,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003079,PETCT_cb83710e3d,autoPET,unknown,unknown,unknown
+BDMAP_00003080,TCIAColon_0197_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003081,TCIAColon_0080_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003082,PETCT_d31c9ebd93,autoPET,unknown,unknown,unknown
+BDMAP_00003083,case_00015,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003084,TCIAColon_0165_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003085,PANCREAS_0004,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00003086,hepaticvessel_035,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003087,CPTAC-CCRCC-C3L-02841_0_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00003088,Case_00226_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003089,Case_00843_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003090,word_0084,WORD,unknown,unknown,unknown
+BDMAP_00003091,LDCT-L209_0_0,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00003092,NIH-LYMPH-ABD-001_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003093,s1394,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003094,lung_069,MSD-Lung,unknown,unknown,unknown
+BDMAP_00003095,lung_062,MSD-Lung,unknown,unknown,unknown
+BDMAP_00003096,PETCT_74bbceaeeb,autoPET,unknown,unknown,unknown
+BDMAP_00003097,hepaticvessel_250,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003098,colon_020,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003099,FLARE23_Ts_0096_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003100,TCIAColon_0126_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003101,PETCT_4dcf62f869,autoPET,unknown,unknown,unknown
+BDMAP_00003102,s0178,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003103,PETCT_6b177a3c28,autoPET,unknown,unknown,unknown
+BDMAP_00003104,s1006,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003105,PETCT_661ddc60e0,autoPET,unknown,unknown,unknown
+BDMAP_00003106,TCIAColon_0253_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003107,s0468,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003108,PETCT_e77124b7f4,autoPET,unknown,unknown,unknown
+BDMAP_00003109,colon_036,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003110,TCIAColon_0174_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003111,colon_117,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003112,amos_0035,AMOS,unknown,unknown,unknown
+BDMAP_00003113,colon_155,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003114,pancreas_006,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003115,volume-131,CT-ORG,unknown,unknown,unknown
+BDMAP_00003116,hepaticvessel_160,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003117,PETCT_05808cf24e,autoPET,unknown,unknown,unknown
+BDMAP_00003118,img0066,BTCV,healthy,healthy,healthy
+BDMAP_00003119,TCIAColon_0035_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003120,hepaticvessel_282,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003121,CPTAC-UCEC-C3N-00866_3_4,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00003122,PETCT_d51bacdaba,autoPET,unknown,unknown,unknown
+BDMAP_00003123,PETCT_0225325b91,autoPET,unknown,unknown,unknown
+BDMAP_00003124,PETCT_8de70b5cd7,autoPET,unknown,unknown,unknown
+BDMAP_00003125,LDCT-L221_0_0,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00003126,TCIAColon_0157_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003127,amos_0190,AMOS,unknown,unknown,unknown
+BDMAP_00003128,case_00582,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003129,case_00165,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003130,pancreas_113,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003131,hepaticvessel_075,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003132,CPTAC-PDA-C3N-03039_2_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003133,pancreas_042,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003134,TCIAColon_0218_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003135,hepaticvessel_057,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003136,TCIAColon_0101_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003137,lung_084,MSD-Lung,unknown,unknown,unknown
+BDMAP_00003138,pancreas_342,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003139,amos_0007,AMOS,unknown,unknown,unknown
+BDMAP_00003140,liver_63,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003141,pancreas_080,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003142,TCIAColon_0185_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003143,case_00016,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003144,NIH-LYMPH-ABD-079_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003145,TCIAColon_0073_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003146,spleen_12,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003147,amos_0384,AMOS,unknown,unknown,unknown
+BDMAP_00003148,volume-62,CT-ORG,unknown,unknown,unknown
+BDMAP_00003149,Case_00026_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003150,case_00113,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003151,liver_52,LiTS,unhealthy,unknown,unknown
+BDMAP_00003152,Case_00036_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003153,case_00556,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003154,PANCREAS_0005,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00003155,word_0079,WORD,unknown,unknown,unknown
+BDMAP_00003156,s0210,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003157,PETCT_901573a747,autoPET,unknown,unknown,unknown
+BDMAP_00003158,spleen_2,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003159,s1224,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003160,s0029,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003161,s0509,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003162,hepaticvessel_011,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003163,hepaticvessel_163,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003164,case_00050,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003165,TCIAColon_0147_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003166,Case_00692_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003167,PETCT_17b46d7275,autoPET,unknown,unknown,unknown
+BDMAP_00003168,pancreas_041,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003169,PETCT_5e339b2ecf,autoPET,unknown,unknown,unknown
+BDMAP_00003170,liver_88,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003171,s0541,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003172,colon_171,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003173,TCIAColon_0200_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003174,img0076,BTCV,healthy,healthy,healthy
+BDMAP_00003175,hepaticvessel_093,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003176,PETCT_72d2d6de52,autoPET,unknown,unknown,unknown
+BDMAP_00003177,hepaticvessel_138,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003178,case_00536,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003179,s0262,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003180,TCIAColon_0146_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003181,word_0009,WORD,unknown,unknown,unknown
+BDMAP_00003182,amos_0005,AMOS,unknown,unknown,unknown
+BDMAP_00003183,case_00231,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003184,PETCT_3b14690fce,autoPET,unknown,unknown,unknown
+BDMAP_00003185,hepaticvessel_046,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003186,hepaticvessel_150,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003187,TCIAColon_0005_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00003188,TCIAColon_0023_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003189,case_00079,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003190,Case_00747_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003191,pancreas_232,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003192,Case_00556_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003193,case_00028,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003194,case_00151,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003195,case_00196,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003196,CPTAC-PDA-C3N-03430_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003197,PETCT_249dd35d0c,autoPET,unknown,unknown,unknown
+BDMAP_00003198,PETCT_7ee7610b9e,autoPET,unknown,unknown,unknown
+BDMAP_00003199,PETCT_7cfaf65584,autoPET,unknown,unknown,unknown
+BDMAP_00003200,case_00155,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003201,word_0105,WORD,unknown,unknown,unknown
+BDMAP_00003202,case_00537,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003203,hepaticvessel_049,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003204,hepaticvessel_210,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003205,case_00061,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003206,PETCT_a4ff5d0d9d,autoPET,unknown,unknown,unknown
+BDMAP_00003207,Case_00414_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003208,TCIAColon_0282_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003209,TCIAColon_0131_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003210,s0600,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003211,s0004,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003212,hepaticvessel_077,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003213,lung_018,MSD-Lung,unknown,unknown,unknown
+BDMAP_00003214,TCIAColon_0175_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003215,pancreas_124,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003216,s1326,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003217,amos_0380,AMOS,unknown,unknown,unknown
+BDMAP_00003218,s0232,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003219,Case_00209_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003220,Case_00681_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003221,PETCT_c395306306,autoPET,unknown,unknown,unknown
+BDMAP_00003222,img0029,BTCV,healthy,healthy,healthy
+BDMAP_00003223,lung_037,MSD-Lung,unknown,unknown,unknown
+BDMAP_00003224,lung_079,MSD-Lung,unknown,unknown,unknown
+BDMAP_00003225,case_00137,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003226,PETCT_642d6c78d6,autoPET,unknown,unknown,unknown
+BDMAP_00003227,amos_0367,AMOS,unknown,unknown,unknown
+BDMAP_00003228,PETCT_3b1c9155f5,autoPET,unknown,unknown,unknown
+BDMAP_00003229,TCIAColon_0135_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003230,TCIAColon_0245_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003231,s1062,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003232,TCIAColon_0004_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003233,word_0042,WORD,unknown,unknown,unknown
+BDMAP_00003234,TCIAColon_0137_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003235,TCIAColon_0103_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003236,liver_85,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003237,TCIAColon_0235_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003238,hepaticvessel_403,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003239,Case_00706_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003240,case_00121,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003241,Case_00892_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003242,amos_0301,AMOS,unknown,unknown,unknown
+BDMAP_00003243,s0682,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003244,pancreas_019,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003245,case_00192,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003246,amos_0254,AMOS,unknown,unknown,unknown
+BDMAP_00003247,TCIAColon_0104_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003248,CPTAC-CCRCC-C3L-01954_3_6,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00003249,PETCT_38106fde84,autoPET,unknown,unknown,unknown
+BDMAP_00003250,case_00277,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003251,TCIAColon_0093_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00003252,case_00577,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003253,TCIAColon_0284_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003254,pancreas_362,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003255,case_00231,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003256,case_00130,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003257,hepaticvessel_106,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003258,hepaticvessel_342,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003259,TCIAColon_0214_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003260,CPTAC-PDA-C3N-01381_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003261,hepaticvessel_074,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003262,Case_00995_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003263,PETCT_760c77b289,autoPET,unknown,unknown,unknown
+BDMAP_00003264,amos_0042,AMOS,unknown,unknown,unknown
+BDMAP_00003265,PANCREAS_0045,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00003266,hepaticvessel_275,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003267,case_00160,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003268,colon_028,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003269,PETCT_9521502dbb,autoPET,unknown,unknown,unknown
+BDMAP_00003270,s0983,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003271,Case_00719_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003272,liver_31,LiTS,unhealthy,unknown,unknown
+BDMAP_00003273,amos_0396,AMOS,unknown,unknown,unknown
+BDMAP_00003274,TCIAColon_0081_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003275,TCIAColon_0208_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00003276,TCIAColon_0291_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003277,pancreas_055,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003278,spleen_22,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003279,TCIAColon_0295_0_5,TCIAColon,unknown,unknown,unknown
+BDMAP_00003280,amos_0388,AMOS,unknown,unknown,unknown
+BDMAP_00003281,case_00540,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003282,lung_070,MSD-Lung,unknown,unknown,unknown
+BDMAP_00003283,pancreas_216,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003284,TCIAColon_0199_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003285,amos_0274,AMOS,unknown,unknown,unknown
+BDMAP_00003286,hepaticvessel_064,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003287,liver_100,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003288,PETCT_9ffd8409b3,autoPET,unknown,unknown,unknown
+BDMAP_00003289,hepaticvessel_453,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003290,s1269,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003291,amos_0121,AMOS,unknown,unknown,unknown
+BDMAP_00003292,TCIAColon_0094_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003293,amos_0057,AMOS,unknown,unknown,unknown
+BDMAP_00003294,case_00449,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003295,hepaticvessel_038,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003296,CPTAC-CCRCC-C3N-00313_0_3,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00003297,amos_0110,AMOS,unknown,unknown,unknown
+BDMAP_00003298,PETCT_5e2da717db,autoPET,unknown,unknown,unknown
+BDMAP_00003299,colon_107,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003300,case_00508,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003301,case_00133,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003302,Case_00767_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003303,amos_0092,AMOS,unknown,unknown,unknown
+BDMAP_00003304,s0482,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003305,Case_00704_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003306,s0830,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003307,PETCT_13b40a817b,autoPET,unknown,unknown,unknown
+BDMAP_00003308,TCIAColon_0197_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003309,NIH-LYMPH-MED-001_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003310,TCIAColon_0275_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003311,Case_00814_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003312,case_00184,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003313,TCIAColon_0239_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003314,TCIAColon_0145_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003315,case_00514,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003316,TCIAColon_0048_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003317,amos_0273,AMOS,unknown,unknown,unknown
+BDMAP_00003318,FLARE23_Ts_0100_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003319,pancreas_001,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003320,CPTAC-PDA-C3N-03039_2_2,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003321,hepaticvessel_434,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003322,s0957,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003323,hepaticvessel_252,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003324,case_00263,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003325,s0102,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003326,pancreas_321,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003327,colon_059,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003328,TCIAColon_0227_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003329,pancreas_101,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003330,liver_111,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003331,lung_061,MSD-Lung,unknown,unknown,unknown
+BDMAP_00003332,PETCT_a3df01d3a3,autoPET,unknown,unknown,unknown
+BDMAP_00003333,colon_054,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003334,PETCT_77bceab232,autoPET,unknown,unknown,unknown
+BDMAP_00003335,NIH-LYMPH-MED-018_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003336,FLARE23_Ts_0094_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003337,hepaticvessel_084,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003338,TCIAColon_0057_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003339,PETCT_ab953a5230,autoPET,unknown,unknown,unknown
+BDMAP_00003340,amos_0245,AMOS,unknown,unknown,unknown
+BDMAP_00003341,pancreas_154,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003342,s0111,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003343,case_00219,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003344,hepaticvessel_110,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003345,TCIAColon_0292_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003346,s0073,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003347,pancreas_135,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003348,TCIAColon_0143_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003349,CPTAC-PDA-C3N-01166_0_5,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003350,TCGA-SARC-A8VG_0_0,TCGA-SARC,unknown,unknown,unknown
+BDMAP_00003351,amos_0180,AMOS,unknown,unknown,unknown
+BDMAP_00003352,Case_00237_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003353,TCIAColon_0263_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003354,volume-135,CT-ORG,unknown,unknown,unknown
+BDMAP_00003355,lung_025,MSD-Lung,unknown,unknown,unknown
+BDMAP_00003356,pancreas_186,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003357,colon_216,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003358,case_00502,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003359,case_00273,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003360,PETCT_9c8842d12e,autoPET,unknown,unknown,unknown
+BDMAP_00003361,liver_94,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003362,PETCT_c2a51a65c6,autoPET,unknown,unknown,unknown
+BDMAP_00003363,case_00533,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003364,case_00103,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003365,hepaticvessel_068,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003366,hepaticvessel_148,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003367,pancreas_015,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003368,colon_019,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003369,TCIAColon_0155_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003370,PETCT_9a418f3b3a,autoPET,unknown,unknown,unknown
+BDMAP_00003371,spleen_9,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003372,img0038,BTCV,healthy,healthy,healthy
+BDMAP_00003373,colon_136,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003374,s0305,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003375,TCIAColon_0043_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003376,pancreas_400,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003377,liver_32,LiTS,healthy,unknown,unknown
+BDMAP_00003378,PETCT_ae46202abb,autoPET,unknown,unknown,unknown
+BDMAP_00003379,PETCT_65a1330e90,autoPET,unknown,unknown,unknown
+BDMAP_00003380,FLARE23_Ts_0029_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003381,case_00147,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003382,LDCT-L237_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00003383,hepaticvessel_069,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003384,case_00024,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003385,pancreas_287,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003386,hepaticvessel_296,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003387,pancreas_502,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003388,PETCT_1781ba966c,autoPET,unknown,unknown,unknown
+BDMAP_00003389,PETCT_a2a68bbe97,autoPET,unknown,unknown,unknown
+BDMAP_00003390,PETCT_3ba0277c0c,autoPET,unknown,unknown,unknown
+BDMAP_00003391,PETCT_d7ca3d4aaa,autoPET,unknown,unknown,unknown
+BDMAP_00003392,case_00018,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003393,PETCT_389462c0ac,autoPET,unknown,unknown,unknown
+BDMAP_00003394,NIH-LYMPH-ABD-009_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003395,hepaticvessel_099,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003396,pancreas_294,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003397,s1145,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003398,TCIAColon_0178_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003399,s0258,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003400,pancreas_386,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003401,PETCT_4776e75543,autoPET,unknown,unknown,unknown
+BDMAP_00003402,TCIAColon_0261_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003403,s0343,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003404,TCIAColon_0298_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003405,PETCT_dd6165ae36,autoPET,unknown,unknown,unknown
+BDMAP_00003406,case_00173,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003407,CPTAC-CCRCC-C3L-01954_1_5,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00003408,TCIAColon_0137_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003409,case_00280,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003410,amos_0160,AMOS,unknown,unknown,unknown
+BDMAP_00003411,case_00418,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003412,colon_075,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003413,s0878,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003414,s1362,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003415,volume-130,CT-ORG,unknown,unknown,unknown
+BDMAP_00003416,PETCT_0f44cec2e6,autoPET,unknown,unknown,unknown
+BDMAP_00003417,hepaticvessel_050,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003418,TCIAColon_0032_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003419,s1395,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003420,hepaticvessel_330,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003421,TCGA-BLCA-4Z-AA7W_0_1,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00003422,case_00022,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003423,amos_0170,AMOS,unknown,unknown,unknown
+BDMAP_00003424,PETCT_3c3ee87fc6,autoPET,unknown,unknown,unknown
+BDMAP_00003425,case_00251,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003426,PETCT_d0ea6c975f,autoPET,unknown,unknown,unknown
+BDMAP_00003427,pancreas_170,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003428,PETCT_a4ccc397a9,autoPET,unknown,unknown,unknown
+BDMAP_00003429,Case_00539_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003430,NIH-LYMPH-ABD-083_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003431,hepaticvessel_264,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003432,s0045,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003433,PETCT_c7dc6f848c,autoPET,unknown,unknown,unknown
+BDMAP_00003434,TCIAColon_0112_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003435,case_00047,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003436,pancreas_034,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003437,s1013,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003438,pancreas_277,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003439,liver_82,LiTS,unhealthy,unknown,unknown
+BDMAP_00003440,pancreas_356,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003441,case_00234,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003442,TCIAColon_0030_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003443,pancreas_212,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003444,case_00585,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003445,amos_0094,AMOS,unknown,unknown,unknown
+BDMAP_00003446,s0711,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003447,case_00253,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003448,case_00088,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003449,FLARE23_Ts_0070_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003450,word_0127,WORD,unknown,unknown,unknown
+BDMAP_00003451,pancreas_291,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003452,pancreas_184,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003453,PETCT_f1ca0f7c4c,autoPET,unknown,unknown,unknown
+BDMAP_00003454,PETCT_d325897ff4,autoPET,unknown,unknown,unknown
+BDMAP_00003455,case_00544,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003456,CPTAC-UCEC-C3L-01744_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00003457,case_00192,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003458,word_0006,WORD,unknown,unknown,unknown
+BDMAP_00003459,s1037,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003460,hepaticvessel_087,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003461,case_00539,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003462,hepaticvessel_382,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003463,word_0051,WORD,unknown,unknown,unknown
+BDMAP_00003464,pancreas_054,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003465,case_00119,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003466,word_0039,WORD,unknown,unknown,unknown
+BDMAP_00003467,CPTAC-UCEC-C3N-00520_0_3,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00003468,word_0121,WORD,unknown,unknown,unknown
+BDMAP_00003469,TCIAColon_0008_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003470,TCIAColon_0148_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003471,amos_0156,AMOS,unknown,unknown,unknown
+BDMAP_00003472,PETCT_4e3d83bfe3,autoPET,unknown,unknown,unknown
+BDMAP_00003473,TCIAColon_0028_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003474,s0040,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003475,PETCT_1a90052cb2,autoPET,unknown,unknown,unknown
+BDMAP_00003476,PETCT_dc15c1d9bb,autoPET,unknown,unknown,unknown
+BDMAP_00003477,spleen_10,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003478,PETCT_e4e99dbb47,autoPET,unknown,unknown,unknown
+BDMAP_00003479,pancreas_098,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003480,img0024,BTCV,healthy,healthy,healthy
+BDMAP_00003481,liver_129,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003482,liver_18,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003483,pancreas_333,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003484,case_00158,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003485,s0161,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003486,case_00053,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003487,TCIAColon_0225_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003488,PETCT_8b74860ce6,autoPET,unknown,unknown,unknown
+BDMAP_00003489,FLARE23_Ts_0063_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003490,hepaticvessel_228,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003491,case_00440,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003492,s0480,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003493,case_00102,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003494,TCGA-STAD-A8DL_0_2,TCGA-STAD,unknown,unknown,unknown
+BDMAP_00003495,PETCT_14c4d2c208,autoPET,unknown,unknown,unknown
+BDMAP_00003496,TCIAColon_0098_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003497,liver_101,LiTS,unhealthy,unknown,unknown
+BDMAP_00003498,s1012,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003499,Case_00940_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003500,TCIAColon_0219_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003501,PETCT_7094acd4c0,autoPET,unknown,unknown,unknown
+BDMAP_00003502,pancreas_180,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003503,amos_0192,AMOS,unknown,unknown,unknown
+BDMAP_00003504,PETCT_da4ce0da01,autoPET,unknown,unknown,unknown
+BDMAP_00003505,CPTAC-UCEC-C3N-00866_2_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00003506,case_00196,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003507,hepaticvessel_040,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003508,hepaticvessel_214,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003509,FLARE23_Ts_0066_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003510,colon_192,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003511,PETCT_a3ce52b2a8,autoPET,unknown,unknown,unknown
+BDMAP_00003512,s1119,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003513,liver_64,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003514,case_00278,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003515,s1199,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003516,case_00549,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003517,TCIAColon_0298_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003518,s0794,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003519,amos_0302,AMOS,unknown,unknown,unknown
+BDMAP_00003520,case_00071,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003521,PETCT_c10f48b173,autoPET,unknown,unknown,unknown
+BDMAP_00003522,30_image,CHAOS,healthy,healthy,healthy
+BDMAP_00003523,PETCT_02ba7e20f5,autoPET,unknown,unknown,unknown
+BDMAP_00003524,liver_57,LiTS,unhealthy,unknown,unknown
+BDMAP_00003525,pancreas_246,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003526,PETCT_86153b2974,autoPET,unknown,unknown,unknown
+BDMAP_00003527,10_image,CHAOS,healthy,healthy,healthy
+BDMAP_00003528,pancreas_363,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003529,case_00010,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003530,case_00126,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003531,case_00135,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003532,PETCT_e5c7be07a8,autoPET,unknown,unknown,unknown
+BDMAP_00003533,PETCT_7d01eb835d,autoPET,unknown,unknown,unknown
+BDMAP_00003534,liver_174,MSD-Liver,unknown,unknown,unknown
+BDMAP_00003535,case_00012,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003536,spleen_24,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003537,PETCT_4bab91dccd,autoPET,unknown,unknown,unknown
+BDMAP_00003538,pancreas_453,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003539,img0002,BTCV,healthy,healthy,healthy
+BDMAP_00003540,PETCT_4a72eeb991,autoPET,unknown,unknown,unknown
+BDMAP_00003541,FLARE23_Ts_0079_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003542,case_00160,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003543,pancreas_050,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003544,s0664,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003545,PETCT_1a1712f7d0,autoPET,unknown,unknown,unknown
+BDMAP_00003546,TCIAColon_0007_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003547,Case_00942_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003548,TCIAColon_0356_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003549,case_00235,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003550,PETCT_35de69968e,autoPET,unknown,unknown,unknown
+BDMAP_00003551,pancreas_089,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003552,colon_034,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003553,case_00185,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003554,TCIAColon_0293_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003555,PETCT_e5b1d6b4bd,autoPET,unknown,unknown,unknown
+BDMAP_00003556,liver_103,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003557,Case_00944_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003558,case_00446,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003559,Case_00007_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003560,colon_194,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003561,PETCT_098c4b7b87,autoPET,unknown,unknown,unknown
+BDMAP_00003562,TCIAColon_0152_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003563,PETCT_d31c9ebd93,autoPET,unknown,unknown,unknown
+BDMAP_00003564,colon_169,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003565,s0339,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003566,TCIAColon_0017_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003567,TCIAColon_0168_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003568,hepaticvessel_438,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003569,liver_96,LiTS,unhealthy,unknown,unknown
+BDMAP_00003570,s0869,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003571,PETCT_4ea806706c,autoPET,unknown,unknown,unknown
+BDMAP_00003572,TCIAColon_0058_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003573,FLARE23_Ts_0048_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003574,amos_0358,AMOS,unknown,unknown,unknown
+BDMAP_00003575,PETCT_69606f6a25,autoPET,unknown,unknown,unknown
+BDMAP_00003576,case_00506,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003577,PANCREAS_0029,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00003578,TCIAColon_0189_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003579,Case_00881_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003580,case_00038,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003581,case_00227,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003582,amos_0401,AMOS,unknown,unknown,unknown
+BDMAP_00003583,PETCT_4c670a5445,autoPET,unknown,unknown,unknown
+BDMAP_00003584,s0644,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003585,PETCT_db3daf78d2,autoPET,unknown,unknown,unknown
+BDMAP_00003586,Case_00718_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003587,FLARE23_Ts_0019_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003588,spleen_26,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003589,s1135,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003590,pancreas_114,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003591,TCIAColon_0029_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003592,pancreas_411,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003593,pancreas_156,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003594,amos_0142,AMOS,unknown,unknown,unknown
+BDMAP_00003595,CPTAC-PDA-C3N-01165_0_6,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003596,s0325,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003597,pancreas_436,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003598,pancreas_268,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003599,TCIAColon_0064_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003600,case_00170,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003601,PETCT_eee2ede9ad,autoPET,unknown,unknown,unknown
+BDMAP_00003602,spleen_46,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003603,case_00021,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003604,hepaticvessel_404,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003605,word_0048,WORD,unknown,unknown,unknown
+BDMAP_00003606,PETCT_c2ffda4725,autoPET,unknown,unknown,unknown
+BDMAP_00003607,s0934,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003608,case_00581,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003609,liver_161,MSD-Liver,unknown,unknown,unknown
+BDMAP_00003610,PETCT_ea9302fb0f,autoPET,unknown,unknown,unknown
+BDMAP_00003611,TCIAColon_0274_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003612,pancreas_149,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003613,Case_00801_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003614,case_00234,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003615,case_00163,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003616,CPTAC-PDA-C3N-02971_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003617,s0222,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003618,TCIAColon_0252_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003619,PETCT_8bf08c9a42,autoPET,unknown,unknown,unknown
+BDMAP_00003620,Case_00873_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003621,TCIAColon_0062_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003622,PETCT_855c7fca12,autoPET,unknown,unknown,unknown
+BDMAP_00003623,s1102,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003624,s0256,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003625,amos_0212,AMOS,unknown,unknown,unknown
+BDMAP_00003626,volume-30,CT-ORG,unknown,unknown,unknown
+BDMAP_00003627,PETCT_777fa0ba88,autoPET,unknown,unknown,unknown
+BDMAP_00003628,Case_00045_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003629,Case_00048_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003630,TCIAColon_0247_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003631,pancreas_313,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003632,s1023,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003633,pancreas_323,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003634,colon_165,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003635,s1086,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003636,Case_00468_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003637,PETCT_ccb9c375b2,autoPET,unknown,unknown,unknown
+BDMAP_00003638,CPTAC-CCRCC-C3N-00380_0_7,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00003639,hepaticvessel_323,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003640,colon_163,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003641,PETCT_29b372cb99,autoPET,unknown,unknown,unknown
+BDMAP_00003642,hepaticvessel_043,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003643,s0661,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003644,PANCREAS_0043,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00003645,Case_00255_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003646,PETCT_978c395243,autoPET,unknown,unknown,unknown
+BDMAP_00003647,img0031,BTCV,healthy,healthy,healthy
+BDMAP_00003648,PETCT_f4a5ebd915,autoPET,unknown,unknown,unknown
+BDMAP_00003649,TCIAColon_0088_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003650,pancreas_412,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003651,CPTAC-UCEC-C3N-01008_0_7,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00003652,s0456,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003653,hepaticvessel_409,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003654,TCIAColon_0293_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003655,NIH-LYMPH-ABD-068_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003656,case_00113,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003657,pancreas_165,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003658,pancreas_155,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003659,PETCT_44bf7dc508,autoPET,unknown,unknown,unknown
+BDMAP_00003660,TCIAColon_0209_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003661,s1244,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003662,Case_00916_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003663,TCIAColon_0271_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003664,liver_110,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003665,Case_00301_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003666,FLARE23_Ts_0053_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003667,TCIAColon_0108_1_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003668,PETCT_48c70dc4a3,autoPET,unknown,unknown,unknown
+BDMAP_00003669,s0472,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003670,TCIAColon_0049_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003671,hepaticvessel_421,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003672,case_00159,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003673,s0586,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003674,word_0107,WORD,unknown,unknown,unknown
+BDMAP_00003675,hepaticvessel_086,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003676,PETCT_4481687483,autoPET,unknown,unknown,unknown
+BDMAP_00003677,PETCT_81debf13a1,autoPET,unknown,unknown,unknown
+BDMAP_00003678,PETCT_e1a4145b63,autoPET,unknown,unknown,unknown
+BDMAP_00003679,s0796,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003680,colon_208,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003681,liver_175,MSD-Liver,unknown,unknown,unknown
+BDMAP_00003682,PETCT_19838cb8e5,autoPET,unknown,unknown,unknown
+BDMAP_00003683,pancreas_415,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003684,s0982,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003685,pancreas_285,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003686,TCIAColon_0130_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003687,Case_00757_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003688,PETCT_ea6c621616,autoPET,unknown,unknown,unknown
+BDMAP_00003689,hepaticvessel_384,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003690,Case_00523_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003691,TCIAColon_0166_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003692,word_0137,WORD,unknown,unknown,unknown
+BDMAP_00003693,word_0080,WORD,unknown,unknown,unknown
+BDMAP_00003694,case_00517,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003695,word_0041,WORD,unknown,unknown,unknown
+BDMAP_00003696,word_0064,WORD,unknown,unknown,unknown
+BDMAP_00003697,CPTAC-CCRCC-C3N-00492_0_3,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00003698,PETCT_275fb00a66,autoPET,unknown,unknown,unknown
+BDMAP_00003699,PETCT_bbb83facb4,autoPET,unknown,unknown,unknown
+BDMAP_00003700,TCIAColon_0127_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003701,case_00117,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003702,img0032,BTCV,healthy,healthy,healthy
+BDMAP_00003703,pancreas_097,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003704,Case_00299_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003705,PETCT_1fa22c576e,autoPET,unknown,unknown,unknown
+BDMAP_00003706,Case_00417_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003707,TCIAColon_0151_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003708,TCIAColon_0148_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00003709,PETCT_cd2ef932b5,autoPET,unknown,unknown,unknown
+BDMAP_00003710,word_0098,WORD,unknown,unknown,unknown
+BDMAP_00003711,CPTAC-PDA-C3N-03039_3_2,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003712,amos_0154,AMOS,unknown,unknown,unknown
+BDMAP_00003713,case_00127,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003714,hepaticvessel_202,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003715,PETCT_5e81863014,autoPET,unknown,unknown,unknown
+BDMAP_00003716,amos_0076,AMOS,unknown,unknown,unknown
+BDMAP_00003717,case_00510,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003718,Case_00660_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003719,PETCT_53dad1785b,autoPET,unknown,unknown,unknown
+BDMAP_00003720,amos_0119,AMOS,unknown,unknown,unknown
+BDMAP_00003721,case_00281,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003722,case_00215,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003723,hepaticvessel_031,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003724,Case_00670_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003725,s0486,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003726,TCIAColon_0263_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003727,case_00229,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003728,PETCT_2ad62beb52,autoPET,unknown,unknown,unknown
+BDMAP_00003729,TCIAColon_0099_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003730,PETCT_89fb723947,autoPET,unknown,unknown,unknown
+BDMAP_00003731,amos_0371,AMOS,unknown,unknown,unknown
+BDMAP_00003732,Case_00700_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003733,word_0026,WORD,unknown,unknown,unknown
+BDMAP_00003734,pancreas_425,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003735,case_00178,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003736,case_00091,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003737,s0705,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003738,TCIAColon_0152_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003739,TCIAColon_0277_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003740,case_00003,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003741,pancreas_245,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003742,s0096,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003743,TCIAColon_0167_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003744,pancreas_071,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003745,s0985,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003746,hepaticvessel_374,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003747,amos_0124,AMOS,unknown,unknown,unknown
+BDMAP_00003748,NIH-LYMPH-MED-020_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003749,case_00527,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003750,hepaticvessel_372,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003751,PETCT_9b1bb2165a,autoPET,unknown,unknown,unknown
+BDMAP_00003752,case_00232,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003753,CPTAC-CCRCC-C3N-00380_0_8,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00003754,PETCT_da6161d7eb,autoPET,unknown,unknown,unknown
+BDMAP_00003755,PETCT_e8624bab41,autoPET,unknown,unknown,unknown
+BDMAP_00003756,hepaticvessel_380,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003757,TCIAColon_0081_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003758,PETCT_1928952a0f,autoPET,unknown,unknown,unknown
+BDMAP_00003759,case_00194,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003760,pancreas_082,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003761,s0393,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003762,case_00069,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003763,liver_51,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003764,Case_00808_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003765,s0402,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003766,s0422,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003767,case_00200,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003768,hepaticvessel_298,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003769,pancreas_262,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003770,s0860,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003771,pancreas_049,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003772,TCGA-BLCA-4Z-AA7W_0_4,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00003773,s0845,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003774,case_00588,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003775,amos_0030,AMOS,unknown,unknown,unknown
+BDMAP_00003776,pancreas_194,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003777,CPTAC-UCEC-C3L-01246_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00003778,PETCT_e03b96666f,autoPET,unknown,unknown,unknown
+BDMAP_00003779,TCIAColon_0255_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003780,hepaticvessel_358,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003781,pancreas_122,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003782,PETCT_6e7c7f8087,autoPET,unknown,unknown,unknown
+BDMAP_00003783,TCIAColon_0136_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003784,pancreas_352,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003785,PETCT_b0e002e974,autoPET,unknown,unknown,unknown
+BDMAP_00003786,PETCT_3c71e8f518,autoPET,unknown,unknown,unknown
+BDMAP_00003787,PETCT_c2b776974a,autoPET,unknown,unknown,unknown
+BDMAP_00003788,hepaticvessel_085,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003789,pancreas_008,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003790,TCIAColon_0144_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003791,amos_0188,AMOS,unknown,unknown,unknown
+BDMAP_00003792,colon_186,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003793,spleen_47,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003794,s0551,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003795,spleen_20,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003796,PETCT_ca29501275,autoPET,unknown,unknown,unknown
+BDMAP_00003797,TCIAColon_0017_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003798,liver_30,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003799,amos_0366,AMOS,unknown,unknown,unknown
+BDMAP_00003800,s0057,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003801,PETCT_69aa8dbfd2,autoPET,unknown,unknown,unknown
+BDMAP_00003802,NIH-LYMPH-MED-022_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003803,TCIAColon_0091_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003804,spleen_19,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003805,s0212,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003806,case_00274,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003807,Case_00985_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003808,case_00236,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003809,pancreas_148,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003810,Case_00051_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003811,case_00176,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003812,pancreas_157,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003813,CPTAC-PDA-C3N-03000_0_2,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003814,img0067,BTCV,healthy,healthy,healthy
+BDMAP_00003815,case_00097,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003816,Case_00407_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003817,TCIAColon_0234_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003818,amos_0043,AMOS,unknown,unknown,unknown
+BDMAP_00003819,amos_0171,AMOS,unknown,unknown,unknown
+BDMAP_00003820,liver_133,MSD-Liver,unknown,unknown,unknown
+BDMAP_00003821,img0068,BTCV,healthy,healthy,healthy
+BDMAP_00003822,pancreas_167,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003823,case_00030,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003824,pancreas_350,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003825,CPTAC-CCRCC-C3L-01459_1_4,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00003826,TCIAColon_0231_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003827,colon_207,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003828,CPTAC-CCRCC-C3N-00435_0_3,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00003829,hepaticvessel_407,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003830,img0070,BTCV,healthy,healthy,healthy
+BDMAP_00003831,hepaticvessel_192,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003832,case_00164,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003833,case_00402,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003834,amos_0381,AMOS,unknown,unknown,unknown
+BDMAP_00003835,hepaticvessel_320,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003836,TCIAColon_0085_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00003837,s0986,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003838,PETCT_f75d25523c,autoPET,unknown,unknown,unknown
+BDMAP_00003839,PETCT_87afc291ec,autoPET,unknown,unknown,unknown
+BDMAP_00003840,pancreas_378,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003841,PETCT_5060603ba4,autoPET,unknown,unknown,unknown
+BDMAP_00003842,s0806,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003843,TCIAColon_0220_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003844,lung_042,MSD-Lung,unknown,unknown,unknown
+BDMAP_00003845,amos_0196,AMOS,unknown,unknown,unknown
+BDMAP_00003846,PETCT_d27451634d,autoPET,unknown,unknown,unknown
+BDMAP_00003847,colon_025,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003848,PETCT_5d10be5b89,autoPET,unknown,unknown,unknown
+BDMAP_00003849,case_00223,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003850,TCIAColon_0300_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00003851,case_00131,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003852,PETCT_515c40c4a6,autoPET,unknown,unknown,unknown
+BDMAP_00003853,pancreas_094,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003854,TCIAColon_0202_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003855,Case_00012_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003856,TCIAColon_0095_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003857,liver_84,LiTS,unhealthy,unknown,unknown
+BDMAP_00003858,img0003,BTCV,healthy,healthy,healthy
+BDMAP_00003859,PETCT_3fd361ec14,autoPET,unknown,unknown,unknown
+BDMAP_00003860,PETCT_ae6a37a9d6,autoPET,unknown,unknown,unknown
+BDMAP_00003861,PETCT_89065bc4ab,autoPET,unknown,unknown,unknown
+BDMAP_00003862,Case_00207_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003863,liver_147,MSD-Liver,unknown,unknown,unknown
+BDMAP_00003864,PETCT_bca97006e4,autoPET,unknown,unknown,unknown
+BDMAP_00003865,TCIAColon_0118_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003866,s1278,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003867,liver_49,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003868,spleen_61,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003869,case_00116,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003870,Case_00787_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003871,s0863,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003872,hepaticvessel_363,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003873,NIH-LYMPH-ABD-069_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003874,TCIAColon_0238_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003875,s0574,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003876,PANCREAS_0008,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00003877,volume-129,CT-ORG,unknown,unknown,unknown
+BDMAP_00003878,PETCT_6e1dba94e8,autoPET,unknown,unknown,unknown
+BDMAP_00003879,TCIAColon_0036_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003880,case_00250,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003881,PETCT_e9e1a391b5,autoPET,unknown,unknown,unknown
+BDMAP_00003882,case_00101,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003883,PETCT_d63f6162a6,autoPET,unknown,unknown,unknown
+BDMAP_00003884,pancreas_105,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003885,TCIAColon_0254_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003886,pancreas_181,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003887,s0939,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003888,PETCT_b926177c87,autoPET,unknown,unknown,unknown
+BDMAP_00003889,CPTAC-PDA-C3N-03430_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003890,colon_053,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003891,PETCT_4c75fa4a5d,autoPET,unknown,unknown,unknown
+BDMAP_00003892,s1400,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003893,Case_00295_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003894,TCIAColon_0165_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003895,PETCT_a41d59682f,autoPET,unknown,unknown,unknown
+BDMAP_00003896,TCGA-BLCA-4Z-AA7M_0_2,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00003897,liver_82,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003898,liver_46,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003899,word_0146,WORD,unknown,unknown,unknown
+BDMAP_00003900,pancreas_067,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003901,TCIAColon_0005_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003902,PETCT_3eac8f16d4,autoPET,unknown,unknown,unknown
+BDMAP_00003903,PETCT_870181d7fd,autoPET,unknown,unknown,unknown
+BDMAP_00003904,PETCT_c29aba73da,autoPET,unknown,unknown,unknown
+BDMAP_00003905,Case_00877_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003906,img0021,BTCV,healthy,healthy,healthy
+BDMAP_00003907,TCIAColon_0163_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003908,amos_0297,AMOS,unknown,unknown,unknown
+BDMAP_00003909,CPTAC-PDA-C3N-03039_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00003910,hepaticvessel_127,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003911,liver_12,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003912,TCIAColon_0139_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003913,TCIAColon_0210_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003914,TCIAColon_0159_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003915,PETCT_8b74860ce6,autoPET,unknown,unknown,unknown
+BDMAP_00003916,colon_056,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003917,FLARE23_Ts_0078_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003918,pancreas_084,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003919,NIH-LYMPH-ABD-070_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00003920,liver_27,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00003921,CPTAC-UCEC-C3L-00962_0_3,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00003922,TCIAColon_0149_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003923,case_00042,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003924,PETCT_7fbb39290e,autoPET,unknown,unknown,unknown
+BDMAP_00003925,s1350,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003926,PETCT_99a7bfad23,autoPET,unknown,unknown,unknown
+BDMAP_00003927,word_0065,WORD,unknown,unknown,unknown
+BDMAP_00003928,case_00075,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003929,s0707,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003930,case_00525,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003931,s1383,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003932,liver_60,LiTS,unhealthy,unknown,unknown
+BDMAP_00003933,TCIAColon_0264_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003934,PETCT_48cb8662f7,autoPET,unknown,unknown,unknown
+BDMAP_00003935,TCGA-BLCA-4Z-AA80_0_4,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00003936,TCIAColon_0294_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003937,Case_01045_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003938,PETCT_141fffa2a9,autoPET,unknown,unknown,unknown
+BDMAP_00003939,pancreas_495,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003940,TCGA-OV-61-1998_1_3,TCGA-OV,unknown,unknown,unknown
+BDMAP_00003941,PETCT_147a9fcff3,autoPET,unknown,unknown,unknown
+BDMAP_00003942,hepaticvessel_351,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00003943,case_00572,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003944,TCIAColon_0185_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00003945,TCIAColon_0105_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00003946,colon_175,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003947,case_00498,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003948,PETCT_f2f28337ba,autoPET,unknown,unknown,unknown
+BDMAP_00003949,amos_0149,AMOS,unknown,unknown,unknown
+BDMAP_00003950,spleen_41,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00003951,PETCT_1bdefef7d5,autoPET,unknown,unknown,unknown
+BDMAP_00003952,case_00504,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003953,word_0055,WORD,unknown,unknown,unknown
+BDMAP_00003954,word_0118,WORD,unknown,unknown,unknown
+BDMAP_00003955,case_00477,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003956,case_00550,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003957,case_00120,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003958,pancreas_394,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00003959,Case_00543_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003960,img0039,BTCV,healthy,healthy,healthy
+BDMAP_00003961,pancreas_024,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00003962,TCIAColon_0276_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003963,case_00082,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003964,TCIAColon_0019_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00003965,s0531,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00003966,LDCT-L299_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00003967,amos_0217,AMOS,unknown,unknown,unknown
+BDMAP_00003968,TCIAColon_0287_0_5,TCIAColon,unknown,unknown,unknown
+BDMAP_00003969,volume-138,CT-ORG,unknown,unknown,unknown
+BDMAP_00003970,word_0133,WORD,unknown,unknown,unknown
+BDMAP_00003971,case_00483,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003972,colon_127,MSD-Colon,unknown,unknown,unknown
+BDMAP_00003973,case_00189,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003974,PETCT_a19c2a0c65,autoPET,unknown,unknown,unknown
+BDMAP_00003975,word_0108,WORD,unknown,unknown,unknown
+BDMAP_00003976,case_00543,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003977,PETCT_28a0a0a163,autoPET,unknown,unknown,unknown
+BDMAP_00003978,hepaticvessel_123,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003979,Case_00971_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003980,hepaticvessel_072,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00003981,FLARE23_Ts_0041_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00003982,img0026,BTCV,healthy,healthy,healthy
+BDMAP_00003983,PETCT_ca62984a81,autoPET,unknown,unknown,unknown
+BDMAP_00003984,hepaticvessel_309,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003985,LDCT-L210_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00003986,Case_00426_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00003987,hepaticvessel_097,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00003988,amos_0406,AMOS,unknown,unknown,unknown
+BDMAP_00003989,PETCT_6dd36a2c19,autoPET,unknown,unknown,unknown
+BDMAP_00003990,PETCT_b79961f3f6,autoPET,unknown,unknown,unknown
+BDMAP_00003991,TCIAColon_0093_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00003992,PETCT_792d7b6ad0,autoPET,unknown,unknown,unknown
+BDMAP_00003993,img0069,BTCV,healthy,healthy,healthy
+BDMAP_00003994,case_00007,KiTS21,unknown,unknown,unhealthy
+BDMAP_00003995,TCIAColon_0240_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00003996,case_00415,KiTS23,unknown,unknown,unhealthy
+BDMAP_00003997,PETCT_2f9aec0275,autoPET,unknown,unknown,unknown
+BDMAP_00003998,case_00144,KiTS19,unknown,unknown,unhealthy
+BDMAP_00003999,amos_0033,AMOS,unknown,unknown,unknown
+BDMAP_00004000,PETCT_a61d8768d0,autoPET,unknown,unknown,unknown
+BDMAP_00004001,s1120,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004002,Case_00958_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004003,FLARE23_Ts_0039_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004004,PETCT_ebb0045704,autoPET,unknown,unknown,unknown
+BDMAP_00004005,pancreas_435,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004006,s0785,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004007,hepaticvessel_158,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004008,amos_0011,AMOS,unknown,unknown,unknown
+BDMAP_00004009,TCIAColon_0283_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004010,hepaticvessel_041,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004011,case_00258,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004012,FLARE23_Ts_0030_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004013,hepaticvessel_132,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004014,case_00045,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004015,pancreas_410,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004016,liver_62,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004017,liver_48,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004018,TCIAColon_0222_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004019,s0264,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004020,PETCT_7323c415d0,autoPET,unknown,unknown,unknown
+BDMAP_00004021,TCIAColon_0217_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004022,word_0093,WORD,unknown,unknown,unknown
+BDMAP_00004023,pancreas_191,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004024,hepaticvessel_284,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004025,TCIAColon_0069_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004026,s0979,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004027,case_00003,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004028,liver_46,LiTS,unhealthy,unknown,unknown
+BDMAP_00004029,PETCT_71ac560ffe,autoPET,unknown,unknown,unknown
+BDMAP_00004030,case_00272,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004031,case_00567,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004032,TCIAColon_0040_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004033,case_00034,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004034,hepaticvessel_377,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004035,s1397,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004036,TCIAColon_0173_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004037,volume-47,CT-ORG,unknown,unknown,unknown
+BDMAP_00004038,TCIAColon_0107_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004039,case_00044,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004040,word_0072,WORD,unknown,unknown,unknown
+BDMAP_00004041,Case_00774_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004042,Case_00003_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004043,CPTAC-PDA-C3N-01382_0_8,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004044,img0080,BTCV,healthy,healthy,healthy
+BDMAP_00004045,hepaticvessel_408,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004046,TCIAColon_0087_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004047,s1171,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004048,FLARE23_Ts_0044_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004049,PETCT_a22ec7f62b,autoPET,unknown,unknown,unknown
+BDMAP_00004050,PETCT_fc0389a486,autoPET,unknown,unknown,unknown
+BDMAP_00004051,s1330,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004052,Case_00945_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004053,hepaticvessel_116,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004054,PETCT_80f7760f65,autoPET,unknown,unknown,unknown
+BDMAP_00004055,amos_0081,AMOS,unknown,unknown,unknown
+BDMAP_00004056,s1367,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004057,PETCT_60baa6979c,autoPET,unknown,unknown,unknown
+BDMAP_00004058,PETCT_57c05962c1,autoPET,unknown,unknown,unknown
+BDMAP_00004059,hepaticvessel_237,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004060,pancreas_360,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004061,s1307,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004062,TCIAColon_0238_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004063,TCIAColon_0143_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004064,volume-132,CT-ORG,unknown,unknown,unknown
+BDMAP_00004065,case_00571,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004066,case_00448,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004067,PETCT_c08293f2b2,autoPET,unknown,unknown,unknown
+BDMAP_00004068,amos_0351,AMOS,unknown,unknown,unknown
+BDMAP_00004069,img0022,BTCV,healthy,healthy,healthy
+BDMAP_00004070,TCIAColon_0057_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004071,hepaticvessel_281,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004072,TCIAColon_0061_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004073,PETCT_a1d93ebc74,autoPET,unknown,unknown,unknown
+BDMAP_00004074,case_00562,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004075,Case_00777_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004076,PETCT_73fda3a382,autoPET,unknown,unknown,unknown
+BDMAP_00004077,pancreas_239,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004078,TCIAColon_0125_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004079,pancreas_020,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004080,s1321,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004081,PETCT_eca59074b9,autoPET,unknown,unknown,unknown
+BDMAP_00004082,PETCT_80d7e682f7,autoPET,unknown,unknown,unknown
+BDMAP_00004083,case_00060,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004084,27_image,CHAOS,healthy,healthy,healthy
+BDMAP_00004085,PETCT_411801de5a,autoPET,unknown,unknown,unknown
+BDMAP_00004086,hepaticvessel_112,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004087,case_00404,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004088,6_image,CHAOS,healthy,healthy,healthy
+BDMAP_00004089,TCIAColon_0289_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004090,PETCT_54bed1d98f,autoPET,unknown,unknown,unknown
+BDMAP_00004091,PETCT_a4ff5d0d9d,autoPET,unknown,unknown,unknown
+BDMAP_00004092,TCIAColon_0051_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00004093,hepaticvessel_045,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004094,img0035,BTCV,healthy,healthy,healthy
+BDMAP_00004095,hepaticvessel_251,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004096,TCIAColon_0251_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004097,pancreas_275,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004098,PANCREAS_0016,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00004099,amos_0111,AMOS,unknown,unknown,unknown
+BDMAP_00004100,TCIAColon_0284_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004101,case_00064,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004102,hepaticvessel_293,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004103,liver_74,LiTS,unhealthy,unknown,unknown
+BDMAP_00004104,case_00125,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004105,TCIAColon_0074_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004106,pancreas_402,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004107,word_0122,WORD,unknown,unknown,unknown
+BDMAP_00004108,volume-108,CT-ORG,unknown,unknown,unknown
+BDMAP_00004109,hepaticvessel_052,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004110,amos_0330,AMOS,unknown,unknown,unknown
+BDMAP_00004111,s0106,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004112,PETCT_e4712dc58c,autoPET,unknown,unknown,unknown
+BDMAP_00004113,case_00419,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004114,Case_00984_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004115,pancreas_081,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004116,s0665,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004117,liver_66,LiTS,unhealthy,unknown,unknown
+BDMAP_00004118,CPTAC-PDA-C3N-01502_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004119,hepaticvessel_225,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004120,TCIAColon_0002_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004121,colon_041,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004122,s0928,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004123,PETCT_993d40ea7e,autoPET,unknown,unknown,unknown
+BDMAP_00004124,s0244,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004125,PETCT_9a583160ea,autoPET,unknown,unknown,unknown
+BDMAP_00004126,PETCT_dc3437c9b9,autoPET,unknown,unknown,unknown
+BDMAP_00004127,hepaticvessel_277,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004128,pancreas_145,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004129,pancreas_175,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004130,case_00098,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004131,case_00253,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004132,colon_109,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004133,PETCT_d65cf203be,autoPET,unknown,unknown,unknown
+BDMAP_00004134,TCIAColon_0138_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004135,TCIAColon_0224_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004136,lung_015,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004137,hepaticvessel_387,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004138,img0074,BTCV,healthy,healthy,healthy
+BDMAP_00004139,PETCT_68a7ade33c,autoPET,unknown,unknown,unknown
+BDMAP_00004140,TCIAColon_0076_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004141,PETCT_d9da6129f8,autoPET,unknown,unknown,unknown
+BDMAP_00004142,PANCREAS_0032,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00004143,TCIAColon_0193_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004144,FLARE23_Ts_0051_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004145,PETCT_0c13e4df10,autoPET,unknown,unknown,unknown
+BDMAP_00004146,PETCT_eafa147e24,autoPET,unknown,unknown,unknown
+BDMAP_00004147,colon_006,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004148,case_00085,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004149,PETCT_65bd1a4c38,autoPET,unknown,unknown,unknown
+BDMAP_00004150,case_00063,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004151,amos_0230,AMOS,unknown,unknown,unknown
+BDMAP_00004152,CPTAC-UCEC-C3N-00732_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004153,amos_0237,AMOS,unknown,unknown,unknown
+BDMAP_00004154,case_00469,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004155,s0754,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004156,PETCT_68f73c4518,autoPET,unknown,unknown,unknown
+BDMAP_00004157,PETCT_14f931f634,autoPET,unknown,unknown,unknown
+BDMAP_00004158,case_00146,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004159,pancreas_487,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004160,TCIAColon_0090_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00004161,PETCT_f65602d938,autoPET,unknown,unknown,unknown
+BDMAP_00004162,PETCT_92c3de23f5,autoPET,unknown,unknown,unknown
+BDMAP_00004163,colon_172,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004164,PETCT_d1f456d229,autoPET,unknown,unknown,unknown
+BDMAP_00004165,colon_042,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004166,s0784,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004167,s0315,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004168,amos_0179,AMOS,unknown,unknown,unknown
+BDMAP_00004169,FLARE23_Ts_0021_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004170,hepaticvessel_391,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004171,PETCT_d3d61785e6,autoPET,unknown,unknown,unknown
+BDMAP_00004172,FLARE23_Ts_0026_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004173,PETCT_75e42ff05f,autoPET,unknown,unknown,unknown
+BDMAP_00004174,PETCT_572fca6b44,autoPET,unknown,unknown,unknown
+BDMAP_00004175,case_00157,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004176,amos_0248,AMOS,unknown,unknown,unknown
+BDMAP_00004177,Case_00412_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004178,CPTAC-UCEC-C3N-01003_0_7,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004179,PETCT_ddbb3c69f0,autoPET,unknown,unknown,unknown
+BDMAP_00004180,pancreas_465,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004181,case_00071,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004182,word_0149,WORD,unknown,unknown,unknown
+BDMAP_00004183,pancreas_382,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004184,pancreas_173,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004185,liver_95,LiTS,unhealthy,unknown,unknown
+BDMAP_00004186,TCIAColon_0104_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004187,liver_55,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004188,s1090,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004189,amos_0259,AMOS,unknown,unknown,unknown
+BDMAP_00004190,s0366,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004191,TCIAColon_0279_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00004192,s0650,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004193,CPTAC-CCRCC-C3L-01465_0_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004194,CPTAC-CCRCC-C3L-01954_3_4,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004195,case_00560,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004196,colon_064,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004197,s0753,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004198,pancreas_318,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004199,liver_35,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004200,CPTAC-UCEC-C3N-01172_0_5,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004201,hepaticvessel_390,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004202,PANCREAS_0009,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00004203,TCIAColon_0144_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004204,CPTAC-UCEC-C3N-01003_0_5,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004205,PETCT_4250de48c8,autoPET,unknown,unknown,unknown
+BDMAP_00004206,hepaticvessel_173,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004207,TCIAColon_0185_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004208,CPTAC-CCRCC-C3N-00491_0_2,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004209,Case_00514_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004210,CPTAC-CCRCC-C3L-01352_0_3,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004211,hepaticvessel_345,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004212,case_00224,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004213,word_0143,WORD,unknown,unknown,unknown
+BDMAP_00004214,s0801,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004215,PETCT_08198c4f0c,autoPET,unknown,unknown,unknown
+BDMAP_00004216,case_00518,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004217,FLARE23_Ts_0060_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004218,s0095,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004219,PETCT_ed9fa4eff1,autoPET,unknown,unknown,unknown
+BDMAP_00004220,PETCT_08cdb15e0b,autoPET,unknown,unknown,unknown
+BDMAP_00004221,NIH-LYMPH-MED-011_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00004222,CPTAC-PDA-C3N-03000_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004223,word_0087,WORD,unknown,unknown,unknown
+BDMAP_00004224,Case_00530_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004225,hepaticvessel_315,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004226,PETCT_40f0749cb7,autoPET,unknown,unknown,unknown
+BDMAP_00004227,Case_00661_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004228,liver_117,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004229,pancreas_389,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004230,TCIAColon_0243_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00004231,pancreas_219,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004232,case_00574,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004233,PETCT_e2a729e21f,autoPET,unknown,unknown,unknown
+BDMAP_00004234,word_0044,WORD,unknown,unknown,unknown
+BDMAP_00004235,CPTAC-UCEC-C3N-00743_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004236,Case_00582_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004237,s0916,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004238,PETCT_88bcc970b3,autoPET,unknown,unknown,unknown
+BDMAP_00004239,hepaticvessel_140,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004240,hepaticvessel_452,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004241,PETCT_55e3e62722,autoPET,unknown,unknown,unknown
+BDMAP_00004242,NIH-LYMPH-ABD-051_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00004243,TCIAColon_0171_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00004244,TCIAColon_0241_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004245,s0970,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004246,PETCT_7a262070a9,autoPET,unknown,unknown,unknown
+BDMAP_00004247,s0440,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004248,PETCT_b53ba7c6bf,autoPET,unknown,unknown,unknown
+BDMAP_00004249,TCIAColon_0252_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004250,case_00029,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004251,hepaticvessel_331,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004252,s0445,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004253,case_00180,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004254,s0703,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004255,TCIAColon_0215_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004256,PETCT_185bac7954,autoPET,unknown,unknown,unknown
+BDMAP_00004257,case_00434,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004258,amos_0321,AMOS,unknown,unknown,unknown
+BDMAP_00004259,PETCT_15a205ffcc,autoPET,unknown,unknown,unknown
+BDMAP_00004260,PETCT_04606080a0,autoPET,unknown,unknown,unknown
+BDMAP_00004261,TCIAColon_0122_1_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00004262,pancreas_303,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004263,s0153,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004264,pancreas_229,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004265,case_00257,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004266,amos_0050,AMOS,unknown,unknown,unknown
+BDMAP_00004267,lung_047,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004268,CPTAC-UCEC-C3L-01246_0_3,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004269,LDCT-L232_0_0,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00004270,PETCT_a86b3fad40,autoPET,unknown,unknown,unknown
+BDMAP_00004271,volume-123,CT-ORG,unknown,unknown,unknown
+BDMAP_00004272,TCIAColon_0013_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004273,CPTAC-PDA-C3N-01715_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004274,CPTAC-PDA-C3N-01715_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004275,PETCT_32219760da,autoPET,unknown,unknown,unknown
+BDMAP_00004276,hepaticvessel_395,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004277,CPTAC-UCEC-C3N-00872_0_9,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004278,case_00247,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004279,hepaticvessel_248,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004280,hepaticvessel_302,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004281,liver_26,LiTS,unhealthy,unknown,unknown
+BDMAP_00004282,TCIAColon_0052_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004283,PETCT_249c02c01c,autoPET,unknown,unknown,unknown
+BDMAP_00004284,PETCT_302613f9a5,autoPET,unknown,unknown,unknown
+BDMAP_00004285,PETCT_4a0d2e5166,autoPET,unknown,unknown,unknown
+BDMAP_00004286,pancreas_234,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004287,lung_041,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004288,pancreas_351,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004289,CPTAC-CCRCC-C3L-00610_0_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004290,FLARE23_Ts_0093_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004291,PETCT_c6730783de,autoPET,unknown,unknown,unknown
+BDMAP_00004292,s0555,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004293,pancreas_395,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004294,case_00006,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004295,pancreas_178,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004296,case_00578,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004297,pancreas_066,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004298,PETCT_cbbc9e2879,autoPET,unknown,unknown,unknown
+BDMAP_00004299,TCIAColon_0271_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004300,s1157,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004301,CPTAC-CCRCC-C3N-00380_0_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004302,TCIAColon_0164_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004303,case_00078,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004304,liver_80,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004305,Case_00049_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004306,FLARE23_Ts_0068_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004307,pancreas_257,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004308,1_image,CHAOS,healthy,healthy,healthy
+BDMAP_00004309,TCIAColon_0221_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004310,TCGA-OV-61-1907_1_1,TCGA-OV,unknown,unknown,unknown
+BDMAP_00004311,hepaticvessel_427,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004312,PETCT_b42adab18e,autoPET,unknown,unknown,unknown
+BDMAP_00004313,TCIAColon_0265_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004314,s0058,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004315,case_00299,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004316,PETCT_20995a0fe1,autoPET,unknown,unknown,unknown
+BDMAP_00004317,case_00073,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004318,PETCT_ccd8f5ff71,autoPET,unknown,unknown,unknown
+BDMAP_00004319,PETCT_29ab45ef17,autoPET,unknown,unknown,unknown
+BDMAP_00004320,PETCT_f144b214af,autoPET,unknown,unknown,unknown
+BDMAP_00004321,NIH-LYMPH-ABD-042_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00004322,PETCT_b66ba83594,autoPET,unknown,unknown,unknown
+BDMAP_00004323,Case_00529_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004324,PETCT_7e56b0b62c,autoPET,unknown,unknown,unknown
+BDMAP_00004325,Case_00957_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004326,liver_171,MSD-Liver,unknown,unknown,unknown
+BDMAP_00004327,case_00047,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004328,case_00063,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004329,Case_00432_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004330,amos_0134,AMOS,unknown,unknown,unknown
+BDMAP_00004331,case_00121,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004332,PETCT_119165872d,autoPET,unknown,unknown,unknown
+BDMAP_00004333,amos_0047,AMOS,unknown,unknown,unknown
+BDMAP_00004334,PETCT_a1db71e797,autoPET,unknown,unknown,unknown
+BDMAP_00004335,s1240,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004336,hepaticvessel_344,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004337,PETCT_31ddf5013a,autoPET,unknown,unknown,unknown
+BDMAP_00004338,TCIAColon_0121_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004339,PETCT_3708f576ec,autoPET,unknown,unknown,unknown
+BDMAP_00004340,PETCT_d539f5a88b,autoPET,unknown,unknown,unknown
+BDMAP_00004341,PETCT_6efefcb92a,autoPET,unknown,unknown,unknown
+BDMAP_00004342,TCIAColon_0128_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004343,case_00161,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004344,amos_0125,AMOS,unknown,unknown,unknown
+BDMAP_00004345,PETCT_be3e55a32f,autoPET,unknown,unknown,unknown
+BDMAP_00004346,PETCT_e77c5fca12,autoPET,unknown,unknown,unknown
+BDMAP_00004347,Case_00668_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004348,pancreas_450,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004349,TCGA-OV-61-2016_2_1,TCGA-OV,unknown,unknown,unknown
+BDMAP_00004350,PETCT_78b89bc739,autoPET,unknown,unknown,unknown
+BDMAP_00004351,amos_0276,AMOS,unknown,unknown,unknown
+BDMAP_00004352,PETCT_30c4b7062b,autoPET,unknown,unknown,unknown
+BDMAP_00004353,PETCT_44d6ba6772,autoPET,unknown,unknown,unknown
+BDMAP_00004354,case_00112,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004355,PETCT_72725f2bda,autoPET,unknown,unknown,unknown
+BDMAP_00004356,Case_00827_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004357,FLARE23_Ts_0082_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004358,TCIAColon_0222_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004359,PETCT_f5c9b0de6c,autoPET,unknown,unknown,unknown
+BDMAP_00004360,PANCREAS_0035,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00004361,hepaticvessel_274,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004362,amos_0268,AMOS,unknown,unknown,unknown
+BDMAP_00004363,s0965,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004364,Case_00264_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004365,TCIAColon_0039_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00004366,s1132,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004367,PETCT_0410759456,autoPET,unknown,unknown,unknown
+BDMAP_00004368,CPTAC-PDA-C3N-03039_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004369,hepaticvessel_108,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004370,FLARE23_Ts_0018_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004371,PETCT_1fc35d02da,autoPET,unknown,unknown,unknown
+BDMAP_00004372,PETCT_30001118d0,autoPET,unknown,unknown,unknown
+BDMAP_00004373,case_00019,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004374,colon_218,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004375,hepaticvessel_182,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004376,FLARE23_Ts_0052_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004377,TCIAColon_0125_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004378,liver_73,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004379,Case_00953_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004380,PETCT_cc7cb73213,autoPET,unknown,unknown,unknown
+BDMAP_00004381,PETCT_048981112f,autoPET,unknown,unknown,unknown
+BDMAP_00004382,s1377,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004383,pancreas_202,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004384,colon_061,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004385,FLARE23_Ts_0077_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004386,hepaticvessel_276,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004387,PETCT_93132553f6,autoPET,unknown,unknown,unknown
+BDMAP_00004388,amos_0353,AMOS,unknown,unknown,unknown
+BDMAP_00004389,case_00553,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004390,lung_009,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004391,PETCT_ed7abf4433,autoPET,unknown,unknown,unknown
+BDMAP_00004392,PETCT_efd619ecbf,autoPET,unknown,unknown,unknown
+BDMAP_00004393,PETCT_9b982e72cb,autoPET,unknown,unknown,unknown
+BDMAP_00004394,lung_092,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004395,case_00493,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004396,s0024,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004397,case_00251,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004398,pancreas_207,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004399,TCGA-BLCA-4Z-AA84_0_2,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00004400,hepaticvessel_183,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004401,TCIAColon_0226_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004402,PETCT_55ae7986e1,autoPET,unknown,unknown,unknown
+BDMAP_00004403,volume-68,CT-ORG,unknown,unknown,unknown
+BDMAP_00004404,hepaticvessel_456,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004405,hepaticvessel_399,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004406,TCIAColon_0213_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004407,case_00457,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004408,word_0089,WORD,unknown,unknown,unknown
+BDMAP_00004409,s0201,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004410,Case_00994_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004411,NIH-LYMPH-ABD-067_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00004412,Case_00201_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004413,TCIAColon_0253_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004414,case_00270,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004415,colon_081,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004416,liver_118,LiTS,unhealthy,unknown,unknown
+BDMAP_00004417,case_00289,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004418,img0004,BTCV,healthy,healthy,healthy
+BDMAP_00004419,TCIAColon_0194_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004420,case_00111,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004421,img0006,BTCV,healthy,healthy,healthy
+BDMAP_00004422,Case_00423_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004423,hepaticvessel_242,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004424,TCIAColon_0270_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004425,Case_00824_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004426,PETCT_644d80e987,autoPET,unknown,unknown,unknown
+BDMAP_00004427,case_00475,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004428,Case_01037_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004429,PETCT_16cdbc8689,autoPET,unknown,unknown,unknown
+BDMAP_00004430,TCGA-BLCA-4Z-AA7S_0_1,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00004431,colon_157,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004432,PETCT_2428b44e92,autoPET,unknown,unknown,unknown
+BDMAP_00004433,CPTAC-UCEC-C3L-01732_0_10,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004434,PETCT_71ae114917,autoPET,unknown,unknown,unknown
+BDMAP_00004435,liver_78,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004436,s1283,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004437,case_00219,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004438,PETCT_fde66dd53a,autoPET,unknown,unknown,unknown
+BDMAP_00004439,hepaticvessel_415,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004440,amos_0263,AMOS,unknown,unknown,unknown
+BDMAP_00004441,CPTAC-UCEC-C3N-00872_0_11,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004442,TCIAColon_0020_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004443,s0674,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004444,PETCT_7b42056ee3,autoPET,unknown,unknown,unknown
+BDMAP_00004445,PETCT_9d6e9223cc,autoPET,unknown,unknown,unknown
+BDMAP_00004446,PETCT_20f4a3aa02,autoPET,unknown,unknown,unknown
+BDMAP_00004447,pancreas_401,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004448,PETCT_52f84711a5,autoPET,unknown,unknown,unknown
+BDMAP_00004449,s0683,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004450,colon_159,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004451,TCIAColon_0116_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004452,hepaticvessel_206,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004453,TCIAColon_0212_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004454,PETCT_82137245b0,autoPET,unknown,unknown,unknown
+BDMAP_00004455,Case_01051_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004456,s0271,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004457,case_00465,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004458,PANCREAS_0022,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00004459,liver_72,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004460,CPTAC-CCRCC-C3L-00766_0_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004461,PETCT_78355d1d4f,autoPET,unknown,unknown,unknown
+BDMAP_00004462,liver_25,LiTS,unhealthy,unknown,unknown
+BDMAP_00004463,amos_0215,AMOS,unknown,unknown,unknown
+BDMAP_00004464,PETCT_bd76c8d299,autoPET,unknown,unknown,unknown
+BDMAP_00004465,s0275,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004466,s1331,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004467,case_00157,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004468,spleen_37,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00004469,hepaticvessel_243,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004470,CPTAC-PDA-C3N-01166_0_11,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004471,s0918,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004472,PETCT_2e44706eaf,autoPET,unknown,unknown,unknown
+BDMAP_00004473,Case_00421_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004474,lung_048,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004475,case_00008,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004476,CPTAC-CCRCC-C3L-00812_0_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004477,PETCT_ae8c77a995,autoPET,unknown,unknown,unknown
+BDMAP_00004478,TCIAColon_0172_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004479,case_00169,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004480,PANCREAS_0010,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00004481,case_00282,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004482,case_00535,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004483,PETCT_e5e1918765,autoPET,unknown,unknown,unknown
+BDMAP_00004484,amos_0173,AMOS,unknown,unknown,unknown
+BDMAP_00004485,PETCT_85b69fed1c,autoPET,unknown,unknown,unknown
+BDMAP_00004486,colon_177,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004487,PETCT_a37b4bca43,autoPET,unknown,unknown,unknown
+BDMAP_00004488,TCIAColon_0182_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004489,case_00045,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004490,TCIAColon_0210_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00004491,colon_065,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004492,PETCT_c73c2685c0,autoPET,unknown,unknown,unknown
+BDMAP_00004493,colon_129,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004494,pancreas_367,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004495,case_00098,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004496,hepaticvessel_261,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004497,PETCT_350e91119a,autoPET,unknown,unknown,unknown
+BDMAP_00004498,liver_135,MSD-Liver,unknown,unknown,unknown
+BDMAP_00004499,case_00167,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004500,TCIAColon_0064_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004501,PETCT_4ef69de4e1,autoPET,unknown,unknown,unknown
+BDMAP_00004502,TCIAColon_0187_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00004503,hepaticvessel_213,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004504,word_0020,WORD,unknown,unknown,unknown
+BDMAP_00004505,TCIAColon_0203_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004506,word_0011,WORD,unknown,unknown,unknown
+BDMAP_00004507,hepaticvessel_204,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004508,liver_79,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004509,liver_83,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004510,case_00513,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004511,pancreas_200,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004512,PETCT_68b75093c5,autoPET,unknown,unknown,unknown
+BDMAP_00004513,hepaticvessel_021,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004514,pancreas_196,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004515,s1349,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004516,word_0135,WORD,unknown,unknown,unknown
+BDMAP_00004517,hepaticvessel_419,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004518,TCIAColon_0133_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004519,TCIAColon_0025_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004520,TCIAColon_0179_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004521,TCIAColon_0023_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00004522,TCIAColon_0259_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004523,PETCT_9abb9583ec,autoPET,unknown,unknown,unknown
+BDMAP_00004524,TCIAColon_0191_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004525,liver_162,MSD-Liver,unknown,unknown,unknown
+BDMAP_00004526,PETCT_6016a6c3af,autoPET,unknown,unknown,unknown
+BDMAP_00004527,PETCT_cb1f5e74ab,autoPET,unknown,unknown,unknown
+BDMAP_00004528,LDCT-L220_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00004529,liver_49,LiTS,unhealthy,unknown,unknown
+BDMAP_00004530,PETCT_3d8b79d739,autoPET,unknown,unknown,unknown
+BDMAP_00004531,PETCT_3b2a4af482,autoPET,unknown,unknown,unknown
+BDMAP_00004532,pancreas_460,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004533,hepaticvessel_316,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004534,TCIAColon_0239_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004535,TCIAColon_0280_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004536,amos_0320,AMOS,unknown,unknown,unknown
+BDMAP_00004537,liver_144,MSD-Liver,unknown,unknown,unknown
+BDMAP_00004538,PETCT_59309d6625,autoPET,unknown,unknown,unknown
+BDMAP_00004539,amos_0387,AMOS,unknown,unknown,unknown
+BDMAP_00004540,amos_0307,AMOS,unknown,unknown,unknown
+BDMAP_00004541,pancreas_032,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004542,hepaticvessel_301,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004543,PETCT_ba717a1f22,autoPET,unknown,unknown,unknown
+BDMAP_00004544,TCIAColon_0103_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004545,amos_0131,AMOS,unknown,unknown,unknown
+BDMAP_00004546,pancreas_406,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004547,CPTAC-PDA-C3N-01907_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004548,PETCT_b6a3c72db6,autoPET,unknown,unknown,unknown
+BDMAP_00004549,pancreas_107,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004550,pancreas_300,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004551,case_00032,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004552,case_00468,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004553,Case_00856_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004554,pancreas_489,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004555,TCIAColon_0289_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004556,TCIAColon_0094_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00004557,word_0145,WORD,unknown,unknown,unknown
+BDMAP_00004558,colon_026,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004559,hepaticvessel_306,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004560,word_0025,WORD,unknown,unknown,unknown
+BDMAP_00004561,case_00482,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004562,hepaticvessel_458,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004563,CPTAC-UCEC-C3L-01247_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004564,PETCT_3d6f425a76,autoPET,unknown,unknown,unknown
+BDMAP_00004565,volume-59,CT-ORG,unknown,unknown,unknown
+BDMAP_00004566,hepaticvessel_337,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004567,word_0081,WORD,unknown,unknown,unknown
+BDMAP_00004568,s0708,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004569,pancreas_424,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004570,TCIAColon_0198_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004571,PETCT_86e54ea60a,autoPET,unknown,unknown,unknown
+BDMAP_00004572,s0300,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004573,hepaticvessel_117,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004574,lung_080,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004575,case_00027,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004576,hepaticvessel_246,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004577,hepaticvessel_414,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004578,case_00048,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004579,case_00028,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004580,s0218,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004581,case_00148,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004582,lung_031,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004583,s0483,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004584,TCIAColon_0076_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004585,TCIAColon_0188_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004586,case_00456,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004587,NIH-LYMPH-ABD-053_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00004588,NIH-LYMPH-ABD-003_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00004589,hepaticvessel_056,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004590,volume-61,CT-ORG,unknown,unknown,unknown
+BDMAP_00004591,s1361,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004592,Case_00053_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004593,PETCT_63508c679d,autoPET,unknown,unknown,unknown
+BDMAP_00004594,img0010,BTCV,healthy,healthy,healthy
+BDMAP_00004595,PETCT_a1bc00ad60,autoPET,unknown,unknown,unknown
+BDMAP_00004596,volume-89,CT-ORG,unknown,unknown,unknown
+BDMAP_00004597,case_00488,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004598,hepaticvessel_118,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004599,s0255,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004600,liver_118,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004601,hepaticvessel_429,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004602,Case_00436_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004603,s0053,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004604,colon_091,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004605,TCIAColon_0002_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004606,TCIAColon_0178_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004607,PETCT_c252d734a0,autoPET,unknown,unknown,unknown
+BDMAP_00004608,case_00183,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004609,case_00088,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004610,s0236,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004611,s0452,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004612,Case_01002_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004613,hepaticvessel_055,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004614,word_0012,WORD,unknown,unknown,unknown
+BDMAP_00004615,colon_124,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004616,case_00001,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004617,PETCT_c16c13211c,autoPET,unknown,unknown,unknown
+BDMAP_00004618,hepaticvessel_412,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004619,amos_0379,AMOS,unknown,unknown,unknown
+BDMAP_00004620,case_00114,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004621,PETCT_6543c58e13,autoPET,unknown,unknown,unknown
+BDMAP_00004622,CPTAC-CCRCC-C3N-00435_0_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004623,PETCT_9d97ac5d4d,autoPET,unknown,unknown,unknown
+BDMAP_00004624,colon_088,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004625,PETCT_64aff75516,autoPET,unknown,unknown,unknown
+BDMAP_00004626,s0204,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004627,CPTAC-PDA-C3N-02697_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004628,amos_0137,AMOS,unknown,unknown,unknown
+BDMAP_00004629,PETCT_1e43e3d692,autoPET,unknown,unknown,unknown
+BDMAP_00004630,amos_0127,AMOS,unknown,unknown,unknown
+BDMAP_00004631,s1207,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004632,TCIAColon_0257_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004633,CPTAC-CCRCC-C3L-00766_0_3,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004634,PETCT_ca16242e89,autoPET,unknown,unknown,unknown
+BDMAP_00004635,TCIAColon_0196_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004636,pancreas_292,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004637,s1386,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004638,PETCT_0ea07b421b,autoPET,unknown,unknown,unknown
+BDMAP_00004639,liver_40,LiTS,unhealthy,unknown,unknown
+BDMAP_00004640,Case_00451_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004641,pancreas_179,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004642,s0788,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004643,hepaticvessel_324,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004644,amos_0067,AMOS,unknown,unknown,unknown
+BDMAP_00004645,case_00148,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004646,TCIAColon_0251_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004647,PETCT_04a4e1c874,autoPET,unknown,unknown,unknown
+BDMAP_00004648,PETCT_a09210d96b,autoPET,unknown,unknown,unknown
+BDMAP_00004649,hepaticvessel_044,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004650,colon_033,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004651,pancreas_102,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004652,case_00099,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004653,s0624,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004654,PETCT_e6f6c84ec5,autoPET,unknown,unknown,unknown
+BDMAP_00004655,FLARE23_Ts_0059_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004656,TCIAColon_0242_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004657,PETCT_2922971d0f,autoPET,unknown,unknown,unknown
+BDMAP_00004658,TCIAColon_0177_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004659,PETCT_7b477e7e0d,autoPET,unknown,unknown,unknown
+BDMAP_00004660,word_0078,WORD,unknown,unknown,unknown
+BDMAP_00004661,TCIAColon_0202_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00004662,PETCT_5c35fcbe89,autoPET,unknown,unknown,unknown
+BDMAP_00004663,PETCT_e6a91fef70,autoPET,unknown,unknown,unknown
+BDMAP_00004664,liver_33,LiTS,unhealthy,unknown,unknown
+BDMAP_00004665,LDCT-L299_0_0,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00004666,liver_140,MSD-Liver,unknown,unknown,unknown
+BDMAP_00004667,volume-67,CT-ORG,unknown,unknown,unknown
+BDMAP_00004668,Case_01029_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004669,amos_0296,AMOS,unknown,unknown,unknown
+BDMAP_00004670,TCIAColon_0264_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004671,case_00154,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004672,pancreas_035,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004673,case_00143,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004674,s0334,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004675,PETCT_6a3477cd9a,autoPET,unknown,unknown,unknown
+BDMAP_00004676,colon_045,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004677,PETCT_f603eeceba,autoPET,unknown,unknown,unknown
+BDMAP_00004678,NIH-LYMPH-ABD-078_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00004679,case_00267,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004680,hepaticvessel_353,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004681,amos_0072,AMOS,unknown,unknown,unknown
+BDMAP_00004682,case_00021,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004683,hepaticvessel_297,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004684,amos_0025,AMOS,unknown,unknown,unknown
+BDMAP_00004685,hepaticvessel_361,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004686,CPTAC-CCRCC-C3L-01465_0_2,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004687,CPTAC-CCRCC-C3N-00380_0_10,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004688,PETCT_6c469107a0,autoPET,unknown,unknown,unknown
+BDMAP_00004689,Case_00537_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004690,TCIAColon_0273_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004691,word_0101,WORD,unknown,unknown,unknown
+BDMAP_00004692,Case_01031_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004693,CPTAC-CCRCC-C3L-01459_0_1,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004694,hepaticvessel_356,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004695,pancreas_251,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004696,Case_00820_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004697,NIH-LYMPH-MED-010_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00004698,Case_01049_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004699,PETCT_e44480b9b8,autoPET,unknown,unknown,unknown
+BDMAP_00004700,PETCT_9c68e484a7,autoPET,unknown,unknown,unknown
+BDMAP_00004701,hepaticvessel_080,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004702,hepaticvessel_135,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004703,PETCT_d69eabbb20,autoPET,unknown,unknown,unknown
+BDMAP_00004704,liver_58,LiTS,unhealthy,unknown,unknown
+BDMAP_00004705,PETCT_5b10c3f524,autoPET,unknown,unknown,unknown
+BDMAP_00004706,PETCT_e207c7a1a4,autoPET,unknown,unknown,unknown
+BDMAP_00004707,word_0082,WORD,unknown,unknown,unknown
+BDMAP_00004708,volume-50,CT-ORG,unknown,unknown,unknown
+BDMAP_00004709,PETCT_5f569fb737,autoPET,unknown,unknown,unknown
+BDMAP_00004710,TCIAColon_0288_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004711,TCIAColon_0194_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004712,case_00039,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004713,s1304,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004714,hepaticvessel_143,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004715,amos_0398,AMOS,unknown,unknown,unknown
+BDMAP_00004716,s1155,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004717,case_00239,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004718,TCIAColon_0089_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00004719,pancreas_005,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004720,NIH-LYMPH-ABD-084_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00004721,TCGA-SARC-A5V2_0_1,TCGA-SARC,unknown,unknown,unknown
+BDMAP_00004722,CPTAC-UCEC-C3N-00866_1_4,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004723,TCIAColon_0039_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004724,TCIAColon_0176_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004725,s0499,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004726,PETCT_ac75e49284,autoPET,unknown,unknown,unknown
+BDMAP_00004727,hepaticvessel_446,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004728,TCIAColon_0150_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004729,Case_00959_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004730,PETCT_ed9dca00d2,autoPET,unknown,unknown,unknown
+BDMAP_00004731,PETCT_c16d325d12,autoPET,unknown,unknown,unknown
+BDMAP_00004732,liver_150,MSD-Liver,unknown,unknown,unknown
+BDMAP_00004733,LDCT-L203_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00004734,word_0027,WORD,unknown,unknown,unknown
+BDMAP_00004735,pancreas_051,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004736,CPTAC-PDA-C3N-01166_0_8,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004737,word_0095,WORD,unknown,unknown,unknown
+BDMAP_00004738,case_00056,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004739,pancreas_208,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004740,hepaticvessel_269,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004741,pancreas_182,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004742,PETCT_4c9e7be363,autoPET,unknown,unknown,unknown
+BDMAP_00004743,TCIAColon_0100_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004744,case_00260,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004745,liver_113,LiTS,unhealthy,unknown,unknown
+BDMAP_00004746,case_00061,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004747,PETCT_5cf118ac06,autoPET,unknown,unknown,unknown
+BDMAP_00004748,lung_020,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004749,FLARE23_Ts_0072_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004750,TCIAColon_0201_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004751,hepaticvessel_234,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004752,s0719,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004753,PETCT_0011f3deaf,autoPET,unknown,unknown,unknown
+BDMAP_00004754,word_0126,WORD,unknown,unknown,unknown
+BDMAP_00004755,PETCT_2d70838805,autoPET,unknown,unknown,unknown
+BDMAP_00004756,TCIAColon_0098_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004757,CPTAC-PDA-C3N-01907_0_2,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004758,TCIAColon_0164_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004759,case_00075,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004760,TCIAColon_0297_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004761,Case_00829_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004762,TCIAColon_0070_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004763,PETCT_1285b86bea,autoPET,unknown,unknown,unknown
+BDMAP_00004764,colon_106,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004765,TCIAColon_0112_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004766,hepaticvessel_440,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004767,PETCT_75d1080946,autoPET,unknown,unknown,unknown
+BDMAP_00004768,TCGA-BLCA-CU-A3QU_0_1,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00004769,s0628,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004770,pancreas_061,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004771,Case_00211_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004772,PETCT_402c061122,autoPET,unknown,unknown,unknown
+BDMAP_00004773,pancreas_140,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004774,s0019,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004775,case_00568,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004776,PETCT_bbb2df1e31,autoPET,unknown,unknown,unknown
+BDMAP_00004777,pancreas_499,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004778,s0065,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004779,amos_0370,AMOS,unknown,unknown,unknown
+BDMAP_00004780,Case_00720_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004781,hepaticvessel_007,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004782,lung_028,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004783,colon_143,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004784,PETCT_ff39795341,autoPET,unknown,unknown,unknown
+BDMAP_00004785,PETCT_22d07bdc49,autoPET,unknown,unknown,unknown
+BDMAP_00004786,s0619,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004787,PETCT_099b3fd402,autoPET,unknown,unknown,unknown
+BDMAP_00004788,hepaticvessel_014,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004789,FLARE23_Ts_0003_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004790,lung_034,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004791,s0150,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004792,hepaticvessel_028,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004793,pancreas_289,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004794,PETCT_d393ab6c7b,autoPET,unknown,unknown,unknown
+BDMAP_00004795,s0933,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004796,pancreas_414,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004797,liver_185,MSD-Liver,unknown,unknown,unknown
+BDMAP_00004798,pancreas_497,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004799,s0994,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004800,TCIAColon_0134_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004801,CPTAC-PDA-C3N-01382_0_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004802,hepaticvessel_433,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004803,PETCT_182bdeba22,autoPET,unknown,unknown,unknown
+BDMAP_00004804,pancreas_028,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004805,TCIAColon_0233_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004806,pancreas_324,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004807,hepaticvessel_299,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004808,case_00224,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004809,hepaticvessel_145,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004810,s0585,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004811,TCIAColon_0147_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004812,CPTAC-PDA-C3N-01165_0_5,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004813,hepaticvessel_451,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004814,PETCT_c7fd7e3e30,autoPET,unknown,unknown,unknown
+BDMAP_00004815,case_00203,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004816,PETCT_36bb0257fc,autoPET,unknown,unknown,unknown
+BDMAP_00004817,case_00156,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004818,s0375,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004819,Case_00866_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004820,lung_074,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004821,Case_00715_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004822,pancreas_260,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004823,Case_01013_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004824,NIH-LYMPH-ABD-061_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00004825,liver_115,LiTS,healthy,unknown,unknown
+BDMAP_00004826,word_0111,WORD,unknown,unknown,unknown
+BDMAP_00004827,s1206,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004828,PETCT_ca9570a0eb,autoPET,unknown,unknown,unknown
+BDMAP_00004829,pancreas_305,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004830,liver_120,LiTS,unhealthy,unknown,unknown
+BDMAP_00004831,s0459,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004832,s1110,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004833,s0959,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004834,case_00279,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004835,hepaticvessel_047,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004836,TCIAColon_0290_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004837,TCIAColon_0181_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00004838,Case_00665_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004839,liver_36,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004840,img0005,BTCV,healthy,healthy,healthy
+BDMAP_00004841,pancreas_210,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004842,hepaticvessel_154,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004843,pancreas_357,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004844,case_00076,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004845,TCIAColon_0041_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00004846,s0516,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004847,pancreas_193,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004848,Case_01008_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004849,PETCT_ca5406d339,autoPET,unknown,unknown,unknown
+BDMAP_00004850,case_00218,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004851,TCGA-STAD-A8DT_0_4,TCGA-STAD,unknown,unknown,unknown
+BDMAP_00004852,TCGA-BLCA-4Z-AA7S_0_3,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00004853,TCIAColon_0183_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004854,Case_00213_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004855,liver_120,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004856,PETCT_18e8b02af3,autoPET,unknown,unknown,unknown
+BDMAP_00004857,Case_01048_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004858,pancreas_129,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004859,PETCT_c517c47bed,autoPET,unknown,unknown,unknown
+BDMAP_00004860,hepaticvessel_156,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004861,TCIAColon_0037_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004862,PETCT_e9be8ec30f,autoPET,unknown,unknown,unknown
+BDMAP_00004863,TCIAColon_0168_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004864,s0529,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004865,TCIAColon_0176_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004866,liver_60,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004867,case_00168,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004868,PETCT_c4a686881e,autoPET,unknown,unknown,unknown
+BDMAP_00004869,volume-94,CT-ORG,unknown,unknown,unknown
+BDMAP_00004870,case_00181,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004871,PETCT_bfd89440db,autoPET,unknown,unknown,unknown
+BDMAP_00004872,TCIAColon_0279_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004873,PETCT_154e94256c,autoPET,unknown,unknown,unknown
+BDMAP_00004874,Case_00023_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004875,TCIAColon_0140_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004876,colon_162,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004877,PETCT_61663450f8,autoPET,unknown,unknown,unknown
+BDMAP_00004878,liver_76,LiTS,unhealthy,unknown,unknown
+BDMAP_00004879,PETCT_21853fc15b,autoPET,unknown,unknown,unknown
+BDMAP_00004880,pancreas_056,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004881,PETCT_af547fa618,autoPET,unknown,unknown,unknown
+BDMAP_00004882,FLARE23_Ts_0071_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004883,s0355,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004884,TCIAColon_0045_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004885,pancreas_344,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004886,case_00109,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004887,case_00213,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004888,liver_44,LiTS,unhealthy,unknown,unknown
+BDMAP_00004889,hepaticvessel_349,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004890,liver_29,LiTS,unhealthy,unknown,unknown
+BDMAP_00004891,CPTAC-PDA-C3N-03039_3_1,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004892,CPTAC-CCRCC-C3N-00380_0_3,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00004893,PETCT_cd2ef932b5,autoPET,unknown,unknown,unknown
+BDMAP_00004894,colon_148,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004895,pancreas_249,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004896,liver_151,MSD-Liver,unknown,unknown,unknown
+BDMAP_00004897,case_00423,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004898,Case_00990_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004899,word_0059,WORD,unknown,unknown,unknown
+BDMAP_00004900,s1111,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004901,case_00250,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004902,TCIAColon_0246_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004903,case_00534,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004904,CPTAC-UCEC-C3L-01247_0_2,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004905,s1247,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004906,Case_00818_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004907,PETCT_e0aa99fa04,autoPET,unknown,unknown,unknown
+BDMAP_00004908,PETCT_13f476b02b,autoPET,unknown,unknown,unknown
+BDMAP_00004909,Case_00834_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004910,colon_038,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004911,PETCT_e4749cda88,autoPET,unknown,unknown,unknown
+BDMAP_00004912,amos_0116,AMOS,unknown,unknown,unknown
+BDMAP_00004913,img0027,BTCV,healthy,healthy,healthy
+BDMAP_00004914,PETCT_7365362464,autoPET,unknown,unknown,unknown
+BDMAP_00004915,s0141,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004916,s0462,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004917,pancreas_074,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004918,PETCT_ddca6cfba6,autoPET,unknown,unknown,unknown
+BDMAP_00004919,s1124,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004920,Case_00546_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004921,s0835,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004922,case_00458,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004923,CPTAC-PDA-C3N-01375_0_2,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004924,PETCT_3a4be713a1,autoPET,unknown,unknown,unknown
+BDMAP_00004925,case_00206,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004926,CPTAC-UCEC-C3N-00866_0_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004927,pancreas_086,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004928,Case_00792_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004929,CPTAC-UCEC-C3N-00866_3_3,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00004930,hepaticvessel_392,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004931,Case_00538_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004932,PETCT_3708f576ec,autoPET,unknown,unknown,unknown
+BDMAP_00004933,PETCT_a7b323b3fd,autoPET,unknown,unknown,unknown
+BDMAP_00004934,FLARE23_Ts_0025_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004935,s0403,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004936,s1262,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004937,amos_0075,AMOS,unknown,unknown,unknown
+BDMAP_00004938,hepaticvessel_305,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004939,TCIAColon_0106_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00004940,FLARE23_Ts_0054_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004941,amos_0226,AMOS,unknown,unknown,unknown
+BDMAP_00004942,FLARE23_Ts_0012_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004943,liver_87,LiTS,healthy,unknown,unknown
+BDMAP_00004944,TCIAColon_0145_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004945,Case_00542_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004946,TCIAColon_0010_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004947,PETCT_b52920038c,autoPET,unknown,unknown,unknown
+BDMAP_00004948,PETCT_f24f3ce1da,autoPET,unknown,unknown,unknown
+BDMAP_00004949,TCIAColon_0003_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004950,colon_178,MSD-Colon,unknown,unknown,unknown
+BDMAP_00004951,PETCT_b04be2846e,autoPET,unknown,unknown,unknown
+BDMAP_00004952,PETCT_b1d6679284,autoPET,unknown,unknown,unknown
+BDMAP_00004953,24_image,CHAOS,healthy,healthy,healthy
+BDMAP_00004954,pancreas_242,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004955,hepaticvessel_335,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004956,case_00486,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004957,TCIAColon_0075_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004958,TCIAColon_0208_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004959,hepaticvessel_062,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00004960,lung_051,MSD-Lung,unknown,unknown,unknown
+BDMAP_00004961,s0248,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004962,s0362,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004963,TCIAColon_0108_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00004964,pancreas_093,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004965,pancreas_060,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004966,hepaticvessel_170,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004967,Case_01026_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00004968,case_00140,KiTS19,unknown,unknown,unhealthy
+BDMAP_00004969,pancreas_043,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004970,PETCT_f082a3d319,autoPET,unknown,unknown,unknown
+BDMAP_00004971,hepaticvessel_030,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004972,TCIAColon_0156_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00004973,PETCT_8fea5f0537,autoPET,unknown,unknown,unknown
+BDMAP_00004974,hepaticvessel_346,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00004975,PETCT_f8aac0e202,autoPET,unknown,unknown,unknown
+BDMAP_00004976,hepaticvessel_235,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004977,s1131,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004978,PETCT_a2676f03c0,autoPET,unknown,unknown,unknown
+BDMAP_00004979,FLARE23_Ts_0049_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00004980,case_00267,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004981,case_00495,KiTS23,unknown,unknown,unhealthy
+BDMAP_00004982,CPTAC-PDA-C3N-01715_0_4,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00004983,TCIAColon_0142_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00004984,volume-136,CT-ORG,unknown,unknown,unknown
+BDMAP_00004985,s0406,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00004986,PETCT_556b56485a,autoPET,unknown,unknown,unknown
+BDMAP_00004987,liver_139,MSD-Liver,unknown,unknown,unknown
+BDMAP_00004988,pancreas_151,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00004989,PETCT_0fa313309d,autoPET,unknown,unknown,unknown
+BDMAP_00004990,case_00132,KiTS21,unknown,unknown,unhealthy
+BDMAP_00004991,volume-98,CT-ORG,unknown,unknown,unknown
+BDMAP_00004992,pancreas_279,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00004993,hepaticvessel_307,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00004994,PETCT_df2e16771a,autoPET,unknown,unknown,unknown
+BDMAP_00004995,liver_63,LiTS,unhealthy,unknown,unknown
+BDMAP_00004996,spleen_44,MSD-Spleen,unknown,unknown,unknown
+BDMAP_00004997,amos_0403,AMOS,unknown,unknown,unknown
+BDMAP_00004998,liver_31,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00004999,word_0004,WORD,unknown,unknown,unknown
+BDMAP_00005000,volume-125,CT-ORG,unknown,unknown,unknown
+BDMAP_00005001,colon_205,MSD-Colon,unknown,unknown,unknown
+BDMAP_00005002,CPTAC-PDA-C3N-03426_0_5,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00005003,TCIAColon_0249_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00005004,Case_01007_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005005,colon_089,MSD-Colon,unknown,unknown,unknown
+BDMAP_00005006,case_00252,KiTS21,unknown,unknown,unhealthy
+BDMAP_00005007,PETCT_631b419cef,autoPET,unknown,unknown,unknown
+BDMAP_00005008,pancreas_433,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00005009,case_00430,KiTS23,unknown,unknown,unhealthy
+BDMAP_00005010,PETCT_a25083ff81,autoPET,unknown,unknown,unknown
+BDMAP_00005011,hepaticvessel_367,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00005012,colon_004,MSD-Colon,unknown,unknown,unknown
+BDMAP_00005013,word_0141,WORD,unknown,unknown,unknown
+BDMAP_00005014,hepaticvessel_162,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00005015,TCIAColon_0218_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00005016,colon_039,MSD-Colon,unknown,unknown,unknown
+BDMAP_00005017,case_00435,KiTS23,unknown,unknown,unhealthy
+BDMAP_00005018,PETCT_1ac497ed9d,autoPET,unknown,unknown,unknown
+BDMAP_00005019,s0975,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005020,pancreas_274,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005021,NIH-LYMPH-ABD-010_0_0,NIH-Lymph,unknown,unknown,unknown
+BDMAP_00005022,pancreas_138,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005023,LDCT-L221_0_1,TCIA-LDCT,unknown,unknown,unknown
+BDMAP_00005024,Case_00216_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005025,s0408,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005026,TCIAColon_0159_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00005027,PETCT_76ebd5c736,autoPET,unknown,unknown,unknown
+BDMAP_00005028,word_0058,WORD,unknown,unknown,unknown
+BDMAP_00005029,FLARE23_Ts_0069_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00005030,case_00053,KiTS19,unknown,unknown,unhealthy
+BDMAP_00005031,PETCT_dec9ff6a91,autoPET,unknown,unknown,unknown
+BDMAP_00005032,lung_036,MSD-Lung,unknown,unknown,unknown
+BDMAP_00005033,CPTAC-UCEC-C3N-01008_1_7,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00005034,PETCT_62a78a061f,autoPET,unknown,unknown,unknown
+BDMAP_00005035,amos_0197,AMOS,unknown,unknown,unknown
+BDMAP_00005036,s0192,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005037,pancreas_302,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005038,TCGA-OV-61-2614_0_1,TCGA-OV,unknown,unknown,unknown
+BDMAP_00005039,CPTAC-CCRCC-C3L-01465_0_6,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00005040,FLARE23_Ts_0034_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00005041,TCGA-BLCA-4Z-AA82_0_3,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00005042,CPTAC-UCEC-C3N-00866_3_2,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00005043,PETCT_1774857f8e,autoPET,unknown,unknown,unknown
+BDMAP_00005044,word_0008,WORD,unknown,unknown,unknown
+BDMAP_00005045,s0522,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005046,TCGA-BLCA-DK-A1A6_0_3,TCGA-BLCA,unknown,unknown,unknown
+BDMAP_00005047,colon_090,MSD-Colon,unknown,unknown,unknown
+BDMAP_00005048,s0431,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005049,word_0071,WORD,unknown,unknown,unknown
+BDMAP_00005050,amos_0184,AMOS,unknown,unknown,unknown
+BDMAP_00005051,PETCT_c852dfc0a5,autoPET,unknown,unknown,unknown
+BDMAP_00005052,TCIAColon_0126_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00005053,amos_0242,AMOS,unknown,unknown,unknown
+BDMAP_00005054,liver_189,MSD-Liver,unknown,unknown,unknown
+BDMAP_00005055,CPTAC-CCRCC-C3L-01465_0_4,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00005056,PETCT_185da4c8b6,autoPET,unknown,unknown,unknown
+BDMAP_00005057,hepaticvessel_318,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00005058,lung_073,MSD-Lung,unknown,unknown,unknown
+BDMAP_00005059,FLARE23_Ts_0056_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00005060,FLARE23_Ts_0075_0000,FLARE23Val,unknown,unknown,unknown
+BDMAP_00005061,PETCT_98cd91d44e,autoPET,unknown,unknown,unknown
+BDMAP_00005062,s0812,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005063,pancreas_264,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005064,pancreas_295,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005065,liver_50,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00005066,PETCT_59e6d1de22,autoPET,unknown,unknown,unknown
+BDMAP_00005067,colon_108,MSD-Colon,unknown,unknown,unknown
+BDMAP_00005068,hepaticvessel_179,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00005069,CPTAC-CCRCC-C3L-01958_0_9,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00005070,pancreas_217,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005071,Case_00815_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005072,s0120,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005073,colon_161,MSD-Colon,unknown,unknown,unknown
+BDMAP_00005074,pancreas_021,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005075,pancreas_380,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005076,TCIAColon_0204_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00005077,case_00207,KiTS21,unknown,unknown,unhealthy
+BDMAP_00005078,pancreas_283,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005079,Case_00249_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005080,hepaticvessel_185,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00005081,case_00439,KiTS23,unknown,unknown,unhealthy
+BDMAP_00005082,hepaticvessel_126,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00005083,liver_1,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00005084,hepaticvessel_149,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00005085,case_00146,KiTS21,unknown,unknown,unhealthy
+BDMAP_00005086,Case_00753_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005087,TCIAColon_0034_0_1,TCIAColon,unknown,unknown,unknown
+BDMAP_00005088,PETCT_0e9a98ecda,autoPET,unknown,unknown,unknown
+BDMAP_00005089,s1379,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005090,hepaticvessel_444,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00005091,PETCT_f11f3d3692,autoPET,unknown,unknown,unknown
+BDMAP_00005092,pancreas_025,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005093,Case_00064_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005094,PETCT_a37c4e231f,autoPET,unknown,unknown,unknown
+BDMAP_00005095,PETCT_07b7e9abfc,autoPET,unknown,unknown,unknown
+BDMAP_00005096,s0038,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005097,liver_4,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00005098,s0764,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005099,case_00433,KiTS23,unknown,unknown,unhealthy
+BDMAP_00005100,PETCT_7ce196485f,autoPET,unknown,unknown,unknown
+BDMAP_00005101,hepaticvessel_174,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00005102,PETCT_48d5467561,autoPET,unknown,unknown,unknown
+BDMAP_00005103,hepaticvessel_073,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00005104,hepaticvessel_232,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00005105,case_00035,KiTS21,unknown,unknown,unhealthy
+BDMAP_00005106,hepaticvessel_209,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00005107,PETCT_4d7b745a7b,autoPET,unknown,unknown,unknown
+BDMAP_00005108,pancreas_278,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005109,pancreas_163,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00005110,PANCREAS_0020,TCIA-Pancreas-CT,unknown,healthy,unknown
+BDMAP_00005111,TCIAColon_0056_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00005112,hepaticvessel_336,MSD-HepaticVessel,unknown,unknown,unknown
+BDMAP_00005113,case_00136,KiTS21,unknown,unknown,unhealthy
+BDMAP_00005114,case_00023,KiTS21,unknown,unknown,unhealthy
+BDMAP_00005115,s0728,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005116,TCIAColon_0109_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00005117,s0797,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005118,Case_00855_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005119,liver_41,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00005120,case_00570,KiTS23,unknown,unknown,unhealthy
+BDMAP_00005121,Case_00721_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005122,amos_0172,AMOS,unknown,unknown,unknown
+BDMAP_00005123,TCIAColon_0014_0_0,TCIAColon,unknown,unknown,unknown
+BDMAP_00005124,hepaticvessel_352,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00005125,CPTAC-CCRCC-C3N-00492_0_4,TCIA-CPTAC-CCRCC,unknown,unknown,unknown
+BDMAP_00005126,s0913,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005127,Case_00673_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005128,PETCT_bacc741e2c,autoPET,unknown,unknown,unknown
+BDMAP_00005129,TCIAColon_0257_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00005130,liver_88,LiTS,unhealthy,unknown,unknown
+BDMAP_00005131,s0578,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005132,hepaticvessel_218,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00005133,TCIAColon_0035_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00005134,PETCT_c018b45a49,autoPET,unknown,unknown,unknown
+BDMAP_00005135,CPTAC-UCEC-C3N-01171_0_8,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00005136,Case_00734_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005137,PETCT_a57bd6b006,autoPET,unknown,unknown,unknown
+BDMAP_00005138,TCIAColon_0085_0_2,TCIAColon,unknown,unknown,unknown
+BDMAP_00005139,liver_15,MSD-Liver,unhealthy,unknown,unknown
+BDMAP_00005140,pancreas_214,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005141,pancreas_169,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005142,img0065,BTCV,healthy,healthy,healthy
+BDMAP_00005143,TCIAColon_0119_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00005144,word_0150,WORD,unknown,unknown,unknown
+BDMAP_00005145,PETCT_dd68a71e0a,autoPET,unknown,unknown,unknown
+BDMAP_00005146,hepaticvessel_166,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00005147,amos_0395,AMOS,unknown,unknown,unknown
+BDMAP_00005148,case_00042,KiTS19,unknown,unknown,unhealthy
+BDMAP_00005149,PETCT_ca89066e44,autoPET,unknown,unknown,unknown
+BDMAP_00005150,case_00284,KiTS19,unknown,unknown,unhealthy
+BDMAP_00005151,case_00474,KiTS23,unknown,unknown,unhealthy
+BDMAP_00005152,case_00137,KiTS19,unknown,unknown,unhealthy
+BDMAP_00005153,pancreas_314,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00005154,case_00244,KiTS21,unknown,unknown,unhealthy
+BDMAP_00005155,pancreas_166,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005156,PETCT_3f5a9f616f,autoPET,unknown,unknown,unknown
+BDMAP_00005157,case_00290,KiTS21,unknown,unknown,unhealthy
+BDMAP_00005158,TCIAColon_0059_0_4,TCIAColon,unknown,unknown,unknown
+BDMAP_00005159,PETCT_44c04dcf65,autoPET,unknown,unknown,unknown
+BDMAP_00005160,colon_144,MSD-Colon,unknown,unknown,unknown
+BDMAP_00005161,hepaticvessel_308,MSD-Hepaticvessel,unknown,unknown,unknown
+BDMAP_00005162,amos_0158,AMOS,unknown,unknown,unknown
+BDMAP_00005163,pancreas_359,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00005164,s0190,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005165,PETCT_36870de2f2,autoPET,unknown,unknown,unknown
+BDMAP_00005166,case_00275,KiTS19,unknown,unknown,unhealthy
+BDMAP_00005167,case_00422,KiTS23,unknown,unknown,unhealthy
+BDMAP_00005168,liver_20,LiTS,unhealthy,unknown,unknown
+BDMAP_00005169,pancreas_172,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005170,colon_154,MSD-Colon,unknown,unknown,unknown
+BDMAP_00005171,case_00067,KiTS19,unknown,unknown,unhealthy
+BDMAP_00005172,PETCT_94986389d4,autoPET,unknown,unknown,unknown
+BDMAP_00005173,Case_00867_0000,AbdomenCT-1K,unknown,unknown,unknown
+BDMAP_00005174,case_00118,KiTS21,unknown,unknown,unhealthy
+BDMAP_00005175,PETCT_caae8d63f0,autoPET,unknown,unknown,unknown
+BDMAP_00005176,s0686,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005177,TCIAColon_0169_0_3,TCIAColon,unknown,unknown,unknown
+BDMAP_00005178,CPTAC-PDA-C3N-02998_0_3,TCIA-CPTAC-PDA,unknown,unknown,unknown
+BDMAP_00005179,hepaticvessel_360,MSD-Hepatic,unknown,unknown,unknown
+BDMAP_00005180,amos_0015,AMOS,unknown,unknown,unknown
+BDMAP_00005181,s1370,TotalSegmentator,unknown,unknown,unknown
+BDMAP_00005182,PETCT_bdd21f5590,autoPET,unknown,unknown,unknown
+BDMAP_00005183,PETCT_7a8a062ed5,autoPET,unknown,unknown,unknown
+BDMAP_00005184,word_0144,WORD,unknown,unknown,unknown
+BDMAP_00005185,pancreas_048,MSD-Pancreas,unknown,unhealthy,unknown
+BDMAP_00005186,liver_34,LiTS,healthy,unknown,unknown
+BDMAP_00005187,pancreas_417,MSD-Pancreas,unknown,unknown,unknown
+BDMAP_00005188,PETCT_11afab3485,autoPET,unknown,unknown,unknown
+BDMAP_00005189,CPTAC-UCEC-C3N-00866_3_1,TCIA-CPTAC-UCEC,unknown,unknown,unknown
+BDMAP_00005190,colon_151,MSD-Colon,unknown,unknown,unknown
+BDMAP_00005191,case_00110,KiTS21,unknown,unknown,unhealthy
+BDMAP_00005192,case_00279,KiTS19,unknown,unknown,unhealthy
+BDMAP_00005193,TCGA-STAD-A8DZ_0_1,TCGA-STAD,unknown,unknown,unknown
+BDMAP_00005194,PETCT_642d6c78d6,autoPET,unknown,unknown,unknown
+BDMAP_00005195,Case_00233_0000,AbdomenCT-1K,unknown,unknown,unknown
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/gen_json.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/gen_json.py
new file mode 100644
index 0000000000000000000000000000000000000000..02cd2a0c632e0a488f9dd701350bc868f7150013
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/gen_json.py
@@ -0,0 +1,64 @@
+# import os
+# from batchgenerators.utilities.file_and_folder_operations import join, load_json, isfile, save_json, maybe_mkdir_p
+# import nibabel as nib
+
+# val_liver = load_json('/storage/chenqi/cvpr25/annotated_exp/nnUNet_preprocessed/Dataset016_AbdomenAtlas2.0_base_liver/splits_final.json')[0]['val']
+# val_pancreas = load_json('/storage/chenqi/cvpr25/annotated_exp/nnUNet_preprocessed/Dataset017_AbdomenAtlas2.0_base_pancreas/splits_final.json')[0]['val']
+# val_kidney = load_json('/storage/chenqi/cvpr25/annotated_exp/nnUNet_preprocessed/Dataset018_AbdomenAtlas2.0_base_kidney/splits_final.json')[0]['val']
+# val_colon = load_json('/storage/chenqi/code/GEM-3D-ct/data_split/splits_final_colon_bdmap.json')[0]['val']
+# val_eso = load_json('/storage/chenqi/code/GEM-3D-ct/data_split/splits_final_eso_bdmap.json')[0]['val']
+# val_ute = load_json('/storage/chenqi/code/GEM-3D-ct/data_split/splits_final_uterus_bdmap.json')[0]['val']
+
+# all_files = os.listdir('/storage/chenqi/data/our_data/nnUNet_preprocessed/Dataset104_AbdomenAtlas2.0_1102/gt_segmentations')
+# all_files = [i.split('.')[0] for i in all_files]
+# train_liver = list(set(all_files ) - set( val_liver) - set(val_pancreas) - set(val_kidney) - set(val_colon) - set(val_eso) - set(val_ute) )
+# # breakpoint()
+# write_content = []
+
+# valid_train_liver = []
+# for name in train_liver:
+# data = nib.load(os.path.join('/storage/chenqi/data/our_data/nnUNet_preprocessed/Dataset104_AbdomenAtlas2.0_1102/gt_segmentations', name)).get_fdata()
+# if (data.shape[0]==512 and data.shape[1]==512):
+# valid_train_liver.append(name)
+
+# write_content.append({"train":train_liver, "val":val_liver})
+
+# # breakpoint()
+
+# splits_file = 'splits_final.json'
+# save_json(write_content, splits_file)
+
+import os
+from batchgenerators.utilities.file_and_folder_operations import join, load_json, isfile, save_json, maybe_mkdir_p
+import nibabel as nib
+from multiprocessing import Pool, cpu_count
+
+def is_valid_file(name):
+ data = nib.load(os.path.join('/storage/chenqi/data/our_data/nnUNet_preprocessed/Dataset105_AbdomenAtlas2.0_1106/gt_segmentations', name+'.nii.gz')).get_fdata()
+ return name if (data.shape[0] == 512 and data.shape[1] == 512) else None
+
+def main():
+ val_liver = load_json('/storage/chenqi/cvpr25/annotated_exp/nnUNet_preprocessed/Dataset016_AbdomenAtlas2.0_base_liver/splits_final.json')[0]['val']
+ val_pancreas = load_json('/storage/chenqi/cvpr25/annotated_exp/nnUNet_preprocessed/Dataset017_AbdomenAtlas2.0_base_pancreas/splits_final.json')[0]['val']
+ val_kidney = load_json('/storage/chenqi/cvpr25/annotated_exp/nnUNet_preprocessed/Dataset018_AbdomenAtlas2.0_base_kidney/splits_final.json')[0]['val']
+ val_colon = load_json('/storage/chenqi/code/GEM-3D-ct/data_split/splits_final_colon_bdmap.json')[0]['val']
+ val_eso = load_json('/storage/chenqi/code/GEM-3D-ct/data_split/splits_final_eso_bdmap.json')[0]['val']
+ val_ute = load_json('/storage/chenqi/code/GEM-3D-ct/data_split/splits_final_uterus_bdmap.json')[0]['val']
+
+ all_files = os.listdir('/storage/chenqi/data/our_data/nnUNet_preprocessed/Dataset105_AbdomenAtlas2.0_1106/gt_segmentations')
+ all_files = [i.split('.')[0] for i in all_files]
+ train_liver = list(set(all_files) - set(val_liver) - set(val_pancreas) - set(val_kidney) - set(val_colon) - set(val_eso) - set(val_ute))
+
+ with Pool(cpu_count()) as pool:
+ valid_train_liver = pool.map(is_valid_file, train_liver)
+
+ valid_train_liver = [name for name in valid_train_liver if name is not None]
+
+ write_content = [{"train": valid_train_liver, "val": val_liver}]
+
+ splits_file = 'splits_final.json'
+ save_json(write_content, splits_file)
+
+if __name__ == "__main__":
+ main()
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/kidney/splits_final.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/kidney/splits_final.json
new file mode 100644
index 0000000000000000000000000000000000000000..02bf33920adba1f196e03e71feac0c0391ea012b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/kidney/splits_final.json
@@ -0,0 +1,999 @@
+[
+ {
+ "train": [
+ "BDMAP_00000442",
+ "BDMAP_00003811",
+ "BDMAP_00002446",
+ "BDMAP_00003456",
+ "BDMAP_00003614",
+ "BDMAP_00004744",
+ "BDMAP_00000037",
+ "BDMAP_00004838",
+ "BDMAP_00003441",
+ "BDMAP_00002933",
+ "BDMAP_00002834",
+ "BDMAP_00003178",
+ "BDMAP_00001413",
+ "BDMAP_00000129",
+ "BDMAP_00003613",
+ "BDMAP_00000516",
+ "BDMAP_00000039",
+ "BDMAP_00000008",
+ "BDMAP_00003032",
+ "BDMAP_00001443",
+ "BDMAP_00004482",
+ "BDMAP_00003300",
+ "BDMAP_00004039",
+ "BDMAP_00002251",
+ "BDMAP_00000626",
+ "BDMAP_00001128",
+ "BDMAP_00004712",
+ "BDMAP_00000159",
+ "BDMAP_00003364",
+ "BDMAP_00002609",
+ "BDMAP_00002511",
+ "BDMAP_00003619",
+ "BDMAP_00000843",
+ "BDMAP_00003337",
+ "BDMAP_00003638",
+ "BDMAP_00003976",
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+ "BDMAP_00001402",
+ "BDMAP_00003183",
+ "BDMAP_00003250",
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+ "BDMAP_00003083",
+ "BDMAP_00004193",
+ "BDMAP_00001223",
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+ "BDMAP_00003529"
+ ],
+ "val": [
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+ "BDMAP_00003205",
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+ "BDMAP_00004250",
+ "BDMAP_00002453",
+ "BDMAP_00003164",
+ "BDMAP_00004578",
+ "BDMAP_00001735"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/liver/eval.txt b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/liver/eval.txt
new file mode 100644
index 0000000000000000000000000000000000000000..316828ac6df3bb23f044e2c9606b6bdd0e4a2150
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/liver/eval.txt
@@ -0,0 +1,23 @@
+imagesTr/liver_26.nii.gz labelsTr/liver_26.nii.gz
+imagesTr/liver_129.nii.gz labelsTr/liver_129.nii.gz
+imagesTr/liver_29.nii.gz labelsTr/liver_29.nii.gz
+imagesTr/liver_13.nii.gz labelsTr/liver_13.nii.gz
+imagesTr/liver_66.nii.gz labelsTr/liver_66.nii.gz
+imagesTr/liver_42.nii.gz labelsTr/liver_42.nii.gz
+imagesTr/liver_88.nii.gz labelsTr/liver_88.nii.gz
+imagesTr/liver_73.nii.gz labelsTr/liver_73.nii.gz
+imagesTr/liver_58.nii.gz labelsTr/liver_58.nii.gz
+imagesTr/liver_82.nii.gz labelsTr/liver_82.nii.gz
+imagesTr/liver_98.nii.gz labelsTr/liver_98.nii.gz
+imagesTr/liver_76.nii.gz labelsTr/liver_76.nii.gz
+imagesTr/liver_71.nii.gz labelsTr/liver_71.nii.gz
+imagesTr/liver_112.nii.gz labelsTr/liver_112.nii.gz
+imagesTr/liver_128.nii.gz labelsTr/liver_128.nii.gz
+imagesTr/liver_10.nii.gz labelsTr/liver_10.nii.gz
+imagesTr/liver_22.nii.gz labelsTr/liver_22.nii.gz
+imagesTr/liver_19.nii.gz labelsTr/liver_19.nii.gz
+imagesTr/liver_15.nii.gz labelsTr/liver_15.nii.gz
+imagesTr/liver_97.nii.gz labelsTr/liver_97.nii.gz
+imagesTr/liver_61.nii.gz labelsTr/liver_61.nii.gz
+imagesTr/liver_31.nii.gz labelsTr/liver_31.nii.gz
+imagesTr/liver_86.nii.gz labelsTr/liver_86.nii.gz
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/liver/train.txt b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/liver/train.txt
new file mode 100644
index 0000000000000000000000000000000000000000..78e54c074e7dc999e652658019ee6e9a5559276e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/liver/train.txt
@@ -0,0 +1,95 @@
+imagesTr/liver_95.nii.gz labelsTr/liver_95.nii.gz
+imagesTr/liver_120.nii.gz labelsTr/liver_120.nii.gz
+imagesTr/liver_12.nii.gz labelsTr/liver_12.nii.gz
+imagesTr/liver_100.nii.gz labelsTr/liver_100.nii.gz
+imagesTr/liver_50.nii.gz labelsTr/liver_50.nii.gz
+imagesTr/liver_111.nii.gz labelsTr/liver_111.nii.gz
+imagesTr/liver_110.nii.gz labelsTr/liver_110.nii.gz
+imagesTr/liver_108.nii.gz labelsTr/liver_108.nii.gz
+imagesTr/liver_72.nii.gz labelsTr/liver_72.nii.gz
+imagesTr/liver_2.nii.gz labelsTr/liver_2.nii.gz
+imagesTr/liver_65.nii.gz labelsTr/liver_65.nii.gz
+imagesTr/liver_96.nii.gz labelsTr/liver_96.nii.gz
+imagesTr/liver_81.nii.gz labelsTr/liver_81.nii.gz
+imagesTr/liver_11.nii.gz labelsTr/liver_11.nii.gz
+imagesTr/liver_109.nii.gz labelsTr/liver_109.nii.gz
+imagesTr/liver_24.nii.gz labelsTr/liver_24.nii.gz
+imagesTr/liver_92.nii.gz labelsTr/liver_92.nii.gz
+imagesTr/liver_124.nii.gz labelsTr/liver_124.nii.gz
+imagesTr/liver_103.nii.gz labelsTr/liver_103.nii.gz
+imagesTr/liver_57.nii.gz labelsTr/liver_57.nii.gz
+imagesTr/liver_63.nii.gz labelsTr/liver_63.nii.gz
+imagesTr/liver_20.nii.gz labelsTr/liver_20.nii.gz
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+imagesTr/liver_43.nii.gz labelsTr/liver_43.nii.gz
+imagesTr/liver_90.nii.gz labelsTr/liver_90.nii.gz
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+imagesTr/liver_80.nii.gz labelsTr/liver_80.nii.gz
+imagesTr/liver_1.nii.gz labelsTr/liver_1.nii.gz
+imagesTr/liver_8.nii.gz labelsTr/liver_8.nii.gz
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+imagesTr/liver_99.nii.gz labelsTr/liver_99.nii.gz
+imagesTr/liver_3.nii.gz labelsTr/liver_3.nii.gz
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+imagesTr/liver_64.nii.gz labelsTr/liver_64.nii.gz
+imagesTr/liver_93.nii.gz labelsTr/liver_93.nii.gz
+imagesTr/liver_83.nii.gz labelsTr/liver_83.nii.gz
+imagesTr/liver_46.nii.gz labelsTr/liver_46.nii.gz
+imagesTr/liver_79.nii.gz labelsTr/liver_79.nii.gz
+imagesTr/liver_126.nii.gz labelsTr/liver_126.nii.gz
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/pancreas/splits_final.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/pancreas/splits_final.json
new file mode 100644
index 0000000000000000000000000000000000000000..b64da3ca97c3a47ccd27c2b1a38a7365f495dfce
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/pancreas/splits_final.json
@@ -0,0 +1,320 @@
+[
+ {
+ "train": [
+ "BDMAP_00004015",
+ "BDMAP_00000438",
+ "BDMAP_00002815",
+ "BDMAP_00002855",
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+ "BDMAP_00002233",
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+ "BDMAP_00000771",
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+ "BDMAP_00000618",
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+ "BDMAP_00002460",
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+ "BDMAP_00004077",
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+ "BDMAP_00002945",
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+ "BDMAP_00001823",
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+ "BDMAP_00002227",
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+ "BDMAP_00002402",
+ "BDMAP_00004097",
+ "BDMAP_00003853",
+ "BDMAP_00003543",
+ "BDMAP_00003744",
+ "BDMAP_00003400",
+ "BDMAP_00005075",
+ "BDMAP_00002616",
+ "BDMAP_00003244",
+ "BDMAP_00003633",
+ "BDMAP_00002021",
+ "BDMAP_00004773",
+ "BDMAP_00000037",
+ "BDMAP_00002309",
+ "BDMAP_00003356",
+ "BDMAP_00000774",
+ "BDMAP_00005064",
+ "BDMAP_00003385",
+ "BDMAP_00000993",
+ "BDMAP_00005092",
+ "BDMAP_00000715",
+ "BDMAP_00004262",
+ "BDMAP_00004719",
+ "BDMAP_00003168",
+ "BDMAP_00002250",
+ "BDMAP_00004264",
+ "BDMAP_00001632",
+ "BDMAP_00000388",
+ "BDMAP_00004286",
+ "BDMAP_00000154",
+ "BDMAP_00001461",
+ "BDMAP_00000683",
+ "BDMAP_00000309",
+ "BDMAP_00001125",
+ "BDMAP_00001898",
+ "BDMAP_00001845",
+ "BDMAP_00001464",
+ "BDMAP_00003658",
+ "BDMAP_00002844",
+ "BDMAP_00003427",
+ "BDMAP_00004636",
+ "BDMAP_00004129",
+ "BDMAP_00001842",
+ "BDMAP_00001646",
+ "BDMAP_00006043",
+ "BDMAP_00004288",
+ "BDMAP_00003631",
+ "BDMAP_00003440",
+ "BDMAP_00004550",
+ "BDMAP_00005070",
+ "BDMAP_00004829",
+ "BDMAP_00000225",
+ "BDMAP_00004060",
+ "BDMAP_00001092",
+ "BDMAP_00002619",
+ "BDMAP_00000701",
+ "BDMAP_00001247",
+ "BDMAP_00002304",
+ "BDMAP_00008128",
+ "BDMAP_00003376",
+ "BDMAP_00000431",
+ "BDMAP_00004546",
+ "BDMAP_00005898",
+ "BDMAP_00003685",
+ "BDMAP_00004992",
+ "BDMAP_00004954",
+ "BDMAP_00004494",
+ "BDMAP_00001617",
+ "BDMAP_00003886",
+ "BDMAP_00003776",
+ "BDMAP_00000692",
+ "BDMAP_00003840",
+ "BDMAP_00001414",
+ "BDMAP_00003277",
+ "BDMAP_00004969",
+ "BDMAP_00000623",
+ "BDMAP_00004847",
+ "BDMAP_00003590",
+ "BDMAP_00001712",
+ "BDMAP_00004895",
+ "BDMAP_00004651",
+ "BDMAP_00001605",
+ "BDMAP_00004183",
+ "BDMAP_00003918",
+ "BDMAP_00002029",
+ "BDMAP_00001597",
+ "BDMAP_00003612",
+ "BDMAP_00003138",
+ "BDMAP_00000558",
+ "BDMAP_00001905",
+ "BDMAP_00001736",
+ "BDMAP_00005063",
+ "BDMAP_00000391",
+ "BDMAP_00001440",
+ "BDMAP_00003613",
+ "BDMAP_00004841",
+ "BDMAP_00004917",
+ "BDMAP_00003329",
+ "BDMAP_00001331",
+ "BDMAP_00002737",
+ "BDMAP_00000197",
+ "BDMAP_00006472",
+ "BDMAP_00003650",
+ "BDMAP_00003260",
+ "BDMAP_00001564",
+ "BDMAP_00003396",
+ "BDMAP_00001057",
+ "BDMAP_00000462",
+ "BDMAP_00003657",
+ "BDMAP_00002580",
+ "BDMAP_00004541",
+ "BDMAP_00000809",
+ "BDMAP_00004023",
+ "BDMAP_00001637",
+ "BDMAP_00004885",
+ "BDMAP_00004184",
+ "BDMAP_00000855",
+ "BDMAP_00005108",
+ "BDMAP_00003769",
+ "BDMAP_00003215",
+ "BDMAP_00003036",
+ "BDMAP_00002171",
+ "BDMAP_00002298",
+ "BDMAP_00001040",
+ "BDMAP_00001368",
+ "BDMAP_00002758",
+ "BDMAP_00000744",
+ "BDMAP_00006533",
+ "BDMAP_00000542",
+ "BDMAP_00004843",
+ "BDMAP_00001804",
+ "BDMAP_00001806",
+ "BDMAP_00000243",
+ "BDMAP_00009738",
+ "BDMAP_00000882",
+ "BDMAP_00001175",
+ "BDMAP_00005037",
+ "BDMAP_00003319",
+ "BDMAP_00003598",
+ "BDMAP_00002829",
+ "BDMAP_00001754",
+ "BDMAP_00001476",
+ "BDMAP_00000411",
+ "BDMAP_00004514",
+ "BDMAP_00001545",
+ "BDMAP_00004293",
+ "BDMAP_00004927",
+ "BDMAP_00001618",
+ "BDMAP_00002690",
+ "BDMAP_00003130",
+ "BDMAP_00000427",
+ "BDMAP_00003141",
+ "BDMAP_00003483",
+ "BDMAP_00005020",
+ "BDMAP_00001067",
+ "BDMAP_00001523",
+ "BDMAP_00005140",
+ "BDMAP_00002863",
+ "BDMAP_00001649",
+ "BDMAP_00003683",
+ "BDMAP_00002328",
+ "BDMAP_00004295",
+ "BDMAP_00000676",
+ "BDMAP_00001064",
+ "BDMAP_00004793"
+ ],
+ "val": [
+ "BDMAP_00000332",
+ "BDMAP_00004858",
+ "BDMAP_00005155",
+ "BDMAP_00001205",
+ "BDMAP_00004770",
+ "BDMAP_00001361",
+ "BDMAP_00002944",
+ "BDMAP_00003961",
+ "BDMAP_00000430",
+ "BDMAP_00000679",
+ "BDMAP_00003809",
+ "BDMAP_00004115",
+ "BDMAP_00003367",
+ "BDMAP_00002899",
+ "BDMAP_00003771",
+ "BDMAP_00003502",
+ "BDMAP_00001628",
+ "BDMAP_00003884",
+ "BDMAP_00005074",
+ "BDMAP_00003114",
+ "BDMAP_00004741",
+ "BDMAP_00001746",
+ "BDMAP_00002603",
+ "BDMAP_00004128"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final2.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final2.json
new file mode 100644
index 0000000000000000000000000000000000000000..edfc56c1528307820a81411ebf43b8392f23af6e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final2.json
@@ -0,0 +1,1255 @@
+[
+ {
+ "train": [
+ "BDMAP_00000008",
+ "BDMAP_00000023",
+ "BDMAP_00000030",
+ "BDMAP_00000034",
+ "BDMAP_00000036",
+ "BDMAP_00000039",
+ "BDMAP_00000043",
+ "BDMAP_00000044",
+ "BDMAP_00000052",
+ "BDMAP_00000059",
+ "BDMAP_00000062",
+ "BDMAP_00000066",
+ "BDMAP_00000069",
+ "BDMAP_00000084",
+ "BDMAP_00000087",
+ "BDMAP_00000090",
+ "BDMAP_00000091",
+ "BDMAP_00000093",
+ "BDMAP_00000102",
+ "BDMAP_00000104",
+ "BDMAP_00000113",
+ "BDMAP_00000117",
+ "BDMAP_00000122",
+ "BDMAP_00000128",
+ "BDMAP_00000129",
+ "BDMAP_00000132",
+ "BDMAP_00000137",
+ "BDMAP_00000138",
+ "BDMAP_00000139",
+ "BDMAP_00000140",
+ "BDMAP_00000149",
+ "BDMAP_00000152",
+ "BDMAP_00000154",
+ "BDMAP_00000159",
+ "BDMAP_00000161",
+ "BDMAP_00000162",
+ "BDMAP_00000167",
+ "BDMAP_00000176",
+ "BDMAP_00000190",
+ "BDMAP_00000192",
+ "BDMAP_00000197",
+ "BDMAP_00000205",
+ "BDMAP_00000219",
+ "BDMAP_00000225",
+ "BDMAP_00000229",
+ "BDMAP_00000232",
+ "BDMAP_00000233",
+ "BDMAP_00000236",
+ "BDMAP_00000240",
+ "BDMAP_00000241",
+ "BDMAP_00000243",
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+ "BDMAP_00000249",
+ "BDMAP_00000259",
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+ "BDMAP_00000273",
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+ "BDMAP_00000388",
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+ "BDMAP_00002619",
+ "BDMAP_00002626",
+ "BDMAP_00002638",
+ "BDMAP_00002653",
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+ "BDMAP_00002655",
+ "BDMAP_00002656",
+ "BDMAP_00002661",
+ "BDMAP_00002663",
+ "BDMAP_00002665",
+ "BDMAP_00002688",
+ "BDMAP_00002689",
+ "BDMAP_00002690",
+ "BDMAP_00002695",
+ "BDMAP_00002696",
+ "BDMAP_00002699",
+ "BDMAP_00002704",
+ "BDMAP_00002707",
+ "BDMAP_00002708",
+ "BDMAP_00002710",
+ "BDMAP_00002712",
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+ "BDMAP_00002720",
+ "BDMAP_00002728",
+ "BDMAP_00002730",
+ "BDMAP_00002737",
+ "BDMAP_00002739",
+ "BDMAP_00002742",
+ "BDMAP_00002746",
+ "BDMAP_00002748",
+ "BDMAP_00002751",
+ "BDMAP_00002758",
+ "BDMAP_00002760",
+ "BDMAP_00002776",
+ "BDMAP_00002788",
+ "BDMAP_00002791",
+ "BDMAP_00002793",
+ "BDMAP_00002798",
+ "BDMAP_00002803",
+ "BDMAP_00002805",
+ "BDMAP_00002807",
+ "BDMAP_00002815",
+ "BDMAP_00002826",
+ "BDMAP_00002828",
+ "BDMAP_00002829",
+ "BDMAP_00002833",
+ "BDMAP_00002834",
+ "BDMAP_00002841",
+ "BDMAP_00002844",
+ "BDMAP_00002845",
+ "BDMAP_00002846",
+ "BDMAP_00002849",
+ "BDMAP_00002854",
+ "BDMAP_00002855",
+ "BDMAP_00002856",
+ "BDMAP_00002863",
+ "BDMAP_00002870",
+ "BDMAP_00002871",
+ "BDMAP_00002873",
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+ "BDMAP_00002884",
+ "BDMAP_00002889",
+ "BDMAP_00002893",
+ "BDMAP_00002896",
+ "BDMAP_00002902",
+ "BDMAP_00002906",
+ "BDMAP_00002909",
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+ "BDMAP_00002924",
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+ "BDMAP_00002930",
+ "BDMAP_00002933",
+ "BDMAP_00002936",
+ "BDMAP_00002940",
+ "BDMAP_00002942",
+ "BDMAP_00002945",
+ "BDMAP_00002947",
+ "BDMAP_00002948",
+ "BDMAP_00002953",
+ "BDMAP_00002959",
+ "BDMAP_00002981",
+ "BDMAP_00002986",
+ "BDMAP_00002990",
+ "BDMAP_00002991",
+ "BDMAP_00002992",
+ "BDMAP_00003002",
+ "BDMAP_00003017",
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+ "BDMAP_00003034",
+ "BDMAP_00003036",
+ "BDMAP_00003052",
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+ "BDMAP_00003063",
+ "BDMAP_00003070",
+ "BDMAP_00003073",
+ "BDMAP_00003074",
+ "BDMAP_00003109",
+ "BDMAP_00003111",
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+ "BDMAP_00003129",
+ "BDMAP_00003130",
+ "BDMAP_00003133",
+ "BDMAP_00003138",
+ "BDMAP_00003140",
+ "BDMAP_00003141",
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+ "BDMAP_00003168",
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+ "BDMAP_00003250",
+ "BDMAP_00003252",
+ "BDMAP_00003254",
+ "BDMAP_00003255",
+ "BDMAP_00003256",
+ "BDMAP_00003268",
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+ "BDMAP_00003315",
+ "BDMAP_00003319",
+ "BDMAP_00003324",
+ "BDMAP_00003326",
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+ "BDMAP_00003329",
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+ "BDMAP_00003333",
+ "BDMAP_00003343",
+ "BDMAP_00003347",
+ "BDMAP_00003356",
+ "BDMAP_00003357",
+ "BDMAP_00003358",
+ "BDMAP_00003359",
+ "BDMAP_00003361",
+ "BDMAP_00003363",
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+ "BDMAP_00003373",
+ "BDMAP_00003376",
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+ "BDMAP_00003384",
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+ "BDMAP_00003400",
+ "BDMAP_00003406",
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+ "BDMAP_00003425",
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+ "BDMAP_00003435",
+ "BDMAP_00003438",
+ "BDMAP_00003440",
+ "BDMAP_00003441",
+ "BDMAP_00003443",
+ "BDMAP_00003444",
+ "BDMAP_00003447",
+ "BDMAP_00003448",
+ "BDMAP_00003451",
+ "BDMAP_00003455",
+ "BDMAP_00003457",
+ "BDMAP_00003461",
+ "BDMAP_00003465",
+ "BDMAP_00003479",
+ "BDMAP_00003482",
+ "BDMAP_00003483",
+ "BDMAP_00003484",
+ "BDMAP_00003491",
+ "BDMAP_00003493",
+ "BDMAP_00003497",
+ "BDMAP_00003506",
+ "BDMAP_00003510",
+ "BDMAP_00003513",
+ "BDMAP_00003514",
+ "BDMAP_00003516",
+ "BDMAP_00003520",
+ "BDMAP_00003524",
+ "BDMAP_00003525",
+ "BDMAP_00003529",
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+ "BDMAP_00003603",
+ "BDMAP_00003608",
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+ "BDMAP_00003930",
+ "BDMAP_00003932",
+ "BDMAP_00003943",
+ "BDMAP_00003946",
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+ "BDMAP_00003952",
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+ "BDMAP_00003971",
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+ "BDMAP_00004017",
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+ "BDMAP_00004074",
+ "BDMAP_00004077",
+ "BDMAP_00004087",
+ "BDMAP_00004097",
+ "BDMAP_00004101",
+ "BDMAP_00004103",
+ "BDMAP_00004104",
+ "BDMAP_00004106",
+ "BDMAP_00004121",
+ "BDMAP_00004129",
+ "BDMAP_00004130",
+ "BDMAP_00004131",
+ "BDMAP_00004147",
+ "BDMAP_00004148",
+ "BDMAP_00004150",
+ "BDMAP_00004154",
+ "BDMAP_00004158",
+ "BDMAP_00004163",
+ "BDMAP_00004165",
+ "BDMAP_00004175",
+ "BDMAP_00004181",
+ "BDMAP_00004183",
+ "BDMAP_00004184",
+ "BDMAP_00004185",
+ "BDMAP_00004187",
+ "BDMAP_00004195",
+ "BDMAP_00004196",
+ "BDMAP_00004198",
+ "BDMAP_00004199",
+ "BDMAP_00004212",
+ "BDMAP_00004216",
+ "BDMAP_00004228",
+ "BDMAP_00004229",
+ "BDMAP_00004231",
+ "BDMAP_00004232",
+ "BDMAP_00004253",
+ "BDMAP_00004257",
+ "BDMAP_00004262",
+ "BDMAP_00004264",
+ "BDMAP_00004265",
+ "BDMAP_00004278",
+ "BDMAP_00004286",
+ "BDMAP_00004288",
+ "BDMAP_00004293",
+ "BDMAP_00004294",
+ "BDMAP_00004295",
+ "BDMAP_00004296",
+ "BDMAP_00004297",
+ "BDMAP_00004303",
+ "BDMAP_00004304",
+ "BDMAP_00004315",
+ "BDMAP_00004317",
+ "BDMAP_00004327",
+ "BDMAP_00004328",
+ "BDMAP_00004331",
+ "BDMAP_00004343",
+ "BDMAP_00004354",
+ "BDMAP_00004373",
+ "BDMAP_00004374",
+ "BDMAP_00004384",
+ "BDMAP_00004389",
+ "BDMAP_00004395",
+ "BDMAP_00004397",
+ "BDMAP_00004398",
+ "BDMAP_00004407",
+ "BDMAP_00004414",
+ "BDMAP_00004415",
+ "BDMAP_00004416",
+ "BDMAP_00004417",
+ "BDMAP_00004420",
+ "BDMAP_00004427",
+ "BDMAP_00004431",
+ "BDMAP_00004435",
+ "BDMAP_00004437",
+ "BDMAP_00004447",
+ "BDMAP_00004450",
+ "BDMAP_00004457",
+ "BDMAP_00004459",
+ "BDMAP_00004462",
+ "BDMAP_00004467",
+ "BDMAP_00004475",
+ "BDMAP_00004479",
+ "BDMAP_00004481",
+ "BDMAP_00004482",
+ "BDMAP_00004489",
+ "BDMAP_00004491",
+ "BDMAP_00004493",
+ "BDMAP_00004494",
+ "BDMAP_00004495",
+ "BDMAP_00004499",
+ "BDMAP_00004508",
+ "BDMAP_00004509",
+ "BDMAP_00004510",
+ "BDMAP_00004511",
+ "BDMAP_00004514",
+ "BDMAP_00004529",
+ "BDMAP_00004541",
+ "BDMAP_00004546",
+ "BDMAP_00004549",
+ "BDMAP_00004550",
+ "BDMAP_00004552",
+ "BDMAP_00004558",
+ "BDMAP_00004561",
+ "BDMAP_00004575",
+ "BDMAP_00004579",
+ "BDMAP_00004581",
+ "BDMAP_00004586",
+ "BDMAP_00004597",
+ "BDMAP_00004600",
+ "BDMAP_00004604",
+ "BDMAP_00004608",
+ "BDMAP_00004609",
+ "BDMAP_00004615",
+ "BDMAP_00004616",
+ "BDMAP_00004620",
+ "BDMAP_00004624",
+ "BDMAP_00004636",
+ "BDMAP_00004639",
+ "BDMAP_00004641",
+ "BDMAP_00004645",
+ "BDMAP_00004650",
+ "BDMAP_00004651",
+ "BDMAP_00004652",
+ "BDMAP_00004664",
+ "BDMAP_00004671",
+ "BDMAP_00004672",
+ "BDMAP_00004673",
+ "BDMAP_00004676",
+ "BDMAP_00004679",
+ "BDMAP_00004682",
+ "BDMAP_00004712",
+ "BDMAP_00004717",
+ "BDMAP_00004719",
+ "BDMAP_00004735",
+ "BDMAP_00004738",
+ "BDMAP_00004744",
+ "BDMAP_00004745",
+ "BDMAP_00004759",
+ "BDMAP_00004764",
+ "BDMAP_00004773",
+ "BDMAP_00004775",
+ "BDMAP_00004783",
+ "BDMAP_00004793",
+ "BDMAP_00004796",
+ "BDMAP_00004804",
+ "BDMAP_00004808",
+ "BDMAP_00004815",
+ "BDMAP_00004817",
+ "BDMAP_00004825",
+ "BDMAP_00004829",
+ "BDMAP_00004830",
+ "BDMAP_00004834",
+ "BDMAP_00004839",
+ "BDMAP_00004841",
+ "BDMAP_00004843",
+ "BDMAP_00004847",
+ "BDMAP_00004850",
+ "BDMAP_00004855",
+ "BDMAP_00004866",
+ "BDMAP_00004867",
+ "BDMAP_00004870",
+ "BDMAP_00004876",
+ "BDMAP_00004880",
+ "BDMAP_00004885",
+ "BDMAP_00004886",
+ "BDMAP_00004887",
+ "BDMAP_00004888",
+ "BDMAP_00004894",
+ "BDMAP_00004895",
+ "BDMAP_00004897",
+ "BDMAP_00004901",
+ "BDMAP_00004903",
+ "BDMAP_00004910",
+ "BDMAP_00004917",
+ "BDMAP_00004922",
+ "BDMAP_00004925",
+ "BDMAP_00004927",
+ "BDMAP_00004943",
+ "BDMAP_00004954",
+ "BDMAP_00004956",
+ "BDMAP_00004964",
+ "BDMAP_00004968",
+ "BDMAP_00004969",
+ "BDMAP_00004980",
+ "BDMAP_00004981",
+ "BDMAP_00004990",
+ "BDMAP_00004992",
+ "BDMAP_00004995",
+ "BDMAP_00005001",
+ "BDMAP_00005005",
+ "BDMAP_00005006",
+ "BDMAP_00005009",
+ "BDMAP_00005016",
+ "BDMAP_00005017",
+ "BDMAP_00005020",
+ "BDMAP_00005022",
+ "BDMAP_00005037",
+ "BDMAP_00005063",
+ "BDMAP_00005064",
+ "BDMAP_00005065",
+ "BDMAP_00005067",
+ "BDMAP_00005070",
+ "BDMAP_00005073",
+ "BDMAP_00005075",
+ "BDMAP_00005077",
+ "BDMAP_00005078",
+ "BDMAP_00005081",
+ "BDMAP_00005083",
+ "BDMAP_00005085",
+ "BDMAP_00005092",
+ "BDMAP_00005097",
+ "BDMAP_00005099",
+ "BDMAP_00005105",
+ "BDMAP_00005108",
+ "BDMAP_00005113",
+ "BDMAP_00005114",
+ "BDMAP_00005119",
+ "BDMAP_00005120",
+ "BDMAP_00005140",
+ "BDMAP_00005141",
+ "BDMAP_00005148",
+ "BDMAP_00005150",
+ "BDMAP_00005151",
+ "BDMAP_00005152",
+ "BDMAP_00005154",
+ "BDMAP_00005157",
+ "BDMAP_00005160",
+ "BDMAP_00005166",
+ "BDMAP_00005167",
+ "BDMAP_00005168",
+ "BDMAP_00005169",
+ "BDMAP_00005170",
+ "BDMAP_00005174",
+ "BDMAP_00005185",
+ "BDMAP_00005186",
+ "BDMAP_00005191",
+ "BDMAP_00005192"
+ ],
+ "val": [
+ "BDMAP_00000100",
+ "BDMAP_00000101",
+ "BDMAP_00000332",
+ "BDMAP_00000375",
+ "BDMAP_00000430",
+ "BDMAP_00000458",
+ "BDMAP_00000487",
+ "BDMAP_00000505",
+ "BDMAP_00000511",
+ "BDMAP_00000608",
+ "BDMAP_00000615",
+ "BDMAP_00000679",
+ "BDMAP_00000745",
+ "BDMAP_00000794",
+ "BDMAP_00000831",
+ "BDMAP_00000833",
+ "BDMAP_00000921",
+ "BDMAP_00001018",
+ "BDMAP_00001198",
+ "BDMAP_00001205",
+ "BDMAP_00001255",
+ "BDMAP_00001309",
+ "BDMAP_00001352",
+ "BDMAP_00001361",
+ "BDMAP_00001413",
+ "BDMAP_00001628",
+ "BDMAP_00001735",
+ "BDMAP_00001746",
+ "BDMAP_00001786",
+ "BDMAP_00001813",
+ "BDMAP_00001962",
+ "BDMAP_00001981",
+ "BDMAP_00002181",
+ "BDMAP_00002214",
+ "BDMAP_00002242",
+ "BDMAP_00002453",
+ "BDMAP_00002463",
+ "BDMAP_00002474",
+ "BDMAP_00002603",
+ "BDMAP_00002631",
+ "BDMAP_00002648",
+ "BDMAP_00002673",
+ "BDMAP_00002717",
+ "BDMAP_00002744",
+ "BDMAP_00002775",
+ "BDMAP_00002804",
+ "BDMAP_00002840",
+ "BDMAP_00002864",
+ "BDMAP_00002899",
+ "BDMAP_00002931",
+ "BDMAP_00002944",
+ "BDMAP_00002955",
+ "BDMAP_00003083",
+ "BDMAP_00003114",
+ "BDMAP_00003143",
+ "BDMAP_00003150",
+ "BDMAP_00003164",
+ "BDMAP_00003170",
+ "BDMAP_00003205",
+ "BDMAP_00003272",
+ "BDMAP_00003367",
+ "BDMAP_00003439",
+ "BDMAP_00003481",
+ "BDMAP_00003486",
+ "BDMAP_00003502",
+ "BDMAP_00003656",
+ "BDMAP_00003771",
+ "BDMAP_00003809",
+ "BDMAP_00003884",
+ "BDMAP_00003897",
+ "BDMAP_00003961",
+ "BDMAP_00004083",
+ "BDMAP_00004115",
+ "BDMAP_00004117",
+ "BDMAP_00004128",
+ "BDMAP_00004250",
+ "BDMAP_00004281",
+ "BDMAP_00004378",
+ "BDMAP_00004551",
+ "BDMAP_00004578",
+ "BDMAP_00004704",
+ "BDMAP_00004741",
+ "BDMAP_00004746",
+ "BDMAP_00004770",
+ "BDMAP_00004844",
+ "BDMAP_00004858",
+ "BDMAP_00004878",
+ "BDMAP_00004890",
+ "BDMAP_00004998",
+ "BDMAP_00005030",
+ "BDMAP_00005074",
+ "BDMAP_00005130",
+ "BDMAP_00005139",
+ "BDMAP_00005155",
+ "BDMAP_00005171"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_all.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_all.json
new file mode 100644
index 0000000000000000000000000000000000000000..4949d7adb115020e79dcfbaf81ec0f52873f5a2d
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_all.json
@@ -0,0 +1,2159 @@
+[
+ {
+ "train": [
+ "BDMAP_00004015",
+ "BDMAP_00000438",
+ "BDMAP_00002815",
+ "BDMAP_00002855",
+ "BDMAP_00000714",
+ "BDMAP_00003438",
+ "BDMAP_00001214",
+ "BDMAP_00005444",
+ "BDMAP_00001584",
+ "BDMAP_00002232",
+ "BDMAP_00002332",
+ "BDMAP_00003525",
+ "BDMAP_00003592",
+ "BDMAP_00000859",
+ "BDMAP_00000338",
+ "BDMAP_00002947",
+ "BDMAP_00000732",
+ "BDMAP_00002244",
+ "BDMAP_00004297",
+ "BDMAP_00004447",
+ "BDMAP_00002313",
+ "BDMAP_00004672",
+ "BDMAP_00004198",
+ "BDMAP_00008525",
+ "BDMAP_00001704",
+ "BDMAP_00001504",
+ "BDMAP_00000589",
+ "BDMAP_00004231",
+ "BDMAP_00005078",
+ "BDMAP_00003900",
+ "BDMAP_00003508",
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+ "BDMAP_00002185",
+ "BDMAP_00001237",
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+ "BDMAP_00004650",
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+ "BDMAP_00001985",
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+ "BDMAP_00001015",
+ "BDMAP_00014516",
+ "BDMAP_00014627",
+ "BDMAP_00014630",
+ "BDMAP_00014552",
+ "BDMAP_00014557",
+ "BDMAP_00014629",
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+ "BDMAP_00014569",
+ "BDMAP_00014493",
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+ "BDMAP_00014576",
+ "BDMAP_00014581",
+ "BDMAP_00014635",
+ "BDMAP_00014521",
+ "BDMAP_00014506",
+ "BDMAP_00014517",
+ "BDMAP_00014567",
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+ "BDMAP_00014520",
+ "BDMAP_00014582",
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+ "BDMAP_00014496",
+ "BDMAP_00014545",
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+ "BDMAP_00014625",
+ "BDMAP_00014513",
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+ "BDMAP_00014632",
+ "BDMAP_00014541",
+ "BDMAP_00014522",
+ "BDMAP_00014508",
+ "BDMAP_00014578",
+ "BDMAP_00014494",
+ "BDMAP_00014562",
+ "BDMAP_00014534",
+ "BDMAP_00014553",
+ "BDMAP_00014566",
+ "BDMAP_00014542",
+ "BDMAP_00014612",
+ "BDMAP_00014614",
+ "BDMAP_00014637",
+ "BDMAP_00014631",
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+ "BDMAP_00014498",
+ "BDMAP_00014570",
+ "BDMAP_00014624",
+ "BDMAP_00014503",
+ "BDMAP_00014573",
+ "BDMAP_00014546",
+ "BDMAP_00014626",
+ "BDMAP_00014526",
+ "BDMAP_00014488",
+ "BDMAP_00014524",
+ "BDMAP_00014528",
+ "BDMAP_00014617",
+ "BDMAP_00014518",
+ "BDMAP_00014571",
+ "BDMAP_00014551",
+ "BDMAP_00014515",
+ "BDMAP_00014561",
+ "BDMAP_00014616",
+ "BDMAP_00014580",
+ "BDMAP_00014492",
+ "BDMAP_00014514",
+ "BDMAP_00014533",
+ "BDMAP_00014620",
+ "BDMAP_00014558",
+ "BDMAP_00014610",
+ "BDMAP_00014510",
+ "BDMAP_00014512",
+ "BDMAP_00014583",
+ "BDMAP_00014628",
+ "BDMAP_00014579",
+ "BDMAP_00014537",
+ "BDMAP_00014615",
+ "BDMAP_00014568",
+ "BDMAP_00014539",
+ "BDMAP_00014556",
+ "BDMAP_00014491",
+ "BDMAP_00014495",
+ "BDMAP_00014549",
+ "BDMAP_00014554",
+ "BDMAP_00014565",
+ "BDMAP_00014504",
+ "BDMAP_00014485",
+ "BDMAP_00014575",
+ "BDMAP_00014563",
+ "BDMAP_00014574",
+ "BDMAP_00014572",
+ "BDMAP_00014499",
+ "BDMAP_00014559",
+ "BDMAP_00014530",
+ "BDMAP_00014505",
+ "BDMAP_00014532",
+ "BDMAP_00014564",
+ "BDMAP_00014497",
+ "BDMAP_00014538",
+ "BDMAP_00014519",
+ "BDMAP_00014544",
+ "BDMAP_00014555",
+ "BDMAP_00014527",
+ "BDMAP_00014611",
+ "BDMAP_00014577",
+ "BDMAP_00014634",
+ "BDMAP_00014613",
+ "BDMAP_00014584",
+ "BDMAP_00014509",
+ "BDMAP_00014523",
+ "BDMAP_00014543",
+ "BDMAP_00014502",
+ "BDMAP_00014622",
+ "BDMAP_00014540",
+ "BDMAP_00014500",
+ "BDMAP_00014548",
+ "BDMAP_00014529",
+ "BDMAP_00014550",
+ "BDMAP_00014623",
+ "BDMAP_00014487",
+ "BDMAP_00014531",
+ "BDMAP_00014501",
+ "BDMAP_00014484",
+ "BDMAP_00014489",
+ "BDMAP_00014511",
+ "BDMAP_00014560",
+ "BDMAP_00014621",
+ "BDMAP_00022490",
+ "BDMAP_00022418",
+ "BDMAP_00022453",
+ "BDMAP_00022438",
+ "BDMAP_00022413",
+ "BDMAP_00022469",
+ "BDMAP_00022436",
+ "BDMAP_00022443",
+ "BDMAP_00022427",
+ "BDMAP_00022416",
+ "BDMAP_00022421",
+ "BDMAP_00022434",
+ "BDMAP_00022473",
+ "BDMAP_00022440",
+ "BDMAP_00022439",
+ "BDMAP_00022433",
+ "BDMAP_00022446",
+ "BDMAP_00022475",
+ "BDMAP_00022414",
+ "BDMAP_00022431",
+ "BDMAP_00022432",
+ "BDMAP_00022428",
+ "BDMAP_00022481",
+ "BDMAP_00022489",
+ "BDMAP_00022462",
+ "BDMAP_00022437",
+ "BDMAP_00022467",
+ "BDMAP_00022420",
+ "BDMAP_00022474",
+ "BDMAP_00022426",
+ "BDMAP_00022466",
+ "BDMAP_00022463",
+ "BDMAP_00022451",
+ "BDMAP_00022461",
+ "BDMAP_00022472",
+ "BDMAP_00022423",
+ "BDMAP_00022456",
+ "BDMAP_00022455",
+ "BDMAP_00022429",
+ "BDMAP_00022487",
+ "BDMAP_00022419",
+ "BDMAP_00022422",
+ "BDMAP_00022448",
+ "BDMAP_00022415",
+ "BDMAP_00022454",
+ "BDMAP_00022477",
+ "BDMAP_00022444",
+ "BDMAP_00022435",
+ "BDMAP_00022417",
+ "BDMAP_00022478",
+ "BDMAP_00022445",
+ "BDMAP_00022442",
+ "BDMAP_00022465",
+ "BDMAP_00022412",
+ "BDMAP_00022424",
+ "BDMAP_00022458"
+ ],
+ "val": [
+ "BDMAP_00000332",
+ "BDMAP_00004858",
+ "BDMAP_00005155",
+ "BDMAP_00001205",
+ "BDMAP_00004770",
+ "BDMAP_00001361",
+ "BDMAP_00002944",
+ "BDMAP_00003961",
+ "BDMAP_00000430",
+ "BDMAP_00000679",
+ "BDMAP_00003809",
+ "BDMAP_00004115",
+ "BDMAP_00003367",
+ "BDMAP_00002899",
+ "BDMAP_00003771",
+ "BDMAP_00003502",
+ "BDMAP_00001628",
+ "BDMAP_00003884",
+ "BDMAP_00005074",
+ "BDMAP_00003114",
+ "BDMAP_00004741",
+ "BDMAP_00001746",
+ "BDMAP_00002603",
+ "BDMAP_00004128",
+ "BDMAP_00000487",
+ "BDMAP_00002631",
+ "BDMAP_00002744",
+ "BDMAP_00000833",
+ "BDMAP_00002648",
+ "BDMAP_00000375",
+ "BDMAP_00000608",
+ "BDMAP_00001352",
+ "BDMAP_00002775",
+ "BDMAP_00002474",
+ "BDMAP_00000458",
+ "BDMAP_00000511",
+ "BDMAP_00003150",
+ "BDMAP_00000794",
+ "BDMAP_00001255",
+ "BDMAP_00002242",
+ "BDMAP_00003205",
+ "BDMAP_00002181",
+ "BDMAP_00003486",
+ "BDMAP_00004250",
+ "BDMAP_00002453",
+ "BDMAP_00003164",
+ "BDMAP_00004578",
+ "BDMAP_00001735",
+ "BDMAP_00004281",
+ "BDMAP_00003481",
+ "BDMAP_00001786",
+ "BDMAP_00000101",
+ "BDMAP_00001813",
+ "BDMAP_00000615",
+ "BDMAP_00003170",
+ "BDMAP_00004378",
+ "BDMAP_00004704",
+ "BDMAP_00003439",
+ "BDMAP_00002717",
+ "BDMAP_00001981",
+ "BDMAP_00000100",
+ "BDMAP_00001018",
+ "BDMAP_00002214",
+ "BDMAP_00001198",
+ "BDMAP_00001962",
+ "BDMAP_00002463",
+ "BDMAP_00005139",
+ "BDMAP_00000831",
+ "BDMAP_00002955",
+ "BDMAP_00003272",
+ "BDMAP_00000745",
+ "BDMAP_00000138",
+ "BDMAP_00000152",
+ "BDMAP_00000264",
+ "BDMAP_00000345",
+ "BDMAP_00000547",
+ "BDMAP_00001095",
+ "BDMAP_00001212",
+ "BDMAP_00001426",
+ "BDMAP_00001445",
+ "BDMAP_00001598",
+ "BDMAP_00001769",
+ "BDMAP_00001835",
+ "BDMAP_00001875",
+ "BDMAP_00002253",
+ "BDMAP_00002465",
+ "BDMAP_00002612",
+ "BDMAP_00002654",
+ "BDMAP_00003111",
+ "BDMAP_00003564",
+ "BDMAP_00003847",
+ "BDMAP_00003890",
+ "BDMAP_00003946",
+ "BDMAP_00004558",
+ "BDMAP_00004783",
+ "BDMAP_00005170",
+ "BDMAP_00014585",
+ "BDMAP_00014586",
+ "BDMAP_00014587",
+ "BDMAP_00014588",
+ "BDMAP_00014589",
+ "BDMAP_00014590",
+ "BDMAP_00014591",
+ "BDMAP_00014592",
+ "BDMAP_00014593",
+ "BDMAP_00014594",
+ "BDMAP_00014595",
+ "BDMAP_00014596",
+ "BDMAP_00014597",
+ "BDMAP_00014598",
+ "BDMAP_00014599",
+ "BDMAP_00014600",
+ "BDMAP_00014601",
+ "BDMAP_00014602",
+ "BDMAP_00014603",
+ "BDMAP_00014604",
+ "BDMAP_00014605",
+ "BDMAP_00014606",
+ "BDMAP_00014607",
+ "BDMAP_00014608",
+ "BDMAP_00014609",
+ "BDMAP_00022471",
+ "BDMAP_00022449",
+ "BDMAP_00022488",
+ "BDMAP_00022479",
+ "BDMAP_00022486",
+ "BDMAP_00022484",
+ "BDMAP_00022450",
+ "BDMAP_00022468",
+ "BDMAP_00022476",
+ "BDMAP_00022459",
+ "BDMAP_00022447",
+ "BDMAP_00022460",
+ "BDMAP_00022425",
+ "BDMAP_00022470",
+ "BDMAP_00022483",
+ "BDMAP_00022457",
+ "BDMAP_00022430",
+ "BDMAP_00022480",
+ "BDMAP_00022482",
+ "BDMAP_00022452",
+ "BDMAP_00022464",
+ "BDMAP_00022441",
+ "BDMAP_00022485"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_all2.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_all2.json
new file mode 100644
index 0000000000000000000000000000000000000000..2105815a1071a9c5d567c0cf4403c94138e2de89
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_all2.json
@@ -0,0 +1,2208 @@
+[
+ {
+ "train": [
+ "BDMAP_00002376",
+ "BDMAP_00001520",
+ "BDMAP_00014542",
+ "BDMAP_00005168",
+ "BDMAP_00002720",
+ "BDMAP_00000549",
+ "BDMAP_00002221",
+ "BDMAP_00002353",
+ "BDMAP_00014502",
+ "BDMAP_00004345",
+ "BDMAP_00002342",
+ "BDMAP_00004384",
+ "BDMAP_00000978",
+ "BDMAP_00002665",
+ "BDMAP_00000802",
+ "BDMAP_00000980",
+ "BDMAP_00002496",
+ "BDMAP_00002380",
+ "BDMAP_00002881",
+ "BDMAP_00002831",
+ "BDMAP_00022463",
+ "BDMAP_00004415",
+ "BDMAP_00001458",
+ "BDMAP_00003525",
+ "BDMAP_00004359",
+ "BDMAP_00001419",
+ "BDMAP_00000347",
+ "BDMAP_00003152",
+ "BDMAP_00002055",
+ "BDMAP_00001987",
+ "BDMAP_00000336",
+ "BDMAP_00001791",
+ "BDMAP_00001225",
+ "BDMAP_00002051",
+ "BDMAP_00003451",
+ "BDMAP_00004066",
+ "BDMAP_00009779",
+ "BDMAP_00001802",
+ "BDMAP_00014539",
+ "BDMAP_00003168",
+ "BDMAP_00003429",
+ "BDMAP_00002171",
+ "BDMAP_00002260",
+ "BDMAP_00001744",
+ "BDMAP_00003514",
+ "BDMAP_00001640",
+ "BDMAP_00014633",
+ "BDMAP_00000844",
+ "BDMAP_00003508",
+ "BDMAP_00008568",
+ "BDMAP_00005009",
+ "BDMAP_00022416",
+ "BDMAP_00022453",
+ "BDMAP_00002580",
+ "BDMAP_00000848",
+ "BDMAP_00000569",
+ "BDMAP_00003028",
+ "BDMAP_00001950",
+ "BDMAP_00000640",
+ "BDMAP_00003666",
+ "BDMAP_00002661",
+ "BDMAP_00001868",
+ "BDMAP_00000305",
+ "BDMAP_00002444",
+ "BDMAP_00004184",
+ "BDMAP_00004407",
+ "BDMAP_00002117",
+ "BDMAP_00003822",
+ "BDMAP_00022437",
+ "BDMAP_00002396",
+ "BDMAP_00000484",
+ "BDMAP_00002199",
+ "BDMAP_00000023",
+ "BDMAP_00001619",
+ "BDMAP_00000447",
+ "BDMAP_00000643",
+ "BDMAP_00002060",
+ "BDMAP_00002417",
+ "BDMAP_00001322",
+ "BDMAP_00003252",
+ "BDMAP_00001721",
+ "BDMAP_00002930",
+ "BDMAP_00001233",
+ "BDMAP_00000363",
+ "BDMAP_00001920",
+ "BDMAP_00001944",
+ "BDMAP_00001906",
+ "BDMAP_00000480",
+ "BDMAP_00002080",
+ "BDMAP_00001830",
+ "BDMAP_00000062",
+ "BDMAP_00014520",
+ "BDMAP_00004031",
+ "BDMAP_00000462",
+ "BDMAP_00003722",
+ "BDMAP_00001107",
+ "BDMAP_00002501",
+ "BDMAP_00004722",
+ "BDMAP_00004995",
+ "BDMAP_00000117",
+ "BDMAP_00004604",
+ "BDMAP_00004928",
+ "BDMAP_00001690",
+ "BDMAP_00001247",
+ "BDMAP_00002359",
+ "BDMAP_00002167",
+ "BDMAP_00000265",
+ "BDMAP_00000283",
+ "BDMAP_00005030",
+ "BDMAP_00002849",
+ "BDMAP_00004259",
+ "BDMAP_00022446",
+ "BDMAP_00000698",
+ "BDMAP_00002216",
+ "BDMAP_00014562",
+ "BDMAP_00000382",
+ "BDMAP_00002347",
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+ "BDMAP_00002090",
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+ "BDMAP_00003851",
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+ "BDMAP_00001487",
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+ "BDMAP_00001610",
+ "BDMAP_00002558",
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+ "BDMAP_00002730",
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+ "BDMAP_00004194",
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+ "BDMAP_00002942",
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+ "BDMAP_00003759",
+ "BDMAP_00000985",
+ "BDMAP_00001437",
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+ "BDMAP_00001967",
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+ "BDMAP_00000444",
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+ "BDMAP_00014496",
+ "BDMAP_00001040",
+ "BDMAP_00004010",
+ "BDMAP_00000017",
+ "BDMAP_00000297",
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+ "BDMAP_00004546",
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+ "BDMAP_00003587",
+ "BDMAP_00001739",
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+ "BDMAP_00001931",
+ "BDMAP_00000137",
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+ "BDMAP_00000595",
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+ "BDMAP_00001414",
+ "BDMAP_00003059",
+ "BDMAP_00002300",
+ "BDMAP_00014559",
+ "BDMAP_00000338",
+ "BDMAP_00000140",
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+ "BDMAP_00014629",
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+ "BDMAP_00001882",
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+ "BDMAP_00003769",
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+ "BDMAP_00001010",
+ "BDMAP_00014498",
+ "BDMAP_00001391",
+ "BDMAP_00001012",
+ "BDMAP_00004289",
+ "BDMAP_00005119",
+ "BDMAP_00002402",
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+ "BDMAP_00022467",
+ "BDMAP_00004836",
+ "BDMAP_00003740",
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+ "BDMAP_00001281",
+ "BDMAP_00002529",
+ "BDMAP_00001654",
+ "BDMAP_00000536",
+ "BDMAP_00000907",
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+ "BDMAP_00004557",
+ "BDMAP_00003329",
+ "BDMAP_00001560",
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+ "BDMAP_00003455",
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+ "BDMAP_00022489",
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+ "BDMAP_00001514",
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+ "BDMAP_00014524",
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+ "BDMAP_00002870",
+ "BDMAP_00000132",
+ "BDMAP_00004761",
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+ "BDMAP_00000575",
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+ "BDMAP_00000670",
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+ "BDMAP_00004097",
+ "BDMAP_00002480",
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+ "BDMAP_00004608",
+ "BDMAP_00002068",
+ "BDMAP_00000873",
+ "BDMAP_00005065",
+ "BDMAP_00002522",
+ "BDMAP_00002302",
+ "BDMAP_00000353",
+ "BDMAP_00003608",
+ "BDMAP_00000589",
+ "BDMAP_00001289",
+ "BDMAP_00005191",
+ "BDMAP_00002404",
+ "BDMAP_00000243",
+ "BDMAP_00000247",
+ "BDMAP_00000445",
+ "BDMAP_00001852",
+ "BDMAP_00000606",
+ "BDMAP_00005006",
+ "BDMAP_00004091",
+ "BDMAP_00000411",
+ "BDMAP_00002433",
+ "BDMAP_00014625",
+ "BDMAP_00002873",
+ "BDMAP_00001226",
+ "BDMAP_00000774",
+ "BDMAP_00001770",
+ "BDMAP_00004271",
+ "BDMAP_00002841",
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+ "BDMAP_00014553",
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+ "BDMAP_00003613",
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+ "BDMAP_00003176",
+ "BDMAP_00002363",
+ "BDMAP_00000463",
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+ "BDMAP_00001673",
+ "BDMAP_00004311",
+ "BDMAP_00005035",
+ "BDMAP_00001450",
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+ "BDMAP_00002875",
+ "BDMAP_00002283",
+ "BDMAP_00000503",
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+ "BDMAP_00001403",
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+ "BDMAP_00000567",
+ "BDMAP_00001452",
+ "BDMAP_00001876",
+ "BDMAP_00000524",
+ "BDMAP_00002815",
+ "BDMAP_00002163",
+ "BDMAP_00004454",
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+ "BDMAP_00003569",
+ "BDMAP_00002441",
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+ "BDMAP_00003083",
+ "BDMAP_00014549",
+ "BDMAP_00003130",
+ "BDMAP_00014568",
+ "BDMAP_00004131",
+ "BDMAP_00006507",
+ "BDMAP_00002200",
+ "BDMAP_00002107",
+ "BDMAP_00000887",
+ "BDMAP_00002114",
+ "BDMAP_00004039",
+ "BDMAP_00001068",
+ "BDMAP_00003448",
+ "BDMAP_00004093",
+ "BDMAP_00001115",
+ "BDMAP_00003656",
+ "BDMAP_00003880",
+ "BDMAP_00001270",
+ "BDMAP_00004117",
+ "BDMAP_00014570",
+ "BDMAP_00002538",
+ "BDMAP_00003024",
+ "BDMAP_00000129",
+ "BDMAP_00000729",
+ "BDMAP_00001704",
+ "BDMAP_00000557",
+ "BDMAP_00003482",
+ "BDMAP_00014533",
+ "BDMAP_00000532",
+ "BDMAP_00000724",
+ "BDMAP_00004370",
+ "BDMAP_00002115",
+ "BDMAP_00001919",
+ "BDMAP_00002159",
+ "BDMAP_00001834",
+ "BDMAP_00002519",
+ "BDMAP_00001130",
+ "BDMAP_00005752",
+ "BDMAP_00001568",
+ "BDMAP_00003516",
+ "BDMAP_00001533",
+ "BDMAP_00002462",
+ "BDMAP_00000520",
+ "BDMAP_00001331",
+ "BDMAP_00003557",
+ "BDMAP_00003928",
+ "BDMAP_00002742",
+ "BDMAP_00001223",
+ "BDMAP_00002074",
+ "BDMAP_00022472",
+ "BDMAP_00003581",
+ "BDMAP_00014527",
+ "BDMAP_00003189",
+ "BDMAP_00004265",
+ "BDMAP_00002537",
+ "BDMAP_00001161",
+ "BDMAP_00003333",
+ "BDMAP_00004354",
+ "BDMAP_00003335",
+ "BDMAP_00000413",
+ "BDMAP_00003135",
+ "BDMAP_00004028",
+ "BDMAP_00014517",
+ "BDMAP_00002787",
+ "BDMAP_00000066",
+ "BDMAP_00005106",
+ "BDMAP_00003704",
+ "BDMAP_00014541",
+ "BDMAP_00001536",
+ "BDMAP_00022414",
+ "BDMAP_00002704",
+ "BDMAP_00004151",
+ "BDMAP_00003456",
+ "BDMAP_00001679",
+ "BDMAP_00001138",
+ "BDMAP_00002393",
+ "BDMAP_00002089",
+ "BDMAP_00000579",
+ "BDMAP_00001169",
+ "BDMAP_00003626",
+ "BDMAP_00000710",
+ "BDMAP_00001712"
+ ],
+ "val": [
+ "BDMAP_00004281",
+ "BDMAP_00003481",
+ "BDMAP_00001786",
+ "BDMAP_00000101",
+ "BDMAP_00001813",
+ "BDMAP_00000615",
+ "BDMAP_00003170",
+ "BDMAP_00004378",
+ "BDMAP_00004704",
+ "BDMAP_00003439",
+ "BDMAP_00002717",
+ "BDMAP_00001981",
+ "BDMAP_00000100",
+ "BDMAP_00001018",
+ "BDMAP_00002214",
+ "BDMAP_00001198",
+ "BDMAP_00001962",
+ "BDMAP_00002463",
+ "BDMAP_00005139",
+ "BDMAP_00000831",
+ "BDMAP_00002955",
+ "BDMAP_00003272",
+ "BDMAP_00000745"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_all3.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_all3.json
new file mode 100644
index 0000000000000000000000000000000000000000..2971ee50af7d5ec33c994e2d9b4ef94578e30aa1
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_all3.json
@@ -0,0 +1,2394 @@
+[
+ {
+ "train": [
+ "BDMAP_00014634",
+ "BDMAP_00014562",
+ "BDMAP_00001263",
+ "BDMAP_00004031",
+ "BDMAP_00003363",
+ "BDMAP_00005097",
+ "BDMAP_00001265",
+ "BDMAP_00001343",
+ "BDMAP_00001223",
+ "BDMAP_00001328",
+ "BDMAP_00008526",
+ "BDMAP_00002986",
+ "BDMAP_00001580",
+ "BDMAP_00004796",
+ "BDMAP_00001794",
+ "BDMAP_00002504",
+ "BDMAP_00000439",
+ "BDMAP_00000301",
+ "BDMAP_00004311",
+ "BDMAP_00001383",
+ "BDMAP_00000812",
+ "BDMAP_00003727",
+ "BDMAP_00002318",
+ "BDMAP_00014516",
+ "BDMAP_00003682",
+ "BDMAP_00000098",
+ "BDMAP_00002458",
+ "BDMAP_00003658",
+ "BDMAP_00000713",
+ "BDMAP_00000271",
+ "BDMAP_00014565",
+ "BDMAP_00001185",
+ "BDMAP_00000122",
+ "BDMAP_00002402",
+ "BDMAP_00003763",
+ "BDMAP_00001024",
+ "BDMAP_00003713",
+ "BDMAP_00003556",
+ "BDMAP_00003543",
+ "BDMAP_00004389",
+ "BDMAP_00000319",
+ "BDMAP_00000038",
+ "BDMAP_00014523",
+ "BDMAP_00003425",
+ "BDMAP_00002937",
+ "BDMAP_00005186",
+ "BDMAP_00008389",
+ "BDMAP_00000396",
+ "BDMAP_00005030",
+ "BDMAP_00000606",
+ "BDMAP_00001907",
+ "BDMAP_00001441",
+ "BDMAP_00008356",
+ "BDMAP_00001429",
+ "BDMAP_00014548",
+ "BDMAP_00000201",
+ "BDMAP_00004209",
+ "BDMAP_00000104",
+ "BDMAP_00003400",
+ "BDMAP_00006589",
+ "BDMAP_00002931",
+ "BDMAP_00000492",
+ "BDMAP_00003836",
+ "BDMAP_00000353",
+ "BDMAP_00001882",
+ "BDMAP_00014613",
+ "BDMAP_00002928",
+ "BDMAP_00000828",
+ "BDMAP_00003971",
+ "BDMAP_00005537",
+ "BDMAP_00003269",
+ "BDMAP_00002347",
+ "BDMAP_00002501",
+ "BDMAP_00002542",
+ "BDMAP_00001228",
+ "BDMAP_00002505",
+ "BDMAP_00004651",
+ "BDMAP_00001766",
+ "BDMAP_00004017",
+ "BDMAP_00003717",
+ "BDMAP_00001288",
+ "BDMAP_00001375",
+ "BDMAP_00003711",
+ "BDMAP_00004427",
+ "BDMAP_00000643",
+ "BDMAP_00000871",
+ "BDMAP_00003824",
+ "BDMAP_00004528",
+ "BDMAP_00022481",
+ "BDMAP_00001905",
+ "BDMAP_00002111",
+ "BDMAP_00002815",
+ "BDMAP_00005083",
+ "BDMAP_00004693",
+ "BDMAP_00003130",
+ "BDMAP_00001230",
+ "BDMAP_00001516",
+ "BDMAP_00014575",
+ "BDMAP_00002203",
+ "BDMAP_00003168",
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+ "BDMAP_00005078",
+ "BDMAP_00000569",
+ "BDMAP_00000516",
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+ "BDMAP_00000129",
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+ "BDMAP_00002711",
+ "BDMAP_00000711",
+ "BDMAP_00004738",
+ "BDMAP_00001450",
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+ "BDMAP_00014557",
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+ "BDMAP_00000533",
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+ "BDMAP_00001059",
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+ "BDMAP_00000498",
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+ "BDMAP_00003138",
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+ "BDMAP_00000207",
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+ "BDMAP_00001707",
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+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_colon.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_colon.json
new file mode 100644
index 0000000000000000000000000000000000000000..b8b1a47188a1bd3aadcd2789dd182c8867a3b1da
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_colon.json
@@ -0,0 +1,134 @@
+[
+ {
+ "train": [
+ "colon_127",
+ "colon_193",
+ "colon_155",
+ "colon_022",
+ "colon_072",
+ "colon_119",
+ "colon_131",
+ "colon_144",
+ "colon_142",
+ "colon_069",
+ "colon_187",
+ "colon_137",
+ "colon_040",
+ "colon_008",
+ "colon_161",
+ "colon_202",
+ "colon_207",
+ "colon_038",
+ "colon_107",
+ "colon_091",
+ "colon_192",
+ "colon_145",
+ "colon_024",
+ "colon_039",
+ "colon_149",
+ "colon_012",
+ "colon_164",
+ "colon_099",
+ "colon_065",
+ "colon_009",
+ "colon_138",
+ "colon_102",
+ "colon_216",
+ "colon_126",
+ "colon_006",
+ "colon_095",
+ "colon_139",
+ "colon_212",
+ "colon_159",
+ "colon_061",
+ "colon_141",
+ "colon_168",
+ "colon_129",
+ "colon_218",
+ "colon_028",
+ "colon_185",
+ "colon_171",
+ "colon_046",
+ "colon_219",
+ "colon_089",
+ "colon_001",
+ "colon_050",
+ "colon_088",
+ "colon_172",
+ "colon_194",
+ "colon_112",
+ "colon_148",
+ "colon_201",
+ "colon_136",
+ "colon_075",
+ "colon_163",
+ "colon_181",
+ "colon_054",
+ "colon_122",
+ "colon_030",
+ "colon_157",
+ "colon_203",
+ "colon_215",
+ "colon_134",
+ "colon_106",
+ "colon_133",
+ "colon_033",
+ "colon_213",
+ "colon_041",
+ "colon_045",
+ "colon_005",
+ "colon_036",
+ "colon_100",
+ "colon_120",
+ "colon_217",
+ "colon_042",
+ "colon_077",
+ "colon_064",
+ "colon_124",
+ "colon_007",
+ "colon_078",
+ "colon_011",
+ "colon_059",
+ "colon_066",
+ "colon_205",
+ "colon_114",
+ "colon_031",
+ "colon_140",
+ "colon_104",
+ "colon_108",
+ "colon_081",
+ "colon_162",
+ "colon_208",
+ "colon_032",
+ "colon_165",
+ "colon_118"
+ ],
+ "val": [
+ "colon_214",
+ "colon_206",
+ "colon_026",
+ "colon_051",
+ "colon_196",
+ "colon_027",
+ "colon_098",
+ "colon_053",
+ "colon_154",
+ "colon_175",
+ "colon_092",
+ "colon_143",
+ "colon_103",
+ "colon_086",
+ "colon_166",
+ "colon_176",
+ "colon_117",
+ "colon_096",
+ "colon_015",
+ "colon_115",
+ "colon_169",
+ "colon_074",
+ "colon_111",
+ "colon_029",
+ "colon_025"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_colon_bdmap.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_colon_bdmap.json
new file mode 100644
index 0000000000000000000000000000000000000000..ba9ae29d77ae00575d76f1007f36fc0370153665
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_colon_bdmap.json
@@ -0,0 +1,134 @@
+[
+ {
+ "train": [
+ "BDMAP_00004147",
+ "BDMAP_00004165",
+ "BDMAP_00002598",
+ "BDMAP_00003268",
+ "BDMAP_00005160",
+ "BDMAP_00003333",
+ "BDMAP_00003827",
+ "BDMAP_00004894",
+ "BDMAP_00002318",
+ "BDMAP_00002739",
+ "BDMAP_00000939",
+ "BDMAP_00004384",
+ "BDMAP_00000030",
+ "BDMAP_00002730",
+ "BDMAP_00000942",
+ "BDMAP_00000069",
+ "BDMAP_00004676",
+ "BDMAP_00003634",
+ "BDMAP_00000373",
+ "BDMAP_00003357",
+ "BDMAP_00001865",
+ "BDMAP_00003299",
+ "BDMAP_00001209",
+ "BDMAP_00001215",
+ "BDMAP_00002230",
+ "BDMAP_00002199",
+ "BDMAP_00002401",
+ "BDMAP_00000873",
+ "BDMAP_00002152",
+ "BDMAP_00002828",
+ "BDMAP_00002451",
+ "BDMAP_00000913",
+ "BDMAP_00001078",
+ "BDMAP_00001024",
+ "BDMAP_00001982",
+ "BDMAP_00003560",
+ "BDMAP_00004196",
+ "BDMAP_00004431",
+ "BDMAP_00002875",
+ "BDMAP_00004604",
+ "BDMAP_00004450",
+ "BDMAP_00002305",
+ "BDMAP_00005001",
+ "BDMAP_00004374",
+ "BDMAP_00001102",
+ "BDMAP_00001917",
+ "BDMAP_00002846",
+ "BDMAP_00001732",
+ "BDMAP_00004910",
+ "BDMAP_00000552",
+ "BDMAP_00002103",
+ "BDMAP_00002704",
+ "BDMAP_00004764",
+ "BDMAP_00003510",
+ "BDMAP_00003972",
+ "BDMAP_00003113",
+ "BDMAP_00004491",
+ "BDMAP_00004615",
+ "BDMAP_00005067",
+ "BDMAP_00003412",
+ "BDMAP_00001809",
+ "BDMAP_00001785",
+ "BDMAP_00000828",
+ "BDMAP_00002039",
+ "BDMAP_00004493",
+ "BDMAP_00000851",
+ "BDMAP_00002185",
+ "BDMAP_00001237",
+ "BDMAP_00004163",
+ "BDMAP_00003373",
+ "BDMAP_00000568",
+ "BDMAP_00001737",
+ "BDMAP_00002986",
+ "BDMAP_00000716",
+ "BDMAP_00003327",
+ "BDMAP_00000690",
+ "BDMAP_00000881",
+ "BDMAP_00004121",
+ "BDMAP_00002845",
+ "BDMAP_00004624",
+ "BDMAP_00003109",
+ "BDMAP_00004876",
+ "BDMAP_00005005",
+ "BDMAP_00003172",
+ "BDMAP_00001924",
+ "BDMAP_00005073",
+ "BDMAP_00003680",
+ "BDMAP_00000874",
+ "BDMAP_00000132",
+ "BDMAP_00001438",
+ "BDMAP_00004650",
+ "BDMAP_00005016",
+ "BDMAP_00002924",
+ "BDMAP_00002458",
+ "BDMAP_00000190",
+ "BDMAP_00003640",
+ "BDMAP_00001514",
+ "BDMAP_00003031",
+ "BDMAP_00001985",
+ "BDMAP_00004415",
+ "BDMAP_00001015"
+ ],
+ "val": [
+ "BDMAP_00000138",
+ "BDMAP_00000152",
+ "BDMAP_00000264",
+ "BDMAP_00000345",
+ "BDMAP_00000547",
+ "BDMAP_00001095",
+ "BDMAP_00001212",
+ "BDMAP_00001426",
+ "BDMAP_00001445",
+ "BDMAP_00001598",
+ "BDMAP_00001769",
+ "BDMAP_00001835",
+ "BDMAP_00001875",
+ "BDMAP_00002253",
+ "BDMAP_00002465",
+ "BDMAP_00002612",
+ "BDMAP_00002654",
+ "BDMAP_00003111",
+ "BDMAP_00003564",
+ "BDMAP_00003847",
+ "BDMAP_00003890",
+ "BDMAP_00003946",
+ "BDMAP_00004558",
+ "BDMAP_00004783",
+ "BDMAP_00005170"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_eso_bdmap.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_eso_bdmap.json
new file mode 100644
index 0000000000000000000000000000000000000000..c84205e041e041ec4d524ae54cc5ad9cdac6e321
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_eso_bdmap.json
@@ -0,0 +1,162 @@
+[
+ {
+ "train": [
+ "BDMAP_00014516",
+ "BDMAP_00014627",
+ "BDMAP_00014630",
+ "BDMAP_00014552",
+ "BDMAP_00014557",
+ "BDMAP_00014629",
+ "BDMAP_00014507",
+ "BDMAP_00014618",
+ "BDMAP_00014569",
+ "BDMAP_00014493",
+ "BDMAP_00014633",
+ "BDMAP_00014576",
+ "BDMAP_00014581",
+ "BDMAP_00014635",
+ "BDMAP_00014521",
+ "BDMAP_00014506",
+ "BDMAP_00014517",
+ "BDMAP_00014567",
+ "BDMAP_00014636",
+ "BDMAP_00014520",
+ "BDMAP_00014582",
+ "BDMAP_00014536",
+ "BDMAP_00014525",
+ "BDMAP_00014486",
+ "BDMAP_00014496",
+ "BDMAP_00014545",
+ "BDMAP_00014547",
+ "BDMAP_00014490",
+ "BDMAP_00014625",
+ "BDMAP_00014513",
+ "BDMAP_00014535",
+ "BDMAP_00014632",
+ "BDMAP_00014541",
+ "BDMAP_00014522",
+ "BDMAP_00014508",
+ "BDMAP_00014578",
+ "BDMAP_00014494",
+ "BDMAP_00014562",
+ "BDMAP_00014534",
+ "BDMAP_00014553",
+ "BDMAP_00014566",
+ "BDMAP_00014542",
+ "BDMAP_00014612",
+ "BDMAP_00014614",
+ "BDMAP_00014637",
+ "BDMAP_00014631",
+ "BDMAP_00014619",
+ "BDMAP_00014498",
+ "BDMAP_00014570",
+ "BDMAP_00014624",
+ "BDMAP_00014503",
+ "BDMAP_00014573",
+ "BDMAP_00014546",
+ "BDMAP_00014626",
+ "BDMAP_00014526",
+ "BDMAP_00014488",
+ "BDMAP_00014524",
+ "BDMAP_00014528",
+ "BDMAP_00014617",
+ "BDMAP_00014518",
+ "BDMAP_00014571",
+ "BDMAP_00014551",
+ "BDMAP_00014515",
+ "BDMAP_00014561",
+ "BDMAP_00014616",
+ "BDMAP_00014580",
+ "BDMAP_00014492",
+ "BDMAP_00014514",
+ "BDMAP_00014533",
+ "BDMAP_00014620",
+ "BDMAP_00014558",
+ "BDMAP_00014610",
+ "BDMAP_00014510",
+ "BDMAP_00014512",
+ "BDMAP_00014583",
+ "BDMAP_00014628",
+ "BDMAP_00014579",
+ "BDMAP_00014537",
+ "BDMAP_00014615",
+ "BDMAP_00014568",
+ "BDMAP_00014539",
+ "BDMAP_00014556",
+ "BDMAP_00014491",
+ "BDMAP_00014495",
+ "BDMAP_00014549",
+ "BDMAP_00014554",
+ "BDMAP_00014565",
+ "BDMAP_00014504",
+ "BDMAP_00014485",
+ "BDMAP_00014575",
+ "BDMAP_00014563",
+ "BDMAP_00014574",
+ "BDMAP_00014572",
+ "BDMAP_00014499",
+ "BDMAP_00014559",
+ "BDMAP_00014530",
+ "BDMAP_00014505",
+ "BDMAP_00014532",
+ "BDMAP_00014564",
+ "BDMAP_00014497",
+ "BDMAP_00014538",
+ "BDMAP_00014519",
+ "BDMAP_00014544",
+ "BDMAP_00014555",
+ "BDMAP_00014527",
+ "BDMAP_00014611",
+ "BDMAP_00014577",
+ "BDMAP_00014634",
+ "BDMAP_00014613",
+ "BDMAP_00014584",
+ "BDMAP_00014509",
+ "BDMAP_00014523",
+ "BDMAP_00014543",
+ "BDMAP_00014502",
+ "BDMAP_00014622",
+ "BDMAP_00014540",
+ "BDMAP_00014500",
+ "BDMAP_00014548",
+ "BDMAP_00014529",
+ "BDMAP_00014550",
+ "BDMAP_00014623",
+ "BDMAP_00014487",
+ "BDMAP_00014531",
+ "BDMAP_00014501",
+ "BDMAP_00014484",
+ "BDMAP_00014489",
+ "BDMAP_00014511",
+ "BDMAP_00014560",
+ "BDMAP_00014621"
+ ],
+ "val": [
+ "BDMAP_00014585",
+ "BDMAP_00014586",
+ "BDMAP_00014587",
+ "BDMAP_00014588",
+ "BDMAP_00014589",
+ "BDMAP_00014590",
+ "BDMAP_00014591",
+ "BDMAP_00014592",
+ "BDMAP_00014593",
+ "BDMAP_00014594",
+ "BDMAP_00014595",
+ "BDMAP_00014596",
+ "BDMAP_00014597",
+ "BDMAP_00014598",
+ "BDMAP_00014599",
+ "BDMAP_00014600",
+ "BDMAP_00014601",
+ "BDMAP_00014602",
+ "BDMAP_00014603",
+ "BDMAP_00014604",
+ "BDMAP_00014605",
+ "BDMAP_00014606",
+ "BDMAP_00014607",
+ "BDMAP_00014608",
+ "BDMAP_00014609"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_esophagus.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_esophagus.json
new file mode 100644
index 0000000000000000000000000000000000000000..34fc7d7be005319469b601dfe3b3ecc690808231
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_esophagus.json
@@ -0,0 +1,134 @@
+[
+ {
+ "train": [
+ "BDMAP_esophagus_00000001",
+ "BDMAP_esophagus_00000002",
+ "BDMAP_esophagus_00000003",
+ "BDMAP_esophagus_00000004",
+ "BDMAP_esophagus_00000005",
+ "BDMAP_esophagus_00000006",
+ "BDMAP_esophagus_00000007",
+ "BDMAP_esophagus_00000008",
+ "BDMAP_esophagus_00000009",
+ "BDMAP_esophagus_00000010",
+ "BDMAP_esophagus_00000011",
+ "BDMAP_esophagus_00000012",
+ "BDMAP_esophagus_00000013",
+ "BDMAP_esophagus_00000014",
+ "BDMAP_esophagus_00000015",
+ "BDMAP_esophagus_00000016",
+ "BDMAP_esophagus_00000017",
+ "BDMAP_esophagus_00000018",
+ "BDMAP_esophagus_00000019",
+ "BDMAP_esophagus_00000020",
+ "BDMAP_esophagus_00000021",
+ "BDMAP_esophagus_00000022",
+ "BDMAP_esophagus_00000023",
+ "BDMAP_esophagus_00000024",
+ "BDMAP_esophagus_00000025",
+ "BDMAP_esophagus_00000026",
+ "BDMAP_esophagus_00000027",
+ "BDMAP_esophagus_00000028",
+ "BDMAP_esophagus_00000029",
+ "BDMAP_esophagus_00000030",
+ "BDMAP_esophagus_00000031",
+ "BDMAP_esophagus_00000032",
+ "BDMAP_esophagus_00000033",
+ "BDMAP_esophagus_00000034",
+ "BDMAP_esophagus_00000035",
+ "BDMAP_esophagus_00000036",
+ "BDMAP_esophagus_00000037",
+ "BDMAP_esophagus_00000038",
+ "BDMAP_esophagus_00000039",
+ "BDMAP_esophagus_00000040",
+ "BDMAP_esophagus_00000041",
+ "BDMAP_esophagus_00000042",
+ "BDMAP_esophagus_00000043",
+ "BDMAP_esophagus_00000044",
+ "BDMAP_esophagus_00000045",
+ "BDMAP_esophagus_00000046",
+ "BDMAP_esophagus_00000047",
+ "BDMAP_esophagus_00000048",
+ "BDMAP_esophagus_00000049",
+ "BDMAP_esophagus_00000050",
+ "BDMAP_esophagus_00000051",
+ "BDMAP_esophagus_00000052",
+ "BDMAP_esophagus_00000053",
+ "BDMAP_esophagus_00000054",
+ "BDMAP_esophagus_00000055",
+ "BDMAP_esophagus_00000056",
+ "BDMAP_esophagus_00000057",
+ "BDMAP_esophagus_00000058",
+ "BDMAP_esophagus_00000059",
+ "BDMAP_esophagus_00000060",
+ "BDMAP_esophagus_00000061",
+ "BDMAP_esophagus_00000062",
+ "BDMAP_esophagus_00000063",
+ "BDMAP_esophagus_00000064",
+ "BDMAP_esophagus_00000065",
+ "BDMAP_esophagus_00000066",
+ "BDMAP_esophagus_00000067",
+ "BDMAP_esophagus_00000068",
+ "BDMAP_esophagus_00000069",
+ "BDMAP_esophagus_00000070",
+ "BDMAP_esophagus_00000071",
+ "BDMAP_esophagus_00000072",
+ "BDMAP_esophagus_00000073",
+ "BDMAP_esophagus_00000074",
+ "BDMAP_esophagus_00000075",
+ "BDMAP_esophagus_00000076",
+ "BDMAP_esophagus_00000077",
+ "BDMAP_esophagus_00000078",
+ "BDMAP_esophagus_00000079",
+ "BDMAP_esophagus_00000080",
+ "BDMAP_esophagus_00000081",
+ "BDMAP_esophagus_00000082",
+ "BDMAP_esophagus_00000083",
+ "BDMAP_esophagus_00000084",
+ "BDMAP_esophagus_00000085",
+ "BDMAP_esophagus_00000086",
+ "BDMAP_esophagus_00000087",
+ "BDMAP_esophagus_00000088",
+ "BDMAP_esophagus_00000089",
+ "BDMAP_esophagus_00000090",
+ "BDMAP_esophagus_00000091",
+ "BDMAP_esophagus_00000092",
+ "BDMAP_esophagus_00000093",
+ "BDMAP_esophagus_00000094",
+ "BDMAP_esophagus_00000095",
+ "BDMAP_esophagus_00000096",
+ "BDMAP_esophagus_00000097",
+ "BDMAP_esophagus_00000098",
+ "BDMAP_esophagus_00000099",
+ "BDMAP_esophagus_00000100",
+ "BDMAP_esophagus_00000101"
+ ],
+ "val": [
+ "BDMAP_esophagus_00000102",
+ "BDMAP_esophagus_00000103",
+ "BDMAP_esophagus_00000104",
+ "BDMAP_esophagus_00000105",
+ "BDMAP_esophagus_00000106",
+ "BDMAP_esophagus_00000107",
+ "BDMAP_esophagus_00000108",
+ "BDMAP_esophagus_00000109",
+ "BDMAP_esophagus_00000110",
+ "BDMAP_esophagus_00000111",
+ "BDMAP_esophagus_00000112",
+ "BDMAP_esophagus_00000113",
+ "BDMAP_esophagus_00000114",
+ "BDMAP_esophagus_00000115",
+ "BDMAP_esophagus_00000116",
+ "BDMAP_esophagus_00000117",
+ "BDMAP_esophagus_00000118",
+ "BDMAP_esophagus_00000119",
+ "BDMAP_esophagus_00000120",
+ "BDMAP_esophagus_00000121",
+ "BDMAP_esophagus_00000122",
+ "BDMAP_esophagus_00000123",
+ "BDMAP_esophagus_00000124",
+ "BDMAP_esophagus_00000125",
+ "BDMAP_esophagus_00000126"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_new.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_new.json
new file mode 100644
index 0000000000000000000000000000000000000000..78ccb8be6929194efebb21c6c811aa3ea4417260
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_new.json
@@ -0,0 +1,1800 @@
+[
+ {
+ "train": [
+ "BDMAP_00004015",
+ "BDMAP_00000438",
+ "BDMAP_00002815",
+ "BDMAP_00002855",
+ "BDMAP_00000714",
+ "BDMAP_00003438",
+ "BDMAP_00001214",
+ "BDMAP_00005444",
+ "BDMAP_00001584",
+ "BDMAP_00002232",
+ "BDMAP_00002332",
+ "BDMAP_00003525",
+ "BDMAP_00003592",
+ "BDMAP_00000859",
+ "BDMAP_00000338",
+ "BDMAP_00002947",
+ "BDMAP_00000732",
+ "BDMAP_00002244",
+ "BDMAP_00004297",
+ "BDMAP_00004447",
+ "BDMAP_00002313",
+ "BDMAP_00004672",
+ "BDMAP_00004198",
+ "BDMAP_00008525",
+ "BDMAP_00001704",
+ "BDMAP_00001504",
+ "BDMAP_00000589",
+ "BDMAP_00004231",
+ "BDMAP_00005078",
+ "BDMAP_00003900",
+ "BDMAP_00003508",
+ "BDMAP_00005169",
+ "BDMAP_00008526",
+ "BDMAP_00002362",
+ "BDMAP_00004964",
+ "BDMAP_00001565",
+ "BDMAP_00002295",
+ "BDMAP_00003812",
+ "BDMAP_00003254",
+ "BDMAP_00009367",
+ "BDMAP_00000324",
+ "BDMAP_00001096",
+ "BDMAP_00000696",
+ "BDMAP_00000972",
+ "BDMAP_00002216",
+ "BDMAP_00000087",
+ "BDMAP_00000940",
+ "BDMAP_00004106",
+ "BDMAP_00002126",
+ "BDMAP_00001288",
+ "BDMAP_00003479",
+ "BDMAP_00000416",
+ "BDMAP_00000192",
+ "BDMAP_00002076",
+ "BDMAP_00002387",
+ "BDMAP_00002411",
+ "BDMAP_00001441",
+ "BDMAP_00004796",
+ "BDMAP_00004804",
+ "BDMAP_00001836",
+ "BDMAP_00001701",
+ "BDMAP_00006623",
+ "BDMAP_00005185",
+ "BDMAP_00001419",
+ "BDMAP_00003133",
+ "BDMAP_00003781",
+ "BDMAP_00004641",
+ "BDMAP_00002896",
+ "BDMAP_00000093",
+ "BDMAP_00000614",
+ "BDMAP_00000447",
+ "BDMAP_00001912",
+ "BDMAP_00001931",
+ "BDMAP_00002233",
+ "BDMAP_00001921",
+ "BDMAP_00007200",
+ "BDMAP_00001408",
+ "BDMAP_00005022",
+ "BDMAP_00000052",
+ "BDMAP_00004398",
+ "BDMAP_00002688",
+ "BDMAP_00005141",
+ "BDMAP_00002278",
+ "BDMAP_00001521",
+ "BDMAP_00007398",
+ "BDMAP_00004880",
+ "BDMAP_00003889",
+ "BDMAP_00000771",
+ "BDMAP_00003451",
+ "BDMAP_00002237",
+ "BDMAP_00004982",
+ "BDMAP_00000618",
+ "BDMAP_00000956",
+ "BDMAP_00001862",
+ "BDMAP_00002188",
+ "BDMAP_00003822",
+ "BDMAP_00002460",
+ "BDMAP_00002793",
+ "BDMAP_00002120",
+ "BDMAP_00003443",
+ "BDMAP_00003551",
+ "BDMAP_00004077",
+ "BDMAP_00002475",
+ "BDMAP_00000236",
+ "BDMAP_00004229",
+ "BDMAP_00002945",
+ "BDMAP_00003824",
+ "BDMAP_00004549",
+ "BDMAP_00002523",
+ "BDMAP_00006534",
+ "BDMAP_00004735",
+ "BDMAP_00003934",
+ "BDMAP_00004359",
+ "BDMAP_00000541",
+ "BDMAP_00003326",
+ "BDMAP_00004511",
+ "BDMAP_00001590",
+ "BDMAP_00001823",
+ "BDMAP_00000470",
+ "BDMAP_00001828",
+ "BDMAP_00003017",
+ "BDMAP_00002227",
+ "BDMAP_00003347",
+ "BDMAP_00002402",
+ "BDMAP_00004097",
+ "BDMAP_00003853",
+ "BDMAP_00003543",
+ "BDMAP_00003744",
+ "BDMAP_00003400",
+ "BDMAP_00005075",
+ "BDMAP_00002616",
+ "BDMAP_00003244",
+ "BDMAP_00003633",
+ "BDMAP_00002021",
+ "BDMAP_00004773",
+ "BDMAP_00000037",
+ "BDMAP_00002309",
+ "BDMAP_00003356",
+ "BDMAP_00000774",
+ "BDMAP_00005064",
+ "BDMAP_00003385",
+ "BDMAP_00000993",
+ "BDMAP_00005092",
+ "BDMAP_00000715",
+ "BDMAP_00004262",
+ "BDMAP_00004719",
+ "BDMAP_00003168",
+ "BDMAP_00002250",
+ "BDMAP_00004264",
+ "BDMAP_00001632",
+ "BDMAP_00000388",
+ "BDMAP_00004286",
+ "BDMAP_00000154",
+ "BDMAP_00001461",
+ "BDMAP_00000683",
+ "BDMAP_00000309",
+ "BDMAP_00001125",
+ "BDMAP_00001898",
+ "BDMAP_00001845",
+ "BDMAP_00001464",
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+ "BDMAP_00004304",
+ "BDMAP_00000258",
+ "BDMAP_00004529",
+ "BDMAP_00003287",
+ "BDMAP_00004991",
+ "BDMAP_00000572",
+ "BDMAP_00003642",
+ "BDMAP_00002394",
+ "BDMAP_00002127",
+ "BDMAP_00004830",
+ "BDMAP_00002495",
+ "BDMAP_00000729",
+ "BDMAP_00003574",
+ "BDMAP_00002841",
+ "BDMAP_00001219",
+ "BDMAP_00008829",
+ "BDMAP_00001631",
+ "BDMAP_00003206",
+ "BDMAP_00000232",
+ "BDMAP_00004271",
+ "BDMAP_00001351",
+ "BDMAP_00001678",
+ "BDMAP_00004093",
+ "BDMAP_00004462",
+ "BDMAP_00000454",
+ "BDMAP_00004509",
+ "BDMAP_00000981",
+ "BDMAP_00004199",
+ "BDMAP_00001012",
+ "BDMAP_00001055",
+ "BDMAP_00002831",
+ "BDMAP_00001596",
+ "BDMAP_00004745",
+ "BDMAP_00001457",
+ "BDMAP_00003228",
+ "BDMAP_00003002",
+ "BDMAP_00001378",
+ "BDMAP_00008344",
+ "BDMAP_00000342",
+ "BDMAP_00000965",
+ "BDMAP_00001114",
+ "BDMAP_00003719",
+ "BDMAP_00002583",
+ "BDMAP_00001192",
+ "BDMAP_00003704",
+ "BDMAP_00005168",
+ "BDMAP_00000815",
+ "BDMAP_00000013",
+ "BDMAP_00001270",
+ "BDMAP_00001839",
+ "BDMAP_00000971",
+ "BDMAP_00005035",
+ "BDMAP_00000943",
+ "BDMAP_00002172",
+ "BDMAP_00004625"
+ ],
+ "val": [
+ "BDMAP_00000332",
+ "BDMAP_00004858",
+ "BDMAP_00005155",
+ "BDMAP_00001205",
+ "BDMAP_00004770",
+ "BDMAP_00001361",
+ "BDMAP_00002944",
+ "BDMAP_00003961",
+ "BDMAP_00000430",
+ "BDMAP_00000679",
+ "BDMAP_00003809",
+ "BDMAP_00004115",
+ "BDMAP_00003367",
+ "BDMAP_00002899",
+ "BDMAP_00003771",
+ "BDMAP_00003502",
+ "BDMAP_00001628",
+ "BDMAP_00003884",
+ "BDMAP_00005074",
+ "BDMAP_00003114",
+ "BDMAP_00004741",
+ "BDMAP_00001746",
+ "BDMAP_00002603",
+ "BDMAP_00004128",
+ "BDMAP_00000487",
+ "BDMAP_00002631",
+ "BDMAP_00002744",
+ "BDMAP_00000833",
+ "BDMAP_00002648",
+ "BDMAP_00000375",
+ "BDMAP_00000608",
+ "BDMAP_00001352",
+ "BDMAP_00002775",
+ "BDMAP_00002474",
+ "BDMAP_00000458",
+ "BDMAP_00000511",
+ "BDMAP_00003150",
+ "BDMAP_00000794",
+ "BDMAP_00001255",
+ "BDMAP_00002242",
+ "BDMAP_00003205",
+ "BDMAP_00002181",
+ "BDMAP_00003486",
+ "BDMAP_00004250",
+ "BDMAP_00002453",
+ "BDMAP_00003164",
+ "BDMAP_00004578",
+ "BDMAP_00001735",
+ "BDMAP_00004281",
+ "BDMAP_00003481",
+ "BDMAP_00001786",
+ "BDMAP_00000101",
+ "BDMAP_00001813",
+ "BDMAP_00000615",
+ "BDMAP_00003170",
+ "BDMAP_00004378",
+ "BDMAP_00004704",
+ "BDMAP_00003439",
+ "BDMAP_00002717",
+ "BDMAP_00001981",
+ "BDMAP_00000100",
+ "BDMAP_00001018",
+ "BDMAP_00002214",
+ "BDMAP_00001198",
+ "BDMAP_00001962",
+ "BDMAP_00002463",
+ "BDMAP_00005139",
+ "BDMAP_00000831",
+ "BDMAP_00002955",
+ "BDMAP_00003272",
+ "BDMAP_00000745"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_uterus.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_uterus.json
new file mode 100644
index 0000000000000000000000000000000000000000..15fdfd5f85dd4464b93319a647b02e8368860eeb
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_uterus.json
@@ -0,0 +1,87 @@
+[
+ {
+ "train": [
+ "BDMAP_endometrioma_00000023",
+ "BDMAP_endometrioma_00000003",
+ "BDMAP_endometrioma_00000035",
+ "BDMAP_endometrioma_00000054",
+ "BDMAP_endometrioma_00000001",
+ "BDMAP_endometrioma_00000012",
+ "BDMAP_endometrioma_00000011",
+ "BDMAP_endometrioma_00000013",
+ "BDMAP_endometrioma_00000070",
+ "BDMAP_endometrioma_00000055",
+ "BDMAP_endometrioma_00000016",
+ "BDMAP_endometrioma_00000033",
+ "BDMAP_endometrioma_00000034",
+ "BDMAP_endometrioma_00000040",
+ "BDMAP_endometrioma_00000005",
+ "BDMAP_endometrioma_00000079",
+ "BDMAP_endometrioma_00000028",
+ "BDMAP_endometrioma_00000076",
+ "BDMAP_endometrioma_00000050",
+ "BDMAP_endometrioma_00000043",
+ "BDMAP_endometrioma_00000066",
+ "BDMAP_endometrioma_00000006",
+ "BDMAP_endometrioma_00000027",
+ "BDMAP_endometrioma_00000004",
+ "BDMAP_endometrioma_00000078",
+ "BDMAP_endometrioma_00000007",
+ "BDMAP_endometrioma_00000025",
+ "BDMAP_endometrioma_00000002",
+ "BDMAP_endometrioma_00000032",
+ "BDMAP_endometrioma_00000037",
+ "BDMAP_endometrioma_00000010",
+ "BDMAP_endometrioma_00000063",
+ "BDMAP_endometrioma_00000018",
+ "BDMAP_endometrioma_00000026",
+ "BDMAP_endometrioma_00000064",
+ "BDMAP_endometrioma_00000058",
+ "BDMAP_endometrioma_00000045",
+ "BDMAP_endometrioma_00000044",
+ "BDMAP_endometrioma_00000047",
+ "BDMAP_endometrioma_00000042",
+ "BDMAP_endometrioma_00000067",
+ "BDMAP_endometrioma_00000052",
+ "BDMAP_endometrioma_00000031",
+ "BDMAP_endometrioma_00000020",
+ "BDMAP_endometrioma_00000015",
+ "BDMAP_endometrioma_00000061",
+ "BDMAP_endometrioma_00000024",
+ "BDMAP_endometrioma_00000022",
+ "BDMAP_endometrioma_00000009",
+ "BDMAP_endometrioma_00000056",
+ "BDMAP_endometrioma_00000008",
+ "BDMAP_endometrioma_00000029",
+ "BDMAP_endometrioma_00000017",
+ "BDMAP_endometrioma_00000062",
+ "BDMAP_endometrioma_00000051",
+ "BDMAP_endometrioma_00000021"
+ ],
+ "val": [
+ "BDMAP_endometrioma_00000060",
+ "BDMAP_endometrioma_00000038",
+ "BDMAP_endometrioma_00000077",
+ "BDMAP_endometrioma_00000068",
+ "BDMAP_endometrioma_00000075",
+ "BDMAP_endometrioma_00000073",
+ "BDMAP_endometrioma_00000039",
+ "BDMAP_endometrioma_00000057",
+ "BDMAP_endometrioma_00000065",
+ "BDMAP_endometrioma_00000048",
+ "BDMAP_endometrioma_00000036",
+ "BDMAP_endometrioma_00000049",
+ "BDMAP_endometrioma_00000014",
+ "BDMAP_endometrioma_00000059",
+ "BDMAP_endometrioma_00000072",
+ "BDMAP_endometrioma_00000046",
+ "BDMAP_endometrioma_00000019",
+ "BDMAP_endometrioma_00000069",
+ "BDMAP_endometrioma_00000071",
+ "BDMAP_endometrioma_00000041",
+ "BDMAP_endometrioma_00000053",
+ "BDMAP_endometrioma_00000030",
+ "BDMAP_endometrioma_00000074"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_uterus_bdmap.json b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_uterus_bdmap.json
new file mode 100644
index 0000000000000000000000000000000000000000..3be5e950d651a5b43033b8174c7b722e766a0c11
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/splits_final_uterus_bdmap.json
@@ -0,0 +1,87 @@
+[
+ {
+ "train": [
+ "BDMAP_00022490",
+ "BDMAP_00022418",
+ "BDMAP_00022453",
+ "BDMAP_00022438",
+ "BDMAP_00022413",
+ "BDMAP_00022469",
+ "BDMAP_00022436",
+ "BDMAP_00022443",
+ "BDMAP_00022427",
+ "BDMAP_00022416",
+ "BDMAP_00022421",
+ "BDMAP_00022434",
+ "BDMAP_00022473",
+ "BDMAP_00022440",
+ "BDMAP_00022439",
+ "BDMAP_00022433",
+ "BDMAP_00022446",
+ "BDMAP_00022475",
+ "BDMAP_00022414",
+ "BDMAP_00022431",
+ "BDMAP_00022432",
+ "BDMAP_00022428",
+ "BDMAP_00022481",
+ "BDMAP_00022489",
+ "BDMAP_00022462",
+ "BDMAP_00022437",
+ "BDMAP_00022467",
+ "BDMAP_00022420",
+ "BDMAP_00022474",
+ "BDMAP_00022426",
+ "BDMAP_00022466",
+ "BDMAP_00022463",
+ "BDMAP_00022451",
+ "BDMAP_00022461",
+ "BDMAP_00022472",
+ "BDMAP_00022423",
+ "BDMAP_00022456",
+ "BDMAP_00022455",
+ "BDMAP_00022429",
+ "BDMAP_00022487",
+ "BDMAP_00022419",
+ "BDMAP_00022422",
+ "BDMAP_00022448",
+ "BDMAP_00022415",
+ "BDMAP_00022454",
+ "BDMAP_00022477",
+ "BDMAP_00022444",
+ "BDMAP_00022435",
+ "BDMAP_00022417",
+ "BDMAP_00022478",
+ "BDMAP_00022445",
+ "BDMAP_00022442",
+ "BDMAP_00022465",
+ "BDMAP_00022412",
+ "BDMAP_00022424",
+ "BDMAP_00022458"
+ ],
+ "val": [
+ "BDMAP_00022471",
+ "BDMAP_00022449",
+ "BDMAP_00022488",
+ "BDMAP_00022479",
+ "BDMAP_00022486",
+ "BDMAP_00022484",
+ "BDMAP_00022450",
+ "BDMAP_00022468",
+ "BDMAP_00022476",
+ "BDMAP_00022459",
+ "BDMAP_00022447",
+ "BDMAP_00022460",
+ "BDMAP_00022425",
+ "BDMAP_00022470",
+ "BDMAP_00022483",
+ "BDMAP_00022457",
+ "BDMAP_00022430",
+ "BDMAP_00022480",
+ "BDMAP_00022482",
+ "BDMAP_00022452",
+ "BDMAP_00022464",
+ "BDMAP_00022441",
+ "BDMAP_00022485"
+ ]
+ }
+]
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/uterus_json.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/uterus_json.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d1bd86dfaab7c096830b33f3918eebe07ee095a
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/data_split/uterus_json.py
@@ -0,0 +1,24 @@
+import os
+from batchgenerators.utilities.file_and_folder_operations import join, load_json, isfile, save_json, maybe_mkdir_p
+import csv
+
+
+train_files = load_json('/storage/chenqi/code/GEM-3D-ct/data_split/splits_final_uterus.json')[0]['train']
+val_files = load_json('/storage/chenqi/code/GEM-3D-ct/data_split/splits_final_uterus.json')[0]['val']
+
+with open('/storage/chenqi/X_data/annotatedtumor_txt/endometrioma_tumor_cases.txt', 'r') as f:
+ all_files=f.readlines()
+all_files = [os.path.basename(i.split(' ')[0]).split('.')[0] for i in all_files]
+all_files = [i.split('\n')[0] for i in all_files]
+print(val_files)
+val_files = ['BDMAP_'+str(int(i.split('BDMAP_endometrioma_')[1])+22411).zfill(8) for i in val_files]
+
+write_content = []
+train_liver = list(set(all_files) - set(val_files))
+breakpoint()
+write_content.append({"train":train_liver, "val":val_files})
+
+# breakpoint()
+
+splits_file = 'splits_uterus.json'
+save_json(write_content, splits_file)
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/environment.yml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/environment.yml
new file mode 100644
index 0000000000000000000000000000000000000000..9fd50656e9218f87ed5c58ad72995d1c9b9dac7e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/environment.yml
@@ -0,0 +1,40 @@
+name: gem_ct
+channels:
+ - pytorch
+ - defaults
+dependencies:
+ - cudatoolkit=11.3
+ - pip=22.3.1
+ - python=3.9.18
+ - pytorch=2.0.0
+ - torchaudio=2.0.0
+ - torchtriton=2.0.0
+ - torchvision=0.15.0
+ - yaml=0.2.5
+ - pip:
+ - argparse==1.4.0
+ - easydict==1.11
+ - einops==0.7.0
+ - imageio==2.31.6
+ - lightning-utilities==0.9.0
+ - lpips==0.1.4
+ - nibabel==5.2.0
+ - numpy==1.26.1
+ - omegaconf==2.1.1
+ - open-clip-torch==2.7.0
+ - opencv-python==4.8.1.78
+ - pandas==2.1.2
+ - pyparsing==3.1.1
+ - pytorch-lightning==1.4.2
+ - scikit-image==0.22.0
+ - scikit-learn==1.3.2
+ - scipy==1.11.3
+ - seaborn==0.13.0
+ - test-tube
+ - tensorboard==2.15.1
+ - tensorboardx==2.6.2.2
+ - torch==2.1.0
+ - torchmetrics==0.5.0
+ - tqdm==4.66.1
+ - transformers==4.29.2
+ - volumentations-3D
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/exp.png b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/exp.png
new file mode 100644
index 0000000000000000000000000000000000000000..c918291052dba03dfa3705c9cac9e857859d319f
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/exp.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c8b04878e46317cce0a4b90ae9c445196add2b6cbc9474c473958b055b122742
+size 112278
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ab9aec087745e0ec59be19fe0263076f44ad6f9
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d.py
@@ -0,0 +1,178 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+import argparse
+
+def compute_orientation(init_axcodes, final_axcodes):
+ """
+ A thin wrapper around ``nib.orientations.ornt_transform``
+
+ :param init_axcodes: Initial orientation codes
+ :param final_axcodes: Target orientation codes
+ :return: orientations array, start_ornt, end_ornt
+ """
+ ornt_init = nib.orientations.axcodes2ornt(init_axcodes)
+ ornt_fin = nib.orientations.axcodes2ornt(final_axcodes)
+
+ ornt_transf = nib.orientations.ornt_transform(ornt_init, ornt_fin)
+
+ return ornt_transf, ornt_init, ornt_fin
+
+def do_reorientation(data_array, init_axcodes, final_axcodes):
+ """
+ source: https://niftynet.readthedocs.io/en/dev/_modules/niftynet/io/misc_io.html#do_reorientation
+ Performs the reorientation (changing order of axes)
+
+ :param data_array: 3D Array to reorient
+ :param init_axcodes: Initial orientation
+ :param final_axcodes: Target orientation
+ :return data_reoriented: New data array in its reoriented form
+ """
+ ornt_transf, ornt_init, ornt_fin = compute_orientation(init_axcodes, final_axcodes)
+ if np.array_equal(ornt_init, ornt_fin):
+ return data_array
+
+ return nib.orientations.apply_orientation(data_array, ornt_transf)
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "-t",
+ "--time_steps",
+ type=int,
+ default=20,
+)
+args = parser.parse_args()
+ddim_steps=args.time_steps
+# breakpoint()
+
+logdir = 'logs/full_ct_3d_with_body_mask'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000053.ckpt")
+
+configs_file = "configs/latent-diffusion/full_ct_3d_with_body_mask.yaml"
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_3d_with_body_mask.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = f'3d_results_step{ddim_steps}_train_latest'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+
+for idx, data in tqdm(enumerate(valloader)):
+
+ if idx >= val_num:
+ break
+
+ name=data['name'][0].split('.')[0]
+ volume_data = data['volume_data']
+ volume_seg = data['volume_seg']
+ # breakpoint()
+
+ window_length = 65
+ latent_lenght=17
+ h = 1
+ slice_num =volume_data.shape[2]
+
+ upper_iters = (slice_num-h) // (window_length-h)+1 if (slice_num-h)%(window_length-h) != 0 else (slice_num-h) // (window_length-h)
+ result = torch.zeros((batch_size, upper_iters*latent_lenght, 16, 64, 64)).cuda()
+ print('upper_iters', upper_iters)
+ # breakpoint()
+ for i in range(upper_iters):
+ print('i', i)
+ input_data={}
+ if i == upper_iters-1:
+ input_data['name'] = data['name']
+ input_data['volume_data'] = data['volume_data'][:, :, -window_length:].to(device)
+ input_data['volume_seg'] = data['volume_seg'][:, :, -window_length:].to(device)
+ input_data['input_text'] = data['input_text']
+ else:
+ input_data['volume_data'] = data['volume_data'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['volume_seg'] = data['volume_seg'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['input_text'] = data['input_text']
+ # breakpoint()
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+ # breakpoint()
+ if i == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ if i == upper_iters-1:
+ result[:, -latent_lenght+h:] = samples_i[:,h:,...]
+ else:
+ if i == 0:
+ result[:, :latent_lenght] = samples_i
+ else:
+ # breakpoint()
+ result[:, i*latent_lenght-i*h+h:(i+1)*latent_lenght-i*h] = samples_i[:, h:]
+ x_minus1 = samples_i[:, -h:,...]
+ # breakpoint()
+ # result = rearrange(result, 'b z c h w -> (b z) c h w')
+ result = result.permute(0,2,1,3,4)
+ x_result = torch.zeros((3,slice_num,512,512))
+ dec_unit = 65
+ dec_latent_unit=17
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ x_result[:,-dec_unit:] = model.decode_first_stage(result[:,:,-latent_lenght:])[0]
+ # breakpoint()
+ else:
+ x_result[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:,:,i*latent_lenght:(i+1)*latent_lenght])[0]
+ # x_result[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:,:,i*latent_lenght:(i+1)*latent_lenght])[0]
+ x_result = x_result*2
+ x_result[x_result>1.0] = 1.0
+ x_result[x_result<-1.0] = -1.0
+ x_result = (x_result+1)/2
+ # breakpoint()
+
+ x_result_ = x_result.mean(axis=0).detach().cpu().numpy()
+ # x_result = x_result[0,:,0,...].detach().cpu().numpy()
+ # breakpoint()
+ x_result = x_result_.transpose(1,2,0)
+ # x_result = np.rot90(x_result, k=1, axes=(0,1))
+ # x_result = np.flip(x_result,axis=(0,1))
+ # import imageio as io
+ # io.imsave('exp.png', (x_result[:,:,400]*255).astype(np.uint8))
+
+ # breakpoint()
+ ref_root = '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ ref_nii = os.path.join(ref_root, name.split('_')[0]+'_'+name.split('_')[1], name.split('_')[0]+'_'+name.split('_')[1]+'_'+name.split('_')[2],name+'.nii.gz')
+ affine = nib.load(ref_nii).affine
+
+ x_result = x_result*2000.0 - 1000.0
+ data_path = os.path.join(save_path, str(f'{name}.nii.gz'))
+ data_nii = nib.Nifti1Image(x_result.astype(np.int16), affine)
+
+ nib.save(data_nii, data_path)
+
+ breakpoint()
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_2.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..18b0e32df562fb8cf06a627eae04edddf656ee0b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_2.py
@@ -0,0 +1,207 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+import argparse
+
+def compute_orientation(init_axcodes, final_axcodes):
+ """
+ A thin wrapper around ``nib.orientations.ornt_transform``
+
+ :param init_axcodes: Initial orientation codes
+ :param final_axcodes: Target orientation codes
+ :return: orientations array, start_ornt, end_ornt
+ """
+ ornt_init = nib.orientations.axcodes2ornt(init_axcodes)
+ ornt_fin = nib.orientations.axcodes2ornt(final_axcodes)
+
+ ornt_transf = nib.orientations.ornt_transform(ornt_init, ornt_fin)
+
+ return ornt_transf, ornt_init, ornt_fin
+
+def do_reorientation(data_array, init_axcodes, final_axcodes):
+ """
+ source: https://niftynet.readthedocs.io/en/dev/_modules/niftynet/io/misc_io.html#do_reorientation
+ Performs the reorientation (changing order of axes)
+
+ :param data_array: 3D Array to reorient
+ :param init_axcodes: Initial orientation
+ :param final_axcodes: Target orientation
+ :return data_reoriented: New data array in its reoriented form
+ """
+ ornt_transf, ornt_init, ornt_fin = compute_orientation(init_axcodes, final_axcodes)
+ if np.array_equal(ornt_init, ornt_fin):
+ return data_array
+
+ return nib.orientations.apply_orientation(data_array, ornt_transf)
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "-t",
+ "--time_steps",
+ type=int,
+ default=20,
+)
+args = parser.parse_args()
+ddim_steps=args.time_steps
+# breakpoint()
+
+logdir = 'logs/full_ct_3d_with_body_mask'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000070.ckpt")
+
+configs_file = "configs/latent-diffusion/full_ct_3d_with_body_mask.yaml"
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_3d_with_body_mask.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = f'3d_results_step{ddim_steps}_train_latest'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+
+for idx, data in tqdm(enumerate(valloader)):
+
+ if idx >= val_num:
+ break
+
+ name=data['name'][0].split('.')[0]
+ volume_data = data['volume_data']
+ volume_seg = data['volume_seg']
+ # breakpoint()
+
+ import math
+ # ---- 必要参数 ----
+ window_length = 65 # 输入窗口长度（切片数）
+ latent_lenght = 17 # 每窗输出长度（latent 轴）
+ overleap_lengh = 1 # ★ 输出侧希望的重叠（用于融合）
+ slice_num = volume_data.shape[2]
+ device = next(model.parameters()).device
+
+ # 1) 输出重叠 -> 输入重叠（按比例换算）
+ overlap_in = int(round(overleap_lengh * window_length / max(1, latent_lenght)))
+ overlap_in = max(0, min(window_length - 1, overlap_in)) # 保证 0 <= overlap_in < window_length
+
+ # 2) 输入步长 & upper_iters（首窗从0开始，末窗贴尾）
+ step_in = window_length - overlap_in
+ extra = max(0, slice_num - window_length)
+ upper_iters = 1 + (extra + step_in - 1) // step_in # 等价于 ceil(extra/step_in)
+ print(f"[plan] overlap_in={overlap_in}, step_in={step_in}, upper_iters={upper_iters}")
+
+ # 3) 输出侧拼接步长（latent轴）
+ stride_out = max(1, latent_lenght - overleap_lengh)
+ total_len = latent_lenght + (upper_iters - 1) * stride_out
+ print(f"[plan] stride_out={stride_out}, total_len={total_len}")
+
+ B = batch_size
+ C, H, W = 16, 64, 64
+
+ result_sum = torch.zeros((B, total_len, C, H, W), device=device, dtype=torch.float32)
+ weight_sum = torch.zeros((1, total_len, 1, 1, 1), device=device, dtype=torch.float32)
+
+ x_minus1 = None # 如不需要历史条件，保持 None 即可
+ cond_h = 0 # 若要保留历史条件，可设为 >0，并在采样后取 samples_i[:, -cond_h:]
+
+ for i in range(upper_iters):
+ # ---- 取片：普通窗走 step_in，末窗贴尾 ----
+ if i < upper_iters - 1:
+ st = i * step_in
+ st = max(0, min(st, slice_num - window_length))
+ ed = st + window_length
+ vol_i = data['volume_data'][:, :, st:ed].to(device)
+ seg_i = data['volume_seg'][:, :, st:ed].to(device)
+ else:
+ vol_i = data['volume_data'][:, :, -window_length:].to(device)
+ seg_i = data['volume_seg'][:, :, -window_length:].to(device)
+
+ input_data = {'name': data.get('name', None),
+ 'volume_data': vol_i, 'volume_seg': seg_i,
+ 'input_text': data['input_text']}
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+ if x_minus1 is None or cond_h == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ # ---- 输出侧拼接：平均融合 ----
+ gstart = i * stride_out
+ gend = gstart + latent_lenght
+ result_sum[:, gstart:gend] += samples_i
+ weight_sum[:, gstart:gend] += 1.0
+
+ # 如需历史条件
+ x_minus1 = samples_i[:, -cond_h:, ...] if cond_h > 0 else None
+
+ # 归一化
+ result = result_sum / torch.clamp_min(weight_sum, 1.0)
+
+
+
+
+ # result = rearrange(result, 'b z c h w -> (b z) c h w')
+ result = result.permute(0,2,1,3,4)
+ x_result = torch.zeros((3,slice_num,512,512))
+ dec_unit = 65
+ res_unit = window_length-dec_unit
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ x_result[:,-dec_unit:] = model.decode_first_stage(result[:,:,-latent_lenght:])[0][:,res_unit:]
+ else:
+ # breakpoint()
+ x_result[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:,:,i*latent_lenght:(i+1)*latent_lenght])[0][:,res_unit:]
+ x_result = x_result*2
+ x_result[x_result>1.0] = 1.0
+ x_result[x_result<-1.0] = -1.0
+ x_result = (x_result+1)/2
+ # breakpoint()
+
+ x_result_ = x_result.mean(axis=0).detach().cpu().numpy()
+ # x_result = x_result[0,:,0,...].detach().cpu().numpy()
+ # breakpoint()
+ x_result = x_result_.transpose(1,2,0)
+ # x_result = np.rot90(x_result, k=1, axes=(0,1))
+ # x_result = np.flip(x_result,axis=(0,1))
+ # import imageio as io
+ # io.imsave('exp.png', (x_result[:,:,400]*255).astype(np.uint8))
+
+ # breakpoint()
+ ref_root = '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ ref_nii = os.path.join(ref_root, name.split('_')[0]+'_'+name.split('_')[1], name.split('_')[0]+'_'+name.split('_')[1]+'_'+name.split('_')[2],name+'.nii.gz')
+ affine = nib.load(ref_nii).affine
+
+ x_result = x_result*2000.0 - 1000.0
+ data_path = os.path.join(save_path, str(f'{name}.nii.gz'))
+ data_nii = nib.Nifti1Image(x_result.astype(np.int16), affine)
+
+ nib.save(data_nii, data_path)
+
+ breakpoint()
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_3.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_3.py
new file mode 100644
index 0000000000000000000000000000000000000000..771d1bbdccc466cc21a15209378848f13be8b59f
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_3.py
@@ -0,0 +1,211 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+import argparse
+
+def compute_orientation(init_axcodes, final_axcodes):
+ """
+ A thin wrapper around ``nib.orientations.ornt_transform``
+
+ :param init_axcodes: Initial orientation codes
+ :param final_axcodes: Target orientation codes
+ :return: orientations array, start_ornt, end_ornt
+ """
+ ornt_init = nib.orientations.axcodes2ornt(init_axcodes)
+ ornt_fin = nib.orientations.axcodes2ornt(final_axcodes)
+
+ ornt_transf = nib.orientations.ornt_transform(ornt_init, ornt_fin)
+
+ return ornt_transf, ornt_init, ornt_fin
+
+def do_reorientation(data_array, init_axcodes, final_axcodes):
+ """
+ source: https://niftynet.readthedocs.io/en/dev/_modules/niftynet/io/misc_io.html#do_reorientation
+ Performs the reorientation (changing order of axes)
+
+ :param data_array: 3D Array to reorient
+ :param init_axcodes: Initial orientation
+ :param final_axcodes: Target orientation
+ :return data_reoriented: New data array in its reoriented form
+ """
+ ornt_transf, ornt_init, ornt_fin = compute_orientation(init_axcodes, final_axcodes)
+ if np.array_equal(ornt_init, ornt_fin):
+ return data_array
+
+ return nib.orientations.apply_orientation(data_array, ornt_transf)
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "-t",
+ "--time_steps",
+ type=int,
+ default=20,
+)
+args = parser.parse_args()
+ddim_steps=args.time_steps
+# breakpoint()
+
+logdir = 'logs/full_ct_3d_with_body_mask'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000070.ckpt")
+
+configs_file = "configs/latent-diffusion/full_ct_3d_with_body_mask.yaml"
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_3d_with_body_mask.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = f'3d_results_step{ddim_steps}_train_latest'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+
+for idx, data in tqdm(enumerate(valloader)):
+
+ if idx >= val_num:
+ break
+
+ name=data['name'][0].split('.')[0]
+ volume_data = data['volume_data']
+ volume_seg = data['volume_seg']
+ # breakpoint()
+
+ import math
+ # ---- 必要参数 ----
+ window_length = 65 # 输入窗口长度（切片数）
+ latent_lenght = 17 # 每窗输出长度（latent 轴）
+ overleap_lengh = 8 # ★ 输出侧希望的重叠（用于融合）
+ slice_num = volume_data.shape[2]
+ device = next(model.parameters()).device
+
+ # 1) 输出重叠 -> 输入重叠（按比例换算）
+ overlap_in = int(round(overleap_lengh * window_length / max(1, latent_lenght)))
+ overlap_in = max(0, min(window_length - 1, overlap_in)) # 保证 0 <= overlap_in < window_length
+
+ # 2) 输入步长 & upper_iters（首窗从0开始，末窗贴尾）
+ step_in = window_length - overlap_in
+ extra = max(0, slice_num - window_length)
+ upper_iters = 1 + (extra + step_in - 1) // step_in # 等价于 ceil(extra/step_in)
+ print(f"[plan] overlap_in={overlap_in}, step_in={step_in}, upper_iters={upper_iters}")
+
+ # 3) 输出侧拼接步长（latent轴）
+ stride_out = max(1, latent_lenght - overleap_lengh)
+ total_len = latent_lenght + (upper_iters - 1) * stride_out
+ print(f"[plan] stride_out={stride_out}, total_len={total_len}")
+
+ B = batch_size
+ C, H, W = 16, 64, 64
+
+ result_sum = torch.zeros((B, total_len, C, H, W), device=device, dtype=torch.float32)
+ weight_sum = torch.zeros((1, total_len, 1, 1, 1), device=device, dtype=torch.float32)
+
+ # 1D 高斯权重（长度=latent_lenght）；sigma 可随重叠自适应
+ z = torch.arange(latent_lenght, device=device, dtype=torch.float32)
+ center = (latent_lenght - 1) / 2.0
+ sigma = max(overleap_lengh / 2.0, 1.0) # 重叠越大，sigma 越大；下限1.0避免过尖
+ g = torch.exp(-0.5 * ((z - center) / sigma) ** 2)
+ g = g / g.max()
+ g = g.view(1, latent_lenght, 1, 1, 1)
+
+ x_minus1 = None
+ cond_h = 0 # 同上，是否启用历史条件
+
+ for i in range(upper_iters):
+ if i < upper_iters - 1:
+ st = i * step_in
+ st = max(0, min(st, slice_num - window_length))
+ ed = st + window_length
+ vol_i = data['volume_data'][:, :, st:ed].to(device)
+ seg_i = data['volume_seg'][:, :, st:ed].to(device)
+ else:
+ vol_i = data['volume_data'][:, :, -window_length:].to(device)
+ seg_i = data['volume_seg'][:, :, -window_length:].to(device)
+
+ input_data = {'name': data.get('name', None),
+ 'volume_data': vol_i, 'volume_seg': seg_i,
+ 'input_text': data['input_text']}
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+ if x_minus1 is None or cond_h == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ gstart = i * stride_out
+ gend = gstart + latent_lenght
+ result_sum[:, gstart:gend] += samples_i * g
+ weight_sum[:, gstart:gend] += g
+
+ x_minus1 = samples_i[:, -cond_h:, ...] if cond_h > 0 else None
+
+ result = result_sum / torch.clamp_min(weight_sum, 1e-8)
+
+
+
+
+ # result = rearrange(result, 'b z c h w -> (b z) c h w')
+ result = result.permute(0,2,1,3,4)
+ x_result = torch.zeros((3,slice_num,512,512))
+ dec_unit = 65
+ res_unit = window_length-dec_unit
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ x_result[:,-dec_unit:] = model.decode_first_stage(result[:,:,-latent_lenght:])[0][:,res_unit:]
+ else:
+ # breakpoint()
+ x_result[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:,:,i*latent_lenght:(i+1)*latent_lenght])[0][:,res_unit:]
+ x_result = x_result*2
+ x_result[x_result>1.0] = 1.0
+ x_result[x_result<-1.0] = -1.0
+ x_result = (x_result+1)/2
+ # breakpoint()
+
+ x_result_ = x_result.mean(axis=0).detach().cpu().numpy()
+ # x_result = x_result[0,:,0,...].detach().cpu().numpy()
+ # breakpoint()
+ x_result = x_result_.transpose(1,2,0)
+ # x_result = np.rot90(x_result, k=1, axes=(0,1))
+ # x_result = np.flip(x_result,axis=(0,1))
+ # import imageio as io
+ # io.imsave('exp.png', (x_result[:,:,400]*255).astype(np.uint8))
+
+ # breakpoint()
+ ref_root = '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ ref_nii = os.path.join(ref_root, name.split('_')[0]+'_'+name.split('_')[1], name.split('_')[0]+'_'+name.split('_')[1]+'_'+name.split('_')[2],name+'.nii.gz')
+ affine = nib.load(ref_nii).affine
+
+ x_result = x_result*2000.0 - 1000.0
+ data_path = os.path.join(save_path, str(f'{name}.nii.gz'))
+ data_nii = nib.Nifti1Image(x_result.astype(np.int16), affine)
+
+ nib.save(data_nii, data_path)
+
+ breakpoint()
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_4.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_4.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b31102794d585b4d1436fdd731557b03a267925
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_4.py
@@ -0,0 +1,203 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+import argparse
+
+def compute_orientation(init_axcodes, final_axcodes):
+ """
+ A thin wrapper around ``nib.orientations.ornt_transform``
+
+ :param init_axcodes: Initial orientation codes
+ :param final_axcodes: Target orientation codes
+ :return: orientations array, start_ornt, end_ornt
+ """
+ ornt_init = nib.orientations.axcodes2ornt(init_axcodes)
+ ornt_fin = nib.orientations.axcodes2ornt(final_axcodes)
+
+ ornt_transf = nib.orientations.ornt_transform(ornt_init, ornt_fin)
+
+ return ornt_transf, ornt_init, ornt_fin
+
+def do_reorientation(data_array, init_axcodes, final_axcodes):
+ """
+ source: https://niftynet.readthedocs.io/en/dev/_modules/niftynet/io/misc_io.html#do_reorientation
+ Performs the reorientation (changing order of axes)
+
+ :param data_array: 3D Array to reorient
+ :param init_axcodes: Initial orientation
+ :param final_axcodes: Target orientation
+ :return data_reoriented: New data array in its reoriented form
+ """
+ ornt_transf, ornt_init, ornt_fin = compute_orientation(init_axcodes, final_axcodes)
+ if np.array_equal(ornt_init, ornt_fin):
+ return data_array
+
+ return nib.orientations.apply_orientation(data_array, ornt_transf)
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "-t",
+ "--time_steps",
+ type=int,
+ default=20,
+)
+args = parser.parse_args()
+ddim_steps=args.time_steps
+# breakpoint()
+
+logdir = 'logs/full_ct_3d_with_body_mask'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000070.ckpt")
+
+configs_file = "configs/latent-diffusion/full_ct_3d_with_body_mask.yaml"
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_3d_with_body_mask.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = f'3d_results_step{ddim_steps}_train_latest'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+
+for idx, data in tqdm(enumerate(valloader)):
+
+ if idx >= val_num:
+ break
+
+ name=data['name'][0].split('.')[0]
+ volume_data = data['volume_data']
+ volume_seg = data['volume_seg']
+ # breakpoint()
+
+ import math
+
+ window_length = 65
+ latent_lenght = 17 # 每窗输出 17
+ keep_len = latent_lenght - 1 # 只保留后 16
+ overlap_slices = 4 # 输入体素重叠，>=1 避免断层
+ slice_num = volume_data.shape[2]
+ device = next(model.parameters()).device
+
+ # 输出长度（以“17通道”为单位）与总写入长度
+ result_len = slice_num // window_length + 1
+ result_total_len = result_len * latent_lenght # 你要求的 result 第二维
+ needed_iters = math.ceil(result_total_len / keep_len) # = ceil(result_len*17/16)
+
+ # 体素维 stride（考虑重叠）
+ stride = max(1, window_length - overlap_slices)
+
+ # 结果缓冲
+ result = torch.zeros((batch_size, result_total_len, 16, 64, 64), device='cuda')
+ print(f"result_total_len={result_total_len}, needed_iters={needed_iters}, stride={stride}")
+
+ write_ptr = 0
+ x_minus1 = None
+
+ for i in range(needed_iters):
+ # 体素窗口 [start:end)，越界则钉在最后一个完整窗
+ start = min(i * stride, max(0, slice_num - window_length))
+ end = start + window_length
+
+ input_data = {
+ 'input_text' : data['input_text'],
+ 'volume_data': data['volume_data'][:, :, start:end].to(device),
+ 'volume_seg' : data['volume_seg'][:, :, start:end].to(device)
+ }
+ if 'name' in data:
+ input_data['name'] = data['name']
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+
+ if x_minus1 is None:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght,
+ ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght,
+ ddim=True, eta=1., ddim_steps=ddim_steps,
+ previous=x_minus1)
+
+ # (b*z) c h w -> b z c h w
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+ samples_keep = samples_i[:, 1:, ...] # 丢弃第0个，只保留后16 -> [B,16,16,64,64]
+
+ # 本窗最多写入 keep_len=16，但可能最后一窗只需一部分
+ n_take = min(keep_len, result_total_len - write_ptr)
+ if n_take <= 0:
+ break
+
+ result[:, write_ptr:write_ptr + n_take] = samples_keep[:, :n_take]
+ write_ptr += n_take
+
+ # 传递上一窗最后1个作为 previous，保证跨窗连续
+ x_minus1 = samples_i[:, -1:, ...] # [B,1,16,64,64]
+
+
+
+ # breakpoint()
+ # result = rearrange(result, 'b z c h w -> (b z) c h w')
+ result = result.permute(0,2,1,3,4)
+ x_result = torch.zeros((3,slice_num,512,512))
+ dec_unit = 65
+ dec_latent_unit=17
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ x_result[:,-dec_unit:] = model.decode_first_stage(result[:,:,-latent_lenght:])[0]
+ # breakpoint()
+ else:
+ # breakpoint()
+ x_result[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:,:,i*latent_lenght:(i+1)*latent_lenght])[0]
+ # x_result[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:,:,i*latent_lenght:(i+1)*latent_lenght])[0]
+ x_result = x_result*2
+ x_result[x_result>1.0] = 1.0
+ x_result[x_result<-1.0] = -1.0
+ x_result = (x_result+1)/2
+ # breakpoint()
+
+ x_result_ = x_result.mean(axis=0).detach().cpu().numpy()
+ # x_result = x_result[0,:,0,...].detach().cpu().numpy()
+ # breakpoint()
+ x_result = x_result_.transpose(1,2,0)
+ # x_result = np.rot90(x_result, k=1, axes=(0,1))
+ # x_result = np.flip(x_result,axis=(0,1))
+ # import imageio as io
+ # io.imsave('exp.png', (x_result[:,:,400]*255).astype(np.uint8))
+
+ # breakpoint()
+ ref_root = '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ ref_nii = os.path.join(ref_root, name.split('_')[0]+'_'+name.split('_')[1], name.split('_')[0]+'_'+name.split('_')[1]+'_'+name.split('_')[2],name+'.nii.gz')
+ affine = nib.load(ref_nii).affine
+
+ x_result = x_result*2000.0 - 1000.0
+ data_path = os.path.join(save_path, str(f'{name}.nii.gz'))
+ data_nii = nib.Nifti1Image(x_result.astype(np.int16), affine)
+
+ nib.save(data_nii, data_path)
+
+ breakpoint()
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_5.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_5.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fc912d026a45a28704c9f185908ba256023baa2
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_5.py
@@ -0,0 +1,220 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+import argparse
+
+def compute_orientation(init_axcodes, final_axcodes):
+ """
+ A thin wrapper around ``nib.orientations.ornt_transform``
+
+ :param init_axcodes: Initial orientation codes
+ :param final_axcodes: Target orientation codes
+ :return: orientations array, start_ornt, end_ornt
+ """
+ ornt_init = nib.orientations.axcodes2ornt(init_axcodes)
+ ornt_fin = nib.orientations.axcodes2ornt(final_axcodes)
+
+ ornt_transf = nib.orientations.ornt_transform(ornt_init, ornt_fin)
+
+ return ornt_transf, ornt_init, ornt_fin
+
+def do_reorientation(data_array, init_axcodes, final_axcodes):
+ """
+ source: https://niftynet.readthedocs.io/en/dev/_modules/niftynet/io/misc_io.html#do_reorientation
+ Performs the reorientation (changing order of axes)
+
+ :param data_array: 3D Array to reorient
+ :param init_axcodes: Initial orientation
+ :param final_axcodes: Target orientation
+ :return data_reoriented: New data array in its reoriented form
+ """
+ ornt_transf, ornt_init, ornt_fin = compute_orientation(init_axcodes, final_axcodes)
+ if np.array_equal(ornt_init, ornt_fin):
+ return data_array
+
+ return nib.orientations.apply_orientation(data_array, ornt_transf)
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "-t",
+ "--time_steps",
+ type=int,
+ default=20,
+)
+args = parser.parse_args()
+ddim_steps=args.time_steps
+# breakpoint()
+
+logdir = 'logs/full_ct_3d_with_body_mask'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000070.ckpt")
+
+configs_file = "configs/latent-diffusion/full_ct_3d_with_body_mask.yaml"
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_3d_with_body_mask.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = f'3d_results_step{ddim_steps}_train_latest'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+
+for idx, data in tqdm(enumerate(valloader)):
+
+ if idx >= val_num:
+ break
+
+ name=data['name'][0].split('.')[0]
+ volume_data = data['volume_data']
+ volume_seg = data['volume_seg']
+ # breakpoint()
+
+ import math
+ # ---- 必要参数 ----
+ window_length = 65 # 输入窗口长度（切片数）
+ latent_lenght = 17 # 每窗原始输出长度（latent 轴）
+ overleap_lengh = 1 # ★ 输出侧希望的重叠（用于融合）
+ slice_num = volume_data.shape[2]
+ device = next(model.parameters()).device
+
+ # === 仅保留后16个（丢弃第0个） ===
+ keep_len = max(0, latent_lenght - 1)
+
+ # 1) 输出重叠 -> 输入重叠（按比例换算；用 keep_len）
+ overlap_in = int(round(overleap_lengh * window_length / max(1, keep_len)))
+ overlap_in = max(0, min(window_length - 1, overlap_in)) # 保证 0 <= overlap_in < window_length
+
+ # 2) 输入步长 & upper_iters（首窗从0开始，末窗贴尾）
+ step_in = window_length - overlap_in
+ extra = max(0, slice_num - window_length)
+ upper_iters = 1 + (extra + step_in - 1) // step_in # 等价于 ceil(extra/step_in)
+ print(f"[plan] overlap_in={overlap_in}, step_in={step_in}, upper_iters={upper_iters}")
+
+ # 3) 输出侧拼接步长（latent轴，用 keep_len）
+ stride_out = max(1, keep_len - overleap_lengh)
+ total_len = keep_len + (upper_iters - 1) * stride_out
+ print(f"[plan] stride_out={stride_out}, total_len={total_len}")
+
+ B = batch_size
+ C, H, W = 16, 64, 64
+
+ result_sum = torch.zeros((B, total_len, C, H, W), device=device, dtype=torch.float32)
+ weight_sum = torch.zeros((1, total_len, 1, 1, 1), device=device, dtype=torch.float32)
+
+ # 1D 高斯权重（长度=keep_len）；sigma 可随重叠自适应
+ z = torch.arange(keep_len, device=device, dtype=torch.float32)
+ center = (keep_len - 1) / 2.0 if keep_len > 0 else 0.0
+ sigma = max(overleap_lengh / 2.0, 1.0) # 重叠越大，sigma 越大；下限1.0避免过尖
+ g = torch.exp(-0.5 * ((z - center) / sigma) ** 2) if keep_len > 0 else torch.ones(1, device=device)
+ g = g / g.max() if keep_len > 0 else g
+ g = g.view(1, keep_len, 1, 1, 1)
+
+ x_minus1 = None
+ cond_h = 0 # 是否启用历史条件
+
+ for i in range(upper_iters):
+ if i < upper_iters - 1:
+ st = i * step_in
+ st = max(0, min(st, slice_num - window_length))
+ ed = st + window_length
+ vol_i = data['volume_data'][:, :, st:ed].to(device)
+ seg_i = data['volume_seg'][:, :, st:ed].to(device)
+ else:
+ vol_i = data['volume_data'][:, :, -window_length:].to(device)
+ seg_i = data['volume_seg'][:, :, -window_length:].to(device)
+
+ input_data = {
+ 'name': data.get('name', None),
+ 'volume_data': vol_i, 'volume_seg': seg_i,
+ 'input_text': data['input_text']
+ }
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+ if x_minus1 is None or cond_h == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ # (b*z, c, h, w) -> (b, z, c, h, w)
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ # === 关键改动：只拼接后 keep_len 个（丢弃第0个）===
+ kept = samples_i[:, 1:, ...] # shape: (B, keep_len, C, H, W)
+
+ gstart = i * stride_out
+ gend = gstart + keep_len
+ result_sum[:, gstart:gend] += kept * g
+ weight_sum[:, gstart:gend] += g
+
+ # 历史条件（若启用），通常取“保留段”的尾部
+ x_minus1 = kept[:, -cond_h:, ...] if cond_h > 0 else None
+
+ # 融合
+ result = result_sum / torch.clamp_min(weight_sum, 1e-8)
+
+
+ # result = rearrange(result, 'b z c h w -> (b z) c h w')
+ result = result.permute(0,2,1,3,4)
+ x_result = torch.zeros((3,slice_num,512,512))
+ dec_unit = 65
+ res_unit = window_length-dec_unit
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ x_result[:,-dec_unit:] = model.decode_first_stage(result[:,:,-latent_lenght:])[0][:,res_unit:]
+ else:
+ # breakpoint()
+ x_result[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:,:,i*latent_lenght:(i+1)*latent_lenght])[0][:,res_unit:]
+ x_result = x_result*2
+ x_result[x_result>1.0] = 1.0
+ x_result[x_result<-1.0] = -1.0
+ x_result = (x_result+1)/2
+ # breakpoint()
+
+ x_result_ = x_result.mean(axis=0).detach().cpu().numpy()
+ # x_result = x_result[0,:,0,...].detach().cpu().numpy()
+ # breakpoint()
+ x_result = x_result_.transpose(1,2,0)
+ # x_result = np.rot90(x_result, k=1, axes=(0,1))
+ # x_result = np.flip(x_result,axis=(0,1))
+ # import imageio as io
+ # io.imsave('exp.png', (x_result[:,:,400]*255).astype(np.uint8))
+
+ # breakpoint()
+ ref_root = '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ ref_nii = os.path.join(ref_root, name.split('_')[0]+'_'+name.split('_')[1], name.split('_')[0]+'_'+name.split('_')[1]+'_'+name.split('_')[2],name+'.nii.gz')
+ affine = nib.load(ref_nii).affine
+
+ x_result = x_result*2000.0 - 1000.0
+ data_path = os.path.join(save_path, str(f'{name}.nii.gz'))
+ data_nii = nib.Nifti1Image(x_result.astype(np.int16), affine)
+
+ nib.save(data_nii, data_path)
+
+ breakpoint()
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdd712d6be125c1130f010f046565fc74f0bdbeb
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new.py
@@ -0,0 +1,285 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+import argparse
+
+def compute_orientation(init_axcodes, final_axcodes):
+ """
+ A thin wrapper around ``nib.orientations.ornt_transform``
+
+ :param init_axcodes: Initial orientation codes
+ :param final_axcodes: Target orientation codes
+ :return: orientations array, start_ornt, end_ornt
+ """
+ ornt_init = nib.orientations.axcodes2ornt(init_axcodes)
+ ornt_fin = nib.orientations.axcodes2ornt(final_axcodes)
+
+ ornt_transf = nib.orientations.ornt_transform(ornt_init, ornt_fin)
+
+ return ornt_transf, ornt_init, ornt_fin
+
+def do_reorientation(data_array, init_axcodes, final_axcodes):
+ """
+ source: https://niftynet.readthedocs.io/en/dev/_modules/niftynet/io/misc_io.html#do_reorientation
+ Performs the reorientation (changing order of axes)
+
+ :param data_array: 3D Array to reorient
+ :param init_axcodes: Initial orientation
+ :param final_axcodes: Target orientation
+ :return data_reoriented: New data array in its reoriented form
+ """
+ ornt_transf, ornt_init, ornt_fin = compute_orientation(init_axcodes, final_axcodes)
+ if np.array_equal(ornt_init, ornt_fin):
+ return data_array
+
+ return nib.orientations.apply_orientation(data_array, ornt_transf)
+
+def alternating_volume_fusion(vol1, vol2, offset=32):
+ """
+ Fuse two volumes by alternating between them in segments
+ Rule: z[0-48] from vol1, z[49-80] from vol2 (offset by 32), and so on
+
+ :param vol1: First volume with shape (x, y, z)
+ :param vol2: Second volume with shape (x, y, z-32), offset by 32 slices
+ :param offset: Offset between vol1 and vol2 (default 32)
+ :return: Fused volume with alternating segments, shape (x, y, z)
+ """
+ x, y, total_slices = vol1.shape
+ fused = np.zeros_like(vol1)
+
+ # First segment: z[0:49] from vol1 (49 slices)
+ segment_1_end = 49
+ fused[:, :, 0:segment_1_end] = vol1[:, :, 0:segment_1_end]
+
+ current_pos = segment_1_end
+ use_vol2 = True
+
+ while current_pos < total_slices:
+ if use_vol2:
+ # Use vol2: z[49-80] from vol2, which corresponds to vol2's z[17:49]
+ vol2_start = current_pos - offset # 49 - 32 = 17
+ segment_length = 32 # z[49:81] is 32 slices
+ segment_end = min(current_pos + segment_length, total_slices)
+ vol2_end = vol2_start + (segment_end - current_pos)
+
+ if vol2_start >= 0 and vol2_start < vol2.shape[2]:
+ actual_end = min(vol2_end, vol2.shape[2])
+ copy_length = actual_end - vol2_start
+ fused[:, :, current_pos:current_pos+copy_length] = vol2[:, :, vol2_start:actual_end]
+ current_pos += copy_length
+ else:
+ break
+
+ else:
+ # Use vol1: z[81:130] from vol1 (49 slices)
+ segment_length = 49
+ segment_end = min(current_pos + segment_length, total_slices)
+ fused[:, :, current_pos:segment_end] = vol1[:, :, current_pos:segment_end]
+ current_pos = segment_end
+
+ use_vol2 = not use_vol2
+
+ return fused
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "-t",
+ "--time_steps",
+ type=int,
+ default=20,
+)
+args = parser.parse_args()
+ddim_steps=args.time_steps
+
+logdir = 'logs/full_ct_3d_with_body_mask'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000070.ckpt")
+
+configs_file = "configs/latent-diffusion/full_ct_3d_with_body_mask.yaml"
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_3d_with_body_mask.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = f'3d_results_step{ddim_steps}_train_latest'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+
+for idx, data in tqdm(enumerate(valloader)):
+
+ if idx >= val_num:
+ break
+
+ name=data['name'][0].split('.')[0]
+ volume_data = data['volume_data']
+ volume_seg = data['volume_seg']
+
+ window_length = 65
+ latent_lenght=17
+ h = 1
+ slice_num =volume_data.shape[2]
+
+ upper_iters = (slice_num-h) // (window_length-h)+1 if (slice_num-h)%(window_length-h) != 0 else (slice_num-h) // (window_length-h)
+ result = torch.zeros((batch_size, upper_iters*latent_lenght, 16, 64, 64)).cuda()
+ print('upper_iters', upper_iters)
+
+ for i in range(upper_iters):
+ print('i', i)
+ input_data={}
+ if i == upper_iters-1:
+ input_data['name'] = data['name']
+ input_data['volume_data'] = data['volume_data'][:, :, -window_length:].to(device)
+ input_data['volume_seg'] = data['volume_seg'][:, :, -window_length:].to(device)
+ input_data['input_text'] = data['input_text']
+ else:
+ input_data['volume_data'] = data['volume_data'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['volume_seg'] = data['volume_seg'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['input_text'] = data['input_text']
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+
+ if i == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ if i == upper_iters-1:
+ result[:, -latent_lenght+h:] = samples_i[:,h:,...]
+ else:
+ if i == 0:
+ result[:, :latent_lenght] = samples_i
+ else:
+ result[:, i*latent_lenght-i*h+h:(i+1)*latent_lenght-i*h] = samples_i[:, h:]
+ x_minus1 = samples_i[:, -h:,...]
+
+ result = result.permute(0,2,1,3,4)
+ x_result = torch.zeros((3,slice_num,512,512))
+ dec_unit = 65
+ dec_latent_unit=17
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ x_result[:,-dec_unit:] = model.decode_first_stage(result[:,:,-latent_lenght:])[0]
+ else:
+ x_result[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:,:,i*latent_lenght:(i+1)*latent_lenght])[0]
+
+ x_result = x_result*2
+ x_result[x_result>1.0] = 1.0
+ x_result[x_result<-1.0] = -1.0
+ x_result = (x_result+1)/2
+ x_result_ = x_result.mean(axis=0).detach().cpu().numpy()
+ x_result = x_result_.transpose(1,2,0)
+
+ # Process second volume with offset
+ data2={}
+ data2['volume_data'] = data['volume_data'][:, :, 32:] # Fixed: should slice all dimensions properly
+ data2['volume_seg'] = data['volume_seg'][:, :, 32:]
+ data2['name'] = data['name']
+ data2['input_text'] = data['input_text']
+
+ volume_data = data2['volume_data']
+ volume_seg = data2['volume_seg']
+
+ window_length = 65
+ latent_lenght=17
+ h = 1
+ slice_num =volume_data.shape[2]
+
+ upper_iters = (slice_num-h) // (window_length-h)+1 if (slice_num-h)%(window_length-h) != 0 else (slice_num-h) // (window_length-h)
+ result2 = torch.zeros((batch_size, upper_iters*latent_lenght, 16, 64, 64)).cuda()
+ print('upper_iters', upper_iters)
+
+ for i in range(upper_iters):
+ print('i', i)
+ input_data={}
+ if i == upper_iters-1:
+ input_data['name'] = data2['name']
+ input_data['volume_data'] = data2['volume_data'][:, :, -window_length:].to(device)
+ input_data['volume_seg'] = data2['volume_seg'][:, :, -window_length:].to(device)
+ input_data['input_text'] = data2['input_text']
+ else:
+ input_data['volume_data'] = data2['volume_data'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['volume_seg'] = data2['volume_seg'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['input_text'] = data2['input_text']
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+
+ if i == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ if i == upper_iters-1:
+ result2[:, -latent_lenght+h:] = samples_i[:,h:,...]
+ else:
+ if i == 0:
+ result2[:, :latent_lenght] = samples_i
+ else:
+ result2[:, i*latent_lenght-i*h+h:(i+1)*latent_lenght-i*h] = samples_i[:, h:]
+ x_minus1 = samples_i[:, -h:,...]
+
+ result2 = result2.permute(0,2,1,3,4)
+ x_result2 = torch.zeros((3,slice_num,512,512))
+ dec_unit = 65
+ dec_latent_unit=17
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ x_result2[:,-dec_unit:] = model.decode_first_stage(result2[:,:,-latent_lenght:])[0] # Fixed: was using result instead of result2
+ else:
+ x_result2[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result2[:,:,i*latent_lenght:(i+1)*latent_lenght])[0] # Fixed: same here
+
+ x_result2 = x_result2*2
+ x_result2[x_result2>1.0] = 1.0
+ x_result2[x_result2<-1.0] = -1.0
+ x_result2 = (x_result2+1)/2 # Fixed: typo was x_resul2t
+ x_result2_ = x_result2.mean(axis=0).detach().cpu().numpy()
+ x_result2 = x_result2_.transpose(1,2,0)
+
+ # Fuse volumes using alternating segments
+ # Rule: z[0-48] from x_result, z[49-80] from x_result2, and so on
+ print("Fusing volumes with alternating segments (0-48 from vol1, 49-80 from vol2)...")
+ x_result_fused = alternating_volume_fusion(x_result, x_result2, offset=32)
+
+ # Load reference and save fused result
+ ref_root = '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ ref_nii = os.path.join(ref_root, name.split('_')[0]+'_'+name.split('_')[1], name.split('_')[0]+'_'+name.split('_')[1]+'_'+name.split('_')[2],name+'.nii.gz')
+ affine = nib.load(ref_nii).affine
+
+ x_result_fused = x_result_fused*2000.0 - 1000.0
+ data_path = os.path.join(save_path, str(f'{name}_fused.nii.gz'))
+ data_nii = nib.Nifti1Image(x_result_fused.astype(np.int16), affine)
+
+ nib.save(data_nii, data_path)
+ print(f"Saved fused volume: {data_path}")
+ breakpoint()
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new2.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new2.py
new file mode 100644
index 0000000000000000000000000000000000000000..925687f91f340560497ba522feb46a53dbe4c8a6
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new2.py
@@ -0,0 +1,326 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+import argparse
+
+def compute_orientation(init_axcodes, final_axcodes):
+ """
+ A thin wrapper around ``nib.orientations.ornt_transform``
+
+ :param init_axcodes: Initial orientation codes
+ :param final_axcodes: Target orientation codes
+ :return: orientations array, start_ornt, end_ornt
+ """
+ ornt_init = nib.orientations.axcodes2ornt(init_axcodes)
+ ornt_fin = nib.orientations.axcodes2ornt(final_axcodes)
+
+ ornt_transf = nib.orientations.ornt_transform(ornt_init, ornt_fin)
+
+ return ornt_transf, ornt_init, ornt_fin
+
+def do_reorientation(data_array, init_axcodes, final_axcodes):
+ """
+ source: https://niftynet.readthedocs.io/en/dev/_modules/niftynet/io/misc_io.html#do_reorientation
+ Performs the reorientation (changing order of axes)
+
+ :param data_array: 3D Array to reorient
+ :param init_axcodes: Initial orientation
+ :param final_axcodes: Target orientation
+ :return data_reoriented: New data array in its reoriented form
+ """
+ ornt_transf, ornt_init, ornt_fin = compute_orientation(init_axcodes, final_axcodes)
+ if np.array_equal(ornt_init, ornt_fin):
+ return data_array
+
+ return nib.orientations.apply_orientation(data_array, ornt_transf)
+
+def compute_fusion_boundaries(upper_iters, latent_length, h, decode_ratio=4, blend_width=16):
+ """
+ Compute fusion boundaries based on result segments
+
+ :param upper_iters: Number of iterations in the generation loop
+ :param latent_length: Length of latent dimension (17)
+ :param h: Overlap parameter (1)
+ :param decode_ratio: Ratio between decoded and latent dimensions (4)
+ :param blend_width: Width of blending region on each side (16 slices)
+ :return: List of boundary dictionaries for fusion
+ """
+ boundaries = []
+
+ for i in range(1, upper_iters):
+ # Calculate boundary position in latent space
+ latent_boundary = i * latent_length - i * h
+
+ # Convert to decoded space (multiply by decode_ratio)
+ decoded_boundary = latent_boundary * decode_ratio
+
+ # Define blending region: boundary ± blend_width
+ blend_start = max(0, decoded_boundary - blend_width)
+ blend_end = decoded_boundary + blend_width
+
+ boundaries.append({
+ 'boundary': decoded_boundary,
+ 'blend_start': blend_start,
+ 'blend_end': blend_end
+ })
+
+ return boundaries
+
+
+def alternating_volume_fusion_with_boundaries(vol1, vol2, boundaries, offset=32, replace_width=16):
+ """
+ Fuse two volumes by replacing regions around boundaries
+
+ :param vol1: First volume with shape (x, y, z) - used as base
+ :param vol2: Second volume with shape (x, y, z-offset), offset by 32 slices
+ :param boundaries: List of boundary dictionaries from compute_fusion_boundaries
+ :param offset: Offset between vol1 and vol2 (default 32)
+ :param replace_width: Width on each side of boundary to replace (default 16)
+ :return: Fused volume with vol2 replacing vol1 at boundary regions
+ """
+ x, y, total_slices = vol1.shape
+ fused = vol1.copy() # Start with vol1 as base
+
+ # Sort boundaries by position
+ sorted_boundaries = sorted(boundaries, key=lambda b: b['boundary'])
+
+ print(f"Fusion starting with vol1 as base")
+
+ for i, boundary_info in enumerate(sorted_boundaries):
+ boundary = boundary_info['boundary']
+
+ # Define replacement region: boundary ± replace_width
+ replace_start = max(0, boundary - replace_width)
+ replace_end = min(boundary + replace_width, total_slices)
+
+ # Map to vol2 coordinates
+ vol2_start = replace_start - offset
+ vol2_end = replace_end - offset
+
+ # Replace this region with vol2 if valid
+ if vol2_start >= 0 and vol2_end <= vol2.shape[2]:
+ fused[:, :, replace_start:replace_end] = vol2[:, :, vol2_start:vol2_end]
+ print(f" Boundary {i+1}: z={boundary}, replaced z[{replace_start}:{replace_end}] with vol2[{vol2_start}:{vol2_end}]")
+ else:
+ print(f" Boundary {i+1}: z={boundary}, skipped (vol2 out of range)")
+
+ print(f"Fusion complete with {len(sorted_boundaries)} boundaries")
+
+ return fused
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "-t",
+ "--time_steps",
+ type=int,
+ default=20,
+)
+args = parser.parse_args()
+ddim_steps=args.time_steps
+
+logdir = 'logs/full_ct_3d_with_body_mask'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000070.ckpt")
+
+configs_file = "configs/latent-diffusion/full_ct_3d_with_body_mask.yaml"
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_3d_with_body_mask.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = f'3d_results_step{ddim_steps}_train_latest'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+
+for idx, data in tqdm(enumerate(valloader)):
+
+ if idx >= val_num:
+ break
+
+ name=data['name'][0].split('.')[0]
+ volume_data = data['volume_data']
+ volume_seg = data['volume_seg']
+
+ window_length = 65
+ latent_lenght=17
+ h = 1
+ slice_num =volume_data.shape[2]
+
+ upper_iters = (slice_num-h) // (window_length-h)+1 if (slice_num-h)%(window_length-h) != 0 else (slice_num-h) // (window_length-h)
+ result = torch.zeros((batch_size, upper_iters*latent_lenght, 16, 64, 64)).cuda()
+ print('upper_iters', upper_iters)
+
+ for i in range(upper_iters):
+ print('i', i)
+ input_data={}
+ if i == upper_iters-1:
+ input_data['name'] = data['name']
+ input_data['volume_data'] = data['volume_data'][:, :, -window_length:].to(device)
+ input_data['volume_seg'] = data['volume_seg'][:, :, -window_length:].to(device)
+ input_data['input_text'] = data['input_text']
+ else:
+ input_data['volume_data'] = data['volume_data'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['volume_seg'] = data['volume_seg'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['input_text'] = data['input_text']
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+
+ if i == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ if i == upper_iters-1:
+ result[:, -latent_lenght+h:] = samples_i[:,h:,...]
+ else:
+ if i == 0:
+ result[:, :latent_lenght] = samples_i
+ else:
+ result[:, i*latent_lenght-i*h+h:(i+1)*latent_lenght-i*h] = samples_i[:, h:]
+ x_minus1 = samples_i[:, -h:,...]
+
+ result = result.permute(0,2,1,3,4)
+ x_result = torch.zeros((3,slice_num,512,512))
+ dec_unit = 65
+ dec_latent_unit=17
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ x_result[:,-dec_unit:] = model.decode_first_stage(result[:,:,-latent_lenght:])[0]
+ else:
+ x_result[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:,:,i*latent_lenght:(i+1)*latent_lenght])[0]
+
+ x_result = x_result*2
+ x_result[x_result>1.0] = 1.0
+ x_result[x_result<-1.0] = -1.0
+ x_result = (x_result+1)/2
+ x_result_ = x_result.mean(axis=0).detach().cpu().numpy()
+ x_result = x_result_.transpose(1,2,0)
+
+ # Process second volume with offset
+ data2={}
+ data2['volume_data'] = data['volume_data'][:, :, 32:]
+ data2['volume_seg'] = data['volume_seg'][:, :, 32:]
+ data2['name'] = data['name']
+ data2['input_text'] = data['input_text']
+
+ volume_data = data2['volume_data']
+ volume_seg = data2['volume_seg']
+
+ window_length = 65
+ latent_lenght=17
+ h = 1
+ slice_num2 = volume_data.shape[2]
+
+ upper_iters2 = (slice_num2-h) // (window_length-h)+1 if (slice_num2-h)%(window_length-h) != 0 else (slice_num2-h) // (window_length-h)
+ result2 = torch.zeros((batch_size, upper_iters2*latent_lenght, 16, 64, 64)).cuda()
+ print('upper_iters2', upper_iters2)
+
+ for i in range(upper_iters2):
+ print('i', i)
+ input_data={}
+ if i == upper_iters2-1:
+ input_data['name'] = data2['name']
+ input_data['volume_data'] = data2['volume_data'][:, :, -window_length:].to(device)
+ input_data['volume_seg'] = data2['volume_seg'][:, :, -window_length:].to(device)
+ input_data['input_text'] = data2['input_text']
+ else:
+ input_data['volume_data'] = data2['volume_data'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['volume_seg'] = data2['volume_seg'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['input_text'] = data2['input_text']
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+
+ if i == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ if i == upper_iters2-1:
+ result2[:, -latent_lenght+h:] = samples_i[:,h:,...]
+ else:
+ if i == 0:
+ result2[:, :latent_lenght] = samples_i
+ else:
+ result2[:, i*latent_lenght-i*h+h:(i+1)*latent_lenght-i*h] = samples_i[:, h:]
+ x_minus1 = samples_i[:, -h:,...]
+
+ result2 = result2.permute(0,2,1,3,4)
+ x_result2 = torch.zeros((3,slice_num2,512,512))
+ dec_unit = 65
+ dec_latent_unit=17
+ num_dec_iter = slice_num2 // dec_unit + 1 if slice_num2 % dec_unit != 0 else slice_num2 // dec_unit
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ x_result2[:,-dec_unit:] = model.decode_first_stage(result2[:,:,-latent_lenght:])[0]
+ else:
+ x_result2[:,i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result2[:,:,i*latent_lenght:(i+1)*latent_lenght])[0]
+
+ x_result2 = x_result2*2
+ x_result2[x_result2>1.0] = 1.0
+ x_result2[x_result2<-1.0] = -1.0
+ x_result2 = (x_result2+1)/2
+ x_result2_ = x_result2.mean(axis=0).detach().cpu().numpy()
+ x_result2 = x_result2_.transpose(1,2,0)
+
+ # Compute fusion boundaries based on result segments
+ boundaries = compute_fusion_boundaries(
+ upper_iters=upper_iters,
+ latent_length=latent_lenght,
+ h=h,
+ decode_ratio=4,
+ blend_width=16
+ )
+
+ # Fuse volumes: start with vol1, replace regions around boundaries with vol2
+ print("Fusing volumes: replacing boundary regions with vol2...")
+ x_result_fused = alternating_volume_fusion_with_boundaries(
+ x_result,
+ x_result2,
+ boundaries,
+ offset=32,
+ replace_width=8
+ )
+
+ # Load reference and save fused result
+ ref_root = '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ ref_nii = os.path.join(ref_root, name.split('_')[0]+'_'+name.split('_')[1], name.split('_')[0]+'_'+name.split('_')[1]+'_'+name.split('_')[2],name+'.nii.gz')
+ affine = nib.load(ref_nii).affine
+
+ x_result_fused = x_result_fused*2000.0 - 1000.0
+ data_path = os.path.join(save_path, str(f'{name}_fused.nii.gz'))
+ data_nii = nib.Nifti1Image(x_result_fused.astype(np.int16), affine)
+
+ nib.save(data_nii, data_path)
+ print(f"Saved fused volume: {data_path}")
+ breakpoint()
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new3.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new3.py
new file mode 100644
index 0000000000000000000000000000000000000000..abb2cef44d45b1b1509f53580f3fca7a7bc20aeb
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new3.py
@@ -0,0 +1,334 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+import argparse
+
+def compute_orientation(init_axcodes, final_axcodes):
+ """
+ A thin wrapper around ``nib.orientations.ornt_transform``
+
+ :param init_axcodes: Initial orientation codes
+ :param final_axcodes: Target orientation codes
+ :return: orientations array, start_ornt, end_ornt
+ """
+ ornt_init = nib.orientations.axcodes2ornt(init_axcodes)
+ ornt_fin = nib.orientations.axcodes2ornt(final_axcodes)
+
+ ornt_transf = nib.orientations.ornt_transform(ornt_init, ornt_fin)
+
+ return ornt_transf, ornt_init, ornt_fin
+
+def do_reorientation(data_array, init_axcodes, final_axcodes):
+ """
+ source: https://niftynet.readthedocs.io/en/dev/_modules/niftynet/io/misc_io.html#do_reorientation
+ Performs the reorientation (changing order of axes)
+
+ :param data_array: 3D Array to reorient
+ :param init_axcodes: Initial orientation
+ :param final_axcodes: Target orientation
+ :return data_reoriented: New data array in its reoriented form
+ """
+ ornt_transf, ornt_init, ornt_fin = compute_orientation(init_axcodes, final_axcodes)
+ if np.array_equal(ornt_init, ornt_fin):
+ return data_array
+
+ return nib.orientations.apply_orientation(data_array, ornt_transf)
+
+def compute_fusion_boundaries(upper_iters, latent_length, h, decode_ratio=4, blend_width=16):
+ """
+ Compute fusion boundaries based on result segments
+
+ :param upper_iters: Number of iterations in the generation loop
+ :param latent_length: Length of latent dimension (17)
+ :param h: Overlap parameter (1)
+ :param decode_ratio: Ratio between decoded and latent dimensions (4)
+ :param blend_width: Width of blending region on each side (16 slices)
+ :return: List of boundary dictionaries for fusion
+ """
+ boundaries = []
+
+ for i in range(1, upper_iters):
+ # Calculate boundary position in latent space
+ latent_boundary = i * latent_length - i * h
+
+ # Convert to decoded space (multiply by decode_ratio)
+ decoded_boundary = latent_boundary * decode_ratio
+
+ # Define blending region: boundary ± blend_width
+ blend_start = max(0, decoded_boundary - blend_width)
+ blend_end = decoded_boundary + blend_width
+
+ boundaries.append({
+ 'boundary': decoded_boundary,
+ 'blend_start': blend_start,
+ 'blend_end': blend_end
+ })
+
+ return boundaries
+
+
+def alternating_volume_fusion_with_boundaries(vol1, vol2, boundaries, offset=32, replace_width=16):
+ """
+ Fuse two volumes by replacing regions around boundaries
+
+ :param vol1: First volume with shape (x, y, z) - used as base
+ :param vol2: Second volume with shape (x, y, z-offset), offset by 32 slices
+ :param boundaries: List of boundary dictionaries from compute_fusion_boundaries
+ :param offset: Offset between vol1 and vol2 (default 32)
+ :param replace_width: Width on each side of boundary to replace (default 16)
+ :return: Fused volume with vol2 replacing vol1 at boundary regions
+ """
+ x, y, total_slices = vol1.shape
+ fused = vol1.copy() # Start with vol1 as base
+
+ # Sort boundaries by position
+ sorted_boundaries = sorted(boundaries, key=lambda b: b['boundary'])
+
+ print(f"Fusion starting with vol1 as base")
+
+ for i, boundary_info in enumerate(sorted_boundaries):
+ boundary = boundary_info['boundary']
+
+ # Define replacement region: boundary ± replace_width
+ replace_start = max(0, boundary - replace_width)
+ replace_end = min(boundary + replace_width, total_slices)
+
+ # Map to vol2 coordinates
+ vol2_start = replace_start - offset
+ vol2_end = replace_end - offset
+
+ # Replace this region with vol2 if valid
+ if vol2_start >= 0 and vol2_end <= vol2.shape[2]:
+ fused[:, :, replace_start:replace_end] = vol2[:, :, vol2_start:vol2_end]
+ print(f" Boundary {i+1}: z={boundary}, replaced z[{replace_start}:{replace_end}] with vol2[{vol2_start}:{vol2_end}]")
+ else:
+ print(f" Boundary {i+1}: z={boundary}, skipped (vol2 out of range)")
+
+ print(f"Fusion complete with {len(sorted_boundaries)} boundaries")
+
+ return fused
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "-t",
+ "--time_steps",
+ type=int,
+ default=20,
+)
+args = parser.parse_args()
+ddim_steps=args.time_steps
+
+logdir = 'logs/full_ct_3d_with_body_mask'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000070.ckpt")
+
+configs_file = "configs/latent-diffusion/full_ct_3d_with_body_mask.yaml"
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_3d_with_body_mask.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = f'3d_results_step{ddim_steps}_train_latest'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+
+for idx, data in tqdm(enumerate(valloader)):
+
+ if idx >= val_num:
+ break
+
+ name=data['name'][0].split('.')[0]
+ volume_data = data['volume_data']
+ volume_seg = data['volume_seg']
+
+ window_length = 65
+ latent_lenght=17
+ h = 1
+ slice_num =volume_data.shape[2]
+
+ upper_iters = (slice_num-h) // (window_length-h)+1 if (slice_num-h)%(window_length-h) != 0 else (slice_num-h) // (window_length-h)
+ result = torch.zeros((batch_size, upper_iters*latent_lenght, 16, 64, 64)).cuda()
+ print('upper_iters', upper_iters)
+
+ for i in range(upper_iters):
+ print('i', i)
+ input_data={}
+ if i == upper_iters-1:
+ input_data['name'] = data['name']
+ input_data['volume_data'] = data['volume_data'][:, :, -window_length:].to(device)
+ input_data['volume_seg'] = data['volume_seg'][:, :, -window_length:].to(device)
+ input_data['input_text'] = data['input_text']
+ else:
+ input_data['volume_data'] = data['volume_data'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['volume_seg'] = data['volume_seg'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['input_text'] = data['input_text']
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+
+ if i == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ if i == upper_iters-1:
+ result[:, -latent_lenght+h:] = samples_i[:,h:,...]
+ else:
+ if i == 0:
+ result[:, :latent_lenght] = samples_i
+ else:
+ result[:, i*latent_lenght-i*h+h:(i+1)*latent_lenght-i*h] = samples_i[:, h:]
+ x_minus1 = samples_i[:, -h:,...]
+
+ result = result.permute(0,2,1,3,4)
+ x_result = torch.zeros((3,slice_num,512,512))
+ dec_unit = 65
+ dec_latent_unit=17
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ # 计算最后一次迭代需要处理的实际切片数
+ remaining_slices = slice_num % dec_unit if slice_num % dec_unit != 0 else dec_unit
+ # 从结果的末尾取对应数量的切片
+ x_result[:, -remaining_slices:] = model.decode_first_stage(result[:, :, -latent_lenght:])[0][:, -remaining_slices:]
+ else:
+ x_result[:, i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:, :, i*latent_lenght:(i+1)*latent_lenght])[0]
+
+
+ x_result = x_result*2
+ x_result[x_result>1.0] = 1.0
+ x_result[x_result<-1.0] = -1.0
+ x_result = (x_result+1)/2
+ x_result_ = x_result.mean(axis=0).detach().cpu().numpy()
+ x_result = x_result_.transpose(1,2,0)
+
+ # Process second volume with offset
+ data2={}
+ data2['volume_data'] = data['volume_data'][:, :, 32:]
+ data2['volume_seg'] = data['volume_seg'][:, :, 32:]
+ data2['name'] = data['name']
+ data2['input_text'] = data['input_text']
+
+ volume_data = data2['volume_data']
+ volume_seg = data2['volume_seg']
+
+ window_length = 65
+ latent_lenght=17
+ h = 1
+ slice_num2 = volume_data.shape[2]
+
+ upper_iters2 = (slice_num2-h) // (window_length-h)+1 if (slice_num2-h)%(window_length-h) != 0 else (slice_num2-h) // (window_length-h)
+ result2 = torch.zeros((batch_size, upper_iters2*latent_lenght, 16, 64, 64)).cuda()
+ print('upper_iters2', upper_iters2)
+
+ for i in range(upper_iters2):
+ print('i', i)
+ input_data={}
+ if i == upper_iters2-1:
+ input_data['name'] = data2['name']
+ input_data['volume_data'] = data2['volume_data'][:, :, -window_length:].to(device)
+ input_data['volume_seg'] = data2['volume_seg'][:, :, -window_length:].to(device)
+ input_data['input_text'] = data2['input_text']
+ else:
+ input_data['volume_data'] = data2['volume_data'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['volume_seg'] = data2['volume_seg'][:, :, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['input_text'] = data2['input_text']
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+
+ if i == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ if i == upper_iters2-1:
+ result2[:, -latent_lenght+h:] = samples_i[:,h:,...]
+ else:
+ if i == 0:
+ result2[:, :latent_lenght] = samples_i
+ else:
+ result2[:, i*latent_lenght-i*h+h:(i+1)*latent_lenght-i*h] = samples_i[:, h:]
+ x_minus1 = samples_i[:, -h:,...]
+
+ result2 = result2.permute(0,2,1,3,4)
+ x_result2 = torch.zeros((3,slice_num2,512,512))
+ dec_unit = 65
+ dec_latent_unit=17
+ num_dec_iter = slice_num2 // dec_unit + 1 if slice_num2 % dec_unit != 0 else slice_num2 // dec_unit
+
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ remaining_slices = slice_num2 % dec_unit if slice_num2 % dec_unit != 0 else dec_unit
+ x_result2[:, -remaining_slices:] = model.decode_first_stage(result2[:, :, -latent_lenght:])[0][:, -remaining_slices:]
+ else:
+ x_result2[:, i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result2[:, :, i*latent_lenght:(i+1)*latent_lenght])[0]
+
+
+ x_result2 = x_result2*2
+ x_result2[x_result2>1.0] = 1.0
+ x_result2[x_result2<-1.0] = -1.0
+ x_result2 = (x_result2+1)/2
+ x_result2_ = x_result2.mean(axis=0).detach().cpu().numpy()
+ x_result2 = x_result2_.transpose(1,2,0)
+
+ # Compute fusion boundaries based on result segments
+ boundaries = compute_fusion_boundaries(
+ upper_iters=upper_iters,
+ latent_length=latent_lenght,
+ h=h,
+ decode_ratio=4,
+ blend_width=16
+ )
+
+ # Fuse volumes: start with vol1, replace regions around boundaries with vol2
+ print("Fusing volumes: replacing boundary regions with vol2...")
+ x_result_fused = alternating_volume_fusion_with_boundaries(
+ x_result,
+ x_result2,
+ boundaries,
+ offset=32,
+ replace_width=8
+ )
+
+ # Load reference and save fused result
+ ref_root = '/sd/shuhan/CT-RATE/dataset/valid_fixed'
+ ref_nii = os.path.join(ref_root, name.split('_')[0]+'_'+name.split('_')[1], name.split('_')[0]+'_'+name.split('_')[1]+'_'+name.split('_')[2],name+'.nii.gz')
+ affine = nib.load(ref_nii).affine
+
+ x_result_fused = x_result_fused*2000.0 - 1000.0
+ data_path = os.path.join(save_path, str(f'{name}_fused.nii.gz'))
+ data_nii = nib.Nifti1Image(x_result_fused.astype(np.int16), affine)
+
+ nib.save(data_nii, data_path)
+ print(f"Saved fused volume: {data_path}")
+ breakpoint()
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new4.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new4.py
new file mode 100644
index 0000000000000000000000000000000000000000..fca8cfc9b4342f1764749409fd765a3da88d7330
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_abdomen_our_3d_new4.py
@@ -0,0 +1,320 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+import argparse
+
+def compute_orientation(init_axcodes, final_axcodes):
+ """
+ A thin wrapper around ``nib.orientations.ornt_transform``
+
+ :param init_axcodes: Initial orientation codes
+ :param final_axcodes: Target orientation codes
+ :return: orientations array, start_ornt, end_ornt
+ """
+ ornt_init = nib.orientations.axcodes2ornt(init_axcodes)
+ ornt_fin = nib.orientations.axcodes2ornt(final_axcodes)
+
+ ornt_transf = nib.orientations.ornt_transform(ornt_init, ornt_fin)
+
+ return ornt_transf, ornt_init, ornt_fin
+
+def do_reorientation(data_array, init_axcodes, final_axcodes):
+ """
+ source: https://niftynet.readthedocs.io/en/dev/_modules/niftynet/io/misc_io.html#do_reorientation
+ Performs the reorientation (changing order of axes)
+
+ :param data_array: 3D Array to reorient
+ :param init_axcodes: Initial orientation
+ :param final_axcodes: Target orientation
+ :return data_reoriented: New data array in its reoriented form
+ """
+ ornt_transf, ornt_init, ornt_fin = compute_orientation(init_axcodes, final_axcodes)
+ if np.array_equal(ornt_init, ornt_fin):
+ return data_array
+
+ return nib.orientations.apply_orientation(data_array, ornt_transf)
+
+def compute_fusion_boundaries(upper_iters, latent_length, decode_ratio=4, blend_width=16):
+ """
+ Compute fusion boundaries based on result segments
+
+ :param upper_iters: Number of iterations in the generation loop
+ :param latent_length: Length of latent dimension (17)
+ :param decode_ratio: Ratio between decoded and latent dimensions (4)
+ :param blend_width: Width of blending region on each side (16 slices)
+ :return: List of boundary dictionaries for fusion
+ """
+ boundaries = []
+
+ for i in range(1, upper_iters):
+ # Calculate boundary position in latent space
+ latent_boundary = i * latent_length
+
+ # Convert to decoded space (multiply by decode_ratio)
+ decoded_boundary = latent_boundary * decode_ratio + 1
+
+ # Define blending region: boundary ± blend_width
+ blend_start = max(0, decoded_boundary - blend_width)
+ blend_end = decoded_boundary + blend_width
+
+ boundaries.append({
+ 'boundary': decoded_boundary,
+ 'blend_start': blend_start,
+ 'blend_end': blend_end
+ })
+
+ return boundaries
+
+
+def alternating_volume_fusion_with_boundaries(vol1, vol2, boundaries, offset=32, replace_width=16):
+ """
+ Fuse two volumes by replacing regions around boundaries
+
+ :param vol1: First volume with shape (x, y, z) - used as base
+ :param vol2: Second volume with shape (x, y, z-offset), offset by 32 slices
+ :param boundaries: List of boundary dictionaries from compute_fusion_boundaries
+ :param offset: Offset between vol1 and vol2 (default 32)
+ :param replace_width: Width on each side of boundary to replace (default 16)
+ :return: Fused volume with vol2 replacing vol1 at boundary regions
+ """
+ x, y, total_slices = vol1.shape
+ fused = vol1.copy() # Start with vol1 as base
+
+ # Sort boundaries by position
+ sorted_boundaries = sorted(boundaries, key=lambda b: b['boundary'])
+
+ print(f"Fusion starting with vol1 as base")
+
+ for i, boundary_info in enumerate(sorted_boundaries):
+ boundary = boundary_info['boundary']
+
+ # Define replacement region: boundary ± replace_width
+ replace_start = max(0, boundary - replace_width)
+ replace_end = min(boundary + replace_width, total_slices)
+
+ # Map to vol2 coordinates
+ vol2_start = replace_start - offset
+ vol2_end = replace_end - offset
+
+ # Replace this region with vol2 if valid
+ if vol2_start >= 0 and vol2_end <= vol2.shape[2]:
+ fused[:, :, replace_start:replace_end] = vol2[:, :, vol2_start:vol2_end]
+ print(f" Boundary {i+1}: z={boundary}, replaced z[{replace_start}:{replace_end}] with vol2[{vol2_start}:{vol2_end}]")
+ else:
+ print(f" Boundary {i+1}: z={boundary}, skipped (vol2 out of range)")
+
+ print(f"Fusion complete with {len(sorted_boundaries)} boundaries")
+
+ return fused
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "-t",
+ "--time_steps",
+ type=int,
+ default=20,
+)
+args = parser.parse_args()
+ddim_steps=args.time_steps
+
+logdir = 'logs/full_ct_3d_with_body_mask2'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000063.ckpt")
+
+configs_file = "configs/latent-diffusion/full_ct_3d_with_body_mask2.yaml"
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_3d_with_body_mask2.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = f'3d_results_step{ddim_steps}_train_latest'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+
+for idx, data in tqdm(enumerate(valloader)):
+
+ if idx >= val_num:
+ break
+
+ name=data['name'][0].split('.')[0]
+ volume_data = data['volume_data']
+ volume_seg = data['volume_seg']
+
+ window_length = 65
+ latent_lenght=17
+ slice_num = volume_data.shape[2]
+
+ upper_iters = slice_num // window_length + 1 if slice_num % window_length != 0 else slice_num // window_length
+ result = torch.zeros((batch_size, upper_iters*latent_lenght, 16, 64, 64)).cuda()
+ print('upper_iters', upper_iters)
+
+ for i in range(upper_iters):
+ print('i', i)
+ input_data={}
+ if i == upper_iters-1:
+ input_data['name'] = data['name']
+ input_data['volume_data'] = data['volume_data'][:, :, -window_length:].to(device)
+ input_data['volume_seg'] = data['volume_seg'][:, :, -window_length:].to(device)
+ input_data['input_text'] = data['input_text']
+ else:
+ input_data['volume_data'] = data['volume_data'][:, :, i*window_length:(i+1)*window_length].to(device)
+ input_data['volume_seg'] = data['volume_seg'][:, :, i*window_length:(i+1)*window_length].to(device)
+ input_data['input_text'] = data['input_text']
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+
+ if i == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ if i == upper_iters-1:
+ result[:, -latent_lenght:] = samples_i
+ else:
+ result[:, i*latent_lenght:(i+1)*latent_lenght] = samples_i
+ x_minus1 = samples_i[:, -1:, ...] # Take last slice for next iteration
+
+ result = result.permute(0,2,1,3,4)
+ x_result = torch.zeros((3,slice_num,512,512))
+ dec_unit = 65
+ dec_latent_unit=17
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ remaining_slices = slice_num % dec_unit if slice_num % dec_unit != 0 else dec_unit
+ x_result[:, -remaining_slices:] = model.decode_first_stage(result[:, :, -latent_lenght:])[0][:, -remaining_slices:]
+ else:
+ x_result[:, i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[:, :, i*latent_lenght:(i+1)*latent_lenght])[0]
+
+ x_result = x_result*2
+ x_result[x_result>1.0] = 1.0
+ x_result[x_result<-1.0] = -1.0
+ x_result = (x_result+1)/2
+ x_result_ = x_result.mean(axis=0).detach().cpu().numpy()
+ x_result = x_result_.transpose(1,2,0)
+
+ # Process second volume with offset
+ data2={}
+ data2['volume_data'] = data['volume_data'][:, :, 32:]
+ data2['volume_seg'] = data['volume_seg'][:, :, 32:]
+ data2['name'] = data['name']
+ data2['input_text'] = data['input_text']
+
+ volume_data = data2['volume_data']
+ volume_seg = data2['volume_seg']
+
+ window_length = 65
+ latent_lenght=17
+ slice_num2 = volume_data.shape[2]
+
+ upper_iters2 = slice_num2 // window_length + 1 if slice_num2 % window_length != 0 else slice_num2 // window_length
+ result2 = torch.zeros((batch_size, upper_iters2*latent_lenght, 16, 64, 64)).cuda()
+ print('upper_iters2', upper_iters2)
+
+ for i in range(upper_iters2):
+ print('i', i)
+ input_data={}
+ if i == upper_iters2-1:
+ input_data['name'] = data2['name']
+ input_data['volume_data'] = data2['volume_data'][:, :, -window_length:].to(device)
+ input_data['volume_seg'] = data2['volume_seg'][:, :, -window_length:].to(device)
+ input_data['input_text'] = data2['input_text']
+ else:
+ input_data['volume_data'] = data2['volume_data'][:, :, i*window_length:(i+1)*window_length].to(device)
+ input_data['volume_seg'] = data2['volume_seg'][:, :, i*window_length:(i+1)*window_length].to(device)
+ input_data['input_text'] = data2['input_text']
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+
+ if i == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=latent_lenght, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=latent_lenght)
+
+ if i == upper_iters2-1:
+ result2[:, -latent_lenght:] = samples_i
+ else:
+ result2[:, i*latent_lenght:(i+1)*latent_lenght] = samples_i
+ x_minus1 = samples_i[:, -1:, ...] # Take last slice for next iteration
+
+ result2 = result2.permute(0,2,1,3,4)
+ x_result2 = torch.zeros((3,slice_num2,512,512))
+ dec_unit = 65
+ dec_latent_unit=17
+ num_dec_iter = slice_num2 // dec_unit + 1 if slice_num2 % dec_unit != 0 else slice_num2 // dec_unit
+
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ remaining_slices = slice_num2 % dec_unit if slice_num2 % dec_unit != 0 else dec_unit
+ x_result2[:, -remaining_slices:] = model.decode_first_stage(result2[:, :, -latent_lenght:])[0][:, -remaining_slices:]
+ else:
+ x_result2[:, i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result2[:, :, i*latent_lenght:(i+1)*latent_lenght])[0]
+
+ x_result2 = x_result2*2
+ x_result2[x_result2>1.0] = 1.0
+ x_result2[x_result2<-1.0] = -1.0
+ x_result2 = (x_result2+1)/2
+ x_result2_ = x_result2.mean(axis=0).detach().cpu().numpy()
+ x_result2 = x_result2_.transpose(1,2,0)
+
+ # Compute fusion boundaries based on result segments
+ boundaries = compute_fusion_boundaries(
+ upper_iters=upper_iters,
+ latent_length=16,
+ decode_ratio=4,
+ blend_width=16
+ )
+
+ # Fuse volumes: start with vol1, replace regions around boundaries with vol2
+ print("Fusing volumes: replacing boundary regions with vol2...")
+ x_result_fused = alternating_volume_fusion_with_boundaries(
+ x_result,
+ x_result2,
+ boundaries,
+ offset=32,
+ replace_width=16
+ )
+
+ # Load reference and save fused result
+ ref_root = '/sd/shuhan/CT-RATE/dataset/train_fixed'
+ ref_nii = os.path.join(ref_root, name.split('_')[0]+'_'+name.split('_')[1], name.split('_')[0]+'_'+name.split('_')[1]+'_'+name.split('_')[2],name+'.nii.gz')
+ affine = nib.load(ref_nii).affine
+
+ x_result_fused = x_result_fused*2000.0 - 1000.0
+ data_path = os.path.join(save_path, str(f'{name}_fused.nii.gz'))
+ data_nii = nib.Nifti1Image(x_result_fused.astype(np.int16), affine)
+
+ nib.save(data_nii, data_path)
+ print(f"Saved fused volume: {data_path}")
+ breakpoint()
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_full_ct_2d_with_body_mask.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_full_ct_2d_with_body_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..93cdb65e63122f50fdaff9a32fa944efb83301f1
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_full_ct_2d_with_body_mask.py
@@ -0,0 +1,80 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+from skimage import io
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+
+
+logdir = 'logs/full_ct_2d_with_body_mask/'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000999.ckpt")
+
+configs_file = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))[1]
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_2d_with_body_mask.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = 'inference'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+# val_num = 10
+# breakpoint()
+for idx, data in tqdm(enumerate(valloader)):
+ # if idx >= val_num:
+ # break
+ name=data['name'][0].split('.')[0]
+ data['volume_data'] = data['volume_data'].to(device)
+ data['volume_seg'] = data['volume_seg'].to(device)
+ input_text = data['input_text']
+ # .to(device)
+
+ x, c = model.get_input(data, model.first_stage_key)
+ # breakpoint()
+ samples, _ = model.sample_log(cond=c, batch_size=17, ddim=True, eta=1., ddim_steps=100)
+ samples=samples.permute(1,0,2,3)
+
+ res = model.decode_first_stage(samples[None,:])
+ res = res*2
+ # breakpoint()
+ res[res>1.0] = 1.0
+ res[res<-1.0] = -1.0
+ res = (res+1)/2
+ # breakpoint()
+ res = res[0].mean(axis=0).detach().cpu().numpy()
+ # slice_ct=(slice_ct+1)/2
+ # slice_seg=(slice_seg+1)/2
+ for z_slice in range(res.shape[0]):
+ # input_img = np.clip(np.repeat(slice_ct[0,0].detach().cpu().numpy(), 3, 0),0,1) * 255
+ res_slice = np.repeat(res[z_slice][None,], 3, 0).transpose(1,2,0)* 255
+ # slice_seg = np.clip(np.repeat(slice_seg[0,0].detach().cpu().numpy(), 3, 0),0,1) * 255
+
+ io.imsave(os.path.join(save_path, str(name) + f'_sample_{z_slice}.png'), res_slice.astype(np.uint8))
+ # io.imsave(os.path.join(save_path, str(name) + f'_input_img_{z_slice}.png'), input_img.astype(np.uint8))
+ # io.imsave(os.path.join(save_path, str(name) + f'_seg_{z_slice}.png'), slice_seg.astype(np.uint8))
+
+ break
+
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_full_ct_3d_with_body_mask.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_full_ct_3d_with_body_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..9db23d841299b4667798128f8c3bdfa53958117b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/inference_full_ct_3d_with_body_mask.py
@@ -0,0 +1,92 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+from skimage import io
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+
+
+logdir = '/sd/qichen/full_ct_gen/GEM-3D-ct-text4/logs/full_ct_3d_with_body_mask'
+ckpt = os.path.join(logdir, "checkpoints", "epoch=000365.ckpt")
+
+configs_file = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))[1]
+configs = OmegaConf.load(configs_file)
+model = instantiate_from_config(configs.model)
+model.init_from_ckpt(ckpt)
+model.eval()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print("Using device", device)
+model = model.to(device)
+
+config = OmegaConf.load('./configs/latent-diffusion/full_ct_3d_with_body_mask.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+save_path = 'inference'
+save_path = os.path.join(logdir, save_path)
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+# val_num = 10
+# breakpoint()
+for idx, data in tqdm(enumerate(valloader)):
+ # if idx >= val_num:
+ # break
+ name=data['name'][0].split('.')[0]
+ volume_data = data['volume_data']
+ volume_seg = data['volume_seg']
+ input_text = data['input_text']
+ # breakpoint()
+
+ for z_slice in range(16):
+ slice_seg = volume_seg[:,:,z_slice:z_slice+16]
+ slice_ct = volume_data[:,:,z_slice:z_slice+16]
+ # print("shape:", slice_ct.shape)
+ # print("class:", torch.unique(slice_seg))
+ # breakpoint()
+ input_data = {}
+ input_data['volume_data']=slice_ct.to(device)
+ input_data['volume_seg']=slice_seg.to(device)
+ input_data['input_text']=input_text
+ # breakpoint()
+ x, c = model.get_input(input_data, model.first_stage_key)
+ # breakpoint()
+ samples, _ = model.sample_log(cond=c, batch_size=16, ddim=True, eta=1., ddim_steps=200)
+
+ res = model.decode_first_stage(samples)
+ res[res>1.0] = 1.0
+ res[res<-1.0] = -1.0
+ res = (res+1)/2
+ # breakpoint()
+ res = res.mean(axis=1).detach().cpu().numpy()
+ # breakpoint()
+ slice_ct=(slice_ct+1)/2
+ slice_seg=(slice_seg+1)/2
+ # breakpoint()
+ for z_id in range(16):
+ input_img = np.clip(np.repeat(slice_ct[0,0,z_id][None,:].detach().cpu().numpy(), 3, 0),0,1) * 255
+ res_ = np.repeat(res[z_id][None,:], 3, 0).transpose(1,2,0)* 255
+ slice_seg_ = np.clip(np.repeat(slice_seg[0,0,z_id][None,:].detach().cpu().numpy(), 3, 0),0,1) * 255
+
+ io.imsave(os.path.join(save_path, str(name) + f'_sample_{z_id}.png'), res_.astype(np.uint8))
+ io.imsave(os.path.join(save_path, str(name) + f'_input_img_{z_id}.png'), input_img.astype(np.uint8))
+ io.imsave(os.path.join(save_path, str(name) + f'_seg_{z_id}.png'), slice_seg_.astype(np.uint8))
+
+ break
+
+ break
+
+
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/base.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d0b0a1a55a9b58d716ce7caeaa041070a77546d
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/base.py
@@ -0,0 +1,23 @@
+from abc import abstractmethod
+from torch.utils.data import Dataset, ConcatDataset, ChainDataset, IterableDataset
+
+
+class Txt2ImgIterableBaseDataset(IterableDataset):
+ '''
+ Define an interface to make the IterableDatasets for text2img data chainable
+ '''
+ def __init__(self, num_records=0, valid_ids=None, size=256):
+ super().__init__()
+ self.num_records = num_records
+ self.valid_ids = valid_ids
+ self.sample_ids = valid_ids
+ self.size = size
+
+ print(f'{self.__class__.__name__} dataset contains {self.__len__()} examples.')
+
+ def __len__(self):
+ return self.num_records
+
+ @abstractmethod
+ def __iter__(self):
+ pass
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/ct_clip_data_inference.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/ct_clip_data_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbe92ae1b925bcb2020e622c3cc8ab08f5fbfbf3
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/ct_clip_data_inference.py
@@ -0,0 +1,348 @@
+import os
+import glob
+import json
+import torch
+import pandas as pd
+import numpy as np
+from PIL import Image
+from torch.utils.data import Dataset
+import torchvision.transforms as transforms
+from functools import partial
+import torch.nn.functional as F
+import tqdm
+import nibabel as nib
+import copy
+def resize_array(array, current_spacing):
+ """
+ Resize the array to match the target spacing.
+
+ Args:
+ array (torch.Tensor): Input array to be resized.
+ current_spacing (tuple): Current voxel spacing (z_spacing, xy_spacing, xy_spacing).
+ target_spacing (tuple): Target voxel spacing (target_z_spacing, target_x_spacing, target_y_spacing).
+
+ Returns:
+ np.ndarray: Resized array.
+ """
+ # Calculate new dimensions
+ original_shape = array.shape[2:]
+
+
+ new_shape = [original_shape[0], 512, 512]
+ scaling_factors = [new_shape[i] / original_shape[i] for i in range(len(original_shape))]
+ resized_spacing = [current_spacing[i] / scaling_factors[i] for i in range(len(original_shape))]
+ # Resize the array
+ resized_array = F.interpolate(array, size=new_shape, mode='trilinear', align_corners=False).cpu().numpy()
+ # breakpoint()
+ return resized_array, resized_spacing
+
+def resize_mask(array, current_spacing):
+ """
+ Resize the array to match the target spacing.
+
+ Args:
+ array (torch.Tensor): Input array to be resized.
+ current_spacing (tuple): Current voxel spacing (z_spacing, xy_spacing, xy_spacing).
+ target_spacing (tuple): Target voxel spacing (target_z_spacing, target_x_spacing, target_y_spacing).
+
+ Returns:
+ np.ndarray: Resized array.
+ """
+ # Calculate new dimensions
+ original_shape = array.shape[2:]
+
+ new_shape = [original_shape[0], 512, 512]
+
+ resized_array = F.interpolate(array, size=new_shape, mode='nearest').cpu().numpy()
+ # breakpoint()
+ return resized_array
+
+class CTReportDatasetinfer(Dataset):
+ def __init__(self, data_folder, csv_file, min_slices=20, resize_dim=500, force_num_frames=True, labels = "labels.csv"):
+ self.data_folder = data_folder
+ self.min_slices = min_slices
+ self.labels = labels
+ self.accession_to_text = self.load_accession_text(csv_file)
+ self.paths=[]
+ self.samples = self.prepare_samples()
+ self.transform = transforms.Compose([
+ transforms.Resize((resize_dim,resize_dim)),
+ transforms.ToTensor()
+ ])
+ self.nii_to_tensor = partial(self.nii_img_to_tensor, transform = self.transform)
+ self.sample_length=65
+
+ def load_accession_text(self, csv_file):
+ df = pd.read_csv(csv_file)
+ accession_to_text = {}
+ for index, row in df.iterrows():
+ accession_to_text[row['VolumeName']] = row["Findings_EN"],row['Impressions_EN']
+ return accession_to_text
+
+
+ def prepare_samples(self):
+ samples = []
+ # patient_folders = glob.glob(os.path.join(self.data_folder, '*'))
+
+ # Read labels once outside the loop
+ test_df = pd.read_csv(self.labels)
+ test_label_cols = list(test_df.columns[1:])
+ test_df['one_hot_labels'] = list(test_df[test_label_cols].values)
+
+ import json
+
+ # with open('/sd/qichen/full_ct_gen/CT-RATE/valid_no_disease_2_checked.json', 'r') as f:
+ # items = [json.loads(line) for line in f]
+
+ # # 2. 提取所有 volume_path
+ # volume_paths = [item['volume_path']
+ # for item in items
+ # if 'volume_path' in item]
+ # organ_masks = [item['organ_mask']
+ # for item in items
+ # if 'organ_mask' in item]
+
+ # for nii_file, organ_mask in tqdm.tqdm(zip(volume_paths, organ_masks)):
+
+ # accession_number = nii_file.split("/")[-1]
+
+ # seg_file = '/sd/shuhan/CT-RATE/'+organ_mask
+ # nii_file = '/sd/shuhan/CT-RATE/'+nii_file
+ # # accession_number = accession_number.replace(".npz", ".nii.gz")
+ # if accession_number not in self.accession_to_text:
+ # continue
+
+ # impression_text = self.accession_to_text[accession_number]
+ # text_final = ""
+ # for text in list(impression_text):
+ # text = str(text)
+ # if text == "Not given.":
+ # text = ""
+
+ # text_final = text_final + text
+
+ # samples.append((nii_file, seg_file, None, text_final, None, None, None))
+
+ with open('/sd/qichen/full_ct_gen/CT-RATE/disease_mask_json/disease_valid_single_prompt_checked_label.json', 'r') as f:
+ items = [json.loads(line) for line in f]
+
+ # 2. 提取所有 volume_path
+ volume_paths = [item['volume_path']
+ for item in items
+ if 'volume_path' in item]
+
+ disease_mask_paths = [item['disease_mask']
+ for item in items
+ if 'disease_mask' in item]
+
+ organ_mask_paths = [item['organ_mask']
+ for item in items
+ if 'organ_mask' in item]
+
+ disease_findings_list = [item['disease_findings']
+ for item in items
+ if 'disease_findings' in item]
+ disease_mask_channels = [item['disease_mask_channel']
+ for item in items
+ if 'disease_mask_channel' in item]
+ disease_labels = [item['disease_label']
+ for item in items
+ if 'disease_label' in item]
+ disease_classes = [item['disease_class']
+ for item in items
+ if 'disease_class' in item]
+ # breakpoint()
+ for (ct_file, organ_mask_file, disease_mask_file, disease_findings, disease_mask_channel, disease_label, disease_class) in tqdm.tqdm(zip(volume_paths, organ_mask_paths, disease_mask_paths, disease_findings_list, disease_mask_channels, disease_labels, disease_classes)):
+
+ accession_number = organ_mask_file.split("/")[-1]
+
+ ct_file = '/sd/shuhan/CT-RATE/'+ct_file
+ organ_file = '/sd/shuhan/CT-RATE/'+organ_mask_file
+ disease_file = '/sd/shuhan/CT-RATE/'+disease_mask_file
+
+ text_final = disease_findings
+
+
+
+ samples.append((ct_file, organ_file, disease_file, text_final, disease_mask_channel, disease_label, disease_class))
+
+
+ return samples
+
+ def __len__(self):
+ return len(self.samples)
+
+ def nii_img_to_tensor(self, ct_file, organ_file, disease_file, disease_mask_channel, disease_label, disease_class, transform):
+ if disease_file == None:
+
+ nii_img = nib.load(str(ct_file))
+ img_data = nii_img.get_fdata()
+
+ df = pd.read_csv("/sd/shuhan/CT-RATE/metadata/all_metadata.csv") #select the metadata
+ file_name = ct_file.split("/")[-1]
+ row = df[df['VolumeName'] == file_name]
+ slope = float(row["RescaleSlope"].iloc[0])
+ intercept = float(row["RescaleIntercept"].iloc[0])
+ xy_spacing = float(row["XYSpacing"].iloc[0][1:][:-2].split(",")[0])
+ z_spacing = float(row["ZSpacing"].iloc[0])
+
+ nii_seg = nib.load(str(organ_file))
+ mask_data = nii_seg.get_fdata()
+
+ current = (z_spacing, xy_spacing, xy_spacing)
+
+ img_data = img_data.transpose(2, 0, 1)
+ tensor = torch.tensor(img_data)
+ tensor = tensor.unsqueeze(0).unsqueeze(0)
+ img_data, target_spacing = resize_array(tensor, current)
+ img_data = img_data[0][0]
+
+ mask_data = mask_data.transpose(2, 0, 1)
+ tensor = torch.tensor(mask_data)
+ tensor = tensor.unsqueeze(0).unsqueeze(0)
+ mask_data = resize_mask(tensor, current)
+ mask_data = mask_data[0][0]
+ # breakpoint()
+ assert mask_data.shape == img_data.shape
+ fg_mask = (mask_data>0).astype(np.uint8)
+ mask_data = (((mask_data ) / 300)).astype(np.float32) * 2 -1
+
+ hu_min, hu_max = -1000, 1000
+ img_data = np.clip(img_data, hu_min, hu_max)
+
+ bg_np = np.ones_like(img_data) * -1000
+
+ img_data = img_data*fg_mask + bg_np*(1-fg_mask)
+
+
+ img_data = (((img_data ) / 1000)).astype(np.float32)
+ start_id = np.random.randint(0, img_data.shape[0]-self.sample_length)
+ img_data = img_data[start_id:start_id+self.sample_length]
+ mask_data = mask_data[start_id:start_id+self.sample_length]
+
+ # slices=[]
+ img_data = img_data/2.0
+ mask_data = mask_data/2.0
+
+ img_data = torch.tensor(img_data)
+ mask_data = torch.tensor(mask_data)
+
+
+ img_data = img_data.unsqueeze(0)
+ mask_data = mask_data.unsqueeze(0)
+
+ return img_data, mask_data, target_spacing, file_name
+ else:
+ ct_nii = nib.load(str(ct_file))
+ img_data = ct_nii.get_fdata()
+
+ df = pd.read_csv("/sd/shuhan/CT-RATE/metadata/all_metadata.csv") #select the metadata
+ file_name = ct_file.split("/")[-1]
+ row = df[df['VolumeName'] == file_name]
+ slope = float(row["RescaleSlope"].iloc[0])
+ intercept = float(row["RescaleIntercept"].iloc[0])
+ xy_spacing = float(row["XYSpacing"].iloc[0][1:][:-2].split(",")[0])
+ z_spacing = float(row["ZSpacing"].iloc[0])
+
+ current = (z_spacing, xy_spacing, xy_spacing)
+ img_data = img_data.transpose(2, 0, 1)
+ tensor = torch.tensor(img_data)
+ tensor = tensor.unsqueeze(0).unsqueeze(0)
+ img_data, target_spacing = resize_array(tensor, current)
+ img_data = img_data[0][0]
+
+ organ_nii = nib.load(str(organ_file))
+ organ_mask = organ_nii.get_fdata()
+ organ_mask = organ_mask.transpose(2, 0, 1)
+ tensor = torch.tensor(organ_mask)
+ tensor = tensor.unsqueeze(0).unsqueeze(0)
+ organ_mask = resize_mask(tensor, current)
+ organ_mask = organ_mask[0][0]
+
+ disease_nii = nib.load(str(disease_file))
+ disease_mask = disease_nii.get_fdata()[int(disease_mask_channel)]
+ disease_mask = disease_mask.transpose(2, 0, 1)
+ tensor = torch.tensor(disease_mask)
+ tensor = tensor.unsqueeze(0).unsqueeze(0)
+ disease_mask = resize_mask(tensor, current)
+ disease_mask = disease_mask[0][0]
+
+ assert organ_mask.shape == img_data.shape
+
+ num_slices = img_data.shape[0]
+
+ # 找到 mask>0 的切片索引
+ valid_indices = np.where(disease_mask.sum(axis=(1, 2)) > 0)[0]
+
+ if len(valid_indices) > 0:
+ # 只保留能完整取 sample_length 的起点
+ valid_start_ids = valid_indices[valid_indices <= num_slices - self.sample_length]
+
+ if len(valid_start_ids) > 0:
+ start_id = np.random.choice(valid_start_ids)
+ else:
+ # 如果没有合法起点就退化到随机，但保证能取 sample_length
+ start_id = np.random.randint(0, num_slices - self.sample_length + 1)
+ else:
+ # 如果 mask 全部为0，则完全随机
+ start_id = np.random.randint(0, num_slices - self.sample_length + 1)
+
+ end_id = start_id + self.sample_length # 保证长度严格等于 sample_length
+ img_data = img_data[start_id:end_id]
+ organ_mask = organ_mask[start_id:end_id]
+ disease_mask = disease_mask[start_id:end_id]
+ # img_data = img_data[start_id]
+ # organ_mask = organ_mask[start_id]
+ # disease_mask = disease_mask[start_id]
+
+ organ_disease_mask = copy.deepcopy(organ_mask)
+ organ_disease_mask[disease_mask>0] = disease_class+100
+
+ organ_mask = (((organ_mask ) / 300)).astype(np.float32) * 2 -1
+ organ_disease_mask = (((organ_disease_mask ) / 300)).astype(np.float32) * 2 -1
+
+ hu_min, hu_max = -1000, 1000
+ img_data = np.clip(img_data, hu_min, hu_max)
+ img_data = (((img_data ) / 1000)).astype(np.float32)
+
+ img_data = torch.tensor(img_data)
+ organ_mask = torch.tensor(organ_mask)
+ organ_disease_mask = torch.tensor(organ_disease_mask)
+
+ img_data = img_data/2.0
+ organ_mask = organ_mask/2.0
+ organ_disease_mask = organ_disease_mask/2.0
+
+ img_data = img_data.unsqueeze(0)
+ organ_mask = organ_mask.unsqueeze(0)
+ organ_disease_mask = organ_disease_mask.unsqueeze(0)
+
+
+ # img_data=img_data.repeat(3,1,1,1)
+ # organ_mask=organ_mask.repeat(3,1,1,1)
+ # organ_disease_mask=organ_disease_mask.repeat(3,1,1,1)
+
+
+ # return img_data, organ_mask, organ_disease_mask, target_spacing, file_name
+ # return img_data, organ_mask, target_spacing, file_name
+ return img_data, organ_disease_mask, target_spacing, file_name
+
+
+
+ def __getitem__(self, index):
+ ct_file, organ_file, disease_file, input_text, disease_mask_channel, disease_label, disease_class = self.samples[index]
+ video_tensor, volume_seg, spacing, file_name = self.nii_to_tensor(ct_file, organ_file, disease_file, disease_mask_channel, disease_label, disease_class)
+ input_text = str(input_text)
+ input_text = input_text.replace('"', '')
+ input_text = input_text.replace('\'', '')
+ input_text = input_text.replace('(', '')
+ input_text = input_text.replace(')', '')
+
+ # return video_tensor, input_text
+ example = {}
+ example['name'] = file_name
+ example['volume_data'] = video_tensor.float()
+ example['volume_seg'] = volume_seg.float()
+ example['spacing'] = spacing
+ example['input_text'] = input_text
+ return example
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/ct_clip_data_train.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/ct_clip_data_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..2996c2a3cee767aee0a68a397171360b4debceea
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/ct_clip_data_train.py
@@ -0,0 +1,352 @@
+import os
+import glob
+import json
+import torch
+import pandas as pd
+import numpy as np
+from PIL import Image
+from torch.utils.data import Dataset
+import torchvision.transforms as transforms
+from functools import partial
+import torch.nn.functional as F
+import nibabel as nib
+import tqdm
+import copy
+def resize_array(array, current_spacing):
+ """
+ Resize the array to match the target spacing.
+
+ Args:
+ array (torch.Tensor): Input array to be resized.
+ current_spacing (tuple): Current voxel spacing (z_spacing, xy_spacing, xy_spacing).
+ target_spacing (tuple): Target voxel spacing (target_z_spacing, target_x_spacing, target_y_spacing).
+
+ Returns:
+ np.ndarray: Resized array.
+ """
+ # Calculate new dimensions
+ original_shape = array.shape[2:]
+
+
+ new_shape = [original_shape[0], 512, 512]
+ scaling_factors = [new_shape[i] / original_shape[i] for i in range(len(original_shape))]
+ resized_spacing = [current_spacing[i] / scaling_factors[i] for i in range(len(original_shape))]
+ # Resize the array
+ resized_array = F.interpolate(array, size=new_shape, mode='trilinear', align_corners=False).cpu().numpy()
+ # breakpoint()
+ return resized_array, resized_spacing
+
+def resize_mask(array, current_spacing):
+ """
+ Resize the array to match the target spacing.
+
+ Args:
+ array (torch.Tensor): Input array to be resized.
+ current_spacing (tuple): Current voxel spacing (z_spacing, xy_spacing, xy_spacing).
+ target_spacing (tuple): Target voxel spacing (target_z_spacing, target_x_spacing, target_y_spacing).
+
+ Returns:
+ np.ndarray: Resized array.
+ """
+ # Calculate new dimensions
+ original_shape = array.shape[2:]
+
+ new_shape = [original_shape[0], 512, 512]
+
+ resized_array = F.interpolate(array, size=new_shape, mode='nearest').cpu().numpy()
+ # breakpoint()
+ return resized_array
+
+class CTReportDataset(Dataset):
+ def __init__(self, data_folder, csv_file, min_slices=20, resize_dim=500, force_num_frames=True):
+ self.data_folder = data_folder
+ self.min_slices = min_slices
+ self.accession_to_text = self.load_accession_text(csv_file)
+ self.paths=[]
+ self.samples = self.prepare_samples()
+ percent = 80
+ num_files = int((len(self.samples) * percent) / 100)
+ #num_files = 2286
+ self.samples = self.samples[:num_files]
+ print(len(self.samples))
+ self.count = 0
+
+
+ #self.resize_dim = resize_dim
+ #self.resize_transform = transforms.Resize((resize_dim, resize_dim))
+ self.transform = transforms.Compose([
+ transforms.Resize((resize_dim,resize_dim)),
+ transforms.ToTensor()
+ ])
+ self.nii_to_tensor = partial(self.nii_img_to_tensor, transform = self.transform)
+ self.sample_length=65
+
+ def load_accession_text(self, csv_file):
+ df = pd.read_csv(csv_file)
+ accession_to_text = {}
+ for index, row in df.iterrows():
+ # breakpoint()
+ accession_to_text[row['VolumeName']] = row["Findings_EN"],row['Impressions_EN']
+
+ return accession_to_text
+
+
+ def prepare_samples(self):
+ samples = []
+ import json
+
+ # with open('/sd/qichen/full_ct_gen/CT-RATE/train_no_disease_2_checked.json', 'r') as f:
+ # items = [json.loads(line) for line in f]
+
+ # # 2. 提取所有 volume_path
+ # volume_paths = [item['volume_path']
+ # for item in items
+ # if 'volume_path' in item]
+ # organ_masks = [item['organ_mask']
+ # for item in items
+ # if 'organ_mask' in item]
+ # for nii_file, organ_mask in tqdm.tqdm(zip(volume_paths, organ_masks)):
+
+ # accession_number = nii_file.split("/")[-1]
+
+ # seg_file = '/sd/shuhan/CT-RATE/'+ organ_mask
+ # nii_file = '/sd/shuhan/CT-RATE/'+nii_file
+
+ # if accession_number not in self.accession_to_text:
+ # continue
+
+ # impression_text = self.accession_to_text[accession_number]
+
+ # if impression_text == "Not given.":
+ # impression_text=""
+
+ # input_text_concat = ""
+ # for text in impression_text:
+ # input_text_concat = input_text_concat + str(text)
+ # input_text_concat = impression_text[0]
+ # input_text = f'{impression_text}'
+ # samples.append((nii_file, seg_file, None, input_text_concat, None, None, None))
+ # self.paths.append(nii_file)
+
+ with open('/sd/qichen/full_ct_gen/CT-RATE/disease_mask_json/disease_train_single_prompt_checked_label.json', 'r') as f:
+ items = [json.loads(line) for line in f]
+
+ # 2. 提取所有 volume_path
+ volume_paths = [item['volume_path']
+ for item in items
+ if 'volume_path' in item]
+
+ effusion_mask_paths = [item['disease_mask']
+ for item in items
+ if 'disease_mask' in item]
+
+ organ_mask_paths = [item['organ_mask']
+ for item in items
+ if 'organ_mask' in item]
+
+ disease_findings_list = [item['disease_findings']
+ for item in items
+ if 'disease_findings' in item]
+
+ disease_mask_channels = [item['disease_mask_channel']
+ for item in items
+ if 'disease_mask_channel' in item]
+ disease_labels = [item['disease_label']
+ for item in items
+ if 'disease_label' in item]
+ disease_classes = [item['disease_class']
+ for item in items
+ if 'disease_class' in item]
+
+ for (ct_file, organ_mask_file, disease_mask_file, disease_findings, disease_mask_channel, disease_label, disease_class) in tqdm.tqdm(zip(volume_paths, organ_mask_paths, effusion_mask_paths, disease_findings_list, disease_mask_channels, disease_labels, disease_classes)):
+
+ accession_number = organ_mask_file.split("/")[-1]
+
+ ct_file = '/sd/shuhan/CT-RATE/'+ct_file
+ organ_file = '/sd/shuhan/CT-RATE/'+organ_mask_file
+ disease_file = '/sd/shuhan/CT-RATE/'+disease_mask_file
+
+ impression_text = disease_findings
+
+ samples.append((ct_file, organ_file, disease_file, impression_text, disease_mask_channel, disease_label, disease_class))
+
+ return samples
+
+ def __len__(self):
+ return len(self.samples)
+
+
+ def nii_img_to_tensor(self, ct_file, organ_file, disease_file, disease_mask_channel, disease_label, disease_class, transform):
+ if disease_file == None:
+
+ nii_img = nib.load(str(ct_file))
+ img_data = nii_img.get_fdata()
+
+ df = pd.read_csv("/sd/shuhan/CT-RATE/metadata/all_metadata.csv") #select the metadata
+ file_name = ct_file.split("/")[-1]
+ row = df[df['VolumeName'] == file_name]
+ slope = float(row["RescaleSlope"].iloc[0])
+ intercept = float(row["RescaleIntercept"].iloc[0])
+ xy_spacing = float(row["XYSpacing"].iloc[0][1:][:-2].split(",")[0])
+ z_spacing = float(row["ZSpacing"].iloc[0])
+
+ nii_seg = nib.load(str(organ_file))
+ mask_data = nii_seg.get_fdata()
+
+ current = (z_spacing, xy_spacing, xy_spacing)
+
+ img_data = img_data.transpose(2, 0, 1)
+ tensor = torch.tensor(img_data)
+ tensor = tensor.unsqueeze(0).unsqueeze(0)
+ img_data, target_spacing = resize_array(tensor, current)
+ img_data = img_data[0][0]
+
+ mask_data = mask_data.transpose(2, 0, 1)
+ tensor = torch.tensor(mask_data)
+ tensor = tensor.unsqueeze(0).unsqueeze(0)
+ mask_data = resize_mask(tensor, current)
+ mask_data = mask_data[0][0]
+ # breakpoint()
+ assert mask_data.shape == img_data.shape
+ fg_mask = (mask_data>0).astype(np.uint8)
+ mask_data = (((mask_data ) / 300)).astype(np.float32) * 2 -1
+
+ hu_min, hu_max = -1000, 1000
+ img_data = np.clip(img_data, hu_min, hu_max)
+
+ bg_np = np.ones_like(img_data) * -1000
+
+ img_data = img_data*fg_mask + bg_np*(1-fg_mask)
+
+
+ img_data = (((img_data ) / 1000)).astype(np.float32)
+ start_id = np.random.randint(0, img_data.shape[0]-self.sample_length)
+ img_data = img_data[start_id:start_id+self.sample_length]
+ mask_data = mask_data[start_id:start_id+self.sample_length]
+
+ # slices=[]
+ img_data = img_data/2.0
+ mask_data = mask_data/2.0
+
+ img_data = torch.tensor(img_data)
+ mask_data = torch.tensor(mask_data)
+
+
+ img_data = img_data.unsqueeze(0)
+ mask_data = mask_data.unsqueeze(0)
+
+ return img_data, mask_data, target_spacing, file_name
+ else:
+ ct_nii = nib.load(str(ct_file))
+ img_data = ct_nii.get_fdata()
+
+ df = pd.read_csv("/sd/shuhan/CT-RATE/metadata/all_metadata.csv") #select the metadata
+ file_name = ct_file.split("/")[-1]
+ row = df[df['VolumeName'] == file_name]
+ slope = float(row["RescaleSlope"].iloc[0])
+ intercept = float(row["RescaleIntercept"].iloc[0])
+ xy_spacing = float(row["XYSpacing"].iloc[0][1:][:-2].split(",")[0])
+ z_spacing = float(row["ZSpacing"].iloc[0])
+
+ current = (z_spacing, xy_spacing, xy_spacing)
+ img_data = img_data.transpose(2, 0, 1)
+ tensor = torch.tensor(img_data)
+ tensor = tensor.unsqueeze(0).unsqueeze(0)
+ img_data, target_spacing = resize_array(tensor, current)
+ img_data = img_data[0][0]
+
+ organ_nii = nib.load(str(organ_file))
+ organ_mask = organ_nii.get_fdata()
+ organ_mask = organ_mask.transpose(2, 0, 1)
+ tensor = torch.tensor(organ_mask)
+ tensor = tensor.unsqueeze(0).unsqueeze(0)
+ organ_mask = resize_mask(tensor, current)
+ organ_mask = organ_mask[0][0]
+
+ disease_nii = nib.load(str(disease_file))
+ disease_mask = disease_nii.get_fdata()[int(disease_mask_channel)]
+ disease_mask = disease_mask.transpose(2, 0, 1)
+ tensor = torch.tensor(disease_mask)
+ tensor = tensor.unsqueeze(0).unsqueeze(0)
+ disease_mask = resize_mask(tensor, current)
+ disease_mask = disease_mask[0][0]
+
+ assert organ_mask.shape == img_data.shape
+
+ num_slices = img_data.shape[0]
+
+ # 找到 mask>0 的切片索引
+ valid_indices = np.where(disease_mask.sum(axis=(1, 2)) > 0)[0]
+
+ if len(valid_indices) > 0:
+ # 只保留能完整取 sample_length 的起点
+ valid_start_ids = valid_indices[valid_indices <= num_slices - self.sample_length]
+
+ if len(valid_start_ids) > 0:
+ start_id = np.random.choice(valid_start_ids)
+ else:
+ # 如果没有合法起点就退化到随机，但保证能取 sample_length
+ start_id = np.random.randint(0, num_slices - self.sample_length + 1)
+ else:
+ # 如果 mask 全部为0，则完全随机
+ start_id = np.random.randint(0, num_slices - self.sample_length + 1)
+
+ end_id = start_id + self.sample_length # 保证长度严格等于 sample_length
+ img_data = img_data[start_id:end_id]
+ organ_mask = organ_mask[start_id:end_id]
+ disease_mask = disease_mask[start_id:end_id]
+ # img_data = img_data[start_id]
+ # organ_mask = organ_mask[start_id]
+ # disease_mask = disease_mask[start_id]
+
+ organ_disease_mask = copy.deepcopy(organ_mask)
+ organ_disease_mask[disease_mask>0] = disease_class+100
+
+ organ_mask = (((organ_mask ) / 300)).astype(np.float32) * 2 -1
+ organ_disease_mask = (((organ_disease_mask ) / 300)).astype(np.float32) * 2 -1
+
+ hu_min, hu_max = -1000, 1000
+ img_data = np.clip(img_data, hu_min, hu_max)
+ img_data = (((img_data ) / 1000)).astype(np.float32)
+
+ img_data = torch.tensor(img_data)
+ organ_mask = torch.tensor(organ_mask)
+ organ_disease_mask = torch.tensor(organ_disease_mask)
+
+ img_data = img_data/2.0
+ organ_mask = organ_mask/2.0
+ organ_disease_mask = organ_disease_mask/2.0
+
+ img_data = img_data.unsqueeze(0)
+ organ_mask = organ_mask.unsqueeze(0)
+ organ_disease_mask = organ_disease_mask.unsqueeze(0)
+
+
+ # img_data=img_data.repeat(3,1,1,1)
+ # organ_mask=organ_mask.repeat(3,1,1,1)
+ # organ_disease_mask=organ_disease_mask.repeat(3,1,1,1)
+
+
+ # return img_data, organ_mask, organ_disease_mask, target_spacing, file_name
+ # return img_data, organ_mask, target_spacing, file_name
+ return img_data, organ_disease_mask, target_spacing, file_name
+
+
+
+ def __getitem__(self, index):
+ ct_file, organ_file, disease_file, input_text, disease_mask_channel, disease_label, disease_class = self.samples[index]
+ video_tensor, volume_seg, spacing, file_name = self.nii_to_tensor(ct_file, organ_file, disease_file, disease_mask_channel, disease_label, disease_class)
+ input_text = str(input_text)
+ input_text = input_text.replace('"', '')
+ input_text = input_text.replace('\'', '')
+ input_text = input_text.replace('(', '')
+ input_text = input_text.replace(')', '')
+
+ # return video_tensor, input_text
+ example = {}
+ example['name'] = file_name
+ example['volume_data'] = video_tensor.float()
+ example['volume_seg'] = volume_seg.float()
+ example['spacing'] = spacing
+ example['input_text'] = input_text
+ return example
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/mask_generation.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/mask_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8ac71405628b5dd6fdc9ab2c599ead4c260845b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/mask_generation.py
@@ -0,0 +1,61 @@
+import os
+import numpy as np
+from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+
+class volume_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_file,
+ data_repeat=1
+ ):
+
+ self.data_root = data_root
+ planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ file_list = []
+ for line in open(data_file):
+ name = line.strip().split()[1].split('.nii.gz')[0]
+ name = name.split('/')[-1]
+ file_list.append(name)
+
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self.sample_length=16
+ self.sample=False
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ seg_npz = np.load(npz_file)['seg'][0]
+
+ volume_seg = volume_seg.astype(np.float32)
+
+ return volume_seg
+
+class volume_train(volume_base):
+ def __init__(self, data_file='data_split/liver/train.txt', **kwargs):
+ super().__init__(data_file=data_file, **kwargs)
+
+
+class volume_val(volume_base):
+ def __init__(self, data_file='data_split/liver/eval.txt', **kwargs):
+ super().__init__(data_file=data_file, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class volume_test(volume_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9387be62d79305474c66be20b32ce6b5d8e9014
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset.py
@@ -0,0 +1,103 @@
+import os
+import numpy as np
+# from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+from batchgenerators.utilities.file_and_folder_operations import join, load_json, isfile, save_json, maybe_mkdir_p
+
+
+class slice_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_repeat=1,
+ planner='nnUNetPlans_3d_fullres',
+ data_file='data_split/liver/train.txt',
+ phase='train',
+ ):
+
+ self.data_root = data_root
+ # planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+
+ file_list = []
+ if 'liver' in data_file:
+ for line in open(data_file):
+ name = line.strip().split()[1].split('.nii.gz')[0]
+ name = name.split('/')[-1]
+ file_list.append(name)
+ else:
+ file_list = load_json(data_file)[0][phase]
+
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+ # print('data_npz', data_npz.shape)
+ # print('seg_npz', seg_npz.shape)
+ # breakpoint()
+ pos_id = np.random.randint(0, data_npz.shape[0])
+ # pos_id = data_npz.shape[0]//2
+ slice_data = data_npz[pos_id]
+ slice_seg = seg_npz[pos_id]
+
+ # slice_data = np.pad(slice_data,((0,0),(8,8),(8,8)),'constant')
+ # breakpoint()
+ slice_data[slice_data <= -175] = -175
+ slice_data[slice_data >= 250] = 250
+ # breakpoint()
+ slice_data = (slice_data+175)/425.0
+
+ # tumor_mask = (slice_seg==2).astype(np.float32)
+ slice_seg = slice_seg.astype(np.float32)
+ foreground_mask = (slice_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*slice_data
+
+ slice_data = slice_data[:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,None].astype(np.float32)
+
+ # tumor_mask = resize(tumor_mask, (64,64), order=0)
+ slice_seg = cv2.resize(slice_seg,(64,64),interpolation=cv2.INTER_NEAREST)
+ slice_seg = slice_seg[:,:,None].astype(np.float32)
+ # print(np.unique(slice_seg))
+ # breakpoint()
+ if (slice_data.shape[0] != 512) or (slice_data.shape[1] != 512):
+ print(self.list[i],slice_data.shape,masked_data.shape,slice_seg.shape)
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['slice_data'] = slice_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = slice_seg
+ # breakpoint()
+ return example
+
+class slice_train(slice_base):
+ def __init__(self, phase='train', **kwargs):
+ super().__init__(phase=phase, **kwargs)
+
+
+class slice_val(slice_base):
+ def __init__(self, phase='val', **kwargs):
+ super().__init__(phase=phase, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class slice_test(slice_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset__.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e53d2b753b22444d3415c651efad0771dca662d
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset__.py
@@ -0,0 +1,81 @@
+import os
+import numpy as np
+# from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+
+class slice_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ # data_file,
+ data_repeat=1
+ ):
+
+ self.data_root = data_root
+ planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+ self.list.sort()
+ # breakpoint()
+ self.list = self.list[:5]
+ # file_list = []
+ # with open(data_file, 'r') as f:
+ # file_list = f.readlines()
+ # file_list = [i.split('\n')[0] for i in file_list]
+ # self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+ # breakpoint()
+ self._length = len(self.list)
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+
+ # pos_id = np.random.randint(0, data_npz.shape[0])
+ # pos_id = data_npz.shape[0]//2
+ # slice_data = data_npz[pos_id]
+ # slice_seg = seg_npz[pos_id]
+
+ # slice_data = np.pad(slice_data,((0,0),(8,8),(8,8)),'constant')
+ # breakpoint()
+ data_npz[data_npz <= -175] = -175
+ data_npz[data_npz >= 250] = 250
+ # breakpoint()
+ data_npz = (data_npz+175)/425.0
+
+ data_npz = data_npz[:,:,:,None].astype(np.float32)
+
+ # breakpoint()
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['slice_data'] = data_npz * 2 - 1
+
+ # breakpoint()
+ return example
+
+class slice_train(slice_base):
+ def __init__(self, **kwargs):
+ super().__init__( **kwargs)
+
+
+class slice_val(slice_base):
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class slice_test(slice_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_28class.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_28class.py
new file mode 100644
index 0000000000000000000000000000000000000000..de79231129330e28794129304585ca12e9b1476c
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_28class.py
@@ -0,0 +1,99 @@
+import os
+import numpy as np
+from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+from batchgenerators.utilities.file_and_folder_operations import load_json
+
+class slice_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_file,
+ data_repeat=1,
+ phase='train',
+ planner='nnUNetPlans_3d_fullres'
+ ):
+
+ self.data_root = data_root
+ # planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+
+ data_splits = load_json(data_file)
+ file_list = data_splits[0][phase]
+ # breakpoint()
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+ # print(npz_file, data_npz.shape, seg_npz.shape)
+ # breakpoint()
+ pos_id = np.random.randint(0, data_npz.shape[0])
+ # pos_id = data_npz.shape[0]//2
+ slice_data = data_npz[pos_id]
+ slice_seg = seg_npz[pos_id]
+
+ # slice_data = np.pad(slice_data,((0,0),(8,8),(8,8)),'constant')
+ # breakpoint()
+ slice_data[slice_data <= -175] = -175
+ slice_data[slice_data >= 250] = 250
+ # breakpoint()
+ slice_data = (slice_data+175)/425.0
+
+
+ # slice_data = resize(slice_data, (512,512))
+ # slice_seg = resize(slice_seg.astype(np.float32), (512,512), order=0, anti_aliasing=False)
+ # breakpoint()
+
+ # tumor_mask = (slice_seg==2).astype(np.float32)
+ slice_seg = slice_seg.astype(np.float32)
+ foreground_mask = (slice_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*slice_data
+
+ slice_data = slice_data[:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,None].astype(np.float32)
+
+ # breakpoint()
+ # tumor_mask = resize(tumor_mask, (64,64), order=0)
+ slice_seg = cv2.resize(slice_seg,(64,64),interpolation=cv2.INTER_NEAREST)
+ slice_seg = slice_seg[:,:,None].astype(np.float32)
+ # print(np.unique(slice_seg))
+ # breakpoint()
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['slice_data'] = slice_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = slice_seg
+ # breakpoint()
+ return example
+
+class slice_train(slice_base):
+ def __init__(self, data_file='data_split/splits_final2.json', phase='train', **kwargs):
+ super().__init__(data_file=data_file, **kwargs)
+
+
+class slice_val(slice_base):
+ def __init__(self, data_file='data_split/splits_final2.json', phase='val', **kwargs):
+ super().__init__(data_file=data_file,phase=phase, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class slice_test(slice_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_28class_randomclass.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_28class_randomclass.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d2095b5a685e098685ee2028c817634cf28feb4
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_28class_randomclass.py
@@ -0,0 +1,127 @@
+import os
+import numpy as np
+from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+# from batchgenerators.utilities.file_and_folder_operations import load_json
+
+import random
+import itertools
+
+def get_random_classes_from_mask(mask):
+ # 分析 mask 中有哪些类别
+ unique_classes = np.unique(mask)
+ unique_classes = unique_classes[unique_classes != 0] # 去除背景类（假设背景类为0）
+
+ # 获取所有可能的类别组合
+ all_combinations = []
+ for i in range(1, len(unique_classes) + 1):
+ all_combinations.extend(itertools.combinations(unique_classes, i))
+
+ # 随机选择一个组合
+ selected_combination = random.choice(all_combinations)
+ return selected_combination
+
+def get_filtered_mask(mask, selected_classes):
+ # 创建只包含选定类别的 mask
+ filtered_mask = np.isin(mask, selected_classes) * mask
+ return filtered_mask
+
+class slice_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_repeat=1,
+ phase='train',
+ planner='nnUNetPlans_3d_fullres',
+ data_file=None
+ ):
+
+ self.data_root = data_root
+ # planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+
+ data_splits = load_json(data_file)
+ file_list = data_splits[0][phase]
+ # breakpoint()
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self._data_repeat = data_repeat
+ self.class_num = 28
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+ # print(npz_file, data_npz.shape, seg_npz.shape)
+ # breakpoint()
+ pos_id = np.random.randint(0, data_npz.shape[0])
+ slice_data = data_npz[pos_id]
+ slice_seg = seg_npz[pos_id]
+
+ # breakpoint()
+ slice_data[slice_data <= -175] = -175
+ slice_data[slice_data >= 250] = 250
+ slice_data = (slice_data+175)/425.0
+
+ # print(np.unique(slice_seg))
+ if len(np.unique(slice_seg)) > 1:
+ selected_classes = get_random_classes_from_mask(slice_seg)
+ slice_seg = get_filtered_mask(slice_seg, selected_classes)
+ # print(np.unique(slice_seg))
+
+ # breakpoint()
+
+ # slice_data = resize(slice_data, (512,512))
+ # slice_seg = resize(slice_seg.astype(np.float32), (512,512), order=0, anti_aliasing=False)
+ # breakpoint()
+
+ # tumor_mask = (slice_seg==2).astype(np.float32)
+ slice_seg = slice_seg.astype(np.float32)
+ foreground_mask = (slice_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*slice_data
+
+ slice_data = slice_data[:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,None].astype(np.float32)
+
+ # breakpoint()
+ # tumor_mask = resize(tumor_mask, (64,64), order=0)
+ slice_seg = cv2.resize(slice_seg,(64,64),interpolation=cv2.INTER_NEAREST)
+ slice_seg = slice_seg[:,:,None].astype(np.float32)
+ if (slice_data.shape[0] != 512) or (slice_data.shape[1] != 512):
+ print(self.list[i],slice_data.shape,masked_data.shape,slice_seg.shape)
+ # print(np.unique(slice_seg))
+ # breakpoint()
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['slice_data'] = slice_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = slice_seg
+ # breakpoint()
+ return example
+
+class slice_train(slice_base):
+ def __init__(self, phase='train', **kwargs):
+ super().__init__(**kwargs)
+
+
+class slice_val(slice_base):
+ def __init__(self, phase='val', **kwargs):
+ super().__init__(phase=phase, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class slice_test(slice_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_28class_randomclass_kl4.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_28class_randomclass_kl4.py
new file mode 100644
index 0000000000000000000000000000000000000000..433c1f35318ec318496c4397c1b6b3bdb1c73831
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_28class_randomclass_kl4.py
@@ -0,0 +1,127 @@
+import os
+import numpy as np
+from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+from batchgenerators.utilities.file_and_folder_operations import load_json
+
+import random
+import itertools
+
+def get_random_classes_from_mask(mask):
+ # 分析 mask 中有哪些类别
+ unique_classes = np.unique(mask)
+ unique_classes = unique_classes[unique_classes != 0] # 去除背景类（假设背景类为0）
+
+ # 获取所有可能的类别组合
+ all_combinations = []
+ for i in range(1, len(unique_classes) + 1):
+ all_combinations.extend(itertools.combinations(unique_classes, i))
+
+ # 随机选择一个组合
+ selected_combination = random.choice(all_combinations)
+ return selected_combination
+
+def get_filtered_mask(mask, selected_classes):
+ # 创建只包含选定类别的 mask
+ filtered_mask = np.isin(mask, selected_classes) * mask
+ return filtered_mask
+
+class slice_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_repeat=1,
+ phase='train',
+ planner='nnUNetPlans_3d_fullres',
+ data_file=None
+ ):
+
+ self.data_root = data_root
+ # planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+
+ data_splits = load_json(data_file)
+ file_list = data_splits[0][phase]
+ # breakpoint()
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self._data_repeat = data_repeat
+ self.class_num = 28
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+ # print(npz_file, data_npz.shape, seg_npz.shape)
+ # breakpoint()
+ pos_id = np.random.randint(0, data_npz.shape[0])
+ slice_data = data_npz[pos_id]
+ slice_seg = seg_npz[pos_id]
+
+ # breakpoint()
+ slice_data[slice_data <= -175] = -175
+ slice_data[slice_data >= 250] = 250
+ slice_data = (slice_data+175)/425.0
+
+ # print(np.unique(slice_seg))
+ if len(np.unique(slice_seg)) > 1:
+ selected_classes = get_random_classes_from_mask(slice_seg)
+ slice_seg = get_filtered_mask(slice_seg, selected_classes)
+ # print(np.unique(slice_seg))
+
+ # breakpoint()
+
+ # slice_data = resize(slice_data, (512,512))
+ # slice_seg = resize(slice_seg.astype(np.float32), (512,512), order=0, anti_aliasing=False)
+ # breakpoint()
+
+ # tumor_mask = (slice_seg==2).astype(np.float32)
+ slice_seg = slice_seg.astype(np.float32)
+ foreground_mask = (slice_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*slice_data
+
+ slice_data = slice_data[:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,None].astype(np.float32)
+
+ # breakpoint()
+ # tumor_mask = resize(tumor_mask, (64,64), order=0)
+ slice_seg = cv2.resize(slice_seg,(128,128),interpolation=cv2.INTER_NEAREST)
+ slice_seg = slice_seg[:,:,None].astype(np.float32)
+ if (slice_data.shape[0] != 512) or (slice_data.shape[1] != 512):
+ print(self.list[i],slice_data.shape,masked_data.shape,slice_seg.shape)
+ # print(np.unique(slice_seg))
+ # breakpoint()
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['slice_data'] = slice_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = slice_seg
+ # breakpoint()
+ return example
+
+class slice_train(slice_base):
+ def __init__(self, phase='train', **kwargs):
+ super().__init__(**kwargs)
+
+
+class slice_val(slice_base):
+ def __init__(self, phase='val', **kwargs):
+ super().__init__(phase=phase, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class slice_test(slice_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_resize.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_resize.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6a1947ac15fb14f791800d0746a7ae1e796e429
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_abdomenatlas_resize.py
@@ -0,0 +1,97 @@
+import os
+import numpy as np
+from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+from batchgenerators.utilities.file_and_folder_operations import load_json
+
+class slice_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_file,
+ data_repeat=1,
+ phase='train'
+ ):
+
+ self.data_root = data_root
+ planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+
+ data_splits = load_json(data_file)
+ file_list = data_splits[0][phase]
+ # breakpoint()
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+ # breakpoint()
+ pos_id = np.random.randint(0, data_npz.shape[0])
+ # pos_id = data_npz.shape[0]//2
+ slice_data = data_npz[pos_id]
+ slice_seg = seg_npz[pos_id]
+
+ # slice_data = np.pad(slice_data,((0,0),(8,8),(8,8)),'constant')
+ # breakpoint()
+ slice_data[slice_data <= -175] = -175
+ slice_data[slice_data >= 250] = 250
+ # breakpoint()
+ slice_data = (slice_data+175)/425.0
+
+
+ slice_data = resize(slice_data, (512,512))
+ slice_seg = resize(slice_seg.astype(np.float32), (512,512), order=0, anti_aliasing=False)
+ # breakpoint()
+
+ # tumor_mask = (slice_seg==2).astype(np.float32)
+ slice_seg = slice_seg.astype(np.float32)
+ foreground_mask = (slice_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*slice_data
+
+ slice_data = slice_data[:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,None].astype(np.float32)
+
+ # breakpoint()
+ # tumor_mask = resize(tumor_mask, (64,64), order=0)
+ slice_seg = cv2.resize(slice_seg,(64,64),interpolation=cv2.INTER_NEAREST)
+ slice_seg = slice_seg[:,:,None].astype(np.float32)
+ # print(np.unique(slice_seg))
+ # breakpoint()
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['slice_data'] = slice_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = slice_seg
+ # breakpoint()
+ return example
+
+class slice_train(slice_base):
+ def __init__(self, data_file='data_split/splits_final.json', phase='train', **kwargs):
+ super().__init__(data_file=data_file, **kwargs)
+
+
+class slice_val(slice_base):
+ def __init__(self, data_file='data_split/splits_final.json', phase='val', **kwargs):
+ super().__init__(data_file=data_file,phase=phase, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class slice_test(slice_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_infer.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..4416966f0e6355055edeb71164fb956153aaae14
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_infer.py
@@ -0,0 +1,94 @@
+import os
+import numpy as np
+# from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+
+class slice_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_file,
+ data_repeat=1
+ ):
+
+ self.data_root = data_root
+ planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+ file_list = []
+ for line in open(data_file):
+ name = line.strip().split()[1].split('.nii.gz')[0]
+ name = name.split('/')[-1]
+ file_list.append(name)
+ # breakpoint()
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+ # breakpoint()
+ # pos_id = np.random.randint(0, data_npz.shape[0])
+ # pos_id = data_npz.shape[0]//2
+ pos_id_list = np.where(np.any(data_npz==2, axis=(1, 2)))[0]
+ pos_id = pos_id_list[len(pos_id_list)//2]
+ # breakpoint()
+ slice_data = data_npz[pos_id]
+ slice_seg = seg_npz[pos_id]
+ # breakpoint()
+ # slice_data = np.pad(slice_data,((0,0),(8,8),(8,8)),'constant')
+ # breakpoint()
+ slice_data[slice_data <= -175] = -175
+ slice_data[slice_data >= 250] = 250
+ # breakpoint()
+ slice_data = (slice_data+175)/425.0
+
+ # tumor_mask = (slice_seg==2).astype(np.float32)
+ slice_seg = slice_seg.astype(np.float32)
+ foreground_mask = (slice_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*slice_data
+
+ slice_data = slice_data[:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,None].astype(np.float32)
+
+ # tumor_mask = resize(tumor_mask, (64,64), order=0)
+ slice_seg = cv2.resize(slice_seg,(64,64),interpolation=cv2.INTER_NEAREST)
+ slice_seg = slice_seg[:,:,None].astype(np.float32)
+ # print(np.unique(slice_seg))
+ # breakpoint()
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['slice_data'] = slice_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = slice_seg
+ # breakpoint()
+ return example
+
+class slice_train(slice_base):
+ def __init__(self, data_file='data_split/liver/train.txt', **kwargs):
+ super().__init__(data_file=data_file, **kwargs)
+
+
+class slice_val(slice_base):
+ def __init__(self, data_file='data_split/liver/eval.txt', **kwargs):
+ super().__init__(data_file=data_file, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class slice_test(slice_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_infer_28class_randomclass.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_infer_28class_randomclass.py
new file mode 100644
index 0000000000000000000000000000000000000000..85a7a9bc7054e71e59c62a25e6f3fdeb31c1b2bf
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/slice_dataset_infer_28class_randomclass.py
@@ -0,0 +1,99 @@
+import os
+import numpy as np
+# from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+
+def get_filtered_mask(mask, selected_classes):
+ # 创建只包含选定类别的 mask
+ filtered_mask = np.isin(mask, selected_classes) * mask
+ return filtered_mask
+
+class slice_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_file,
+ data_repeat=1
+ ):
+
+ self.data_root = data_root
+ planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+ file_list = []
+ for line in open(data_file):
+ name = line.strip().split()[1].split('.nii.gz')[0]
+ name = name.split('/')[-1]
+ file_list.append(name)
+ # breakpoint()
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+ # breakpoint()
+ # pos_id = np.random.randint(0, data_npz.shape[0])
+ # pos_id = data_npz.shape[0]//2
+ pos_id_list = np.where(np.any(data_npz==2, axis=(1, 2)))[0]
+ pos_id = pos_id_list[len(pos_id_list)//2]
+ # breakpoint()
+ slice_data = data_npz[pos_id]
+ slice_seg = seg_npz[pos_id]
+
+ slice_data[slice_data <= -175] = -175
+ slice_data[slice_data >= 250] = 250
+ slice_data = (slice_data+175)/425.0
+
+ slice_seg[slice_seg==1] = 5
+ slice_seg[slice_seg==2] = 26
+
+ # tumor_mask = (slice_seg==2).astype(np.float32)
+ slice_seg = slice_seg.astype(np.float32)
+ foreground_mask = (slice_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*slice_data
+
+ slice_data = slice_data[:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,None].astype(np.float32)
+
+ # tumor_mask = resize(tumor_mask, (64,64), order=0)
+ slice_seg = cv2.resize(slice_seg,(64,64),interpolation=cv2.INTER_NEAREST)
+ slice_seg = slice_seg[:,:,None].astype(np.float32)
+ # print(np.unique(slice_seg))
+ # breakpoint()
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['slice_data'] = slice_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = slice_seg
+ # breakpoint()
+ return example
+
+class slice_train(slice_base):
+ def __init__(self, data_file='data_split/liver/train.txt', **kwargs):
+ super().__init__(data_file=data_file, **kwargs)
+
+
+class slice_val(slice_base):
+ def __init__(self, data_file='data_split/liver/eval.txt', **kwargs):
+ super().__init__(data_file=data_file, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class slice_test(slice_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset copy.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset copy.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c011677e051b1ea7ac4c555c456c1bd979232fa
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset copy.py
@@ -0,0 +1,83 @@
+import os
+import numpy as np
+from skimage.transform import resize
+from torch.utils.data import Dataset
+
+
+class volume_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ sample=True,
+ sample_length=16,
+ data_repeat=1
+ ):
+
+ self.data_root = data_root
+ self.data_name = data_name
+ self.sample = sample
+ self.sample_length = sample_length
+
+ data_root = os.path.join(data_root, data_name + '/nnUNetPlans_3d_fullres')
+ self.list = [os.path.join(data_name + '/nnUNetPlans_3d_fullres',f) for f in os.listdir(data_root) if f.endswith('.npz')]
+ self._length = len(self.list)
+ self._data_repeat = data_repeat
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data']
+ seg_npz = np.load(npz_file)['seg']
+
+ volume_data = data_npz[0, :, :, :]
+ volume_seg = seg_npz[0, :, :, :]
+
+ volume_data = (volume_data - np.min(data_npz)) / (np.max(data_npz)-np.min(data_npz))
+ if 'Abdomen' in self.data_name:
+ max_seg = 4
+ elif 'Brain' in self.data_name:
+ max_seg = 3
+ volume_seg = volume_seg / max_seg
+
+ volume_data = resize(volume_data, (volume_data.shape[0],512,512))
+ volume_seg = resize(volume_seg, (volume_seg.shape[0],512,512), order=0, anti_aliasing=False)
+
+ volume_data = volume_data[:, :, :, None].astype(np.float32)
+ volume_seg = volume_seg[:, :, :, None].astype(np.float32)
+
+ if self.sample:
+ start_id = np.random.randint(0, data_npz.shape[1]-self.sample_length)
+ volume_data = volume_data[start_id:start_id+self.sample_length]
+ volume_seg = volume_seg[start_id:start_id+self.sample_length]
+
+ rand_idx = np.random.randint(0, volume_data.shape[0])
+ volume_ref = volume_data[rand_idx:rand_idx+1]
+
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ example['volume_data'] = volume_data * 2 - 1
+ example['volume_seg'] = volume_seg * 2 - 1
+ example['volume_ref'] = volume_ref * 2 - 1
+
+ return example
+
+class volume_train(volume_base):
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+
+class volume_val(volume_base):
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+ def __len__(self):
+ return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class volume_test(volume_base):
+ def __init__(self, **kwargs):
+ super().__init__(sample=False, **kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9362a689c518d4716dbbdae17c6b18f06e7b2d6
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset.py
@@ -0,0 +1,98 @@
+import os
+import numpy as np
+from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+from batchgenerators.utilities.file_and_folder_operations import join, load_json, isfile, save_json, maybe_mkdir_p
+
+class volume_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_file,
+ data_repeat=1,
+ planner='nnUNetPlans_3d_fullres',
+ phase='train',
+ ):
+
+ self.data_root = data_root
+ # planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+ file_list = []
+ if 'liver' in data_file:
+ for line in open(data_file):
+ name = line.strip().split()[1].split('.nii.gz')[0]
+ name = name.split('/')[-1]
+ file_list.append(name)
+ else:
+ file_list = load_json(data_file)[0][phase]
+ # breakpoint()
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self.sample_length=16
+ self.sample=True
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+
+ # breakpoint()
+
+ if self.sample:
+ start_id = np.random.randint(0, data_npz.shape[0]-self.sample_length)
+ volume_data = data_npz[start_id:start_id+self.sample_length]
+ volume_seg = seg_npz[start_id:start_id+self.sample_length]
+
+ volume_data[volume_data <= -175] = -175
+ volume_data[volume_data >= 250] = 250
+ volume_data = (volume_data+175)/425.0
+
+
+ volume_seg = volume_seg.astype(np.float32)
+ foreground_mask = (volume_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*volume_data
+
+ volume_data = volume_data[:,:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,:,None].astype(np.float32)
+
+ # breakpoint()
+ volume_seg = resize(volume_seg, (volume_seg.shape[0],64,64), order=0, anti_aliasing=False)
+ # print(np.unique(resize(volume_seg, (volume_seg.shape[0],64,64), order=0, anti_aliasing=False)))
+ volume_seg = volume_seg[:,:,:,None].astype(np.float32)
+
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['volume_data'] = volume_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = volume_seg
+ # breakpoint()
+ return example
+
+class volume_train(volume_base):
+ def __init__(self, phase='train', **kwargs):
+ super().__init__(phase=phase, **kwargs)
+
+
+class volume_val(volume_base):
+ def __init__(self, phase='val', **kwargs):
+ super().__init__(phase=phase, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class volume_test(volume_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_13class_randomclass.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_13class_randomclass.py
new file mode 100644
index 0000000000000000000000000000000000000000..12c02a0588538a0a4376761ef44f9d6c059329fa
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_13class_randomclass.py
@@ -0,0 +1,122 @@
+import os
+import numpy as np
+from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+import random
+import itertools
+from batchgenerators.utilities.file_and_folder_operations import load_json
+
+def get_random_classes_from_mask(mask):
+ # 分析 mask 中有哪些类别
+ unique_classes = np.unique(mask)
+ unique_classes = unique_classes[unique_classes != 0] # 去除背景类（假设背景类为0）
+
+ # 获取所有可能的类别组合
+ all_combinations = []
+ for i in range(1, len(unique_classes) + 1):
+ all_combinations.extend(itertools.combinations(unique_classes, i))
+
+ # 随机选择一个组合
+ selected_combination = random.choice(all_combinations)
+ return selected_combination
+
+def get_filtered_mask(mask, selected_classes):
+ # 创建只包含选定类别的 mask
+ filtered_mask = np.isin(mask, selected_classes) * mask
+ return filtered_mask
+
+class volume_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_file,
+ data_repeat=1,
+ phase='train'
+ ):
+
+ self.data_root = data_root
+ planner = 'nnUNetPlans_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+
+ data_splits = load_json(data_file)
+ file_list = data_splits[0][phase]
+ # breakpoint()
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self.class_num = 13
+
+ self.sample_length=16
+ self.sample=True
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+
+ # breakpoint()
+
+ if self.sample:
+ start_id = np.random.randint(0, data_npz.shape[0]-self.sample_length)
+ volume_data = data_npz[start_id:start_id+self.sample_length]
+ volume_seg = seg_npz[start_id:start_id+self.sample_length]
+
+ # breakpoint()
+
+
+ volume_data[volume_data <= -175] = -175
+ volume_data[volume_data >= 250] = 250
+ volume_data = (volume_data+175)/425.0
+
+ if len(np.unique(volume_seg)) > 1:
+ selected_classes = get_random_classes_from_mask(volume_seg)
+ volume_seg = get_filtered_mask(volume_seg, selected_classes)
+
+
+ volume_seg = volume_seg.astype(np.float32)
+ foreground_mask = (volume_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*volume_data
+
+ volume_data = volume_data[:,:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,:,None].astype(np.float32)
+
+ # breakpoint()
+ volume_seg = resize(volume_seg, (volume_seg.shape[0],64,64), order=0, anti_aliasing=False)
+ # print(np.unique(resize(volume_seg, (volume_seg.shape[0],64,64), order=0, anti_aliasing=False)))
+ volume_seg = volume_seg[:,:,:,None].astype(np.float32)
+
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['volume_data'] = volume_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = volume_seg
+ # breakpoint()
+ return example
+
+class volume_train(volume_base):
+ def __init__(self, phase='train', **kwargs):
+ super().__init__( phase=phase, **kwargs)
+
+
+class volume_val(volume_base):
+ def __init__(self, phase='val', **kwargs):
+ super().__init__(phase=phase, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class volume_test(volume_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_28class_randomclass.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_28class_randomclass.py
new file mode 100644
index 0000000000000000000000000000000000000000..16224ea7b6d4d164626aa478f51788dbdcf32210
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_28class_randomclass.py
@@ -0,0 +1,122 @@
+import os
+import numpy as np
+from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+import random
+import itertools
+from batchgenerators.utilities.file_and_folder_operations import load_json
+
+def get_random_classes_from_mask(mask):
+ # 分析 mask 中有哪些类别
+ unique_classes = np.unique(mask)
+ unique_classes = unique_classes[unique_classes != 0] # 去除背景类（假设背景类为0）
+
+ # 获取所有可能的类别组合
+ all_combinations = []
+ for i in range(1, len(unique_classes) + 1):
+ all_combinations.extend(itertools.combinations(unique_classes, i))
+
+ # 随机选择一个组合
+ selected_combination = random.choice(all_combinations)
+ return selected_combination
+
+def get_filtered_mask(mask, selected_classes):
+ # 创建只包含选定类别的 mask
+ filtered_mask = np.isin(mask, selected_classes) * mask
+ return filtered_mask
+
+class volume_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_file,
+ data_repeat=1,
+ phase='train'
+ ):
+
+ self.data_root = data_root
+ planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+
+ data_splits = load_json(data_file)
+ file_list = data_splits[0][phase]
+ # breakpoint()
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self.class_num = 28
+
+ self.sample_length=16
+ self.sample=True
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+
+ # breakpoint()
+
+ if self.sample:
+ start_id = np.random.randint(0, data_npz.shape[0]-self.sample_length)
+ volume_data = data_npz[start_id:start_id+self.sample_length]
+ volume_seg = seg_npz[start_id:start_id+self.sample_length]
+
+ # breakpoint()
+
+
+ volume_data[volume_data <= -175] = -175
+ volume_data[volume_data >= 250] = 250
+ volume_data = (volume_data+175)/425.0
+
+ if len(np.unique(volume_seg)) > 1:
+ selected_classes = get_random_classes_from_mask(volume_seg)
+ volume_seg = get_filtered_mask(volume_seg, selected_classes)
+
+
+ volume_seg = volume_seg.astype(np.float32)
+ foreground_mask = (volume_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*volume_data
+
+ volume_data = volume_data[:,:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,:,None].astype(np.float32)
+
+ # breakpoint()
+ volume_seg = resize(volume_seg, (volume_seg.shape[0],64,64), order=0, anti_aliasing=False)
+ # print(np.unique(resize(volume_seg, (volume_seg.shape[0],64,64), order=0, anti_aliasing=False)))
+ volume_seg = volume_seg[:,:,:,None].astype(np.float32)
+
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['volume_data'] = volume_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = volume_seg
+ # breakpoint()
+ return example
+
+class volume_train(volume_base):
+ def __init__(self, data_file='data_split/splits_final2.json', phase='train', **kwargs):
+ super().__init__(data_file=data_file, phase=phase, **kwargs)
+
+
+class volume_val(volume_base):
+ def __init__(self, data_file='data_split/splits_final2.json', phase='val', **kwargs):
+ super().__init__(data_file=data_file, phase=phase, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class volume_test(volume_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_base.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb62286c96eea7e2b0cda0d0ab58197dbae2263e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_base.py
@@ -0,0 +1,79 @@
+import os
+import numpy as np
+from skimage.transform import resize
+from torch.utils.data import Dataset
+
+
+class volume_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ sample=True,
+ sample_length=16,
+ data_repeat=1
+ ):
+
+ self.data_root = data_root
+ self.data_name = data_name
+ self.sample = sample
+ self.sample_length = sample_length
+
+ data_root = os.path.join(data_root, data_name + '/nnUNetPlans_3d_fullres')
+ self.list = [os.path.join(data_name + '/nnUNetPlans_3d_fullres',f) for f in os.listdir(data_root) if f.endswith('.npz')]
+ self._length = len(self.list)
+ self._data_repeat = data_repeat
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data']
+ seg_npz = np.load(npz_file)['seg']
+
+ volume_data = data_npz[0, :, :, :]
+ volume_seg = seg_npz[0, :, :, :]
+
+ volume_data = (volume_data - np.min(data_npz)) / (np.max(data_npz)-np.min(data_npz))
+ if 'Abdomen' in self.data_name:
+ max_seg = 4
+ elif 'Brain' in self.data_name:
+ max_seg = 3
+ volume_seg = volume_seg / max_seg
+
+ volume_data = resize(volume_data, (volume_data.shape[0],512,512))
+ volume_seg = resize(volume_seg, (volume_seg.shape[0],512,512), order=0, anti_aliasing=False)
+
+ volume_data = volume_data[:, :, :, None].astype(np.float32)
+ volume_seg = volume_seg[:, :, :, None].astype(np.float32)
+
+ if self.sample:
+ start_id = np.random.randint(0, data_npz.shape[1]-self.sample_length)
+ volume_data = volume_data[start_id:start_id+self.sample_length]
+ volume_seg = volume_seg[start_id:start_id+self.sample_length]
+
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ example['volume_data'] = volume_data * 2 - 1
+ example['volume_seg'] = volume_seg * 2 - 1
+
+ return example
+
+class volume_train(volume_base):
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+
+class volume_val(volume_base):
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+ def __len__(self):
+ return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class volume_test(volume_base):
+ def __init__(self, **kwargs):
+ super().__init__(sample=False, **kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_infer.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..162569c1ce5d07b9d918ac4c15a0b12949188e7b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/data/volume_dataset_infer.py
@@ -0,0 +1,96 @@
+import os
+import numpy as np
+from skimage.transform import resize
+import cv2
+from torch.utils.data import Dataset
+import copy
+
+class volume_base(Dataset):
+ def __init__(self,
+ data_root,
+ data_name,
+ data_file,
+ data_repeat=1
+ ):
+
+ self.data_root = data_root
+ planner = 'nnUNetResEncUNetLPlans_torchres_3d_fullres' # nnUNetResEncUNetLPlans_torchres_3d_fullres nnUNetPlans_3d_fullres
+ data_root = os.path.join(data_root, data_name , planner)
+ # self.list = [os.path.join(data_name, planner ,f) for f in os.listdir(data_root) if f.endswith('.npz')]
+ file_list = []
+ for line in open(data_file):
+ name = line.strip().split()[1].split('.nii.gz')[0]
+ name = name.split('/')[-1]
+ file_list.append(name)
+ # breakpoint()
+ self.list = [os.path.join(data_name, planner , i+'.npz') for i in file_list]
+
+ self._length = len(self.list)
+ self.sample_length=16
+ self.sample=False
+ self._data_repeat = data_repeat
+
+
+ def __len__(self):
+ return self._length * self._data_repeat
+
+ def __getitem__(self, i):
+ i = i % self._length
+
+ npz_file = os.path.join(self.data_root, self.list[i])
+ data_npz = np.load(npz_file)['data'][0] # np.load(npz_file)['seg']
+ seg_npz = np.load(npz_file)['seg'][0]
+ # breakpoint()
+
+ if self.sample:
+ start_id = np.random.randint(0, data_npz.shape[0]-self.sample_length)
+ volume_data = data_npz[start_id:start_id+self.sample_length]
+ volume_seg = seg_npz[start_id:start_id+self.sample_length]
+ else:
+ volume_data=data_npz
+ volume_seg = seg_npz
+ # breakpoint()
+
+
+ volume_data[volume_data <= -175] = -175
+ volume_data[volume_data >= 250] = 250
+ volume_data = (volume_data+175)/425.0
+
+
+ volume_seg = volume_seg.astype(np.float32)
+ foreground_mask = (volume_seg>0).astype(np.float32)
+ masked_data = (1-foreground_mask)*volume_data
+
+ volume_data = volume_data[:,:,:,None].astype(np.float32)
+ masked_data = masked_data[:,:,:,None].astype(np.float32)
+
+ # breakpoint()
+ volume_seg = resize(volume_seg, (volume_seg.shape[0],64,64), order=0, anti_aliasing=False)
+ # print(np.unique(resize(volume_seg, (volume_seg.shape[0],64,64), order=0, anti_aliasing=False)))
+ volume_seg = volume_seg[:,:,:,None].astype(np.float32)
+
+ example = {}
+ example['name'] = self.list[i].split('/')[-1].split('.')[0]
+ # example['pos_id'] = pos_id
+ example['volume_data'] = volume_data * 2 - 1
+ example['masked_data'] = masked_data * 2 - 1
+ example['tumor_mask'] = volume_seg
+ # breakpoint()
+ return example
+
+class volume_train(volume_base):
+ def __init__(self, data_file='data_split/liver/train.txt', **kwargs):
+ super().__init__(data_file=data_file, **kwargs)
+
+
+class volume_val(volume_base):
+ def __init__(self, data_file='data_split/liver/eval.txt', **kwargs):
+ super().__init__(data_file=data_file, **kwargs)
+
+ # def __len__(self):
+ # return 2 if super().__len__() // 10000 < 2 else super().__len__() // 10000
+
+class volume_test(volume_base):
+ def __init__(self, data_file='data_splits/test.txt', **kwargs):
+ super().__init__(**kwargs)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/lr_scheduler.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/lr_scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..be39da9ca6dacc22bf3df9c7389bbb403a4a3ade
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/lr_scheduler.py
@@ -0,0 +1,98 @@
+import numpy as np
+
+
+class LambdaWarmUpCosineScheduler:
+ """
+ note: use with a base_lr of 1.0
+ """
+ def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
+ self.lr_warm_up_steps = warm_up_steps
+ self.lr_start = lr_start
+ self.lr_min = lr_min
+ self.lr_max = lr_max
+ self.lr_max_decay_steps = max_decay_steps
+ self.last_lr = 0.
+ self.verbosity_interval = verbosity_interval
+
+ def schedule(self, n, **kwargs):
+ if self.verbosity_interval > 0:
+ if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
+ if n < self.lr_warm_up_steps:
+ lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
+ self.last_lr = lr
+ return lr
+ else:
+ t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
+ t = min(t, 1.0)
+ lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
+ 1 + np.cos(t * np.pi))
+ self.last_lr = lr
+ return lr
+
+ def __call__(self, n, **kwargs):
+ return self.schedule(n,**kwargs)
+
+
+class LambdaWarmUpCosineScheduler2:
+ """
+ supports repeated iterations, configurable via lists
+ note: use with a base_lr of 1.0.
+ """
+ def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
+ assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
+ self.lr_warm_up_steps = warm_up_steps
+ self.f_start = f_start
+ self.f_min = f_min
+ self.f_max = f_max
+ self.cycle_lengths = cycle_lengths
+ self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
+ self.last_f = 0.
+ self.verbosity_interval = verbosity_interval
+
+ def find_in_interval(self, n):
+ interval = 0
+ for cl in self.cum_cycles[1:]:
+ if n <= cl:
+ return interval
+ interval += 1
+
+ def schedule(self, n, **kwargs):
+ cycle = self.find_in_interval(n)
+ n = n - self.cum_cycles[cycle]
+ if self.verbosity_interval > 0:
+ if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
+ f"current cycle {cycle}")
+ if n < self.lr_warm_up_steps[cycle]:
+ f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
+ self.last_f = f
+ return f
+ else:
+ t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
+ t = min(t, 1.0)
+ f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
+ 1 + np.cos(t * np.pi))
+ self.last_f = f
+ return f
+
+ def __call__(self, n, **kwargs):
+ return self.schedule(n, **kwargs)
+
+
+class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
+
+ def schedule(self, n, **kwargs):
+ cycle = self.find_in_interval(n)
+ n = n - self.cum_cycles[cycle]
+ if self.verbosity_interval > 0:
+ if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
+ f"current cycle {cycle}")
+
+ if n < self.lr_warm_up_steps[cycle]:
+ f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
+ self.last_f = f
+ return f
+ else:
+ f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
+ self.last_f = f
+ return f
+
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/autoencoder.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..d122549995ce2cd64092c81a58419ed4a15a02fd
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/autoencoder.py
@@ -0,0 +1,219 @@
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
+from ldm.util import instantiate_from_config
+from ldm.modules.ema import LitEma
+
+
+class AutoencoderKL(pl.LightningModule):
+ def __init__(self,
+ ddconfig,
+ lossconfig,
+ embed_dim,
+ ckpt_path=None,
+ ignore_keys=[],
+ image_key="image",
+ colorize_nlabels=None,
+ monitor=None,
+ ema_decay=None,
+ learn_logvar=False
+ ):
+ super().__init__()
+ self.learn_logvar = learn_logvar
+ self.image_key = image_key
+ self.encoder = Encoder(**ddconfig)
+ self.decoder = Decoder(**ddconfig)
+ self.loss = instantiate_from_config(lossconfig)
+ assert ddconfig["double_z"]
+ self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
+ self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+ self.embed_dim = embed_dim
+ if colorize_nlabels is not None:
+ assert type(colorize_nlabels)==int
+ self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+ if monitor is not None:
+ self.monitor = monitor
+
+ self.use_ema = ema_decay is not None
+ if self.use_ema:
+ self.ema_decay = ema_decay
+ assert 0. < ema_decay < 1.
+ self.model_ema = LitEma(self, decay=ema_decay)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+ def init_from_ckpt(self, path, ignore_keys=list()):
+ sd = torch.load(path, map_location="cpu")["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ self.load_state_dict(sd, strict=False)
+ print(f"Restored from {path}")
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.parameters())
+ self.model_ema.copy_to(self)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self)
+
+ def encode(self, x):
+ h = self.encoder(x)
+ moments = self.quant_conv(h)
+ posterior = DiagonalGaussianDistribution(moments)
+ return posterior
+
+ def decode(self, z):
+ z = self.post_quant_conv(z)
+ dec = self.decoder(z)
+ return dec
+
+ def forward(self, input, sample_posterior=True):
+ posterior = self.encode(input)
+ if sample_posterior:
+ z = posterior.sample()
+ else:
+ z = posterior.mode()
+ dec = self.decode(z)
+ return dec, posterior
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+ return x
+
+ def training_step(self, batch, batch_idx, optimizer_idx):
+ inputs = self.get_input(batch, self.image_key)
+ reconstructions, posterior = self(inputs)
+
+ if optimizer_idx == 0:
+ # train encoder+decoder+logvar
+ aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+ self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ return aeloss
+
+ if optimizer_idx == 1:
+ # train the discriminator
+ discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+
+ self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ return discloss
+
+ def validation_step(self, batch, batch_idx):
+ log_dict = self._validation_step(batch, batch_idx)
+ with self.ema_scope():
+ log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
+ return log_dict
+
+ def _validation_step(self, batch, batch_idx, postfix=""):
+ inputs = self.get_input(batch, self.image_key)
+ reconstructions, posterior = self(inputs)
+ aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
+ last_layer=self.get_last_layer(), split="val"+postfix)
+
+ discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
+ last_layer=self.get_last_layer(), split="val"+postfix)
+
+ self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
+ self.log_dict(log_dict_ae)
+ self.log_dict(log_dict_disc)
+ return self.log_dict
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list(
+ self.quant_conv.parameters()) + list(self.post_quant_conv.parameters())
+ if self.learn_logvar:
+ print(f"{self.__class__.__name__}: Learning logvar")
+ ae_params_list.append(self.loss.logvar)
+ opt_ae = torch.optim.Adam(ae_params_list,
+ lr=lr, betas=(0.5, 0.9))
+ opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+ lr=lr, betas=(0.5, 0.9))
+ return [opt_ae, opt_disc], []
+
+ def get_last_layer(self):
+ return self.decoder.conv_out.weight
+
+ @torch.no_grad()
+ def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.image_key)
+ x = x.to(self.device)
+ if not only_inputs:
+ xrec, posterior = self(x)
+ if x.shape[1] > 3:
+ # colorize with random projection
+ assert xrec.shape[1] > 3
+ x = self.to_rgb(x)
+ xrec = self.to_rgb(xrec)
+ log["samples"] = self.decode(torch.randn_like(posterior.sample()))
+ log["reconstructions"] = xrec
+ if log_ema or self.use_ema:
+ with self.ema_scope():
+ xrec_ema, posterior_ema = self(x)
+ if x.shape[1] > 3:
+ # colorize with random projection
+ assert xrec_ema.shape[1] > 3
+ xrec_ema = self.to_rgb(xrec_ema)
+ log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample()))
+ log["reconstructions_ema"] = xrec_ema
+ log["inputs"] = x
+ return log
+
+ def to_rgb(self, x):
+ assert self.image_key == "segmentation"
+ if not hasattr(self, "colorize"):
+ self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+ x = F.conv2d(x, weight=self.colorize)
+ x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+ return x
+
+
+class IdentityFirstStage(torch.nn.Module):
+ def __init__(self, *args, vq_interface=False, **kwargs):
+ self.vq_interface = vq_interface
+ super().__init__()
+
+ def encode(self, x, *args, **kwargs):
+ return x
+
+ def decode(self, x, *args, **kwargs):
+ return x
+
+ def quantize(self, x, *args, **kwargs):
+ if self.vq_interface:
+ return x, None, [None, None, None]
+ return x
+
+ def forward(self, x, *args, **kwargs):
+ return x
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/autoencoder__.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/autoencoder__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a9c4f45498561953b8085981609b2a3298a5473
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/autoencoder__.py
@@ -0,0 +1,443 @@
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+
+from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
+
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
+from ldm.util import instantiate_from_config
+
+
+class VQModel(pl.LightningModule):
+ def __init__(self,
+ ddconfig,
+ lossconfig,
+ n_embed,
+ embed_dim,
+ ckpt_path=None,
+ ignore_keys=[],
+ image_key="image",
+ colorize_nlabels=None,
+ monitor=None,
+ batch_resize_range=None,
+ scheduler_config=None,
+ lr_g_factor=1.0,
+ remap=None,
+ sane_index_shape=False, # tell vector quantizer to return indices as bhw
+ use_ema=False
+ ):
+ super().__init__()
+ self.embed_dim = embed_dim
+ self.n_embed = n_embed
+ self.image_key = image_key
+ self.encoder = Encoder(**ddconfig)
+ self.decoder = Decoder(**ddconfig)
+ self.loss = instantiate_from_config(lossconfig)
+ self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
+ remap=remap,
+ sane_index_shape=sane_index_shape)
+ self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
+ self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+ if colorize_nlabels is not None:
+ assert type(colorize_nlabels)==int
+ self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+ if monitor is not None:
+ self.monitor = monitor
+ self.batch_resize_range = batch_resize_range
+ if self.batch_resize_range is not None:
+ print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
+
+ self.use_ema = use_ema
+ if self.use_ema:
+ self.model_ema = LitEma(self)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+ self.scheduler_config = scheduler_config
+ self.lr_g_factor = lr_g_factor
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.parameters())
+ self.model_ema.copy_to(self)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ def init_from_ckpt(self, path, ignore_keys=list()):
+ sd = torch.load(path, map_location="cpu")["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ missing, unexpected = self.load_state_dict(sd, strict=False)
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if len(missing) > 0:
+ print(f"Missing Keys: {missing}")
+ print(f"Unexpected Keys: {unexpected}")
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self)
+
+ def encode(self, x):
+ h = self.encoder(x)
+ h = self.quant_conv(h)
+ quant, emb_loss, info = self.quantize(h)
+ return quant, emb_loss, info
+
+ def encode_to_prequant(self, x):
+ h = self.encoder(x)
+ h = self.quant_conv(h)
+ return h
+
+ def decode(self, quant):
+ quant = self.post_quant_conv(quant)
+ dec = self.decoder(quant)
+ return dec
+
+ def decode_code(self, code_b):
+ quant_b = self.quantize.embed_code(code_b)
+ dec = self.decode(quant_b)
+ return dec
+
+ def forward(self, input, return_pred_indices=False):
+ quant, diff, (_,_,ind) = self.encode(input)
+ dec = self.decode(quant)
+ if return_pred_indices:
+ return dec, diff, ind
+ return dec, diff
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+ if self.batch_resize_range is not None:
+ lower_size = self.batch_resize_range[0]
+ upper_size = self.batch_resize_range[1]
+ if self.global_step <= 4:
+ # do the first few batches with max size to avoid later oom
+ new_resize = upper_size
+ else:
+ new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
+ if new_resize != x.shape[2]:
+ x = F.interpolate(x, size=new_resize, mode="bicubic")
+ x = x.detach()
+ return x
+
+ def training_step(self, batch, batch_idx, optimizer_idx):
+ # https://github.com/pytorch/pytorch/issues/37142
+ # try not to fool the heuristics
+ x = self.get_input(batch, self.image_key)
+ xrec, qloss, ind = self(x, return_pred_indices=True)
+
+ if optimizer_idx == 0:
+ # autoencode
+ aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train",
+ predicted_indices=ind)
+
+ self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+ return aeloss
+
+ if optimizer_idx == 1:
+ # discriminator
+ discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+ self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+ return discloss
+
+ def validation_step(self, batch, batch_idx):
+ log_dict = self._validation_step(batch, batch_idx)
+ with self.ema_scope():
+ log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+ return log_dict
+
+ def _validation_step(self, batch, batch_idx, suffix=""):
+ x = self.get_input(batch, self.image_key)
+ xrec, qloss, ind = self(x, return_pred_indices=True)
+ aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split="val"+suffix,
+ predicted_indices=ind
+ )
+
+ discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
+ self.global_step,
+ last_layer=self.get_last_layer(),
+ split="val"+suffix,
+ predicted_indices=ind
+ )
+ rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
+ self.log(f"val{suffix}/rec_loss", rec_loss,
+ prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+ self.log(f"val{suffix}/aeloss", aeloss,
+ prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+ if version.parse(pl.__version__) >= version.parse('1.4.0'):
+ del log_dict_ae[f"val{suffix}/rec_loss"]
+ self.log_dict(log_dict_ae)
+ self.log_dict(log_dict_disc)
+ return self.log_dict
+
+ def configure_optimizers(self):
+ lr_d = self.learning_rate
+ lr_g = self.lr_g_factor*self.learning_rate
+ print("lr_d", lr_d)
+ print("lr_g", lr_g)
+ opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+ list(self.decoder.parameters())+
+ list(self.quantize.parameters())+
+ list(self.quant_conv.parameters())+
+ list(self.post_quant_conv.parameters()),
+ lr=lr_g, betas=(0.5, 0.9))
+ opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+ lr=lr_d, betas=(0.5, 0.9))
+
+ if self.scheduler_config is not None:
+ scheduler = instantiate_from_config(self.scheduler_config)
+
+ print("Setting up LambdaLR scheduler...")
+ scheduler = [
+ {
+ 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ },
+ {
+ 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ },
+ ]
+ return [opt_ae, opt_disc], scheduler
+ return [opt_ae, opt_disc], []
+
+ def get_last_layer(self):
+ return self.decoder.conv_out.weight
+
+ def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.image_key)
+ x = x.to(self.device)
+ if only_inputs:
+ log["inputs"] = x
+ return log
+ xrec, _ = self(x)
+ if x.shape[1] > 3:
+ # colorize with random projection
+ assert xrec.shape[1] > 3
+ x = self.to_rgb(x)
+ xrec = self.to_rgb(xrec)
+ log["inputs"] = x
+ log["reconstructions"] = xrec
+ if plot_ema:
+ with self.ema_scope():
+ xrec_ema, _ = self(x)
+ if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
+ log["reconstructions_ema"] = xrec_ema
+ return log
+
+ def to_rgb(self, x):
+ assert self.image_key == "segmentation"
+ if not hasattr(self, "colorize"):
+ self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+ x = F.conv2d(x, weight=self.colorize)
+ x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+ return x
+
+
+class VQModelInterface(VQModel):
+ def __init__(self, embed_dim, *args, **kwargs):
+ super().__init__(embed_dim=embed_dim, *args, **kwargs)
+ self.embed_dim = embed_dim
+
+ def encode(self, x):
+ h = self.encoder(x)
+ h = self.quant_conv(h)
+ return h
+
+ def decode(self, h, force_not_quantize=False):
+ # also go through quantization layer
+ if not force_not_quantize:
+ quant, emb_loss, info = self.quantize(h)
+ else:
+ quant = h
+ quant = self.post_quant_conv(quant)
+ dec = self.decoder(quant)
+ return dec
+
+
+class AutoencoderKL(pl.LightningModule):
+ def __init__(self,
+ ddconfig,
+ lossconfig,
+ embed_dim,
+ ckpt_path=None,
+ ignore_keys=[],
+ image_key="image",
+ colorize_nlabels=None,
+ monitor=None,
+ ):
+ super().__init__()
+ self.image_key = image_key
+ self.encoder = Encoder(**ddconfig)
+ self.decoder = Decoder(**ddconfig)
+ self.loss = instantiate_from_config(lossconfig)
+ assert ddconfig["double_z"]
+ self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
+ self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+ self.embed_dim = embed_dim
+ if colorize_nlabels is not None:
+ assert type(colorize_nlabels)==int
+ self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+ if monitor is not None:
+ self.monitor = monitor
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+ def init_from_ckpt(self, path, ignore_keys=list()):
+ sd = torch.load(path, map_location="cpu")["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ self.load_state_dict(sd, strict=False)
+ print(f"Restored from {path}")
+
+ def encode(self, x):
+ h = self.encoder(x)
+ moments = self.quant_conv(h)
+ posterior = DiagonalGaussianDistribution(moments)
+ return posterior
+
+ def decode(self, z):
+ z = self.post_quant_conv(z)
+ dec = self.decoder(z)
+ return dec
+
+ def forward(self, input, sample_posterior=True):
+ posterior = self.encode(input)
+ if sample_posterior:
+ z = posterior.sample()
+ else:
+ z = posterior.mode()
+ dec = self.decode(z)
+ return dec, posterior
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+ return x
+
+ def training_step(self, batch, batch_idx, optimizer_idx):
+ inputs = self.get_input(batch, self.image_key)
+ reconstructions, posterior = self(inputs)
+
+ if optimizer_idx == 0:
+ # train encoder+decoder+logvar
+ aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+ self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ return aeloss
+
+ if optimizer_idx == 1:
+ # train the discriminator
+ discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+
+ self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ return discloss
+
+ def validation_step(self, batch, batch_idx):
+ inputs = self.get_input(batch, self.image_key)
+ reconstructions, posterior = self(inputs)
+ aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
+ last_layer=self.get_last_layer(), split="val")
+
+ discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
+ last_layer=self.get_last_layer(), split="val")
+
+ self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
+ self.log_dict(log_dict_ae)
+ self.log_dict(log_dict_disc)
+ return self.log_dict
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+ list(self.decoder.parameters())+
+ list(self.quant_conv.parameters())+
+ list(self.post_quant_conv.parameters()),
+ lr=lr, betas=(0.5, 0.9))
+ opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+ lr=lr, betas=(0.5, 0.9))
+ return [opt_ae, opt_disc], []
+
+ def get_last_layer(self):
+ return self.decoder.conv_out.weight
+
+ @torch.no_grad()
+ def log_images(self, batch, only_inputs=False, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.image_key)
+ x = x.to(self.device)
+ if not only_inputs:
+ xrec, posterior = self(x)
+ if x.shape[1] > 3:
+ # colorize with random projection
+ assert xrec.shape[1] > 3
+ x = self.to_rgb(x)
+ xrec = self.to_rgb(xrec)
+ log["samples"] = self.decode(torch.randn_like(posterior.sample()))
+ log["reconstructions"] = xrec
+ log["inputs"] = x
+ return log
+
+ def to_rgb(self, x):
+ assert self.image_key == "segmentation"
+ if not hasattr(self, "colorize"):
+ self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+ x = F.conv2d(x, weight=self.colorize)
+ x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+ return x
+
+
+class IdentityFirstStage(torch.nn.Module):
+ def __init__(self, *args, vq_interface=False, **kwargs):
+ self.vq_interface = vq_interface # TODO: Should be true by default but check to not break older stuff
+ super().__init__()
+
+ def encode(self, x, *args, **kwargs):
+ return x
+
+ def decode(self, x, *args, **kwargs):
+ return x
+
+ def quantize(self, x, *args, **kwargs):
+ if self.vq_interface:
+ return x, None, [None, None, None]
+ return x
+
+ def forward(self, x, *args, **kwargs):
+ return x
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddim.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddim.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f8dc584daffe533c0a02a721daa35918a9487f9
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddim.py
@@ -0,0 +1,365 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+from einops import rearrange
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
+
+
+class DDIMSampler(object):
+ def __init__(self, model, schedule="linear", **kwargs):
+ super().__init__()
+ self.model = model
+ self.ddpm_num_timesteps = model.num_timesteps
+ self.schedule = schedule
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+ self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ alphas_cumprod = self.model.alphas_cumprod
+ assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+ self.register_buffer('betas', to_torch(self.model.betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+ # ddim sampling parameters
+ ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,verbose=verbose)
+ self.register_buffer('ddim_sigmas', ddim_sigmas)
+ self.register_buffer('ddim_alphas', ddim_alphas)
+ self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+ self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+ (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+ 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+ self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ dynamic_threshold=None,
+ ucg_schedule=None,
+ previous=None,
+ previous_reverse=False,
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ ctmp = conditioning[list(conditioning.keys())[0]]
+ while isinstance(ctmp, list): ctmp = ctmp[0]
+ cbs = ctmp.shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ elif isinstance(conditioning, list):
+ for ctmp in conditioning:
+ if ctmp.shape[0] != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+ print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+ # breakpoint()
+ samples, intermediates = self.ddim_sampling(conditioning, size,
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask, x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ previous=previous,
+ previous_reverse=previous_reverse,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold,
+ ucg_schedule=ucg_schedule
+ )
+ return samples, intermediates
+
+ @torch.no_grad()
+ def ddim_sampling(self, cond, shape,
+ x_T=None, ddim_use_original_steps=False,
+ callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, log_every_t=100,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
+ ucg_schedule=None, previous=None, previous_reverse=False):
+ device = self.model.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ if previous is not None:
+ img = rearrange(img, '(b t) c h w -> b t c h w', b=previous.shape[0])
+ alphas = self.model.alphas_cumprod
+ c0 = torch.sqrt(alphas[-1])
+ c1 = torch.sqrt(1-alphas[-1])
+ previous_noisy = c0*previous+c1*torch.randn_like(previous)
+ if not previous_reverse:
+ img[:, :previous.shape[1]] = previous_noisy
+ else:
+ img[:, -previous.shape[1]:] = previous_noisy
+ img = rearrange(img, 'b t c h w -> (b t) c h w')
+
+ if timesteps is None:
+ timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ elif timesteps is not None and not ddim_use_original_steps:
+ subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ timesteps = self.ddim_timesteps[:subset_end]
+
+ intermediates = {'x_inter': [img], 'pred_x0': [img]}
+ time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+ total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+ print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.model.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if ucg_schedule is not None:
+ assert len(ucg_schedule) == len(time_range)
+ unconditional_guidance_scale = ucg_schedule[i]
+
+ outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+ quantize_denoised=quantize_denoised, temperature=temperature,
+ noise_dropout=noise_dropout, score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold)
+ img, pred_x0 = outs
+ if callback: callback(i)
+ if img_callback: img_callback(pred_x0, i)
+
+ if previous is not None:
+ img = rearrange(img, '(b t) c h w -> b t c h w', b=previous.shape[0])
+ alphas = self.model.alphas_cumprod
+ c0 = torch.sqrt(alphas[index])
+ c1 = torch.sqrt(1-alphas[index])
+ previous_noisy = c0*previous+c1*torch.randn_like(previous)
+ if not previous_reverse:
+ img[:, :previous.shape[1]] = previous_noisy
+ else:
+ img[:, -previous.shape[1]:] = previous_noisy
+ img = rearrange(img, 'b t c h w -> (b t) c h w')
+
+ if index % log_every_t == 0 or index == total_steps - 1:
+ intermediates['x_inter'].append(img)
+ intermediates['pred_x0'].append(pred_x0)
+
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None,
+ dynamic_threshold=None):
+ b, *_, device = *x.shape, x.device
+
+ if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ model_output = self.model.apply_model(x, t, c)
+ else:
+ x_in = torch.cat([x] * 2)
+ t_in = torch.cat([t] * 2)
+ if isinstance(c, dict):
+ assert isinstance(unconditional_conditioning, dict)
+ c_in = dict()
+ for k in c:
+ if isinstance(c[k], list):
+ c_in[k] = [torch.cat([
+ unconditional_conditioning[k][i],
+ c[k][i]]) for i in range(len(c[k]))]
+ else:
+ c_in[k] = torch.cat([
+ unconditional_conditioning[k],
+ c[k]])
+ elif isinstance(c, list):
+ c_in = list()
+ assert isinstance(unconditional_conditioning, list)
+ for i in range(len(c)):
+ c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
+ else:
+ c_in = torch.cat([unconditional_conditioning, c])
+ model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+ model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
+
+ if self.model.parameterization == "v":
+ e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
+ else:
+ e_t = model_output
+
+ if score_corrector is not None:
+ assert self.model.parameterization == "eps", 'not implemented'
+ e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+ sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+ sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+ # select parameters corresponding to the currently considered timestep
+ a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+ a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+ sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+ sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+ # current prediction for x_0
+ if self.model.parameterization != "v":
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ else:
+ pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
+
+ if quantize_denoised:
+ pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+
+ if dynamic_threshold is not None:
+ raise NotImplementedError()
+
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+ noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+ return x_prev, pred_x0
+
+ @torch.no_grad()
+ def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
+ unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
+ num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
+
+ assert t_enc <= num_reference_steps
+ num_steps = t_enc
+
+ if use_original_steps:
+ alphas_next = self.alphas_cumprod[:num_steps]
+ alphas = self.alphas_cumprod_prev[:num_steps]
+ else:
+ alphas_next = self.ddim_alphas[:num_steps]
+ alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
+
+ x_next = x0
+ intermediates = []
+ inter_steps = []
+ for i in tqdm(range(num_steps), desc='Encoding Image'):
+ t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
+ if unconditional_guidance_scale == 1.:
+ noise_pred = self.model.apply_model(x_next, t, c)
+ else:
+ assert unconditional_conditioning is not None
+ e_t_uncond, noise_pred = torch.chunk(
+ self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
+ torch.cat((unconditional_conditioning, c))), 2)
+ noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
+
+ xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
+ weighted_noise_pred = alphas_next[i].sqrt() * (
+ (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
+ x_next = xt_weighted + weighted_noise_pred
+ if return_intermediates and i % (
+ num_steps // return_intermediates) == 0 and i < num_steps - 1:
+ intermediates.append(x_next)
+ inter_steps.append(i)
+ elif return_intermediates and i >= num_steps - 2:
+ intermediates.append(x_next)
+ inter_steps.append(i)
+ if callback: callback(i)
+
+ out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
+ if return_intermediates:
+ out.update({'intermediates': intermediates})
+ return x_next, out
+
+ @torch.no_grad()
+ def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+ # fast, but does not allow for exact reconstruction
+ # t serves as an index to gather the correct alphas
+ if use_original_steps:
+ sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+ sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+ else:
+ sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+ sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+ if noise is None:
+ noise = torch.randn_like(x0)
+ return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+
+ @torch.no_grad()
+ def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+ use_original_steps=False, callback=None):
+
+ timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+ timesteps = timesteps[:t_start]
+
+ time_range = np.flip(timesteps)
+ total_steps = timesteps.shape[0]
+ print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+ x_dec = x_latent
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+ x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning)
+ if callback: callback(i)
+ return x_dec
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..a48c20da2b526c989996ce755d280f3c9c63d2ef
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm.py
@@ -0,0 +1,1447 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+
+import enum
+
+# 保存原始实现（可选，用于调试或回滚）
+_enum_format_orig = enum.Enum.__format__
+
+def _enum_format_value(self, format_spec: str) -> str:
+ # 如果指定了 format 规范，就交给 value 本身的格式化；否则直接返回 value
+ return format(self.value, format_spec) if format_spec else str(self.value)
+
+# 全局替换 Enum.__format__
+enum.Enum.__format__ = _enum_format_value
+
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager, nullcontext
+from functools import partial
+import itertools
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from omegaconf import ListConfig
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+from transformers import BertTokenizer,BertModel
+
+__conditioning_keys__ = {'concat': 'c_concat',
+ 'crossattn': 'c_crossattn',
+ 'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+ return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPM(pl.LightningModule):
+ # classic DDPM with Gaussian diffusion, in image space
+ def __init__(self,
+ unet_config,
+ timesteps=1000,
+ beta_schedule="linear",
+ loss_type="l2",
+ ckpt_path=None,
+ ignore_keys=[],
+ load_only_unet=False,
+ monitor="val/loss",
+ use_ema=True,
+ first_stage_key="image",
+ image_size=256,
+ channels=3,
+ log_every_t=100,
+ clip_denoised=True,
+ linear_start=1e-4,
+ linear_end=2e-2,
+ cosine_s=8e-3,
+ given_betas=None,
+ original_elbo_weight=0.,
+ v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+ l_simple_weight=1.,
+ conditioning_key=None,
+ parameterization="eps", # all assuming fixed variance schedules
+ scheduler_config=None,
+ use_positional_encodings=False,
+ learn_logvar=False,
+ logvar_init=0.,
+ make_it_fit=False,
+ ucg_training=None,
+ reset_ema=False,
+ reset_num_ema_updates=False,
+ ):
+ super().__init__()
+ assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
+ self.parameterization = parameterization
+ print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+ self.cond_stage_model = None
+ self.clip_denoised = clip_denoised
+ self.log_every_t = log_every_t
+ self.first_stage_key = first_stage_key
+ self.image_size = image_size # try conv?
+ self.channels = channels
+
+ self.use_positional_encodings = use_positional_encodings
+ self.model = DiffusionWrapper(unet_config, conditioning_key)
+ count_params(self.model, verbose=True)
+ self.use_ema = use_ema
+ if self.use_ema:
+ self.model_ema = LitEma(self.model)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ self.use_scheduler = scheduler_config is not None
+ if self.use_scheduler:
+ self.scheduler_config = scheduler_config
+
+ self.v_posterior = v_posterior
+ self.original_elbo_weight = original_elbo_weight
+ self.l_simple_weight = l_simple_weight
+
+ if monitor is not None:
+ self.monitor = monitor
+ self.make_it_fit = make_it_fit
+ if reset_ema: assert exists(ckpt_path)
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+ if reset_ema:
+ assert self.use_ema
+ print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+ self.model_ema = LitEma(self.model)
+ if reset_num_ema_updates:
+ print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+ assert self.use_ema
+ self.model_ema.reset_num_updates()
+
+ self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+ linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+ self.loss_type = loss_type
+
+ self.learn_logvar = learn_logvar
+ logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+ if self.learn_logvar:
+ self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+ else:
+ self.register_buffer('logvar', logvar)
+
+ self.ucg_training = ucg_training or dict()
+ if self.ucg_training:
+ self.ucg_prng = np.random.RandomState()
+
+ def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if exists(given_betas):
+ betas = given_betas
+ else:
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
+ posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+ 1. - alphas_cumprod) + self.v_posterior * betas
+ # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+ self.register_buffer('posterior_variance', to_torch(posterior_variance))
+ # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+ self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+ self.register_buffer('posterior_mean_coef1', to_torch(
+ betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+ self.register_buffer('posterior_mean_coef2', to_torch(
+ (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+ if self.parameterization == "eps":
+ lvlb_weights = self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+ elif self.parameterization == "x0":
+ lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+ elif self.parameterization == "v":
+ lvlb_weights = torch.ones_like(self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
+ else:
+ raise NotImplementedError("mu not supported")
+ lvlb_weights[0] = lvlb_weights[1]
+ self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+ assert not torch.isnan(self.lvlb_weights).all()
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.model.parameters())
+ self.model_ema.copy_to(self.model)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.model.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ @torch.no_grad()
+ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+ sd = torch.load(path, map_location="cpu")
+ if "state_dict" in list(sd.keys()):
+ sd = sd["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ if self.make_it_fit:
+ n_params = len([name for name, _ in
+ itertools.chain(self.named_parameters(),
+ self.named_buffers())])
+ for name, param in tqdm(
+ itertools.chain(self.named_parameters(),
+ self.named_buffers()),
+ desc="Fitting old weights to new weights",
+ total=n_params
+ ):
+ if not name in sd:
+ continue
+ old_shape = sd[name].shape
+ new_shape = param.shape
+ assert len(old_shape) == len(new_shape)
+ if len(new_shape) > 2:
+ # we only modify first two axes
+ assert new_shape[2:] == old_shape[2:]
+ # assumes first axis corresponds to output dim
+ if not new_shape == old_shape:
+ new_param = param.clone()
+ old_param = sd[name]
+ if len(new_shape) == 1:
+ for i in range(new_param.shape[0]):
+ new_param[i] = old_param[i % old_shape[0]]
+ elif len(new_shape) >= 2:
+ for i in range(new_param.shape[0]):
+ for j in range(new_param.shape[1]):
+ new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
+
+ n_used_old = torch.ones(old_shape[1])
+ for j in range(new_param.shape[1]):
+ n_used_old[j % old_shape[1]] += 1
+ n_used_new = torch.zeros(new_shape[1])
+ for j in range(new_param.shape[1]):
+ n_used_new[j] = n_used_old[j % old_shape[1]]
+
+ n_used_new = n_used_new[None, :]
+ while len(n_used_new.shape) < len(new_shape):
+ n_used_new = n_used_new.unsqueeze(-1)
+ new_param /= n_used_new
+
+ sd[name] = new_param
+
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+ sd, strict=False)
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if len(missing) > 0:
+ print(f"Missing Keys:\n {missing}")
+ if len(unexpected) > 0:
+ print(f"\nUnexpected Keys:\n {unexpected}")
+
+ def q_mean_variance(self, x_start, t):
+ """
+ Get the distribution q(x_t | x_0).
+ :param x_start: the [N x C x ...] tensor of noiseless inputs.
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+ :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+ """
+ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+ variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+ log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+ return mean, variance, log_variance
+
+ def predict_start_from_noise(self, x_t, t, noise):
+ return (
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+ )
+
+ def predict_start_from_z_and_v(self, x_t, t, v):
+ # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
+ )
+
+ def predict_eps_from_z_and_v(self, x_t, t, v):
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
+ )
+
+ def q_posterior(self, x_start, x_t, t):
+ posterior_mean = (
+ extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+ )
+ posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+ posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+ return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+ def p_mean_variance(self, x, t, clip_denoised: bool):
+ model_out = self.model(x, t)
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+ b, *_, device = *x.shape, x.device
+ model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+ noise = noise_like(x.shape, device, repeat_noise)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def p_sample_loop(self, shape, return_intermediates=False):
+ device = self.betas.device
+ b = shape[0]
+ img = torch.randn(shape, device=device)
+ intermediates = [img]
+ for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+ img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+ clip_denoised=self.clip_denoised)
+ if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+ intermediates.append(img)
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, batch_size=16, return_intermediates=False):
+ image_size = self.image_size
+ channels = self.channels
+ return self.p_sample_loop((batch_size, channels, image_size, image_size),
+ return_intermediates=return_intermediates)
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def get_v(self, x, noise, t):
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
+ )
+
+ def get_loss(self, pred, target, mean=True):
+ if self.loss_type == 'l1':
+ loss = (target - pred).abs()
+ if mean:
+ loss = loss.mean()
+ elif self.loss_type == 'l2':
+ if mean:
+ loss = torch.nn.functional.mse_loss(target, pred)
+ else:
+ loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+ else:
+ raise NotImplementedError("unknown loss type '{loss_type}'")
+
+ return loss
+
+ def p_losses(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_out = self.model(x_noisy, t)
+
+ loss_dict = {}
+ if self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "v":
+ target = self.get_v(x_start, noise, t)
+ else:
+ raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
+
+ loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+ log_prefix = 'train' if self.training else 'val'
+
+ loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+ loss_simple = loss.mean() * self.l_simple_weight
+
+ loss_vlb = (self.lvlb_weights[t] * loss).mean()
+ loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+ loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+ loss_dict.update({f'{log_prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def forward(self, x, *args, **kwargs):
+ # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+ # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ return self.p_losses(x, t, *args, **kwargs)
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = rearrange(x, 'b h w c -> b c h w')
+ x = x.to(memory_format=torch.contiguous_format).float()
+ return x
+
+ def shared_step(self, batch):
+ x = self.get_input(batch, self.first_stage_key)
+ loss, loss_dict = self(x)
+ return loss, loss_dict
+
+ def training_step(self, batch, batch_idx):
+ for k in self.ucg_training:
+ p = self.ucg_training[k]["p"]
+ val = self.ucg_training[k]["val"]
+ if val is None:
+ val = ""
+ for i in range(len(batch[k])):
+ if self.ucg_prng.choice(2, p=[1 - p, p]):
+ batch[k][i] = val
+
+ loss, loss_dict = self.shared_step(batch)
+
+ self.log_dict(loss_dict, prog_bar=True,
+ logger=True, on_step=True, on_epoch=True)
+
+ self.log("global_step", self.global_step,
+ prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ if self.use_scheduler:
+ lr = self.optimizers().param_groups[0]['lr']
+ self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ return loss
+
+ @torch.no_grad()
+ def validation_step(self, batch, batch_idx):
+ _, loss_dict_no_ema = self.shared_step(batch)
+ with self.ema_scope():
+ _, loss_dict_ema = self.shared_step(batch)
+ loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+ self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+ self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self.model)
+
+ def _get_rows_from_list(self, samples):
+ n_imgs_per_row = len(samples)
+ denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.first_stage_key)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ x = x.to(self.device)[:N]
+ log["inputs"] = x
+
+ # get diffusion row
+ diffusion_row = list()
+ x_start = x[:n_row]
+
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(x_start)
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ diffusion_row.append(x_noisy)
+
+ log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+ if sample:
+ # get denoise row
+ with self.ema_scope("Plotting"):
+ samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+ log["samples"] = samples
+ log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.learn_logvar:
+ params = params + [self.logvar]
+ opt = torch.optim.AdamW(params, lr=lr)
+ return opt
+
+
+class LatentDiffusion(DDPM):
+ """main class"""
+
+ def __init__(self,
+ num_timesteps_cond=None,
+ cond_stage_key="image",
+ cond_stage_trainable=False,
+ concat_mode=True,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ scale_factor=1.0,
+ scale_by_std=False,
+ force_null_conditioning=False,
+ text_enc='custom',
+ *args, **kwargs):
+ self.force_null_conditioning = force_null_conditioning
+ self.num_timesteps_cond = default(num_timesteps_cond, 1)
+ self.scale_by_std = scale_by_std
+ assert self.num_timesteps_cond <= kwargs['timesteps']
+ # for backwards compatibility after implementation of DiffusionWrapper
+ if conditioning_key is None:
+ conditioning_key = 'concat' if concat_mode else 'crossattn'
+
+ ckpt_path = kwargs.pop("ckpt_path", None)
+ reset_ema = kwargs.pop("reset_ema", False)
+ reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
+ ignore_keys = kwargs.pop("ignore_keys", [])
+ super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+ self.concat_mode = concat_mode
+ self.cond_stage_trainable = cond_stage_trainable
+ self.cond_stage_key = cond_stage_key
+
+ if not scale_by_std:
+ self.scale_factor = scale_factor
+ else:
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
+ # breakpoint()
+ self.instantiate_first_stage()
+ # self.instantiate_cond_stage()
+
+ self.cond_stage_forward = cond_stage_forward
+ self.clip_denoised = False
+ self.bbox_tokenizer = None
+ self.tokenizer=BertTokenizer.from_pretrained('microsoft/BiomedVLP-CXR-BERT-specialized',do_lower_case=True)
+ self.text_encode_without_mask=False
+ self.text_transformer=BertModel.from_pretrained("microsoft/BiomedVLP-CXR-BERT-specialized")
+ self.text_enc=text_enc
+
+ self.restarted_from_ckpt = False
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys)
+ self.restarted_from_ckpt = True
+ if reset_ema:
+ assert self.use_ema
+ print(
+ f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+ self.model_ema = LitEma(self.model)
+ if reset_num_ema_updates:
+ print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+ assert self.use_ema
+ self.model_ema.reset_num_updates()
+
+
+ def make_cond_schedule(self, ):
+ self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+ ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+ self.cond_ids[:self.num_timesteps_cond] = ids
+
+ @rank_zero_only
+ @torch.no_grad()
+ def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+ # only for very first batch
+ if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+ assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+ # set rescale weight to 1./std of encodings
+ # breakpoint()
+ print("### USING STD-RESCALING ###")
+ x = super().get_input(batch, self.first_stage_key)
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+ del self.scale_factor
+ self.register_buffer('scale_factor', 1. / z.flatten().std())
+ print(f"setting self.scale_factor to {self.scale_factor}")
+ print("### USING STD-RESCALING ###")
+
+ def register_schedule(self,
+ given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+ self.shorten_cond_schedule = self.num_timesteps_cond > 1
+ if self.shorten_cond_schedule:
+ self.make_cond_schedule()
+
+ def instantiate_first_stage(self):
+ from LeanVAE import LeanVAE
+ vqgan_ckpt='LeanVAE/ckpts/LeanVAE-dim16.ckpt'
+ model = LeanVAE.load_from_checkpoint(vqgan_ckpt, strict=False)
+ self.first_stage_model = model.eval()
+ self.first_stage_model.train = disabled_train
+ for param in self.first_stage_model.parameters():
+ param.requires_grad = False
+
+ def instantiate_cond_stage(self, config):
+ # breakpoint()
+ if not self.cond_stage_trainable:
+ if config == "__is_first_stage__":
+ print("Using first stage also as cond stage.")
+ self.cond_stage_model = self.first_stage_model
+ elif config == "__is_unconditional__":
+ print(f"Training {self.__class__.__name__} as an unconditional model.")
+ self.cond_stage_model = None
+ # self.be_unconditional = True
+ else:
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model.eval()
+ self.cond_stage_model.train = disabled_train
+ for param in self.cond_stage_model.parameters():
+ param.requires_grad = False
+ else:
+ assert config != '__is_first_stage__'
+ assert config != '__is_unconditional__'
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model
+
+ def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+ denoise_row = []
+ for zd in tqdm(samples, desc=desc):
+ denoise_row.append(self.decode_first_stage(zd.to(self.device),
+ force_not_quantize=force_no_decoder_quantization))
+ n_imgs_per_row = len(denoise_row)
+ denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
+ denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ def get_first_stage_encoding(self, encoder_posterior):
+ # breakpoint()
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+ z = encoder_posterior.sample()
+ elif isinstance(encoder_posterior, torch.Tensor):
+ z = encoder_posterior
+ else:
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ return self.scale_factor * z
+
+ def get_learned_conditioning(self, c):
+ if self.cond_stage_forward is None:
+ if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+ c = self.cond_stage_model.encode(c)
+ if isinstance(c, DiagonalGaussianDistribution):
+ c = c.mode()
+ else:
+ c = self.cond_stage_model(c)
+ else:
+ assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+ c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+ return c
+
+ def meshgrid(self, h, w):
+ y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+ x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+ arr = torch.cat([y, x], dim=-1)
+ return arr
+
+ def delta_border(self, h, w):
+ """
+ :param h: height
+ :param w: width
+ :return: normalized distance to image border,
+ wtith min distance = 0 at border and max dist = 0.5 at image center
+ """
+ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+ arr = self.meshgrid(h, w) / lower_right_corner
+ dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+ dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+ edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+ return edge_dist
+
+ def get_weighting(self, h, w, Ly, Lx, device):
+ weighting = self.delta_border(h, w)
+ weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+ self.split_input_params["clip_max_weight"], )
+ weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+ if self.split_input_params["tie_braker"]:
+ L_weighting = self.delta_border(Ly, Lx)
+ L_weighting = torch.clip(L_weighting,
+ self.split_input_params["clip_min_tie_weight"],
+ self.split_input_params["clip_max_tie_weight"])
+
+ L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+ weighting = weighting * L_weighting
+ return weighting
+
+ def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
+ """
+ :param x: img of size (bs, c, h, w)
+ :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+ """
+ bs, nc, h, w = x.shape
+
+ # number of crops in image
+ Ly = (h - kernel_size[0]) // stride[0] + 1
+ Lx = (w - kernel_size[1]) // stride[1] + 1
+
+ if uf == 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+ weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+ elif uf > 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+ dilation=1, padding=0,
+ stride=(stride[0] * uf, stride[1] * uf))
+ fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+ elif df > 1 and uf == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+ dilation=1, padding=0,
+ stride=(stride[0] // df, stride[1] // df))
+ fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+ else:
+ raise NotImplementedError
+
+ return fold, unfold, normalization, weighting
+
+ @torch.no_grad()
+ def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+ cond_key=None, return_original_cond=False, bs=None, return_x=False):
+ # breakpoint()
+ if k != 'volume_data' and k != 'slice_data':
+ x = super().get_input(batch, k)
+ elif k == 'volume_data':
+ x = batch[k]
+ x = x.repeat(1,3,1,1,1)
+ x = x.to(memory_format=torch.contiguous_format).float()
+
+ if bs is not None:
+ x = x[:bs]
+ x = x.to(self.device)
+
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+ z = rearrange(z, 'b c z h w -> (b z) c h w')
+ # breakpoint()
+
+ if self.model.conditioning_key is not None and not self.force_null_conditioning:
+ if cond_key is None:
+ cond_key = self.cond_stage_key
+ if cond_key != self.first_stage_key:
+ if cond_key in ['caption', 'coordinates_bbox', "txt", "pos_id"]:
+ xc = batch[cond_key]
+ elif cond_key in ['class_label', 'cls']:
+ xc = batch
+ elif cond_key == 'volume_seg_and_text':
+ xc = batch['volume_seg']
+ xc = xc.repeat(1,3,1,1,1)
+
+ if bs is not None:
+ xc = xc[:bs]
+ xc = self.get_first_stage_encoding(self.encode_first_stage(xc)).detach()
+ z_len=xc.shape[2]
+ xc = rearrange(xc, 'b c z h w -> (b z) c h w')
+ xc = xc.to(memory_format=torch.contiguous_format).float()
+
+ # breakpoint()
+ text = batch['input_text']
+ text = list(text)
+ text_tokens=self.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=512).to(self.device)
+ text_embeddings = self.text_transformer(text_tokens.input_ids, attention_mask = text_tokens.attention_mask )
+ enc_text = text_embeddings[0]
+ enc_text=enc_text[:,None]
+ enc_text = enc_text.repeat(1,z_len,1,1)
+ enc_text = rearrange(enc_text, 'b z l c -> (b z) l c')
+ xc={'c_concat': xc, 'c_crossattn': enc_text}
+ # breakpoint()
+
+ elif cond_key == 'ref_and_volume_seg':
+ xc1 = batch['volume_seg']
+ slice_num = xc1.shape[1]
+ xc1 = rearrange(xc1, 'b z h w c -> (b z) c h w')
+ xc1 = xc1.repeat(1,3,1,1)
+ if bs is not None:
+ xc1 = xc1[:bs]
+ xc1 = self.get_first_stage_encoding(self.encode_first_stage(xc1)).detach()
+ xc1 = xc1.to(memory_format=torch.contiguous_format).float()
+ xc2 = batch['volume_ref']
+ xc2 = xc2.repeat(1,slice_num,1,1,1)
+ xc2 = rearrange(xc2, 'b z h w c -> (b z) c h w')
+ xc2 = xc2.repeat(1,3,1,1)
+ if bs is not None:
+ xc2 = xc2[:bs]
+ xc2 = self.get_first_stage_encoding(self.encode_first_stage(xc2)).detach()
+ xc2 = xc2.to(memory_format=torch.contiguous_format).float()
+ xc = torch.cat([xc1, xc2], dim=1)
+ else:
+ xc = super().get_input(batch, cond_key).to(self.device)
+ else:
+ xc = x
+ # breakpoint()
+ if (not self.cond_stage_trainable or force_c_encode) and k != 'volume_data' and cond_key != 'masked_slice' and cond_key != 'masked_slice':
+ if isinstance(xc, dict) or isinstance(xc, list):
+ c = self.get_learned_conditioning(xc)
+ else:
+ c = self.get_learned_conditioning(xc.to(self.device))
+ else:
+ c = xc
+ if bs is not None:
+ c = c[:bs]
+ # breakpoint()
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ ckey = __conditioning_keys__[self.model.conditioning_key]
+ c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+ else:
+ c = None
+ xc = None
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ c = {'pos_x': pos_x, 'pos_y': pos_y}
+ out = [z, c]
+ if return_first_stage_outputs:
+ xrec = self.decode_first_stage(z)
+ out.extend([x, xrec])
+ if return_x:
+ out.extend([x])
+ if return_original_cond:
+ out.append(xc)
+ return out
+
+ @torch.no_grad()
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+ return self.first_stage_model.decode(z)
+
+ @torch.no_grad()
+ def encode_first_stage(self, x):
+ return self.first_stage_model.encode(x)
+
+ def shared_step(self, batch, **kwargs):
+ x, c = self.get_input(batch, self.first_stage_key)
+ loss = self(x, c)
+ return loss
+
+ def forward(self, x, c, *args, **kwargs):
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ if self.model.conditioning_key is not None:
+ assert c is not None
+ if self.cond_stage_trainable:
+ c = self.get_learned_conditioning(c)
+ if self.shorten_cond_schedule: # TODO: drop this option
+ tc = self.cond_ids[t].to(self.device)
+ c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+ return self.p_losses(x, c, t, *args, **kwargs)
+
+ def apply_model(self, x_noisy, t, cond, return_ids=False):
+ if isinstance(cond, dict):
+ # hybrid case, cond is expected to be a dict
+ pass
+ else:
+ if not isinstance(cond, list):
+ cond = [cond]
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+ cond = {key: cond}
+ # breakpoint()
+ x_recon = self.model(x_noisy, t, **cond)
+
+ if isinstance(x_recon, tuple) and not return_ids:
+ return x_recon[0]
+ else:
+ return x_recon
+
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+ return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+ def _prior_bpd(self, x_start):
+ """
+ Get the prior KL term for the variational lower-bound, measured in
+ bits-per-dim.
+ This term can't be optimized, as it only depends on the encoder.
+ :param x_start: the [N x C x ...] tensor of inputs.
+ :return: a batch of [N] KL values (in bits), one per batch element.
+ """
+ batch_size = x_start.shape[0]
+ t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+ return mean_flat(kl_prior) / np.log(2.0)
+
+ def p_losses(self, x_start, cond, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_output = self.apply_model(x_noisy, t, cond)
+
+ loss_dict = {}
+ prefix = 'train' if self.training else 'val'
+
+ if self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "v":
+ target = self.get_v(x_start, noise, t)
+ else:
+ raise NotImplementedError()
+
+ loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+ loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+ logvar_t = self.logvar[t].to(self.device)
+ loss = loss_simple / torch.exp(logvar_t) + logvar_t
+ # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+ if self.learn_logvar:
+ loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+ loss_dict.update({'logvar': self.logvar.data.mean()})
+
+ loss = self.l_simple_weight * loss.mean()
+
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+ loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+ loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+ loss += (self.original_elbo_weight * loss_vlb)
+ loss_dict.update({f'{prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+ return_x0=False, score_corrector=None, corrector_kwargs=None):
+ t_in = t
+ # breakpoint()
+ model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+ if score_corrector is not None:
+ assert self.parameterization == "eps"
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+ if return_codebook_ids:
+ model_out, logits = model_out
+
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ else:
+ raise NotImplementedError()
+
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+ if quantize_denoised:
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ if return_codebook_ids:
+ return model_mean, posterior_variance, posterior_log_variance, logits
+ elif return_x0:
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
+ else:
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+ return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+ b, *_, device = *x.shape, x.device
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+ return_codebook_ids=return_codebook_ids,
+ quantize_denoised=quantize_denoised,
+ return_x0=return_x0,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if return_codebook_ids:
+ raise DeprecationWarning("Support dropped.")
+ model_mean, _, model_log_variance, logits = outputs
+ elif return_x0:
+ model_mean, _, model_log_variance, x0 = outputs
+ else:
+ model_mean, _, model_log_variance = outputs
+
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+ if return_codebook_ids:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+ if return_x0:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+ else:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+ img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+ score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+ log_every_t=None):
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ timesteps = self.num_timesteps
+ if batch_size is not None:
+ b = batch_size if batch_size is not None else shape[0]
+ shape = [batch_size] + list(shape)
+ else:
+ b = batch_size = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=self.device)
+ else:
+ img = x_T
+ intermediates = []
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+ total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+ if type(temperature) == float:
+ temperature = [temperature] * timesteps
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img, x0_partial = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised, return_x0=True,
+ temperature=temperature[i], noise_dropout=noise_dropout,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(x0_partial)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_loop(self, cond, shape, return_intermediates=False,
+ x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, start_T=None,
+ log_every_t=None):
+
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ device = self.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ intermediates = [img]
+ if timesteps is None:
+ timesteps = self.num_timesteps
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+
+ if mask is not None:
+ assert x0 is not None
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+ # breakpoint()
+ img = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised)
+ if mask is not None:
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(img)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+ verbose=True, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, shape=None, **kwargs):
+ if shape is None:
+ shape = (batch_size, self.channels, self.image_size, self.image_size)
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+ return self.p_sample_loop(cond,
+ shape,
+ return_intermediates=return_intermediates, x_T=x_T,
+ verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+ mask=mask, x0=x0)
+
+ @torch.no_grad()
+ def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
+ if ddim:
+ ddim_sampler = DDIMSampler(self)
+ # breakpoint()
+ # if self.model.conditioning_key == 'crossattn' or self.model.conditioning_key == 'hybrid':
+ # shape = (self.channels, self.image_size, self.image_size)
+ # else:
+ # shape = ((self.channels)//2, self.image_size, self.image_size)
+ # breakpoint()
+ shape = (16, self.image_size, self.image_size)
+ samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
+ shape, cond, verbose=False, **kwargs)
+
+ else:
+ samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+ return_intermediates=True, **kwargs)
+
+ return samples, intermediates
+
+ @torch.no_grad()
+ def get_unconditional_conditioning(self, batch_size, null_label=None):
+ if null_label is not None:
+ xc = null_label
+ if isinstance(xc, ListConfig):
+ xc = list(xc)
+ if isinstance(xc, dict) or isinstance(xc, list):
+ c = self.get_learned_conditioning(xc)
+ else:
+ if hasattr(xc, "to"):
+ xc = xc.to(self.device)
+ c = self.get_learned_conditioning(xc)
+ else:
+ if self.cond_stage_key in ["class_label", "cls"]:
+ xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
+ return self.get_learned_conditioning(xc)
+ else:
+ raise NotImplementedError("todo")
+ if isinstance(c, list): # in case the encoder gives us a list
+ for i in range(len(c)):
+ c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
+ else:
+ c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
+ return c
+
+ @torch.no_grad()
+ def log_images(self, batch, N=32, n_row=4, sample=True, ddim_steps=20, ddim_eta=0., return_keys=None,
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+ plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+ use_ema_scope=True,
+ **kwargs):
+ ema_scope = self.ema_scope if use_ema_scope else nullcontext
+ use_ddim = ddim_steps is not None
+
+ plot_diffusion_rows = False
+ plot_progressive_rows = False
+ inpaint = False
+
+ log = dict()
+
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+ return_first_stage_outputs=True,
+ force_c_encode=True,
+ return_original_cond=True,
+ # bs=N
+ )
+ N = x.shape[0]
+ # N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ # breakpoint()
+ log["inputs"] = x
+ log["reconstruction"] = xrec
+ # if self.model.conditioning_key is not None:
+ # if hasattr(self.cond_stage_model, "decode"):
+ # # xc = self.cond_stage_model.decode(c)
+ # if c.shape[1] == 8:
+ # xc = self.decode_first_stage(c[:,:4])
+ # elif c.shape[1] == 6:
+ # xc = self.decode_first_stage(c[:, :3])
+ # else:
+ # xc = self.decode_first_stage(c)
+ # log["conditioning"] = xc
+ # elif self.cond_stage_key in ["caption", "txt"]:
+ # xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+ # log["conditioning"] = xc
+ # elif self.cond_stage_key in ['class_label', "cls"]:
+ # try:
+ # xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+ # log['conditioning'] = xc
+ # except KeyError:
+ # # probably no "human_label" in batch
+ # pass
+ # elif isimage(xc):
+ # log["conditioning"] = xc
+ # if ismap(xc):
+ # log["original_conditioning"] = self.to_rgb(xc)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ # breakpoint()
+ if sample:
+ # get denoise row
+ with ema_scope("Sampling"):
+ # breakpoint()
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+ self.first_stage_model, IdentityFirstStage):
+ # also display when quantizing x0 while sampling
+ with ema_scope("Plotting Quantized Denoised"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ quantize_denoised=True)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+ # quantize_denoised=True)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_x0_quantized"] = x_samples
+
+ if unconditional_guidance_scale > 1.0:
+ uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+ if self.model.conditioning_key == "crossattn-adm":
+ uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
+ with ema_scope("Sampling with classifier-free guidance"):
+ samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ if inpaint:
+ # make a simple center square
+ b, h, w = z.shape[0], z.shape[2], z.shape[3]
+ mask = torch.ones(N, h, w).to(self.device)
+ # zeros will be filled in
+ mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+ mask = mask[:, None, ...]
+ with ema_scope("Plotting Inpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_inpainting"] = x_samples
+ log["mask"] = mask
+
+ # outpaint
+ mask = 1. - mask
+ with ema_scope("Plotting Outpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_outpainting"] = x_samples
+
+ if plot_progressive_rows:
+ with ema_scope("Plotting Progressives"):
+ img, progressives = self.progressive_denoising(c,
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N)
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+ log["progressive_row"] = prog_row
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.cond_stage_trainable:
+ print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+ params = params + list(self.cond_stage_model.parameters())
+ if self.learn_logvar:
+ print('Diffusion model optimizing logvar')
+ params.append(self.logvar)
+ opt = torch.optim.AdamW(params, lr=lr)
+ if self.use_scheduler:
+ assert 'target' in self.scheduler_config
+ scheduler = instantiate_from_config(self.scheduler_config)
+
+ print("Setting up LambdaLR scheduler...")
+ scheduler = [
+ {
+ 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ }]
+ return [opt], scheduler
+ return opt
+
+ @torch.no_grad()
+ def to_rgb(self, x):
+ x = x.float()
+ if not hasattr(self, "colorize"):
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+ x = nn.functional.conv2d(x, weight=self.colorize)
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+ return x
+
+
+class DiffusionWrapper(pl.LightningModule):
+ def __init__(self, diff_model_config, conditioning_key):
+ super().__init__()
+ self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False)
+ self.diffusion_model = instantiate_from_config(diff_model_config)
+ self.conditioning_key = conditioning_key
+ assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
+
+ def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None):
+ # breakpoint()
+ if self.conditioning_key is None:
+ out = self.diffusion_model(x, t)
+ elif self.conditioning_key == 'concat':
+ xc = torch.cat([x] + c_concat, dim=1)
+ out = self.diffusion_model(xc, t)
+ elif self.conditioning_key == 'crossattn':
+ # breakpoint()
+ xc = torch.cat([x] + [c_concat], dim=1)
+ cc = torch.cat([c_crossattn], 1)
+ out = self.diffusion_model(xc, t, context=cc)
+ elif self.conditioning_key == 'hybrid':
+ xc = torch.cat([x] + c_crossattn, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc)
+ elif self.conditioning_key == 'hybrid-adm':
+ assert c_adm is not None
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc, y=c_adm)
+ elif self.conditioning_key == 'crossattn-adm':
+ assert c_adm is not None
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(x, t, context=cc, y=c_adm)
+ elif self.conditioning_key == 'adm':
+ cc = c_crossattn[0]
+ out = self.diffusion_model(x, t, y=cc)
+ else:
+ raise NotImplementedError()
+
+ return out
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm_pseudo3D.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm_pseudo3D.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb42735f1851b9e50450ea481657fbeddcdaea54
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm_pseudo3D.py
@@ -0,0 +1,1463 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager, nullcontext
+from functools import partial
+import itertools
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from omegaconf import ListConfig
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+from transformers import BertTokenizer,BertModel
+
+
+__conditioning_keys__ = {'concat': 'c_concat',
+ 'crossattn': 'c_crossattn',
+ 'adm': 'y'}
+
+weights = [2000 - i for i in range(1000)]
+weights = torch.tensor(weights, dtype=torch.float)
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+ return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPM(pl.LightningModule):
+ # classic DDPM with Gaussian diffusion, in image space
+ def __init__(self,
+ unet_config,
+ timesteps=1000,
+ beta_schedule="linear",
+ loss_type="l2",
+ ckpt_path=None,
+ ignore_keys=[],
+ load_only_unet=False,
+ monitor="val/loss",
+ use_ema=True,
+ first_stage_key="image",
+ image_size=256,
+ channels=3,
+ log_every_t=100,
+ clip_denoised=True,
+ linear_start=1e-4,
+ linear_end=2e-2,
+ cosine_s=8e-3,
+ given_betas=None,
+ original_elbo_weight=0.,
+ v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+ l_simple_weight=1.,
+ conditioning_key=None,
+ parameterization="eps", # all assuming fixed variance schedules
+ scheduler_config=None,
+ use_positional_encodings=False,
+ learn_logvar=False,
+ logvar_init=0.,
+ make_it_fit=False,
+ ucg_training=None,
+ reset_ema=False,
+ reset_num_ema_updates=False,
+ ):
+ super().__init__()
+ assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
+ self.parameterization = parameterization
+ print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+ self.cond_stage_model = None
+ self.clip_denoised = clip_denoised
+ self.log_every_t = log_every_t
+ self.first_stage_key = first_stage_key
+ self.image_size = image_size # try conv?
+ self.channels = channels
+ self.use_positional_encodings = use_positional_encodings
+ self.model = DiffusionWrapper(unet_config, conditioning_key)
+ count_params(self.model, verbose=True)
+ self.use_ema = use_ema
+ if self.use_ema:
+ self.model_ema = LitEma(self.model)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ self.use_scheduler = scheduler_config is not None
+ if self.use_scheduler:
+ self.scheduler_config = scheduler_config
+
+ self.v_posterior = v_posterior
+ self.original_elbo_weight = original_elbo_weight
+ self.l_simple_weight = l_simple_weight
+
+ if monitor is not None:
+ self.monitor = monitor
+ self.make_it_fit = make_it_fit
+ if reset_ema: assert exists(ckpt_path)
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+ if reset_ema:
+ assert self.use_ema
+ print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+ self.model_ema = LitEma(self.model)
+ if reset_num_ema_updates:
+ print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+ assert self.use_ema
+ self.model_ema.reset_num_updates()
+
+ self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+ linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+ self.loss_type = loss_type
+
+ self.learn_logvar = learn_logvar
+ logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+ if self.learn_logvar:
+ self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+ else:
+ self.register_buffer('logvar', logvar)
+
+ self.ucg_training = ucg_training or dict()
+ if self.ucg_training:
+ self.ucg_prng = np.random.RandomState()
+
+ def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if exists(given_betas):
+ betas = given_betas
+ else:
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
+ posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+ 1. - alphas_cumprod) + self.v_posterior * betas
+ # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+ self.register_buffer('posterior_variance', to_torch(posterior_variance))
+ # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+ self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+ self.register_buffer('posterior_mean_coef1', to_torch(
+ betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+ self.register_buffer('posterior_mean_coef2', to_torch(
+ (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+ if self.parameterization == "eps":
+ lvlb_weights = self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+ elif self.parameterization == "x0":
+ lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+ elif self.parameterization == "v":
+ lvlb_weights = torch.ones_like(self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
+ else:
+ raise NotImplementedError("mu not supported")
+ lvlb_weights[0] = lvlb_weights[1]
+ self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+ assert not torch.isnan(self.lvlb_weights).all()
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.model.parameters())
+ self.model_ema.copy_to(self.model)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.model.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ @torch.no_grad()
+ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+ sd = torch.load(path, map_location="cpu")
+ if "state_dict" in list(sd.keys()):
+ sd = sd["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ if self.make_it_fit:
+ n_params = len([name for name, _ in
+ itertools.chain(self.named_parameters(),
+ self.named_buffers())])
+ for name, param in tqdm(
+ itertools.chain(self.named_parameters(),
+ self.named_buffers()),
+ desc="Fitting old weights to new weights",
+ total=n_params
+ ):
+ if not name in sd:
+ continue
+ old_shape = sd[name].shape
+ new_shape = param.shape
+ assert len(old_shape) == len(new_shape)
+ if len(new_shape) > 2:
+ # we only modify first two axes
+ assert new_shape[2:] == old_shape[2:]
+ # assumes first axis corresponds to output dim
+ if not new_shape == old_shape:
+ new_param = param.clone()
+ old_param = sd[name]
+ if len(new_shape) == 1:
+ for i in range(new_param.shape[0]):
+ new_param[i] = old_param[i % old_shape[0]]
+ elif len(new_shape) >= 2:
+ for i in range(new_param.shape[0]):
+ for j in range(new_param.shape[1]):
+ new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
+
+ n_used_old = torch.ones(old_shape[1])
+ for j in range(new_param.shape[1]):
+ n_used_old[j % old_shape[1]] += 1
+ n_used_new = torch.zeros(new_shape[1])
+ for j in range(new_param.shape[1]):
+ n_used_new[j] = n_used_old[j % old_shape[1]]
+
+ n_used_new = n_used_new[None, :]
+ while len(n_used_new.shape) < len(new_shape):
+ n_used_new = n_used_new.unsqueeze(-1)
+ new_param /= n_used_new
+
+ sd[name] = new_param
+
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+ sd, strict=False)
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if len(missing) > 0:
+ print(f"Missing Keys:\n {missing}")
+ if len(unexpected) > 0:
+ print(f"\nUnexpected Keys:\n {unexpected}")
+
+ def q_mean_variance(self, x_start, t):
+ """
+ Get the distribution q(x_t | x_0).
+ :param x_start: the [N x C x ...] tensor of noiseless inputs.
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+ :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+ """
+ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+ variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+ log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+ return mean, variance, log_variance
+
+ def predict_start_from_noise(self, x_t, t, noise):
+ return (
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+ )
+
+ def predict_start_from_z_and_v(self, x_t, t, v):
+ # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
+ )
+
+ def predict_eps_from_z_and_v(self, x_t, t, v):
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
+ )
+
+ def q_posterior(self, x_start, x_t, t):
+ posterior_mean = (
+ extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+ )
+ posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+ posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+ return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+ def p_mean_variance(self, x, t, clip_denoised: bool):
+ model_out = self.model(x, t)
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+ b, *_, device = *x.shape, x.device
+ model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+ noise = noise_like(x.shape, device, repeat_noise)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def p_sample_loop(self, shape, return_intermediates=False):
+ device = self.betas.device
+ b = shape[0]
+ img = torch.randn(shape, device=device)
+ intermediates = [img]
+ for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+ img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+ clip_denoised=self.clip_denoised)
+ if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+ intermediates.append(img)
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, batch_size=16, return_intermediates=False):
+ image_size = self.image_size
+ channels = self.channels
+ return self.p_sample_loop((batch_size, channels, image_size, image_size),
+ return_intermediates=return_intermediates)
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ # breakpoint()
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def get_v(self, x, noise, t):
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
+ )
+
+ def get_loss(self, pred, target, mean=True):
+ if self.loss_type == 'l1':
+ loss = (target - pred).abs()
+ if mean:
+ loss = loss.mean()
+ elif self.loss_type == 'l2':
+ if mean:
+ loss = torch.nn.functional.mse_loss(target, pred)
+ else:
+ loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+ else:
+ raise NotImplementedError("unknown loss type '{loss_type}'")
+
+ return loss
+
+ def p_losses(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_out = self.model(x_noisy, t)
+
+ loss_dict = {}
+ if self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "v":
+ target = self.get_v(x_start, noise, t)
+ else:
+ raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
+
+ loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+ log_prefix = 'train' if self.training else 'val'
+
+ loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+ loss_simple = loss.mean() * self.l_simple_weight
+
+ loss_vlb = (self.lvlb_weights[t] * loss).mean()
+ loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+ loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+ loss_dict.update({f'{log_prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def forward(self, x, *args, **kwargs):
+ # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+ # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ return self.p_losses(x, t, *args, **kwargs)
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = rearrange(x, 'b h w c -> b c h w')
+ x = x.to(memory_format=torch.contiguous_format).float()
+ return x
+
+ def shared_step(self, batch):
+ x = self.get_input(batch, self.first_stage_key)
+ loss, loss_dict = self(x)
+ return loss, loss_dict
+
+ def training_step(self, batch, batch_idx):
+ for k in self.ucg_training:
+ p = self.ucg_training[k]["p"]
+ val = self.ucg_training[k]["val"]
+ if val is None:
+ val = ""
+ for i in range(len(batch[k])):
+ if self.ucg_prng.choice(2, p=[1 - p, p]):
+ batch[k][i] = val
+
+ loss, loss_dict = self.shared_step(batch)
+
+ self.log_dict(loss_dict, prog_bar=True,
+ logger=True, on_step=True, on_epoch=True)
+
+ self.log("global_step", self.global_step,
+ prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ if self.use_scheduler:
+ lr = self.optimizers().param_groups[0]['lr']
+ self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ return loss
+
+ @torch.no_grad()
+ def validation_step(self, batch, batch_idx):
+ _, loss_dict_no_ema = self.shared_step(batch)
+ with self.ema_scope():
+ _, loss_dict_ema = self.shared_step(batch)
+ loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+ self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+ self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self.model)
+
+ def _get_rows_from_list(self, samples):
+ n_imgs_per_row = len(samples)
+ denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.first_stage_key)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ x = x.to(self.device)[:N]
+ log["inputs"] = x
+
+ # get diffusion row
+ diffusion_row = list()
+ x_start = x[:n_row]
+
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(x_start)
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ diffusion_row.append(x_noisy)
+
+ log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+ if sample:
+ # get denoise row
+ with self.ema_scope("Plotting"):
+ samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+ log["samples"] = samples
+ log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.learn_logvar:
+ params = params + [self.logvar]
+ opt = torch.optim.AdamW(params, lr=lr)
+ return opt
+
+
+class LatentDiffusion(DDPM):
+ """main class"""
+
+ def __init__(self,
+ num_timesteps_cond=None,
+ cond_stage_key="image",
+ cond_stage_trainable=False,
+ concat_mode=True,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ scale_factor=1.0,
+ scale_by_std=False,
+ force_null_conditioning=False,
+ fix_t=False,
+ text_enc='custom',
+ *args, **kwargs):
+ self.force_null_conditioning = force_null_conditioning
+ self.num_timesteps_cond = default(num_timesteps_cond, 1)
+ self.scale_by_std = scale_by_std
+ assert self.num_timesteps_cond <= kwargs['timesteps']
+ # for backwards compatibility after implementation of DiffusionWrapper
+ if conditioning_key is None:
+ conditioning_key = 'concat' if concat_mode else 'crossattn'
+
+ ckpt_path = kwargs.pop("ckpt_path", None)
+ reset_ema = kwargs.pop("reset_ema", False)
+ reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
+ ignore_keys = kwargs.pop("ignore_keys", [])
+ super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+ self.concat_mode = concat_mode
+ self.cond_stage_trainable = cond_stage_trainable
+ self.cond_stage_key = cond_stage_key
+
+ if not scale_by_std:
+ self.scale_factor = scale_factor
+ else:
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
+ self.instantiate_first_stage()
+ # self.instantiate_cond_stage()
+ self.cond_stage_forward = cond_stage_forward
+ self.clip_denoised = False
+ self.bbox_tokenizer = None
+ self.tokenizer=BertTokenizer.from_pretrained('microsoft/BiomedVLP-CXR-BERT-specialized',do_lower_case=True)
+ self.text_encode_without_mask=False
+ self.text_transformer=BertModel.from_pretrained("microsoft/BiomedVLP-CXR-BERT-specialized")
+
+ self.restarted_from_ckpt = False
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys)
+ self.restarted_from_ckpt = True
+ if reset_ema:
+ assert self.use_ema
+ print(
+ f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+ self.model_ema = LitEma(self.model)
+ if reset_num_ema_updates:
+ print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+ assert self.use_ema
+ self.model_ema.reset_num_updates()
+
+ self.fix_t = fix_t
+
+ def make_cond_schedule(self, ):
+ self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+ ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+ self.cond_ids[:self.num_timesteps_cond] = ids
+
+ @rank_zero_only
+ @torch.no_grad()
+ def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+ # only for very first batch
+ if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+ assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+ # set rescale weight to 1./std of encodings
+ print("### USING STD-RESCALING ###")
+ x = super().get_input(batch, self.first_stage_key)
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+ del self.scale_factor
+ self.register_buffer('scale_factor', 1. / z.flatten().std())
+ print(f"setting self.scale_factor to {self.scale_factor}")
+ print("### USING STD-RESCALING ###")
+
+ def register_schedule(self,
+ given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+ self.shorten_cond_schedule = self.num_timesteps_cond > 1
+ if self.shorten_cond_schedule:
+ self.make_cond_schedule()
+
+ def instantiate_first_stage(self):
+ from LeanVAE import LeanVAE
+ vqgan_ckpt='LeanVAE/ckpts/LeanVAE-dim16.ckpt'
+ model = LeanVAE.load_from_checkpoint(vqgan_ckpt, strict=False)
+ self.first_stage_model = model.eval()
+ self.first_stage_model.train = disabled_train
+ for param in self.first_stage_model.parameters():
+ param.requires_grad = False
+
+ def instantiate_cond_stage(self, config):
+ if not self.cond_stage_trainable:
+ if config == "__is_first_stage__":
+ print("Using first stage also as cond stage.")
+ self.cond_stage_model = self.first_stage_model
+ elif config == "__is_unconditional__":
+ print(f"Training {self.__class__.__name__} as an unconditional model.")
+ self.cond_stage_model = None
+ # self.be_unconditional = True
+ else:
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model.eval()
+ self.cond_stage_model.train = disabled_train
+ for param in self.cond_stage_model.parameters():
+ param.requires_grad = False
+ else:
+ assert config != '__is_first_stage__'
+ assert config != '__is_unconditional__'
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model
+
+ def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+ denoise_row = []
+ for zd in tqdm(samples, desc=desc):
+ denoise_row.append(self.decode_first_stage(zd.to(self.device),
+ force_not_quantize=force_no_decoder_quantization))
+ n_imgs_per_row = len(denoise_row)
+ denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
+ denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ def get_first_stage_encoding(self, encoder_posterior):
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+ z = encoder_posterior.sample()
+ elif isinstance(encoder_posterior, torch.Tensor):
+ z = encoder_posterior
+ else:
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ return self.scale_factor * z
+
+ def get_learned_conditioning(self, c):
+ if self.cond_stage_forward is None:
+ if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+ c = self.cond_stage_model.encode(c)
+ if isinstance(c, DiagonalGaussianDistribution):
+ c = c.mode()
+ else:
+ c = self.cond_stage_model(c)
+ else:
+ assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+ c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+ return c
+
+ def meshgrid(self, h, w):
+ y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+ x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+ arr = torch.cat([y, x], dim=-1)
+ return arr
+
+ def delta_border(self, h, w):
+ """
+ :param h: height
+ :param w: width
+ :return: normalized distance to image border,
+ wtith min distance = 0 at border and max dist = 0.5 at image center
+ """
+ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+ arr = self.meshgrid(h, w) / lower_right_corner
+ dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+ dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+ edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+ return edge_dist
+
+ def get_weighting(self, h, w, Ly, Lx, device):
+ weighting = self.delta_border(h, w)
+ weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+ self.split_input_params["clip_max_weight"], )
+ weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+ if self.split_input_params["tie_braker"]:
+ L_weighting = self.delta_border(Ly, Lx)
+ L_weighting = torch.clip(L_weighting,
+ self.split_input_params["clip_min_tie_weight"],
+ self.split_input_params["clip_max_tie_weight"])
+
+ L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+ weighting = weighting * L_weighting
+ return weighting
+
+ def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
+ """
+ :param x: img of size (bs, c, h, w)
+ :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+ """
+ bs, nc, h, w = x.shape
+
+ # number of crops in image
+ Ly = (h - kernel_size[0]) // stride[0] + 1
+ Lx = (w - kernel_size[1]) // stride[1] + 1
+
+ if uf == 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+ weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+ elif uf > 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+ dilation=1, padding=0,
+ stride=(stride[0] * uf, stride[1] * uf))
+ fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+ elif df > 1 and uf == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+ dilation=1, padding=0,
+ stride=(stride[0] // df, stride[1] // df))
+ fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+ else:
+ raise NotImplementedError
+
+ return fold, unfold, normalization, weighting
+
+ @torch.no_grad()
+ def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+ cond_key=None, return_original_cond=False, bs=None, return_x=False):
+ # breakpoint()
+ if k != 'volume_data':
+ x = super().get_input(batch, k)
+ else:
+ x = batch[k]
+ x = x.repeat(1,3,1,1,1)
+ x = x.to(memory_format=torch.contiguous_format).float()
+ if bs is not None:
+ x = x[:bs]
+ x = x.to(self.device)
+ # breakpoint()
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+ z = rearrange(z, 'b c z h w -> (b z) c h w')
+
+ if self.model.conditioning_key is not None and not self.force_null_conditioning:
+ if cond_key is None:
+ cond_key = self.cond_stage_key
+ if cond_key != self.first_stage_key:
+ if cond_key in ['caption', 'coordinates_bbox', "txt"]:
+ xc = batch[cond_key]
+ elif cond_key in ['class_label', 'cls']:
+ xc = batch
+ elif cond_key == 'volume_seg':
+ xc = batch['volume_seg']
+ xc = rearrange(xc, 'b z h w c -> (b z) c h w')
+ xc = xc.repeat(1,3,1,1)
+ if bs is not None:
+ xc = xc[:bs]
+ xc = self.get_first_stage_encoding(self.encode_first_stage(xc)).detach()
+ xc = xc.to(memory_format=torch.contiguous_format).float()
+ elif cond_key == 'volume_seg_and_text':
+ xc = batch['volume_seg']
+ xc = xc.repeat(1,3,1,1,1)
+
+ if bs is not None:
+ xc = xc[:bs]
+ xc = self.get_first_stage_encoding(self.encode_first_stage(xc)).detach()
+ z_len=xc.shape[2]
+ xc = rearrange(xc, 'b c z h w -> (b z) c h w')
+ xc = xc.to(memory_format=torch.contiguous_format).float()
+
+ # breakpoint()
+ text = batch['input_text']
+ text = list(text)
+ text_tokens=self.tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=512).to(self.device)
+ text_embeddings = self.text_transformer(text_tokens.input_ids, attention_mask = text_tokens.attention_mask )
+ enc_text = text_embeddings[0]
+ enc_text=enc_text[:,None]
+ enc_text = enc_text.repeat(1,z_len,1,1)
+ enc_text = rearrange(enc_text, 'b z l c -> (b z) l c')
+ xc={'c_concat': xc, 'c_crossattn': enc_text}
+
+ elif cond_key == 'ref_and_volume_seg':
+ xc1 = batch['volume_seg']
+ slice_num = xc1.shape[1]
+ xc1 = rearrange(xc1, 'b z h w c -> (b z) c h w')
+ xc1 = xc1.repeat(1,3,1,1)
+ if bs is not None:
+ xc1 = xc1[:bs]
+ xc1 = self.get_first_stage_encoding(self.encode_first_stage(xc1)).detach()
+ xc1 = xc1.to(memory_format=torch.contiguous_format).float()
+ xc2 = batch['volume_ref']
+ xc2 = xc2.repeat(1,slice_num,1,1,1)
+ xc2 = rearrange(xc2, 'b z h w c -> (b z) c h w')
+ xc2 = xc2.repeat(1,3,1,1)
+ if bs is not None:
+ xc2 = xc2[:bs]
+ xc2 = self.get_first_stage_encoding(self.encode_first_stage(xc2)).detach()
+ xc2 = xc2.to(memory_format=torch.contiguous_format).float()
+ xc = torch.cat([xc1, xc2], dim=1)
+ elif cond_key == 'masked_volume':
+ xc = batch['masked_data']
+ mask_tumor = batch['tumor_mask']
+ # breakpoint()
+ mask_tumor = rearrange(mask_tumor, 'b z h w c -> (b z) c h w')
+ xc = rearrange(xc, 'b z h w c -> (b z) c h w')
+ xc = xc.repeat(1,3,1,1)
+ if bs is not None:
+ xc = xc[:bs]
+ mask_tumor=mask_tumor[:bs]
+ xc = self.get_first_stage_encoding(self.encode_first_stage(xc)).detach()
+ # breakpoint()
+ xc = torch.cat([xc, mask_tumor], dim=1).detach()
+ xc = xc.to(memory_format=torch.contiguous_format).float()
+ else:
+ xc = super().get_input(batch, cond_key).to(self.device)
+ else:
+ xc = x
+ if (not self.cond_stage_trainable or force_c_encode) and k != 'volume_data':
+ if isinstance(xc, dict) or isinstance(xc, list):
+ c = self.get_learned_conditioning(xc)
+ else:
+ c = self.get_learned_conditioning(xc.to(self.device))
+ else:
+ c = xc
+ if bs is not None:
+ c = c[:bs]
+
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ ckey = __conditioning_keys__[self.model.conditioning_key]
+ c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+ else:
+ c = None
+ xc = None
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ c = {'pos_x': pos_x, 'pos_y': pos_y}
+ out = [z, c]
+ if return_first_stage_outputs:
+ xrec = self.decode_first_stage(z)
+ out.extend([x, xrec])
+ if return_x:
+ out.extend([x])
+ if return_original_cond:
+ out.append(xc)
+ return out
+
+ @torch.no_grad()
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+ return self.first_stage_model.decode(z)
+
+ @torch.no_grad()
+ def encode_first_stage(self, x):
+ return self.first_stage_model.encode(x)
+
+ def shared_step(self, batch, **kwargs):
+ x, c = self.get_input(batch, self.first_stage_key)
+ loss = self(x, c)
+ return loss
+
+ def forward(self, x, c, *args, **kwargs):
+ if not self.fix_t:
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ else:
+ t = torch.multinomial(weights, x.shape[0]//17, replacement=True)
+ t = t.reshape(((x.shape[0]//17,))).long().to(self.device)
+ t = t.unsqueeze(1)
+ t = t.repeat(1,17)
+ t = rearrange(t, 'b t-> (b t)')
+ # breakpoint()
+ if self.model.conditioning_key is not None:
+ assert c is not None
+ if self.cond_stage_trainable:
+ c = self.get_learned_conditioning(c)
+ if self.shorten_cond_schedule: # TODO: drop this option
+ tc = self.cond_ids[t].to(self.device)
+ c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+ return self.p_losses(x, c, t, *args, **kwargs)
+
+ def apply_model(self, x_noisy, t, cond, return_ids=False):
+ if isinstance(cond, dict):
+ # hybrid case, cond is expected to be a dict
+ pass
+ else:
+ if not isinstance(cond, list):
+ cond = [cond]
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+ cond = {key: cond}
+
+ x_recon = self.model(x_noisy, t, **cond)
+
+ if isinstance(x_recon, tuple) and not return_ids:
+ return x_recon[0]
+ else:
+ return x_recon
+
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+ return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+ def _prior_bpd(self, x_start):
+ """
+ Get the prior KL term for the variational lower-bound, measured in
+ bits-per-dim.
+ This term can't be optimized, as it only depends on the encoder.
+ :param x_start: the [N x C x ...] tensor of inputs.
+ :return: a batch of [N] KL values (in bits), one per batch element.
+ """
+ batch_size = x_start.shape[0]
+ t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+ return mean_flat(kl_prior) / np.log(2.0)
+
+ def p_losses(self, x_start, cond, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_output = self.apply_model(x_noisy, t, cond)
+
+ loss_dict = {}
+ prefix = 'train' if self.training else 'val'
+
+ if self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "v":
+ target = self.get_v(x_start, noise, t)
+ else:
+ raise NotImplementedError()
+
+ loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+ loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+ logvar_t = self.logvar[t].to(self.device)
+ loss = loss_simple / torch.exp(logvar_t) + logvar_t
+ # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+ if self.learn_logvar:
+ loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+ loss_dict.update({'logvar': self.logvar.data.mean()})
+
+ loss = self.l_simple_weight * loss.mean()
+
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+ loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+ loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+ loss += (self.original_elbo_weight * loss_vlb)
+ loss_dict.update({f'{prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+ return_x0=False, score_corrector=None, corrector_kwargs=None):
+ t_in = t
+ model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+ if score_corrector is not None:
+ assert self.parameterization == "eps"
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+ if return_codebook_ids:
+ model_out, logits = model_out
+
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ else:
+ raise NotImplementedError()
+
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+ if quantize_denoised:
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ if return_codebook_ids:
+ return model_mean, posterior_variance, posterior_log_variance, logits
+ elif return_x0:
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
+ else:
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+ return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+ b, *_, device = *x.shape, x.device
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+ return_codebook_ids=return_codebook_ids,
+ quantize_denoised=quantize_denoised,
+ return_x0=return_x0,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if return_codebook_ids:
+ raise DeprecationWarning("Support dropped.")
+ model_mean, _, model_log_variance, logits = outputs
+ elif return_x0:
+ model_mean, _, model_log_variance, x0 = outputs
+ else:
+ model_mean, _, model_log_variance = outputs
+
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+ if return_codebook_ids:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+ if return_x0:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+ else:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+ img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+ score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+ log_every_t=None):
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ timesteps = self.num_timesteps
+ if batch_size is not None:
+ b = batch_size if batch_size is not None else shape[0]
+ shape = [batch_size] + list(shape)
+ else:
+ b = batch_size = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=self.device)
+ else:
+ img = x_T
+ intermediates = []
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+ total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+ if type(temperature) == float:
+ temperature = [temperature] * timesteps
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img, x0_partial = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised, return_x0=True,
+ temperature=temperature[i], noise_dropout=noise_dropout,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(x0_partial)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_loop(self, cond, shape, return_intermediates=False,
+ x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, start_T=None,
+ log_every_t=None):
+
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ device = self.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ intermediates = [img]
+ if timesteps is None:
+ timesteps = self.num_timesteps
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+
+ if mask is not None:
+ assert x0 is not None
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised)
+ if mask is not None:
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(img)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+ verbose=True, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, shape=None, **kwargs):
+ if shape is None:
+ shape = (batch_size, self.channels, self.image_size, self.image_size)
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+ return self.p_sample_loop(cond,
+ shape,
+ return_intermediates=return_intermediates, x_T=x_T,
+ verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+ mask=mask, x0=x0)
+
+ @torch.no_grad()
+ def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
+ if ddim:
+ ddim_sampler = DDIMSampler(self)
+ if self.model.conditioning_key == 'crossattn' or self.model.conditioning_key == 'hybrid':
+ shape = (self.channels, self.image_size, self.image_size)
+ else:
+ shape = ((self.channels)//2, self.image_size, self.image_size)
+ samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
+ shape, cond, verbose=False, **kwargs)
+
+ else:
+ samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+ return_intermediates=True, **kwargs)
+
+ return samples, intermediates
+
+ @torch.no_grad()
+ def get_unconditional_conditioning(self, batch_size, null_label=None):
+ if null_label is not None:
+ xc = null_label
+ if isinstance(xc, ListConfig):
+ xc = list(xc)
+ if isinstance(xc, dict) or isinstance(xc, list):
+ c = self.get_learned_conditioning(xc)
+ else:
+ if hasattr(xc, "to"):
+ xc = xc.to(self.device)
+ c = self.get_learned_conditioning(xc)
+ else:
+ if self.cond_stage_key in ["class_label", "cls"]:
+ xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
+ return self.get_learned_conditioning(xc)
+ else:
+ raise NotImplementedError("todo")
+ if isinstance(c, list): # in case the encoder gives us a list
+ for i in range(len(c)):
+ c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
+ else:
+ c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
+ return c
+
+ @torch.no_grad()
+ def log_images(self, batch, N=16, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+ plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+ use_ema_scope=True,
+ **kwargs):
+ ema_scope = self.ema_scope if use_ema_scope else nullcontext
+ use_ddim = ddim_steps is not None
+
+ if self.fix_t == True:
+ sample = True
+ plot_denoise_rows = False
+ plot_progressive_rows = False
+ plot_diffusion_rows = False
+ inpaint = False
+
+ log = dict()
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+ return_first_stage_outputs=True,
+ force_c_encode=True,
+ return_original_cond=True,
+ bs=N)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ log["inputs"] = x
+ log["reconstruction"] = xrec
+ # if self.model.conditioning_key is not None:
+ # if hasattr(self.cond_stage_model, "decode"):
+ # # xc = self.cond_stage_model.decode(c)
+ # if c.shape[1] == 8:
+ # xc = self.decode_first_stage(c[:,:4])
+ # elif c.shape[1] == 6:
+ # xc = self.decode_first_stage(c[:, :3])
+ # else:
+ # xc = self.decode_first_stage(c)
+ # log["conditioning"] = xc
+ # elif self.cond_stage_key in ["caption", "txt"]:
+ # xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+ # log["conditioning"] = xc
+ # elif self.cond_stage_key in ['class_label', "cls"]:
+ # try:
+ # xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+ # log['conditioning'] = xc
+ # except KeyError:
+ # # probably no "human_label" in batch
+ # pass
+ # elif isimage(xc):
+ # log["conditioning"] = xc
+ # if ismap(xc):
+ # log["original_conditioning"] = self.to_rgb(xc)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ with ema_scope("Sampling"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+ self.first_stage_model, IdentityFirstStage):
+ # also display when quantizing x0 while sampling
+ with ema_scope("Plotting Quantized Denoised"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ quantize_denoised=True)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+ # quantize_denoised=True)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_x0_quantized"] = x_samples
+
+ if unconditional_guidance_scale > 1.0:
+ uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+ if self.model.conditioning_key == "crossattn-adm":
+ uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
+ with ema_scope("Sampling with classifier-free guidance"):
+ samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ if c.shape[1] == 8 or c.shape[1] == 6:
+ inpaint = False
+ if inpaint:
+ # make a simple center square
+ b, h, w = z.shape[0], z.shape[2], z.shape[3]
+ mask = torch.ones(N, h, w).to(self.device)
+ # zeros will be filled in
+ mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+ mask = mask[:, None, ...]
+ with ema_scope("Plotting Inpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_inpainting"] = x_samples
+ log["mask"] = mask
+
+ # outpaint
+ mask = 1. - mask
+ with ema_scope("Plotting Outpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_outpainting"] = x_samples
+
+ if plot_progressive_rows:
+ with ema_scope("Plotting Progressives"):
+ img, progressives = self.progressive_denoising(c,
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N)
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+ log["progressive_row"] = prog_row
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = []
+ for name, p in self.model.named_parameters():
+ if 'tem' in name:
+ params.append(p)
+ # params = list(self.model.parameters())
+ if self.cond_stage_trainable:
+ print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+ params = params + list(self.cond_stage_model.parameters())
+ if self.learn_logvar:
+ print('Diffusion model optimizing logvar')
+ params.append(self.logvar)
+ opt = torch.optim.AdamW(params, lr=lr)
+ if self.use_scheduler:
+ assert 'target' in self.scheduler_config
+ scheduler = instantiate_from_config(self.scheduler_config)
+
+ print("Setting up LambdaLR scheduler...")
+ scheduler = [
+ {
+ 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ }]
+ return [opt], scheduler
+ return opt
+
+ @torch.no_grad()
+ def to_rgb(self, x):
+ x = x.float()
+ if not hasattr(self, "colorize"):
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+ x = nn.functional.conv2d(x, weight=self.colorize)
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+ return x
+
+
+class DiffusionWrapper(pl.LightningModule):
+ def __init__(self, diff_model_config, conditioning_key):
+ super().__init__()
+ self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False)
+ self.diffusion_model = instantiate_from_config(diff_model_config)
+ self.conditioning_key = conditioning_key
+ assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
+
+ def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None):
+ if self.conditioning_key is None:
+ out = self.diffusion_model(x, t)
+ elif self.conditioning_key == 'concat':
+ # breakpoint()
+ xc = torch.cat([x] + c_concat, dim=1)
+ out = self.diffusion_model(xc, t)
+ elif self.conditioning_key == 'crossattn':
+ # if not self.sequential_cross_attn:
+ # cc = torch.cat(c_crossattn, 1)
+ # else:
+ # cc = c_crossattn
+ # out = self.diffusion_model(x, t, context=cc)
+ # breakpoint()
+ xc = torch.cat([x] + [c_concat], dim=1)
+ cc = torch.cat([c_crossattn], 1)
+ out = self.diffusion_model(xc, t, context=cc)
+ elif self.conditioning_key == 'hybrid':
+ xc = torch.cat([x] + c_crossattn, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc)
+ elif self.conditioning_key == 'hybrid-adm':
+ assert c_adm is not None
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc, y=c_adm)
+ elif self.conditioning_key == 'crossattn-adm':
+ assert c_adm is not None
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(x, t, context=cc, y=c_adm)
+ elif self.conditioning_key == 'adm':
+ cc = c_crossattn[0]
+ out = self.diffusion_model(x, t, y=cc)
+ else:
+ raise NotImplementedError()
+
+ return out
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm_recon.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm_recon.py
new file mode 100644
index 0000000000000000000000000000000000000000..af03934332de891dee007853077d9de5f57d1641
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm_recon.py
@@ -0,0 +1,141 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager, nullcontext
+from functools import partial
+import itertools
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from omegaconf import ListConfig
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
+__conditioning_keys__ = {'concat': 'c_concat',
+ 'crossattn': 'c_crossattn',
+ 'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+ return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+
+class Latent_Recon(pl.LightningModule):
+ """main class"""
+
+ def __init__(self,
+ first_stage_config,
+ cond_stage_config,
+ num_timesteps_cond=None,
+ cond_stage_key="image",
+ cond_stage_trainable=False,
+ concat_mode=True,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ scale_factor=1.0,
+ scale_by_std=False,
+ force_null_conditioning=False,
+ *args, **kwargs):
+ super().__init__()
+ self.scale_by_std = scale_by_std
+
+ try:
+ self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+ except:
+ self.num_downs = 0
+ if not scale_by_std:
+ self.scale_factor = scale_factor
+ else:
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
+ # breakpoint()
+ self.instantiate_first_stage(first_stage_config)
+
+ def instantiate_first_stage(self, config):
+ model = instantiate_from_config(config)
+ self.first_stage_model = model.eval()
+ self.first_stage_model.train = disabled_train
+ for param in self.first_stage_model.parameters():
+ param.requires_grad = False
+
+ def get_first_stage_encoding(self, encoder_posterior):
+ # breakpoint()
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+ z = encoder_posterior.sample()
+ elif isinstance(encoder_posterior, torch.Tensor):
+ z = encoder_posterior
+ else:
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ return self.scale_factor * z
+
+
+ @torch.no_grad()
+ def get_input(self, batch):
+
+ x = rearrange(batch, 'b h w c -> b c h w')
+ x = x.repeat(1,3,1,1)
+ x = x.to(memory_format=torch.contiguous_format).float()
+
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+ xrec = self.decode_first_stage(z)
+ return xrec
+
+ @torch.no_grad()
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+ return self.first_stage_model.decode(z)
+
+ @torch.no_grad()
+ def encode_first_stage(self, x):
+ return self.first_stage_model.encode(x)
+
+ def forward(self, batch, *args, **kwargs):
+ xrec = self.get_input(batch)
+ return xrec
+
+ @torch.no_grad()
+ def validation_step(self, batch, batch_idx):
+ xrec = self.forward(batch)
+ return xrec
+
+ @torch.no_grad()
+ def to_rgb(self, x):
+ x = x.float()
+ if not hasattr(self, "colorize"):
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+ x = nn.functional.conv2d(x, weight=self.colorize)
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+ return x
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm_recon_vq.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm_recon_vq.py
new file mode 100644
index 0000000000000000000000000000000000000000..7bf3d28fb83fafcf8a2c3017df6c717f9eacc1f9
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/ddpm_recon_vq.py
@@ -0,0 +1,144 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager, nullcontext
+from functools import partial
+import itertools
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from omegaconf import ListConfig
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
+__conditioning_keys__ = {'concat': 'c_concat',
+ 'crossattn': 'c_crossattn',
+ 'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+ return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+
+class Latent_Recon(pl.LightningModule):
+ """main class"""
+
+ def __init__(self,
+ first_stage_config,
+ cond_stage_config,
+ num_timesteps_cond=None,
+ cond_stage_key="image",
+ cond_stage_trainable=False,
+ concat_mode=True,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ scale_factor=1.0,
+ scale_by_std=False,
+ force_null_conditioning=False,
+ *args, **kwargs):
+ super().__init__()
+ self.scale_by_std = scale_by_std
+
+ try:
+ self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+ except:
+ self.num_downs = 0
+ if not scale_by_std:
+ self.scale_factor = scale_factor
+ else:
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
+ # breakpoint()
+ self.instantiate_first_stage(first_stage_config)
+
+ def instantiate_first_stage(self, config):
+ model = instantiate_from_config(config)
+ self.first_stage_model = model.eval()
+ self.first_stage_model.train = disabled_train
+ for param in self.first_stage_model.parameters():
+ param.requires_grad = False
+
+ def get_first_stage_encoding(self, encoder_posterior):
+ # breakpoint()
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+ z = encoder_posterior.sample()
+ elif isinstance(encoder_posterior, torch.Tensor):
+ z = encoder_posterior
+ elif isinstance(encoder_posterior, tuple):
+ z = encoder_posterior[0]
+ else:
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ # breakpoint()
+ return self.scale_factor * z
+
+
+ @torch.no_grad()
+ def get_input(self, batch):
+
+ x = rearrange(batch, 'b h w c -> b c h w')
+ x = x.repeat(1,3,1,1)
+ x = x.to(memory_format=torch.contiguous_format).float()
+
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+ xrec = self.decode_first_stage(z)
+ return xrec
+
+ @torch.no_grad()
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+ return self.first_stage_model.decode(z)
+
+ @torch.no_grad()
+ def encode_first_stage(self, x):
+ return self.first_stage_model.encode(x)
+
+ def forward(self, batch, *args, **kwargs):
+ xrec = self.get_input(batch)
+ return xrec
+
+ @torch.no_grad()
+ def validation_step(self, batch, batch_idx):
+ xrec = self.forward(batch)
+ return xrec
+
+ @torch.no_grad()
+ def to_rgb(self, x):
+ x = x.float()
+ if not hasattr(self, "colorize"):
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+ x = nn.functional.conv2d(x, weight=self.colorize)
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+ return x
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/plms.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/plms.py
new file mode 100644
index 0000000000000000000000000000000000000000..7002a365d27168ced0a04e9a4d83e088f8284eae
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/plms.py
@@ -0,0 +1,244 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+from functools import partial
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
+from ldm.models.diffusion.sampling_util import norm_thresholding
+
+
+class PLMSSampler(object):
+ def __init__(self, model, schedule="linear", **kwargs):
+ super().__init__()
+ self.model = model
+ self.ddpm_num_timesteps = model.num_timesteps
+ self.schedule = schedule
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+ if ddim_eta != 0:
+ raise ValueError('ddim_eta must be 0 for PLMS')
+ self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ alphas_cumprod = self.model.alphas_cumprod
+ assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+ self.register_buffer('betas', to_torch(self.model.betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+ # ddim sampling parameters
+ ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,verbose=verbose)
+ self.register_buffer('ddim_sigmas', ddim_sigmas)
+ self.register_buffer('ddim_alphas', ddim_alphas)
+ self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+ self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+ (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+ 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+ self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None,
+ # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ dynamic_threshold=None,
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+ print(f'Data shape for PLMS sampling is {size}')
+
+ samples, intermediates = self.plms_sampling(conditioning, size,
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask, x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold,
+ )
+ return samples, intermediates
+
+ @torch.no_grad()
+ def plms_sampling(self, cond, shape,
+ x_T=None, ddim_use_original_steps=False,
+ callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, log_every_t=100,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None,
+ dynamic_threshold=None):
+ device = self.model.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ if timesteps is None:
+ timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ elif timesteps is not None and not ddim_use_original_steps:
+ subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ timesteps = self.ddim_timesteps[:subset_end]
+
+ intermediates = {'x_inter': [img], 'pred_x0': [img]}
+ time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
+ total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+ print(f"Running PLMS Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
+ old_eps = []
+
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+ ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
+
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
+ img = img_orig * mask + (1. - mask) * img
+
+ outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+ quantize_denoised=quantize_denoised, temperature=temperature,
+ noise_dropout=noise_dropout, score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ old_eps=old_eps, t_next=ts_next,
+ dynamic_threshold=dynamic_threshold)
+ img, pred_x0, e_t = outs
+ old_eps.append(e_t)
+ if len(old_eps) >= 4:
+ old_eps.pop(0)
+ if callback: callback(i)
+ if img_callback: img_callback(pred_x0, i)
+
+ if index % log_every_t == 0 or index == total_steps - 1:
+ intermediates['x_inter'].append(img)
+ intermediates['pred_x0'].append(pred_x0)
+
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
+ dynamic_threshold=None):
+ b, *_, device = *x.shape, x.device
+
+ def get_model_output(x, t):
+ if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ e_t = self.model.apply_model(x, t, c)
+ else:
+ x_in = torch.cat([x] * 2)
+ t_in = torch.cat([t] * 2)
+ c_in = torch.cat([unconditional_conditioning, c])
+ e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+ e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+ if score_corrector is not None:
+ assert self.model.parameterization == "eps"
+ e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+ return e_t
+
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+ sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+ sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+ def get_x_prev_and_pred_x0(e_t, index):
+ # select parameters corresponding to the currently considered timestep
+ a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+ a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+ sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+ sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+ # current prediction for x_0
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ if quantize_denoised:
+ pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+ if dynamic_threshold is not None:
+ pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+ noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+ return x_prev, pred_x0
+
+ e_t = get_model_output(x, t)
+ if len(old_eps) == 0:
+ # Pseudo Improved Euler (2nd order)
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+ e_t_next = get_model_output(x_prev, t_next)
+ e_t_prime = (e_t + e_t_next) / 2
+ elif len(old_eps) == 1:
+ # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (3 * e_t - old_eps[-1]) / 2
+ elif len(old_eps) == 2:
+ # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+ elif len(old_eps) >= 3:
+ # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+ return x_prev, pred_x0, e_t
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/sampling_util.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/sampling_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eff02be6d7c54d43ee6680636ac0698dd3b3f33
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/models/diffusion/sampling_util.py
@@ -0,0 +1,22 @@
+import torch
+import numpy as np
+
+
+def append_dims(x, target_dims):
+ """Appends dimensions to the end of a tensor until it has target_dims dimensions.
+ From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
+ dims_to_append = target_dims - x.ndim
+ if dims_to_append < 0:
+ raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+ return x[(...,) + (None,) * dims_to_append]
+
+
+def norm_thresholding(x0, value):
+ s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
+ return x0 * (value / s)
+
+
+def spatial_norm_thresholding(x0, value):
+ # b c h w
+ s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
+ return x0 * (value / s)
\ No newline at end of file
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/attention.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7e06ed79f47b0b7b869531e69e1f9de6f69c2a7
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/attention.py
@@ -0,0 +1,354 @@
+from inspect import isfunction
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+from typing import Optional, Any
+
+from ldm.modules.diffusionmodules.util import checkpoint
+
+
+try:
+ import xformers
+ import xformers.ops
+ XFORMERS_IS_AVAILBLE = True
+except:
+ XFORMERS_IS_AVAILBLE = False
+
+# CrossAttn precision handling
+import os
+_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
+
+def exists(val):
+ return val is not None
+
+
+def uniq(arr):
+ return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+ return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+ dim = tensor.shape[-1]
+ std = 1 / math.sqrt(dim)
+ tensor.uniform_(-std, std)
+ return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+ def __init__(self, dim_in, dim_out):
+ super().__init__()
+ self.proj = nn.Linear(dim_in, dim_out * 2)
+
+ def forward(self, x):
+ x, gate = self.proj(x).chunk(2, dim=-1)
+ return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ dim_out = default(dim_out, dim)
+ project_in = nn.Sequential(
+ nn.Linear(dim, inner_dim),
+ nn.GELU()
+ ) if not glu else GEGLU(dim, inner_dim)
+
+ self.net = nn.Sequential(
+ project_in,
+ nn.Dropout(dropout),
+ nn.Linear(inner_dim, dim_out)
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+
+def zero_module(module):
+ """
+ Zero out the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+
+def Normalize(in_channels):
+ return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class SpatialSelfAttention(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b,c,h,w = q.shape
+ q = rearrange(q, 'b c h w -> b (h w) c')
+ k = rearrange(k, 'b c h w -> b c (h w)')
+ w_ = torch.einsum('bij,bjk->bik', q, k)
+
+ w_ = w_ * (int(c)**(-0.5))
+ w_ = torch.nn.functional.softmax(w_, dim=2)
+
+ # attend to values
+ v = rearrange(v, 'b c h w -> b c (h w)')
+ w_ = rearrange(w_, 'b i j -> b j i')
+ h_ = torch.einsum('bij,bjk->bik', v, w_)
+ h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
+ h_ = self.proj_out(h_)
+
+ return x+h_
+
+
+class CrossAttention(nn.Module):
+ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
+ super().__init__()
+ inner_dim = dim_head * heads
+ context_dim = default(context_dim, query_dim)
+
+ self.scale = dim_head ** -0.5
+ self.heads = heads
+
+ self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+ self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+ self.to_out = nn.Sequential(
+ nn.Linear(inner_dim, query_dim),
+ nn.Dropout(dropout)
+ )
+
+ def forward(self, x, context=None, mask=None):
+
+ h = self.heads
+
+ # breakpoint()
+ q = self.to_q(x)
+ context = default(context, x)
+ k = self.to_k(context)
+ v = self.to_v(context)
+ # breakpoint()
+ if len(k.shape) == 2:
+ k = rearrange(k, 'b d -> b 1 d')
+ v = rearrange(v, 'b d -> b 1 d')
+ k = repeat(k, 'b 1 d -> b n d', n=q.shape[1])
+ v = repeat(v, 'b 1 d -> b n d', n=q.shape[1])
+
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+ # force cast to fp32 to avoid overflowing
+ if _ATTN_PRECISION =="fp32":
+ with torch.autocast(enabled=False, device_type = 'cuda'):
+ q, k = q.float(), k.float()
+ sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+ else:
+ sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+ del q, k
+
+ if exists(mask):
+ mask = rearrange(mask, 'b ... -> b (...)')
+ max_neg_value = -torch.finfo(sim.dtype).max
+ mask = repeat(mask, 'b j -> (b h) () j', h=h)
+ sim.masked_fill_(~mask, max_neg_value)
+
+ # attention, what we cannot get enough of
+ sim = sim.softmax(dim=-1)
+
+ out = einsum('b i j, b j d -> b i d', sim, v)
+ out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+ return self.to_out(out)
+
+
+class MemoryEfficientCrossAttention(nn.Module):
+ # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
+ super().__init__()
+ print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
+ f"{heads} heads.")
+ inner_dim = dim_head * heads
+ context_dim = default(context_dim, query_dim)
+
+ self.heads = heads
+ self.dim_head = dim_head
+
+ self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+ self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+ self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+ self.attention_op: Optional[Any] = None
+
+ def forward(self, x, context=None, mask=None):
+ q = self.to_q(x)
+ context = default(context, x)
+ k = self.to_k(context)
+ v = self.to_v(context)
+
+ b, _, _ = q.shape
+ q, k, v = map(
+ lambda t: t.unsqueeze(3)
+ .reshape(b, t.shape[1], self.heads, self.dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b * self.heads, t.shape[1], self.dim_head)
+ .contiguous(),
+ (q, k, v),
+ )
+
+ # actually compute the attention, what we cannot get enough of
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+ if exists(mask):
+ raise NotImplementedError
+ out = (
+ out.unsqueeze(0)
+ .reshape(b, self.heads, out.shape[1], self.dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b, out.shape[1], self.heads * self.dim_head)
+ )
+ return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+ ATTENTION_MODES = {
+ "softmax": CrossAttention, # vanilla attention
+ "softmax-xformers": MemoryEfficientCrossAttention
+ }
+ def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
+ disable_self_attn=False):
+ super().__init__()
+ attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
+ # breakpoint()
+ assert attn_mode in self.ATTENTION_MODES
+ attn_cls = self.ATTENTION_MODES[attn_mode]
+ self.disable_self_attn = disable_self_attn
+ self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
+ context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn
+ self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+ self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
+ heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
+ self.norm1 = nn.LayerNorm(dim)
+ self.norm2 = nn.LayerNorm(dim)
+ self.norm3 = nn.LayerNorm(dim)
+ self.checkpoint = checkpoint
+
+ def forward(self, x, context=None):
+ return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+
+ def _forward(self, x, context=None):
+ x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
+ x = self.attn2(self.norm2(x), context=context) + x
+ x = self.ff(self.norm3(x)) + x
+ return x
+
+
+class SpatialTransformer(nn.Module):
+ """
+ Transformer block for image-like data.
+ First, project the input (aka embedding)
+ and reshape to b, t, d.
+ Then apply standard transformer action.
+ Finally, reshape to image
+ NEW: use_linear for more efficiency instead of the 1x1 convs
+ """
+ def __init__(self, in_channels, n_heads, d_head,
+ depth=1, dropout=0., context_dim=None,
+ disable_self_attn=False, use_linear=False,
+ use_checkpoint=True):
+ super().__init__()
+ if exists(context_dim) and not isinstance(context_dim, list):
+ context_dim = [context_dim]
+ self.in_channels = in_channels
+ inner_dim = n_heads * d_head
+ self.norm = Normalize(in_channels)
+ if not use_linear:
+ self.proj_in = nn.Conv2d(in_channels,
+ inner_dim,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ else:
+ self.proj_in = nn.Linear(in_channels, inner_dim)
+
+ self.transformer_blocks = nn.ModuleList(
+ [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
+ disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
+ for d in range(depth)]
+ )
+ if not use_linear:
+ self.proj_out = zero_module(nn.Conv2d(inner_dim,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0))
+ else:
+ self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
+ self.use_linear = use_linear
+
+ def forward(self, x, context=None):
+ # note: if no context is given, cross-attention defaults to self-attention
+ if not isinstance(context, list):
+ context = [context]
+ b, c, h, w = x.shape
+ x_in = x
+ x = self.norm(x)
+ # breakpoint()
+ if not self.use_linear:
+ x = self.proj_in(x)
+
+ x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
+ # context = [rearrange(context[0], 'b c h w -> b (h w) c').contiguous()]
+ # breakpoint()
+ if self.use_linear:
+ x = self.proj_in(x)
+ for i, block in enumerate(self.transformer_blocks):
+ x = block(x, context=context[i])
+ if self.use_linear:
+ x = self.proj_out(x)
+ x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
+ if not self.use_linear:
+ x = self.proj_out(x)
+ return x + x_in
+
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--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/model.py
@@ -0,0 +1,852 @@
+# pytorch_diffusion + derived encoder decoder
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import rearrange
+from typing import Optional, Any
+
+from ldm.modules.attention import MemoryEfficientCrossAttention
+
+try:
+ import xformers
+ import xformers.ops
+ XFORMERS_IS_AVAILBLE = True
+except:
+ XFORMERS_IS_AVAILBLE = False
+ print("No module 'xformers'. Proceeding without it.")
+
+
+def get_timestep_embedding(timesteps, embedding_dim):
+ """
+ This matches the implementation in Denoising Diffusion Probabilistic Models:
+ From Fairseq.
+ Build sinusoidal embeddings.
+ This matches the implementation in tensor2tensor, but differs slightly
+ from the description in Section 3.5 of "Attention Is All You Need".
+ """
+ assert len(timesteps.shape) == 1
+
+ half_dim = embedding_dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+ emb = emb.to(device=timesteps.device)
+ emb = timesteps.float()[:, None] * emb[None, :]
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+ if embedding_dim % 2 == 1: # zero pad
+ emb = torch.nn.functional.pad(emb, (0,1,0,0))
+ return emb
+
+
+def nonlinearity(x):
+ # swish
+ return x*torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32):
+ return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class Upsample(nn.Module):
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ if self.with_conv:
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+ if self.with_conv:
+ x = self.conv(x)
+ return x
+
+
+class Downsample(nn.Module):
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ if self.with_conv:
+ # no asymmetric padding in torch conv, must do it ourselves
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=3,
+ stride=2,
+ padding=0)
+
+ def forward(self, x):
+ if self.with_conv:
+ pad = (0,1,0,1)
+ x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+ x = self.conv(x)
+ else:
+ x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+ return x
+
+
+class ResnetBlock(nn.Module):
+ def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
+ dropout, temb_channels=512):
+ super().__init__()
+ self.in_channels = in_channels
+ out_channels = in_channels if out_channels is None else out_channels
+ self.out_channels = out_channels
+ self.use_conv_shortcut = conv_shortcut
+
+ self.norm1 = Normalize(in_channels)
+ self.conv1 = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ if temb_channels > 0:
+ self.temb_proj = torch.nn.Linear(temb_channels,
+ out_channels)
+ self.norm2 = Normalize(out_channels)
+ self.dropout = torch.nn.Dropout(dropout)
+ self.conv2 = torch.nn.Conv2d(out_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ self.conv_shortcut = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ else:
+ self.nin_shortcut = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x, temb):
+ h = x
+ h = self.norm1(h)
+ h = nonlinearity(h)
+ h = self.conv1(h)
+
+ if temb is not None:
+ h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+
+ h = self.norm2(h)
+ h = nonlinearity(h)
+ h = self.dropout(h)
+ h = self.conv2(h)
+
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ x = self.conv_shortcut(x)
+ else:
+ x = self.nin_shortcut(x)
+
+ return x+h
+
+
+class AttnBlock(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b,c,h,w = q.shape
+ q = q.reshape(b,c,h*w)
+ q = q.permute(0,2,1) # b,hw,c
+ k = k.reshape(b,c,h*w) # b,c,hw
+ w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+ w_ = w_ * (int(c)**(-0.5))
+ w_ = torch.nn.functional.softmax(w_, dim=2)
+
+ # attend to values
+ v = v.reshape(b,c,h*w)
+ w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
+ h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+ h_ = h_.reshape(b,c,h,w)
+
+ h_ = self.proj_out(h_)
+
+ return x+h_
+
+class MemoryEfficientAttnBlock(nn.Module):
+ """
+ Uses xformers efficient implementation,
+ see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+ Note: this is a single-head self-attention operation
+ """
+ #
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.attention_op: Optional[Any] = None
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ B, C, H, W = q.shape
+ q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
+
+ q, k, v = map(
+ lambda t: t.unsqueeze(3)
+ .reshape(B, t.shape[1], 1, C)
+ .permute(0, 2, 1, 3)
+ .reshape(B * 1, t.shape[1], C)
+ .contiguous(),
+ (q, k, v),
+ )
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+ out = (
+ out.unsqueeze(0)
+ .reshape(B, 1, out.shape[1], C)
+ .permute(0, 2, 1, 3)
+ .reshape(B, out.shape[1], C)
+ )
+ out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
+ out = self.proj_out(out)
+ return x+out
+
+
+class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
+ def forward(self, x, context=None, mask=None):
+ b, c, h, w = x.shape
+ x = rearrange(x, 'b c h w -> b (h w) c')
+ out = super().forward(x, context=context, mask=mask)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
+ return x + out
+
+
+def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
+ assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
+ if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
+ attn_type = "vanilla-xformers"
+ print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+ if attn_type == "vanilla":
+ assert attn_kwargs is None
+ return AttnBlock(in_channels)
+ elif attn_type == "vanilla-xformers":
+ print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
+ return MemoryEfficientAttnBlock(in_channels)
+ elif type == "memory-efficient-cross-attn":
+ attn_kwargs["query_dim"] = in_channels
+ return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
+ elif attn_type == "none":
+ return nn.Identity(in_channels)
+ else:
+ raise NotImplementedError()
+
+
+class Model(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = self.ch*4
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+
+ self.use_timestep = use_timestep
+ if self.use_timestep:
+ # timestep embedding
+ self.temb = nn.Module()
+ self.temb.dense = nn.ModuleList([
+ torch.nn.Linear(self.ch,
+ self.temb_ch),
+ torch.nn.Linear(self.temb_ch,
+ self.temb_ch),
+ ])
+
+ # downsampling
+ self.conv_in = torch.nn.Conv2d(in_channels,
+ self.ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ curr_res = resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = ch*in_ch_mult[i_level]
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions-1:
+ down.downsample = Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+ self.down.append(down)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = ch*ch_mult[i_level]
+ skip_in = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks+1):
+ if i_block == self.num_res_blocks:
+ skip_in = ch*in_ch_mult[i_level]
+ block.append(ResnetBlock(in_channels=block_in+skip_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+ self.up.insert(0, up) # prepend to get consistent order
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x, t=None, context=None):
+ #assert x.shape[2] == x.shape[3] == self.resolution
+ if context is not None:
+ # assume aligned context, cat along channel axis
+ x = torch.cat((x, context), dim=1)
+ if self.use_timestep:
+ # timestep embedding
+ assert t is not None
+ temb = get_timestep_embedding(t, self.ch)
+ temb = self.temb.dense[0](temb)
+ temb = nonlinearity(temb)
+ temb = self.temb.dense[1](temb)
+ else:
+ temb = None
+
+ # downsampling
+ hs = [self.conv_in(x)]
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](hs[-1], temb)
+ if len(self.down[i_level].attn) > 0:
+ h = self.down[i_level].attn[i_block](h)
+ hs.append(h)
+ if i_level != self.num_resolutions-1:
+ hs.append(self.down[i_level].downsample(hs[-1]))
+
+ # middle
+ h = hs[-1]
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # upsampling
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h = self.up[i_level].block[i_block](
+ torch.cat([h, hs.pop()], dim=1), temb)
+ if len(self.up[i_level].attn) > 0:
+ h = self.up[i_level].attn[i_block](h)
+ if i_level != 0:
+ h = self.up[i_level].upsample(h)
+
+ # end
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+ def get_last_layer(self):
+ return self.conv_out.weight
+
+
+class Encoder(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
+ **ignore_kwargs):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+
+ # downsampling
+ self.conv_in = torch.nn.Conv2d(in_channels,
+ self.ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ curr_res = resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+ self.in_ch_mult = in_ch_mult
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = ch*in_ch_mult[i_level]
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions-1:
+ down.downsample = Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+ self.down.append(down)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ 2*z_channels if double_z else z_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ # timestep embedding
+ temb = None
+
+ # downsampling
+ hs = [self.conv_in(x)]
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](hs[-1], temb)
+ if len(self.down[i_level].attn) > 0:
+ h = self.down[i_level].attn[i_block](h)
+ hs.append(h)
+ if i_level != self.num_resolutions-1:
+ hs.append(self.down[i_level].downsample(hs[-1]))
+
+ # middle
+ h = hs[-1]
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # end
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+
+class Decoder(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+ attn_type="vanilla", **ignorekwargs):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+ self.give_pre_end = give_pre_end
+ self.tanh_out = tanh_out
+
+ # compute in_ch_mult, block_in and curr_res at lowest res
+ in_ch_mult = (1,)+tuple(ch_mult)
+ block_in = ch*ch_mult[self.num_resolutions-1]
+ curr_res = resolution // 2**(self.num_resolutions-1)
+ self.z_shape = (1,z_channels,curr_res,curr_res)
+ print("Working with z of shape {} = {} dimensions.".format(
+ self.z_shape, np.prod(self.z_shape)))
+
+ # z to block_in
+ self.conv_in = torch.nn.Conv2d(z_channels,
+ block_in,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks+1):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+ self.up.insert(0, up) # prepend to get consistent order
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, z):
+ #assert z.shape[1:] == self.z_shape[1:]
+ self.last_z_shape = z.shape
+
+ # timestep embedding
+ temb = None
+
+ # z to block_in
+ h = self.conv_in(z)
+
+ # middle
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # upsampling
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h = self.up[i_level].block[i_block](h, temb)
+ if len(self.up[i_level].attn) > 0:
+ h = self.up[i_level].attn[i_block](h)
+ if i_level != 0:
+ h = self.up[i_level].upsample(h)
+
+ # end
+ if self.give_pre_end:
+ return h
+
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ if self.tanh_out:
+ h = torch.tanh(h)
+ return h
+
+
+class SimpleDecoder(nn.Module):
+ def __init__(self, in_channels, out_channels, *args, **kwargs):
+ super().__init__()
+ self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
+ ResnetBlock(in_channels=in_channels,
+ out_channels=2 * in_channels,
+ temb_channels=0, dropout=0.0),
+ ResnetBlock(in_channels=2 * in_channels,
+ out_channels=4 * in_channels,
+ temb_channels=0, dropout=0.0),
+ ResnetBlock(in_channels=4 * in_channels,
+ out_channels=2 * in_channels,
+ temb_channels=0, dropout=0.0),
+ nn.Conv2d(2*in_channels, in_channels, 1),
+ Upsample(in_channels, with_conv=True)])
+ # end
+ self.norm_out = Normalize(in_channels)
+ self.conv_out = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ for i, layer in enumerate(self.model):
+ if i in [1,2,3]:
+ x = layer(x, None)
+ else:
+ x = layer(x)
+
+ h = self.norm_out(x)
+ h = nonlinearity(h)
+ x = self.conv_out(h)
+ return x
+
+
+class UpsampleDecoder(nn.Module):
+ def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
+ ch_mult=(2,2), dropout=0.0):
+ super().__init__()
+ # upsampling
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ block_in = in_channels
+ curr_res = resolution // 2 ** (self.num_resolutions - 1)
+ self.res_blocks = nn.ModuleList()
+ self.upsample_blocks = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ res_block = []
+ block_out = ch * ch_mult[i_level]
+ for i_block in range(self.num_res_blocks + 1):
+ res_block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ self.res_blocks.append(nn.ModuleList(res_block))
+ if i_level != self.num_resolutions - 1:
+ self.upsample_blocks.append(Upsample(block_in, True))
+ curr_res = curr_res * 2
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ # upsampling
+ h = x
+ for k, i_level in enumerate(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks + 1):
+ h = self.res_blocks[i_level][i_block](h, None)
+ if i_level != self.num_resolutions - 1:
+ h = self.upsample_blocks[k](h)
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+
+class LatentRescaler(nn.Module):
+ def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
+ super().__init__()
+ # residual block, interpolate, residual block
+ self.factor = factor
+ self.conv_in = nn.Conv2d(in_channels,
+ mid_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+ out_channels=mid_channels,
+ temb_channels=0,
+ dropout=0.0) for _ in range(depth)])
+ self.attn = AttnBlock(mid_channels)
+ self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+ out_channels=mid_channels,
+ temb_channels=0,
+ dropout=0.0) for _ in range(depth)])
+
+ self.conv_out = nn.Conv2d(mid_channels,
+ out_channels,
+ kernel_size=1,
+ )
+
+ def forward(self, x):
+ x = self.conv_in(x)
+ for block in self.res_block1:
+ x = block(x, None)
+ x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
+ x = self.attn(x)
+ for block in self.res_block2:
+ x = block(x, None)
+ x = self.conv_out(x)
+ return x
+
+
+class MergedRescaleEncoder(nn.Module):
+ def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True,
+ ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
+ super().__init__()
+ intermediate_chn = ch * ch_mult[-1]
+ self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
+ z_channels=intermediate_chn, double_z=False, resolution=resolution,
+ attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
+ out_ch=None)
+ self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
+ mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
+
+ def forward(self, x):
+ x = self.encoder(x)
+ x = self.rescaler(x)
+ return x
+
+
+class MergedRescaleDecoder(nn.Module):
+ def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
+ dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
+ super().__init__()
+ tmp_chn = z_channels*ch_mult[-1]
+ self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
+ resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
+ ch_mult=ch_mult, resolution=resolution, ch=ch)
+ self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
+ out_channels=tmp_chn, depth=rescale_module_depth)
+
+ def forward(self, x):
+ x = self.rescaler(x)
+ x = self.decoder(x)
+ return x
+
+
+class Upsampler(nn.Module):
+ def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
+ super().__init__()
+ assert out_size >= in_size
+ num_blocks = int(np.log2(out_size//in_size))+1
+ factor_up = 1.+ (out_size % in_size)
+ print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
+ self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
+ out_channels=in_channels)
+ self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
+ attn_resolutions=[], in_channels=None, ch=in_channels,
+ ch_mult=[ch_mult for _ in range(num_blocks)])
+
+ def forward(self, x):
+ x = self.rescaler(x)
+ x = self.decoder(x)
+ return x
+
+
+class Resize(nn.Module):
+ def __init__(self, in_channels=None, learned=False, mode="bilinear"):
+ super().__init__()
+ self.with_conv = learned
+ self.mode = mode
+ if self.with_conv:
+ print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
+ raise NotImplementedError()
+ assert in_channels is not None
+ # no asymmetric padding in torch conv, must do it ourselves
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=4,
+ stride=2,
+ padding=1)
+
+ def forward(self, x, scale_factor=1.0):
+ if scale_factor==1.0:
+ return x
+ else:
+ x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
+ return x
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/openaimodel.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/openaimodel.py
new file mode 100644
index 0000000000000000000000000000000000000000..af442ac6b189aa0cc558126373e7a104afd31556
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/openaimodel.py
@@ -0,0 +1,788 @@
+from abc import abstractmethod
+import math
+
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ldm.modules.diffusionmodules.util import (
+ checkpoint,
+ conv_nd,
+ linear,
+ avg_pool_nd,
+ zero_module,
+ normalization,
+ timestep_embedding,
+)
+from ldm.modules.attention import SpatialTransformer
+from ldm.util import exists
+
+
+# dummy replace
+def convert_module_to_f16(x):
+ pass
+
+def convert_module_to_f32(x):
+ pass
+
+
+## go
+class AttentionPool2d(nn.Module):
+ """
+ Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+ """
+
+ def __init__(
+ self,
+ spacial_dim: int,
+ embed_dim: int,
+ num_heads_channels: int,
+ output_dim: int = None,
+ ):
+ super().__init__()
+ self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
+ self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+ self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+ self.num_heads = embed_dim // num_heads_channels
+ self.attention = QKVAttention(self.num_heads)
+
+ def forward(self, x):
+ b, c, *_spatial = x.shape
+ x = x.reshape(b, c, -1) # NC(HW)
+ x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
+ x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
+ x = self.qkv_proj(x)
+ x = self.attention(x)
+ x = self.c_proj(x)
+ return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+ """
+ Any module where forward() takes timestep embeddings as a second argument.
+ """
+
+ @abstractmethod
+ def forward(self, x, emb):
+ """
+ Apply the module to `x` given `emb` timestep embeddings.
+ """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+ """
+ A sequential module that passes timestep embeddings to the children that
+ support it as an extra input.
+ """
+
+ def forward(self, x, emb, context=None):
+ for layer in self:
+ if isinstance(layer, TimestepBlock):
+ x = layer(x, emb)
+ elif isinstance(layer, SpatialTransformer):
+ x = layer(x, context)
+ else:
+ x = layer(x)
+ return x
+
+
+class Upsample(nn.Module):
+ """
+ An upsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ upsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ if use_conv:
+ self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ if self.dims == 3:
+ x = F.interpolate(
+ x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+ )
+ else:
+ x = F.interpolate(x, scale_factor=2, mode="nearest")
+ if self.use_conv:
+ x = self.conv(x)
+ return x
+
+class TransposedUpsample(nn.Module):
+ 'Learned 2x upsampling without padding'
+ def __init__(self, channels, out_channels=None, ks=5):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+
+ self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
+
+ def forward(self,x):
+ return self.up(x)
+
+
+class Downsample(nn.Module):
+ """
+ A downsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ downsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ stride = 2 if dims != 3 else (1, 2, 2)
+ if use_conv:
+ self.op = conv_nd(
+ dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+ )
+ else:
+ assert self.channels == self.out_channels
+ self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+ """
+ A residual block that can optionally change the number of channels.
+ :param channels: the number of input channels.
+ :param emb_channels: the number of timestep embedding channels.
+ :param dropout: the rate of dropout.
+ :param out_channels: if specified, the number of out channels.
+ :param use_conv: if True and out_channels is specified, use a spatial
+ convolution instead of a smaller 1x1 convolution to change the
+ channels in the skip connection.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param use_checkpoint: if True, use gradient checkpointing on this module.
+ :param up: if True, use this block for upsampling.
+ :param down: if True, use this block for downsampling.
+ """
+
+ def __init__(
+ self,
+ channels,
+ emb_channels,
+ dropout,
+ out_channels=None,
+ use_conv=False,
+ use_scale_shift_norm=False,
+ dims=2,
+ use_checkpoint=False,
+ up=False,
+ down=False,
+ ):
+ super().__init__()
+ self.channels = channels
+ self.emb_channels = emb_channels
+ self.dropout = dropout
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.use_checkpoint = use_checkpoint
+ self.use_scale_shift_norm = use_scale_shift_norm
+
+ self.in_layers = nn.Sequential(
+ normalization(channels),
+ nn.SiLU(),
+ conv_nd(dims, channels, self.out_channels, 3, padding=1),
+ )
+
+ self.updown = up or down
+
+ if up:
+ self.h_upd = Upsample(channels, False, dims)
+ self.x_upd = Upsample(channels, False, dims)
+ elif down:
+ self.h_upd = Downsample(channels, False, dims)
+ self.x_upd = Downsample(channels, False, dims)
+ else:
+ self.h_upd = self.x_upd = nn.Identity()
+
+ self.emb_layers = nn.Sequential(
+ nn.SiLU(),
+ linear(
+ emb_channels,
+ 2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+ ),
+ )
+ self.out_layers = nn.Sequential(
+ normalization(self.out_channels),
+ nn.SiLU(),
+ nn.Dropout(p=dropout),
+ zero_module(
+ conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+ ),
+ )
+
+ if self.out_channels == channels:
+ self.skip_connection = nn.Identity()
+ elif use_conv:
+ self.skip_connection = conv_nd(
+ dims, channels, self.out_channels, 3, padding=1
+ )
+ else:
+ self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+ def forward(self, x, emb):
+ """
+ Apply the block to a Tensor, conditioned on a timestep embedding.
+ :param x: an [N x C x ...] Tensor of features.
+ :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ return checkpoint(
+ self._forward, (x, emb), self.parameters(), self.use_checkpoint
+ )
+
+
+ def _forward(self, x, emb):
+ if self.updown:
+ in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+ h = in_rest(x)
+ h = self.h_upd(h)
+ x = self.x_upd(x)
+ h = in_conv(h)
+ else:
+ h = self.in_layers(x)
+ emb_out = self.emb_layers(emb).type(h.dtype)
+ while len(emb_out.shape) < len(h.shape):
+ emb_out = emb_out[..., None]
+ if self.use_scale_shift_norm:
+ out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+ scale, shift = th.chunk(emb_out, 2, dim=1)
+ h = out_norm(h) * (1 + scale) + shift
+ h = out_rest(h)
+ else:
+ h = h + emb_out
+ h = self.out_layers(h)
+ return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+ """
+ An attention block that allows spatial positions to attend to each other.
+ Originally ported from here, but adapted to the N-d case.
+ https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+ """
+
+ def __init__(
+ self,
+ channels,
+ num_heads=1,
+ num_head_channels=-1,
+ use_checkpoint=False,
+ use_new_attention_order=False,
+ ):
+ super().__init__()
+ self.channels = channels
+ if num_head_channels == -1:
+ self.num_heads = num_heads
+ else:
+ assert (
+ channels % num_head_channels == 0
+ ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+ self.num_heads = channels // num_head_channels
+ self.use_checkpoint = use_checkpoint
+ self.norm = normalization(channels)
+ self.qkv = conv_nd(1, channels, channels * 3, 1)
+ if use_new_attention_order:
+ # split qkv before split heads
+ self.attention = QKVAttention(self.num_heads)
+ else:
+ # split heads before split qkv
+ self.attention = QKVAttentionLegacy(self.num_heads)
+
+ self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+ def forward(self, x):
+ return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+ #return pt_checkpoint(self._forward, x) # pytorch
+
+ def _forward(self, x):
+ b, c, *spatial = x.shape
+ x = x.reshape(b, c, -1)
+ qkv = self.qkv(self.norm(x))
+ h = self.attention(qkv)
+ h = self.proj_out(h)
+ return (x + h).reshape(b, c, *spatial)
+
+
+def count_flops_attn(model, _x, y):
+ """
+ A counter for the `thop` package to count the operations in an
+ attention operation.
+ Meant to be used like:
+ macs, params = thop.profile(
+ model,
+ inputs=(inputs, timestamps),
+ custom_ops={QKVAttention: QKVAttention.count_flops},
+ )
+ """
+ b, c, *spatial = y[0].shape
+ num_spatial = int(np.prod(spatial))
+ # We perform two matmuls with the same number of ops.
+ # The first computes the weight matrix, the second computes
+ # the combination of the value vectors.
+ matmul_ops = 2 * b * (num_spatial ** 2) * c
+ model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+ """
+ A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+ """
+
+ def __init__(self, n_heads):
+ super().__init__()
+ self.n_heads = n_heads
+
+ def forward(self, qkv):
+ """
+ Apply QKV attention.
+ :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+ :return: an [N x (H * C) x T] tensor after attention.
+ """
+ bs, width, length = qkv.shape
+ assert width % (3 * self.n_heads) == 0
+ ch = width // (3 * self.n_heads)
+ q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+ scale = 1 / math.sqrt(math.sqrt(ch))
+ weight = th.einsum(
+ "bct,bcs->bts", q * scale, k * scale
+ ) # More stable with f16 than dividing afterwards
+ weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+ a = th.einsum("bts,bcs->bct", weight, v)
+ return a.reshape(bs, -1, length)
+
+ @staticmethod
+ def count_flops(model, _x, y):
+ return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+ """
+ A module which performs QKV attention and splits in a different order.
+ """
+
+ def __init__(self, n_heads):
+ super().__init__()
+ self.n_heads = n_heads
+
+ def forward(self, qkv):
+ """
+ Apply QKV attention.
+ :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+ :return: an [N x (H * C) x T] tensor after attention.
+ """
+ bs, width, length = qkv.shape
+ assert width % (3 * self.n_heads) == 0
+ ch = width // (3 * self.n_heads)
+ q, k, v = qkv.chunk(3, dim=1)
+ scale = 1 / math.sqrt(math.sqrt(ch))
+ weight = th.einsum(
+ "bct,bcs->bts",
+ (q * scale).view(bs * self.n_heads, ch, length),
+ (k * scale).view(bs * self.n_heads, ch, length),
+ ) # More stable with f16 than dividing afterwards
+ weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+ a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+ return a.reshape(bs, -1, length)
+
+ @staticmethod
+ def count_flops(model, _x, y):
+ return count_flops_attn(model, _x, y)
+
+
+class UNetModel(nn.Module):
+ """
+ The full UNet model with attention and timestep embedding.
+ :param in_channels: channels in the input Tensor.
+ :param model_channels: base channel count for the model.
+ :param out_channels: channels in the output Tensor.
+ :param num_res_blocks: number of residual blocks per downsample.
+ :param attention_resolutions: a collection of downsample rates at which
+ attention will take place. May be a set, list, or tuple.
+ For example, if this contains 4, then at 4x downsampling, attention
+ will be used.
+ :param dropout: the dropout probability.
+ :param channel_mult: channel multiplier for each level of the UNet.
+ :param conv_resample: if True, use learned convolutions for upsampling and
+ downsampling.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param num_classes: if specified (as an int), then this model will be
+ class-conditional with `num_classes` classes.
+ :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+ :param num_heads: the number of attention heads in each attention layer.
+ :param num_heads_channels: if specified, ignore num_heads and instead use
+ a fixed channel width per attention head.
+ :param num_heads_upsample: works with num_heads to set a different number
+ of heads for upsampling. Deprecated.
+ :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+ :param resblock_updown: use residual blocks for up/downsampling.
+ :param use_new_attention_order: use a different attention pattern for potentially
+ increased efficiency.
+ """
+
+ def __init__(
+ self,
+ image_size,
+ in_channels,
+ model_channels,
+ out_channels,
+ num_res_blocks,
+ attention_resolutions,
+ dropout=0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ num_classes=None,
+ use_checkpoint=False,
+ use_fp16=False,
+ num_heads=-1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ use_new_attention_order=False,
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ legacy=True,
+ disable_self_attentions=None,
+ num_attention_blocks=None,
+ disable_middle_self_attn=False,
+ use_linear_in_transformer=False,
+ ):
+ super().__init__()
+ if use_spatial_transformer:
+ assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+ if context_dim is not None:
+ assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ from omegaconf.listconfig import ListConfig
+ if type(context_dim) == ListConfig:
+ context_dim = list(context_dim)
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.image_size = image_size
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ self.out_channels = out_channels
+ if isinstance(num_res_blocks, int):
+ self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+ else:
+ if len(num_res_blocks) != len(channel_mult):
+ raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+ "as a list/tuple (per-level) with the same length as channel_mult")
+ self.num_res_blocks = num_res_blocks
+ if disable_self_attentions is not None:
+ # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+ assert len(disable_self_attentions) == len(channel_mult)
+ if num_attention_blocks is not None:
+ assert len(num_attention_blocks) == len(self.num_res_blocks)
+ assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+ print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+ f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+ f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+ f"attention will still not be set.")
+
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.num_classes = num_classes
+ self.use_checkpoint = use_checkpoint
+ self.dtype = th.float16 if use_fp16 else th.float32
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.predict_codebook_ids = n_embed is not None
+
+ time_embed_dim = model_channels * 4
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+
+ if self.num_classes is not None:
+ if isinstance(self.num_classes, int):
+ self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+ elif self.num_classes == "continuous":
+ print("setting up linear c_adm embedding layer")
+ self.label_emb = nn.Linear(1, time_embed_dim)
+ else:
+ raise ValueError()
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ conv_nd(dims, in_channels, model_channels, 3, padding=1)
+ )
+ ]
+ )
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+ for level, mult in enumerate(channel_mult):
+ for nr in range(self.num_res_blocks[level]):
+ layers = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=mult * model_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = mult * model_channels
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True,
+ )
+ if resblock_updown
+ else Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ ds *= 2
+ self._feature_size += ch
+
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ self.middle_block = TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ ),
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ )
+ self._feature_size += ch
+
+ self.output_blocks = nn.ModuleList([])
+ for level, mult in list(enumerate(channel_mult))[::-1]:
+ for i in range(self.num_res_blocks[level] + 1):
+ ich = input_block_chans.pop()
+ layers = [
+ ResBlock(
+ ch + ich,
+ time_embed_dim,
+ dropout,
+ out_channels=model_channels * mult,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = model_channels * mult
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads_upsample,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ if level and i == self.num_res_blocks[level]:
+ out_ch = ch
+ layers.append(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ up=True,
+ )
+ if resblock_updown
+ else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+ )
+ ds //= 2
+ self.output_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+
+ self.out = nn.Sequential(
+ normalization(ch),
+ nn.SiLU(),
+ zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+ )
+ if self.predict_codebook_ids:
+ self.id_predictor = nn.Sequential(
+ normalization(ch),
+ conv_nd(dims, model_channels, n_embed, 1),
+ #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
+ )
+
+ def convert_to_fp16(self):
+ """
+ Convert the torso of the model to float16.
+ """
+ self.input_blocks.apply(convert_module_to_f16)
+ self.middle_block.apply(convert_module_to_f16)
+ self.output_blocks.apply(convert_module_to_f16)
+
+ def convert_to_fp32(self):
+ """
+ Convert the torso of the model to float32.
+ """
+ self.input_blocks.apply(convert_module_to_f32)
+ self.middle_block.apply(convert_module_to_f32)
+ self.output_blocks.apply(convert_module_to_f32)
+
+ def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+ """
+ Apply the model to an input batch.
+ :param x: an [N x C x ...] Tensor of inputs.
+ :param timesteps: a 1-D batch of timesteps.
+ :param context: conditioning plugged in via crossattn
+ :param y: an [N] Tensor of labels, if class-conditional.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ # breakpoint()
+ assert (y is not None) == (
+ self.num_classes is not None
+ ), "must specify y if and only if the model is class-conditional"
+ hs = []
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ emb = self.time_embed(t_emb)
+
+ if self.num_classes is not None:
+ assert y.shape[0] == x.shape[0]
+ emb = emb + self.label_emb(y)
+
+ h = x.type(self.dtype)
+ # breakpoint()
+ for module in self.input_blocks:
+ h = module(h, emb, context)
+ hs.append(h)
+ h = self.middle_block(h, emb, context)
+ for module in self.output_blocks:
+ h = th.cat([h, hs.pop()], dim=1)
+ h = module(h, emb, context)
+ h = h.type(x.dtype)
+ if self.predict_codebook_ids:
+ return self.id_predictor(h)
+ else:
+ return self.out(h)
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/openaimodel_pseudo3D.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/openaimodel_pseudo3D.py
new file mode 100644
index 0000000000000000000000000000000000000000..8035f9e5cd3197e7f79a23fb484300605e0d118b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/openaimodel_pseudo3D.py
@@ -0,0 +1,818 @@
+from abc import abstractmethod
+from functools import partial
+import math
+from typing import Iterable
+
+from einops import rearrange
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ldm.modules.diffusionmodules.util import (
+ checkpoint,
+ conv_nd,
+ linear,
+ avg_pool_nd,
+ zero_module,
+ normalization,
+ timestep_embedding,
+)
+from ldm.modules.attention import SpatialTransformer
+
+
+def exists(val):
+ return val is not None
+
+def default(val, d):
+ return val if exists(val) else d
+
+# dummy replace
+def convert_module_to_f16(x):
+ pass
+
+def convert_module_to_f32(x):
+ pass
+
+class PseudoConv3d(nn.Module):
+ def __init__(
+ self,
+ dim,
+ dim_out = None,
+ kernel_size = 3,
+ *,
+ temporal_kernel_size = None,
+ **kwargs
+ ):
+ super().__init__()
+ dim_out = default(dim_out, dim)
+ temporal_kernel_size = default(temporal_kernel_size, kernel_size)
+
+ self.temporal_conv = nn.Conv1d(dim_out, dim_out, kernel_size = temporal_kernel_size, padding = temporal_kernel_size // 2) if kernel_size > 1 else None
+
+ if exists(self.temporal_conv):
+ nn.init.dirac_(self.temporal_conv.weight.data) # initialized to be identity
+ nn.init.zeros_(self.temporal_conv.bias.data)
+
+ def forward(
+ self,
+ x,
+ enable_time = True
+ ):
+ _, _, h, w = x.shape
+
+ if not enable_time or not exists(self.temporal_conv):
+ return x
+
+ x = rearrange(x, '(b f) c h w -> (b h w) c f', f = 17)
+ x = self.temporal_conv(x)
+ x = rearrange(x, '(b h w) c f -> (b f) c h w', h = h, w = w)
+
+ return x
+
+
+## go
+class AttentionPool2d(nn.Module):
+ """
+ Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+ """
+
+ def __init__(
+ self,
+ spacial_dim: int,
+ embed_dim: int,
+ num_heads_channels: int,
+ output_dim: int = None,
+ ):
+ super().__init__()
+ self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
+ self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+ self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+ self.num_heads = embed_dim // num_heads_channels
+ self.attention = QKVAttention(self.num_heads)
+
+ def forward(self, x):
+ b, c, *_spatial = x.shape
+ x = x.reshape(b, c, -1) # NC(HW)
+ x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
+ x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
+ x = self.qkv_proj(x)
+ x = self.attention(x)
+ x = self.c_proj(x)
+ return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+ """
+ Any module where forward() takes timestep embeddings as a second argument.
+ """
+
+ @abstractmethod
+ def forward(self, x, emb):
+ """
+ Apply the module to `x` given `emb` timestep embeddings.
+ """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+ """
+ A sequential module that passes timestep embeddings to the children that
+ support it as an extra input.
+ """
+
+ def forward(self, x, emb, context=None):
+ for layer in self:
+ if isinstance(layer, TimestepBlock):
+ x = layer(x, emb)
+ elif isinstance(layer, SpatialTransformer):
+ x = layer(x, context)
+ else:
+ x = layer(x)
+ return x
+
+
+class Upsample(nn.Module):
+ """
+ An upsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ upsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ if use_conv:
+ self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+ self.conv_tem = PseudoConv3d(self.out_channels, self.out_channels, 3)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ if self.dims == 3:
+ x = F.interpolate(
+ x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+ )
+ else:
+ x = F.interpolate(x, scale_factor=2, mode="nearest")
+ if self.use_conv:
+ x = self.conv(x)
+ x = self.conv_tem(x)
+ return x
+
+class TransposedUpsample(nn.Module):
+ 'Learned 2x upsampling without padding'
+ def __init__(self, channels, out_channels=None, ks=5):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+
+ self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
+
+ def forward(self,x):
+ return self.up(x)
+
+
+class Downsample(nn.Module):
+ """
+ A downsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ downsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ stride = 2 if dims != 3 else (1, 2, 2)
+ if use_conv:
+ self.op = conv_nd(
+ dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+ )
+ self.op_tem = PseudoConv3d(self.out_channels, self.out_channels, 3)
+ else:
+ assert self.channels == self.out_channels
+ self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ if self.use_conv:
+ x = self.op(x)
+ x = self.op_tem(x)
+ else:
+ x = self.op(x)
+ return x
+
+
+class ResBlock(TimestepBlock):
+ """
+ A residual block that can optionally change the number of channels.
+ :param channels: the number of input channels.
+ :param emb_channels: the number of timestep embedding channels.
+ :param dropout: the rate of dropout.
+ :param out_channels: if specified, the number of out channels.
+ :param use_conv: if True and out_channels is specified, use a spatial
+ convolution instead of a smaller 1x1 convolution to change the
+ channels in the skip connection.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param use_checkpoint: if True, use gradient checkpointing on this module.
+ :param up: if True, use this block for upsampling.
+ :param down: if True, use this block for downsampling.
+ """
+
+ def __init__(
+ self,
+ channels,
+ emb_channels,
+ dropout,
+ out_channels=None,
+ use_conv=False,
+ use_scale_shift_norm=False,
+ dims=2,
+ use_checkpoint=False,
+ up=False,
+ down=False,
+ ):
+ super().__init__()
+ self.channels = channels
+ self.emb_channels = emb_channels
+ self.dropout = dropout
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.use_checkpoint = use_checkpoint
+ self.use_scale_shift_norm = use_scale_shift_norm
+
+ self.in_layers = nn.Sequential(
+ normalization(channels),
+ nn.SiLU(),
+ conv_nd(dims, channels, self.out_channels, 3, padding=1),
+ )
+ self.in_layers_tem = PseudoConv3d(channels, self.out_channels, 3)
+
+ self.updown = up or down
+
+ if up:
+ self.h_upd = Upsample(channels, False, dims)
+ self.x_upd = Upsample(channels, False, dims)
+ elif down:
+ self.h_upd = Downsample(channels, False, dims)
+ self.x_upd = Downsample(channels, False, dims)
+ else:
+ self.h_upd = self.x_upd = nn.Identity()
+
+ self.emb_layers = nn.Sequential(
+ nn.SiLU(),
+ linear(
+ emb_channels,
+ 2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+ ),
+ )
+ self.out_layers = nn.Sequential(
+ normalization(self.out_channels),
+ nn.SiLU(),
+ nn.Dropout(p=dropout),
+ zero_module(
+ conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+ ),
+ )
+ self.out_layers_tem = PseudoConv3d(self.out_channels, self.out_channels, 3)
+
+ if self.out_channels == channels:
+ self.skip_connection = nn.Identity()
+ elif use_conv:
+ self.skip_connection = conv_nd(
+ dims, channels, self.out_channels, 3, padding=1
+ )
+ else:
+ self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+ def forward(self, x, emb):
+ """
+ Apply the block to a Tensor, conditioned on a timestep embedding.
+ :param x: an [N x C x ...] Tensor of features.
+ :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ return checkpoint(
+ self._forward, (x, emb), self.parameters(), self.use_checkpoint
+ )
+
+
+ def _forward(self, x, emb):
+ if self.updown:
+ in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+ h = in_rest(x)
+ h = self.h_upd(h)
+ x = self.x_upd(x)
+ h = in_conv(h)
+ h = self.in_layers_tem(h)
+ else:
+ h = self.in_layers(x)
+ h = self.in_layers_tem(h)
+ emb_out = self.emb_layers(emb).type(h.dtype)
+ while len(emb_out.shape) < len(h.shape):
+ emb_out = emb_out[..., None]
+ if self.use_scale_shift_norm:
+ out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+ scale, shift = th.chunk(emb_out, 2, dim=1)
+ h = out_norm(h) * (1 + scale) + shift
+ h = out_rest(h)
+ h = self.out_layers_tem(h)
+ else:
+ h = h + emb_out
+ h = self.out_layers(h)
+ h = self.out_layers_tem(h)
+ return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+ """
+ An attention block that allows spatial positions to attend to each other.
+ Originally ported from here, but adapted to the N-d case.
+ https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+ """
+
+ def __init__(
+ self,
+ channels,
+ num_heads=1,
+ num_head_channels=-1,
+ use_checkpoint=False,
+ use_new_attention_order=False,
+ ):
+ super().__init__()
+ self.channels = channels
+ if num_head_channels == -1:
+ self.num_heads = num_heads
+ else:
+ assert (
+ channels % num_head_channels == 0
+ ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+ self.num_heads = channels // num_head_channels
+ self.use_checkpoint = use_checkpoint
+ self.norm = normalization(channels)
+ self.qkv = conv_nd(1, channels, channels * 3, 1)
+ self.qkv_tem = conv_nd(1, channels, channels * 3, 1)
+
+ if use_new_attention_order:
+ # split qkv before split heads
+ self.attention = QKVAttention(self.num_heads)
+ self.attention_tem = QKVAttention(self.num_heads)
+ else:
+ # split heads before split qkv
+ self.attention = QKVAttentionLegacy(self.num_heads)
+ self.attention_tem = QKVAttentionLegacy(self.num_heads)
+
+ self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+ self.proj_out_tem = zero_module(conv_nd(1, channels, channels, 1))
+
+ def forward(self, x):
+ return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+ #return pt_checkpoint(self._forward, x) # pytorch
+
+ def _forward(self, x):
+ b, c, *spatial = x.shape
+ x = x.reshape(b, c, -1)
+ qkv = self.qkv(self.norm(x))
+ h = self.attention(qkv)
+ h = self.proj_out(h)
+
+ x = (x + h).reshape(b, c, *spatial)
+ x = rearrange(x, '(b f) c h w -> (b h w) c f', f = 16)
+
+ qkv_tem = self.qkv_tem(self.norm(x))
+ h_tem = self.attention_tem(qkv_tem)
+ h_tem = self.proj_out_tem(h_tem)
+
+ x = x+h_tem
+ x = rearrange(x, '(b h w) c f -> (b f) c h w', w = spatial[0], h = spatial[1])
+ return x
+
+
+def count_flops_attn(model, _x, y):
+ """
+ A counter for the `thop` package to count the operations in an
+ attention operation.
+ Meant to be used like:
+ macs, params = thop.profile(
+ model,
+ inputs=(inputs, timestamps),
+ custom_ops={QKVAttention: QKVAttention.count_flops},
+ )
+ """
+ b, c, *spatial = y[0].shape
+ num_spatial = int(np.prod(spatial))
+ # We perform two matmuls with the same number of ops.
+ # The first computes the weight matrix, the second computes
+ # the combination of the value vectors.
+ matmul_ops = 2 * b * (num_spatial ** 2) * c
+ model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+ """
+ A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+ """
+
+ def __init__(self, n_heads):
+ super().__init__()
+ self.n_heads = n_heads
+
+ def forward(self, qkv):
+ """
+ Apply QKV attention.
+ :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+ :return: an [N x (H * C) x T] tensor after attention.
+ """
+ bs, width, length = qkv.shape
+ assert width % (3 * self.n_heads) == 0
+ ch = width // (3 * self.n_heads)
+ q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+ scale = 1 / math.sqrt(math.sqrt(ch))
+ weight = th.einsum(
+ "bct,bcs->bts", q * scale, k * scale
+ ) # More stable with f16 than dividing afterwards
+ weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+ a = th.einsum("bts,bcs->bct", weight, v)
+ return a.reshape(bs, -1, length)
+
+ @staticmethod
+ def count_flops(model, _x, y):
+ return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+ """
+ A module which performs QKV attention and splits in a different order.
+ """
+
+ def __init__(self, n_heads):
+ super().__init__()
+ self.n_heads = n_heads
+
+ def forward(self, qkv):
+ """
+ Apply QKV attention.
+ :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+ :return: an [N x (H * C) x T] tensor after attention.
+ """
+ bs, width, length = qkv.shape
+ assert width % (3 * self.n_heads) == 0
+ ch = width // (3 * self.n_heads)
+ q, k, v = qkv.chunk(3, dim=1)
+ scale = 1 / math.sqrt(math.sqrt(ch))
+ weight = th.einsum(
+ "bct,bcs->bts",
+ (q * scale).view(bs * self.n_heads, ch, length),
+ (k * scale).view(bs * self.n_heads, ch, length),
+ ) # More stable with f16 than dividing afterwards
+ weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+ a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+ return a.reshape(bs, -1, length)
+
+ @staticmethod
+ def count_flops(model, _x, y):
+ return count_flops_attn(model, _x, y)
+
+
+class UNetModel(nn.Module):
+ """
+ The full UNet model with attention and timestep embedding.
+ :param in_channels: channels in the input Tensor.
+ :param model_channels: base channel count for the model.
+ :param out_channels: channels in the output Tensor.
+ :param num_res_blocks: number of residual blocks per downsample.
+ :param attention_resolutions: a collection of downsample rates at which
+ attention will take place. May be a set, list, or tuple.
+ For example, if this contains 4, then at 4x downsampling, attention
+ will be used.
+ :param dropout: the dropout probability.
+ :param channel_mult: channel multiplier for each level of the UNet.
+ :param conv_resample: if True, use learned convolutions for upsampling and
+ downsampling.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param num_classes: if specified (as an int), then this model will be
+ class-conditional with `num_classes` classes.
+ :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+ :param num_heads: the number of attention heads in each attention layer.
+ :param num_heads_channels: if specified, ignore num_heads and instead use
+ a fixed channel width per attention head.
+ :param num_heads_upsample: works with num_heads to set a different number
+ of heads for upsampling. Deprecated.
+ :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+ :param resblock_updown: use residual blocks for up/downsampling.
+ :param use_new_attention_order: use a different attention pattern for potentially
+ increased efficiency.
+ """
+
+ def __init__(
+ self,
+ image_size,
+ in_channels,
+ model_channels,
+ out_channels,
+ num_res_blocks,
+ attention_resolutions,
+ dropout=0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ num_classes=None,
+ use_checkpoint=False,
+ use_fp16=False,
+ num_heads=-1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ use_new_attention_order=False,
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ legacy=True,
+ ):
+ super().__init__()
+ if use_spatial_transformer:
+ assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+ if context_dim is not None:
+ assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ from omegaconf.listconfig import ListConfig
+ if type(context_dim) == ListConfig:
+ context_dim = list(context_dim)
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.image_size = image_size
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ self.out_channels = out_channels
+ self.num_res_blocks = num_res_blocks
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.num_classes = num_classes
+ self.use_checkpoint = use_checkpoint
+ self.dtype = th.float16 if use_fp16 else th.float32
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.predict_codebook_ids = n_embed is not None
+
+ time_embed_dim = model_channels * 4
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+
+ if self.num_classes is not None:
+ self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ conv_nd(dims, in_channels, model_channels, 3, padding=1),
+ PseudoConv3d(model_channels, model_channels, 3)
+ )
+ ]
+ )
+
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+ for level, mult in enumerate(channel_mult):
+ for _ in range(num_res_blocks):
+ layers = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=mult * model_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = mult * model_channels
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, use_checkpoint=use_checkpoint
+ )
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True,
+ )
+ if resblock_updown
+ else Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ ds *= 2
+ self._feature_size += ch
+
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ self.middle_block = TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, use_checkpoint=use_checkpoint
+ ),
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ )
+ self._feature_size += ch
+
+ self.output_blocks = nn.ModuleList([])
+ for level, mult in list(enumerate(channel_mult))[::-1]:
+ for i in range(num_res_blocks + 1):
+ ich = input_block_chans.pop()
+ layers = [
+ ResBlock(
+ ch + ich,
+ time_embed_dim,
+ dropout,
+ out_channels=model_channels * mult,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = model_channels * mult
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads_upsample,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, use_checkpoint=use_checkpoint
+ )
+ )
+ if level and i == num_res_blocks:
+ out_ch = ch
+ layers.append(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ up=True,
+ )
+ if resblock_updown
+ else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+ )
+ ds //= 2
+ self.output_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+
+ self.out = nn.Sequential(
+ normalization(ch),
+ nn.SiLU(),
+ zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+ )
+ self.out_tem = PseudoConv3d(out_channels, out_channels, 3)
+
+ if self.predict_codebook_ids:
+ self.id_predictor = nn.Sequential(
+ normalization(ch),
+ conv_nd(dims, model_channels, n_embed, 1),
+ #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
+ )
+
+ def convert_to_fp16(self):
+ """
+ Convert the torso of the model to float16.
+ """
+ self.input_blocks.apply(convert_module_to_f16)
+ self.middle_block.apply(convert_module_to_f16)
+ self.output_blocks.apply(convert_module_to_f16)
+
+ def convert_to_fp32(self):
+ """
+ Convert the torso of the model to float32.
+ """
+ self.input_blocks.apply(convert_module_to_f32)
+ self.middle_block.apply(convert_module_to_f32)
+ self.output_blocks.apply(convert_module_to_f32)
+
+ def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+ """
+ Apply the model to an input batch.
+ :param x: an [N x C x ...] Tensor of inputs.
+ :param timesteps: a 1-D batch of timesteps.
+ :param context: conditioning plugged in via crossattn
+ :param y: an [N] Tensor of labels, if class-conditional.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ assert (y is not None) == (
+ self.num_classes is not None
+ ), "must specify y if and only if the model is class-conditional"
+ hs = []
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ emb = self.time_embed(t_emb)
+
+ if self.num_classes is not None:
+ assert y.shape == (x.shape[0],)
+ emb = emb + self.label_emb(y)
+
+ h = x.type(self.dtype)
+ for module in self.input_blocks:
+ h = module(h, emb, context)
+ hs.append(h)
+ h = self.middle_block(h, emb, context)
+ for module in self.output_blocks:
+ h = th.cat([h, hs.pop()], dim=1)
+ h = module(h, emb, context)
+ h = h.type(x.dtype)
+ if self.predict_codebook_ids:
+ return self.id_predictor(h)
+ else:
+ return self.out_tem(self.out(h))
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/upscaling.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/upscaling.py
new file mode 100644
index 0000000000000000000000000000000000000000..03816662098ce1ffac79bd939b892e867ab91988
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/upscaling.py
@@ -0,0 +1,81 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from functools import partial
+
+from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
+from ldm.util import default
+
+
+class AbstractLowScaleModel(nn.Module):
+ # for concatenating a downsampled image to the latent representation
+ def __init__(self, noise_schedule_config=None):
+ super(AbstractLowScaleModel, self).__init__()
+ if noise_schedule_config is not None:
+ self.register_schedule(**noise_schedule_config)
+
+ def register_schedule(self, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def forward(self, x):
+ return x, None
+
+ def decode(self, x):
+ return x
+
+
+class SimpleImageConcat(AbstractLowScaleModel):
+ # no noise level conditioning
+ def __init__(self):
+ super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
+ self.max_noise_level = 0
+
+ def forward(self, x):
+ # fix to constant noise level
+ return x, torch.zeros(x.shape[0], device=x.device).long()
+
+
+class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
+ def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
+ super().__init__(noise_schedule_config=noise_schedule_config)
+ self.max_noise_level = max_noise_level
+
+ def forward(self, x, noise_level=None):
+ if noise_level is None:
+ noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
+ else:
+ assert isinstance(noise_level, torch.Tensor)
+ z = self.q_sample(x, noise_level)
+ return z, noise_level
+
+
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/util.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..637363dfe34799e70cfdbcd11445212df9d9ca1f
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/diffusionmodules/util.py
@@ -0,0 +1,270 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+
+import os
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import repeat
+
+from ldm.util import instantiate_from_config
+
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if schedule == "linear":
+ betas = (
+ torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+ )
+
+ elif schedule == "cosine":
+ timesteps = (
+ torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+ )
+ alphas = timesteps / (1 + cosine_s) * np.pi / 2
+ alphas = torch.cos(alphas).pow(2)
+ alphas = alphas / alphas[0]
+ betas = 1 - alphas[1:] / alphas[:-1]
+ betas = np.clip(betas, a_min=0, a_max=0.999)
+
+ elif schedule == "sqrt_linear":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+ elif schedule == "sqrt":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+ else:
+ raise ValueError(f"schedule '{schedule}' unknown.")
+ return betas.numpy()
+
+
+def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+ if ddim_discr_method == 'uniform':
+ c = num_ddpm_timesteps // num_ddim_timesteps
+ ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+ elif ddim_discr_method == 'quad':
+ ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+ else:
+ raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+
+ # assert ddim_timesteps.shape[0] == num_ddim_timesteps
+ # add one to get the final alpha values right (the ones from first scale to data during sampling)
+ steps_out = ddim_timesteps + 1
+ if verbose:
+ print(f'Selected timesteps for ddim sampler: {steps_out}')
+ return steps_out
+
+
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+ # select alphas for computing the variance schedule
+ alphas = alphacums[ddim_timesteps]
+ alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+
+ # according the the formula provided in https://arxiv.org/abs/2010.02502
+ sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+ if verbose:
+ print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+ print(f'For the chosen value of eta, which is {eta}, '
+ f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+ return sigmas, alphas, alphas_prev
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+ """
+ Create a beta schedule that discretizes the given alpha_t_bar function,
+ which defines the cumulative product of (1-beta) over time from t = [0,1].
+ :param num_diffusion_timesteps: the number of betas to produce.
+ :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+ produces the cumulative product of (1-beta) up to that
+ part of the diffusion process.
+ :param max_beta: the maximum beta to use; use values lower than 1 to
+ prevent singularities.
+ """
+ betas = []
+ for i in range(num_diffusion_timesteps):
+ t1 = i / num_diffusion_timesteps
+ t2 = (i + 1) / num_diffusion_timesteps
+ betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+ return np.array(betas)
+
+
+def extract_into_tensor(a, t, x_shape):
+ b, *_ = t.shape
+ out = a.gather(-1, t)
+ return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def checkpoint(func, inputs, params, flag):
+ """
+ Evaluate a function without caching intermediate activations, allowing for
+ reduced memory at the expense of extra compute in the backward pass.
+ :param func: the function to evaluate.
+ :param inputs: the argument sequence to pass to `func`.
+ :param params: a sequence of parameters `func` depends on but does not
+ explicitly take as arguments.
+ :param flag: if False, disable gradient checkpointing.
+ """
+ if flag:
+ args = tuple(inputs) + tuple(params)
+ return CheckpointFunction.apply(func, len(inputs), *args)
+ else:
+ return func(*inputs)
+
+
+class CheckpointFunction(torch.autograd.Function):
+ @staticmethod
+ def forward(ctx, run_function, length, *args):
+ ctx.run_function = run_function
+ ctx.input_tensors = list(args[:length])
+ ctx.input_params = list(args[length:])
+ ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
+ "dtype": torch.get_autocast_gpu_dtype(),
+ "cache_enabled": torch.is_autocast_cache_enabled()}
+ with torch.no_grad():
+ output_tensors = ctx.run_function(*ctx.input_tensors)
+ return output_tensors
+
+ @staticmethod
+ def backward(ctx, *output_grads):
+ ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+ with torch.enable_grad(), \
+ torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
+ # Fixes a bug where the first op in run_function modifies the
+ # Tensor storage in place, which is not allowed for detach()'d
+ # Tensors.
+ shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+ output_tensors = ctx.run_function(*shallow_copies)
+ input_grads = torch.autograd.grad(
+ output_tensors,
+ ctx.input_tensors + ctx.input_params,
+ output_grads,
+ allow_unused=True,
+ )
+ del ctx.input_tensors
+ del ctx.input_params
+ del output_tensors
+ return (None, None) + input_grads
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+ """
+ Create sinusoidal timestep embeddings.
+ :param timesteps: a 1-D Tensor of N indices, one per batch element.
+ These may be fractional.
+ :param dim: the dimension of the output.
+ :param max_period: controls the minimum frequency of the embeddings.
+ :return: an [N x dim] Tensor of positional embeddings.
+ """
+ if not repeat_only:
+ half = dim // 2
+ freqs = torch.exp(
+ -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+ ).to(device=timesteps.device)
+ args = timesteps[:, None].float() * freqs[None]
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+ if dim % 2:
+ embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+ else:
+ embedding = repeat(timesteps, 'b -> b d', d=dim)
+ return embedding
+
+
+def zero_module(module):
+ """
+ Zero out the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+
+def scale_module(module, scale):
+ """
+ Scale the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().mul_(scale)
+ return module
+
+
+def mean_flat(tensor):
+ """
+ Take the mean over all non-batch dimensions.
+ """
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def normalization(channels):
+ """
+ Make a standard normalization layer.
+ :param channels: number of input channels.
+ :return: an nn.Module for normalization.
+ """
+ return GroupNorm32(32, channels)
+
+
+# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
+class SiLU(nn.Module):
+ def forward(self, x):
+ return x * torch.sigmoid(x)
+
+
+class GroupNorm32(nn.GroupNorm):
+ def forward(self, x):
+ return super().forward(x.float()).type(x.dtype)
+
+def conv_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D convolution module.
+ """
+ if dims == 1:
+ return nn.Conv1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.Conv2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.Conv3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+ """
+ Create a linear module.
+ """
+ return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D average pooling module.
+ """
+ if dims == 1:
+ return nn.AvgPool1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.AvgPool2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.AvgPool3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class HybridConditioner(nn.Module):
+
+ def __init__(self, c_concat_config, c_crossattn_config):
+ super().__init__()
+ self.concat_conditioner = instantiate_from_config(c_concat_config)
+ self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+ def forward(self, c_concat, c_crossattn):
+ c_concat = self.concat_conditioner(c_concat)
+ c_crossattn = self.crossattn_conditioner(c_crossattn)
+ return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
+
+
+def noise_like(shape, device, repeat=False):
+ repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+ noise = lambda: torch.randn(shape, device=device)
+ return repeat_noise() if repeat else noise()
\ No newline at end of file
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/distributions/distributions.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/distributions/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2b8ef901130efc171aa69742ca0244d94d3f2e9
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/distributions/distributions.py
@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+ def sample(self):
+ raise NotImplementedError()
+
+ def mode(self):
+ raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+ def __init__(self, value):
+ self.value = value
+
+ def sample(self):
+ return self.value
+
+ def mode(self):
+ return self.value
+
+
+class DiagonalGaussianDistribution(object):
+ def __init__(self, parameters, deterministic=False):
+ self.parameters = parameters
+ self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+ self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+ self.deterministic = deterministic
+ self.std = torch.exp(0.5 * self.logvar)
+ self.var = torch.exp(self.logvar)
+ if self.deterministic:
+ self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+ def sample(self):
+ x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+ return x
+
+ def kl(self, other=None):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ else:
+ if other is None:
+ return 0.5 * torch.sum(torch.pow(self.mean, 2)
+ + self.var - 1.0 - self.logvar,
+ dim=[1, 2, 3])
+ else:
+ return 0.5 * torch.sum(
+ torch.pow(self.mean - other.mean, 2) / other.var
+ + self.var / other.var - 1.0 - self.logvar + other.logvar,
+ dim=[1, 2, 3])
+
+ def nll(self, sample, dims=[1,2,3]):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ logtwopi = np.log(2.0 * np.pi)
+ return 0.5 * torch.sum(
+ logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+ dim=dims)
+
+ def mode(self):
+ return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+ """
+ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+ Compute the KL divergence between two gaussians.
+ Shapes are automatically broadcasted, so batches can be compared to
+ scalars, among other use cases.
+ """
+ tensor = None
+ for obj in (mean1, logvar1, mean2, logvar2):
+ if isinstance(obj, torch.Tensor):
+ tensor = obj
+ break
+ assert tensor is not None, "at least one argument must be a Tensor"
+
+ # Force variances to be Tensors. Broadcasting helps convert scalars to
+ # Tensors, but it does not work for torch.exp().
+ logvar1, logvar2 = [
+ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+ for x in (logvar1, logvar2)
+ ]
+
+ return 0.5 * (
+ -1.0
+ + logvar2
+ - logvar1
+ + torch.exp(logvar1 - logvar2)
+ + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+ )
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/ema.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/ema.py
new file mode 100644
index 0000000000000000000000000000000000000000..bded25019b9bcbcd0260f0b8185f8c7859ca58c4
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/ema.py
@@ -0,0 +1,80 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+ def __init__(self, model, decay=0.9999, use_num_upates=True):
+ super().__init__()
+ if decay < 0.0 or decay > 1.0:
+ raise ValueError('Decay must be between 0 and 1')
+
+ self.m_name2s_name = {}
+ self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+ self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
+ else torch.tensor(-1, dtype=torch.int))
+
+ for name, p in model.named_parameters():
+ if p.requires_grad:
+ # remove as '.'-character is not allowed in buffers
+ s_name = name.replace('.', '')
+ self.m_name2s_name.update({name: s_name})
+ self.register_buffer(s_name, p.clone().detach().data)
+
+ self.collected_params = []
+
+ def reset_num_updates(self):
+ del self.num_updates
+ self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
+
+ def forward(self, model):
+ decay = self.decay
+
+ if self.num_updates >= 0:
+ self.num_updates += 1
+ decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
+
+ one_minus_decay = 1.0 - decay
+
+ with torch.no_grad():
+ m_param = dict(model.named_parameters())
+ shadow_params = dict(self.named_buffers())
+
+ for key in m_param:
+ if m_param[key].requires_grad:
+ sname = self.m_name2s_name[key]
+ shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+ shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+ else:
+ assert not key in self.m_name2s_name
+
+ def copy_to(self, model):
+ m_param = dict(model.named_parameters())
+ shadow_params = dict(self.named_buffers())
+ for key in m_param:
+ if m_param[key].requires_grad:
+ m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+ else:
+ assert not key in self.m_name2s_name
+
+ def store(self, parameters):
+ """
+ Save the current parameters for restoring later.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ temporarily stored.
+ """
+ self.collected_params = [param.clone() for param in parameters]
+
+ def restore(self, parameters):
+ """
+ Restore the parameters stored with the `store` method.
+ Useful to validate the model with EMA parameters without affecting the
+ original optimization process. Store the parameters before the
+ `copy_to` method. After validation (or model saving), use this to
+ restore the former parameters.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ updated with the stored parameters.
+ """
+ for c_param, param in zip(self.collected_params, parameters):
+ param.data.copy_(c_param.data)
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new file mode 100644
index 0000000000000000000000000000000000000000..6b51d5ed9737f017ba6a16c742e36aafc86958b0
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/encoders/modules.py
@@ -0,0 +1,325 @@
+import math
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+
+from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
+from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
+
+import open_clip
+from ldm.util import default, count_params
+from einops import repeat
+
+class AbstractEncoder(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def encode(self, *args, **kwargs):
+ raise NotImplementedError
+
+
+class IdentityEncoder(AbstractEncoder):
+
+ def encode(self, x):
+ return x
+
+
+class ClassEmbedder(nn.Module):
+ def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
+ super().__init__()
+ self.key = key
+ self.embedding = nn.Embedding(n_classes, embed_dim)
+ self.n_classes = n_classes
+ self.ucg_rate = ucg_rate
+
+ def forward(self, batch, key=None, disable_dropout=False):
+ if key is None:
+ key = self.key
+ # this is for use in crossattn
+ c = batch[key][:, None]
+ if self.ucg_rate > 0. and not disable_dropout:
+ mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
+ c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1)
+ c = c.long()
+ c = self.embedding(c)
+ return c
+
+ def get_unconditional_conditioning(self, bs, device="cuda"):
+ uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
+ uc = torch.ones((bs,), device=device) * uc_class
+ uc = {self.key: uc}
+ return uc
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+class BERTTokenizer(AbstractEncoder):
+ """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
+ def __init__(self, device="cuda", vq_interface=True, max_length=77):
+ super().__init__()
+ from transformers import BertTokenizerFast # TODO: add to reuquirements
+ self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
+ self.device = device
+ self.vq_interface = vq_interface
+ self.max_length = max_length
+
+ def forward(self, text):
+ batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+ return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+ tokens = batch_encoding["input_ids"].to(self.device)
+ return tokens
+
+ @torch.no_grad()
+ def encode(self, text):
+ tokens = self(text)
+ if not self.vq_interface:
+ return tokens
+ return None, None, [None, None, tokens]
+
+ def decode(self, text):
+ return text
+
+class BERTEmbedder(AbstractEncoder):
+ """Uses the BERT tokenizr model and add some transformer encoder layers"""
+ def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
+ device="cuda",use_tokenizer=True, embedding_dropout=0.0):
+ super().__init__()
+ self.use_tknz_fn = use_tokenizer
+ if self.use_tknz_fn:
+ self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
+ self.device = device
+ self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
+ attn_layers=Encoder(dim=n_embed, depth=n_layer),
+ emb_dropout=embedding_dropout)
+
+ def forward(self, text):
+ if self.use_tknz_fn:
+ tokens = self.tknz_fn(text)#.to(self.device)
+ else:
+ tokens = text
+ z = self.transformer(tokens, return_embeddings=True)
+ return z
+
+ def encode(self, text):
+ # output of length 77
+ return self(text)
+
+class MedBERTEmbedder(AbstractEncoder):
+ """Uses the BERT tokenizr model and add some transformer encoder layers"""
+ def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77,
+ device="cuda",use_tokenizer=True, embedding_dropout=0.0):
+ super().__init__()
+ self.use_tknz_fn = use_tokenizer
+ if self.use_tknz_fn:
+ self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len)
+ self.device = device
+ self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len,
+ attn_layers=Encoder(dim=n_embed, depth=n_layer),
+ emb_dropout=embedding_dropout)
+
+ def forward(self, text):
+ if self.use_tknz_fn:
+ tokens = self.tknz_fn(text)#.to(self.device)
+ else:
+ tokens = text
+ z = self.transformer(tokens, return_embeddings=True)
+ return z
+
+ def encode(self, text):
+ # output of length 77
+ return self(text)
+
+class FrozenT5Embedder(AbstractEncoder):
+ """Uses the T5 transformer encoder for text"""
+ def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+ super().__init__()
+ self.tokenizer = T5Tokenizer.from_pretrained(version)
+ self.transformer = T5EncoderModel.from_pretrained(version)
+ self.device = device
+ self.max_length = max_length # TODO: typical value?
+ if freeze:
+ self.freeze()
+
+ def freeze(self):
+ self.transformer = self.transformer.eval()
+ #self.train = disabled_train
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+ return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+ tokens = batch_encoding["input_ids"].to(self.device)
+ outputs = self.transformer(input_ids=tokens)
+
+ z = outputs.last_hidden_state
+ return z
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenCLIPEmbedder(AbstractEncoder):
+ """Uses the CLIP transformer encoder for text (from huggingface)"""
+ LAYERS = [
+ "last",
+ "pooled",
+ "hidden"
+ ]
+ def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
+ freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32
+ super().__init__()
+ assert layer in self.LAYERS
+ self.tokenizer = CLIPTokenizer.from_pretrained(version)
+ self.transformer = CLIPTextModel.from_pretrained(version)
+ self.device = device
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ self.layer_idx = layer_idx
+ if layer == "hidden":
+ assert layer_idx is not None
+ assert 0 <= abs(layer_idx) <= 12
+
+ def freeze(self):
+ self.transformer = self.transformer.eval()
+ #self.train = disabled_train
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+ return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+ tokens = batch_encoding["input_ids"].to(self.device)
+ outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
+ if self.layer == "last":
+ z = outputs.last_hidden_state
+ elif self.layer == "pooled":
+ z = outputs.pooler_output[:, None, :]
+ else:
+ z = outputs.hidden_states[self.layer_idx]
+ return z
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenOpenCLIPEmbedder(AbstractEncoder):
+ """
+ Uses the OpenCLIP transformer encoder for text
+ """
+ LAYERS = [
+ #"pooled",
+ "last",
+ "penultimate"
+ ]
+ def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
+ freeze=True, layer="last"):
+ super().__init__()
+ assert layer in self.LAYERS
+ model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
+ del model.visual
+ self.model = model
+
+ self.device = device
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ if self.layer == "last":
+ self.layer_idx = 0
+ elif self.layer == "penultimate":
+ self.layer_idx = 1
+ else:
+ raise NotImplementedError()
+
+ def freeze(self):
+ self.model = self.model.eval()
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ tokens = open_clip.tokenize(text)
+ z = self.encode_with_transformer(tokens.to(self.device))
+ return z
+
+ def encode_with_transformer(self, text):
+ x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
+ x = x + self.model.positional_embedding
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
+ x = x.permute(1, 0, 2) # LND -> NLD
+ x = self.model.ln_final(x)
+ return x
+
+ def text_transformer_forward(self, x: torch.Tensor, attn_mask = None):
+ for i, r in enumerate(self.model.transformer.resblocks):
+ if i == len(self.model.transformer.resblocks) - self.layer_idx:
+ break
+ if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
+ x = checkpoint(r, x, attn_mask)
+ else:
+ x = r(x, attn_mask=attn_mask)
+ return x
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenCLIPT5Encoder(AbstractEncoder):
+ def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
+ clip_max_length=77, t5_max_length=77):
+ super().__init__()
+ self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
+ self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
+ print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
+ f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
+
+ def encode(self, text):
+ return self(text)
+
+ def forward(self, text):
+ clip_z = self.clip_encoder.encode(text)
+ t5_z = self.t5_encoder.encode(text)
+ return [clip_z, t5_z]
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+ """
+ Create sinusoidal timestep embeddings.
+ :param timesteps: a 1-D Tensor of N indices, one per batch element.
+ These may be fractional.
+ :param dim: the dimension of the output.
+ :param max_period: controls the minimum frequency of the embeddings.
+ :return: an [N x dim] Tensor of positional embeddings.
+ """
+ if not repeat_only:
+ half = dim // 2
+ freqs = torch.exp(
+ -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+ ).to(device=timesteps.device)
+ args = timesteps[:, None].float() * freqs[None]
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+ if dim % 2:
+ embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+ else:
+ embedding = repeat(timesteps, 'b -> b d', d=dim)
+ return embedding
+
+
+class PositionalEmbedder(AbstractEncoder):
+ def __init__(self, dim, max_period=10000, repeat_only=False):
+ super().__init__()
+ self.dim = dim
+ self.max_period = max_period
+ self.repeat_only = repeat_only
+
+ def encode(self, positional_id):
+ return self(positional_id)
+
+ def forward(self, positional_id):
+ return timestep_embedding(1000*positional_id, self.dim, self.max_period, self.repeat_only)
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/x_transformer.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/x_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fc15bf9cfe0111a910e7de33d04ffdec3877576
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/modules/x_transformer.py
@@ -0,0 +1,641 @@
+"""shout-out to https://github.com/lucidrains/x-transformers/tree/main/x_transformers"""
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+from functools import partial
+from inspect import isfunction
+from collections import namedtuple
+from einops import rearrange, repeat, reduce
+
+# constants
+
+DEFAULT_DIM_HEAD = 64
+
+Intermediates = namedtuple('Intermediates', [
+ 'pre_softmax_attn',
+ 'post_softmax_attn'
+])
+
+LayerIntermediates = namedtuple('Intermediates', [
+ 'hiddens',
+ 'attn_intermediates'
+])
+
+
+class AbsolutePositionalEmbedding(nn.Module):
+ def __init__(self, dim, max_seq_len):
+ super().__init__()
+ self.emb = nn.Embedding(max_seq_len, dim)
+ self.init_()
+
+ def init_(self):
+ nn.init.normal_(self.emb.weight, std=0.02)
+
+ def forward(self, x):
+ n = torch.arange(x.shape[1], device=x.device)
+ return self.emb(n)[None, :, :]
+
+
+class FixedPositionalEmbedding(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+ self.register_buffer('inv_freq', inv_freq)
+
+ def forward(self, x, seq_dim=1, offset=0):
+ t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
+ sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
+ emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
+ return emb[None, :, :]
+
+
+# helpers
+
+def exists(val):
+ return val is not None
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+
+def always(val):
+ def inner(*args, **kwargs):
+ return val
+ return inner
+
+
+def not_equals(val):
+ def inner(x):
+ return x != val
+ return inner
+
+
+def equals(val):
+ def inner(x):
+ return x == val
+ return inner
+
+
+def max_neg_value(tensor):
+ return -torch.finfo(tensor.dtype).max
+
+
+# keyword argument helpers
+
+def pick_and_pop(keys, d):
+ values = list(map(lambda key: d.pop(key), keys))
+ return dict(zip(keys, values))
+
+
+def group_dict_by_key(cond, d):
+ return_val = [dict(), dict()]
+ for key in d.keys():
+ match = bool(cond(key))
+ ind = int(not match)
+ return_val[ind][key] = d[key]
+ return (*return_val,)
+
+
+def string_begins_with(prefix, str):
+ return str.startswith(prefix)
+
+
+def group_by_key_prefix(prefix, d):
+ return group_dict_by_key(partial(string_begins_with, prefix), d)
+
+
+def groupby_prefix_and_trim(prefix, d):
+ kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
+ kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+ return kwargs_without_prefix, kwargs
+
+
+# classes
+class Scale(nn.Module):
+ def __init__(self, value, fn):
+ super().__init__()
+ self.value = value
+ self.fn = fn
+
+ def forward(self, x, **kwargs):
+ x, *rest = self.fn(x, **kwargs)
+ return (x * self.value, *rest)
+
+
+class Rezero(nn.Module):
+ def __init__(self, fn):
+ super().__init__()
+ self.fn = fn
+ self.g = nn.Parameter(torch.zeros(1))
+
+ def forward(self, x, **kwargs):
+ x, *rest = self.fn(x, **kwargs)
+ return (x * self.g, *rest)
+
+
+class ScaleNorm(nn.Module):
+ def __init__(self, dim, eps=1e-5):
+ super().__init__()
+ self.scale = dim ** -0.5
+ self.eps = eps
+ self.g = nn.Parameter(torch.ones(1))
+
+ def forward(self, x):
+ norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+ return x / norm.clamp(min=self.eps) * self.g
+
+
+class RMSNorm(nn.Module):
+ def __init__(self, dim, eps=1e-8):
+ super().__init__()
+ self.scale = dim ** -0.5
+ self.eps = eps
+ self.g = nn.Parameter(torch.ones(dim))
+
+ def forward(self, x):
+ norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+ return x / norm.clamp(min=self.eps) * self.g
+
+
+class Residual(nn.Module):
+ def forward(self, x, residual):
+ return x + residual
+
+
+class GRUGating(nn.Module):
+ def __init__(self, dim):
+ super().__init__()
+ self.gru = nn.GRUCell(dim, dim)
+
+ def forward(self, x, residual):
+ gated_output = self.gru(
+ rearrange(x, 'b n d -> (b n) d'),
+ rearrange(residual, 'b n d -> (b n) d')
+ )
+
+ return gated_output.reshape_as(x)
+
+
+# feedforward
+
+class GEGLU(nn.Module):
+ def __init__(self, dim_in, dim_out):
+ super().__init__()
+ self.proj = nn.Linear(dim_in, dim_out * 2)
+
+ def forward(self, x):
+ x, gate = self.proj(x).chunk(2, dim=-1)
+ return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ dim_out = default(dim_out, dim)
+ project_in = nn.Sequential(
+ nn.Linear(dim, inner_dim),
+ nn.GELU()
+ ) if not glu else GEGLU(dim, inner_dim)
+
+ self.net = nn.Sequential(
+ project_in,
+ nn.Dropout(dropout),
+ nn.Linear(inner_dim, dim_out)
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+
+# attention.
+class Attention(nn.Module):
+ def __init__(
+ self,
+ dim,
+ dim_head=DEFAULT_DIM_HEAD,
+ heads=8,
+ causal=False,
+ mask=None,
+ talking_heads=False,
+ sparse_topk=None,
+ use_entmax15=False,
+ num_mem_kv=0,
+ dropout=0.,
+ on_attn=False
+ ):
+ super().__init__()
+ if use_entmax15:
+ raise NotImplementedError("Check out entmax activation instead of softmax activation!")
+ self.scale = dim_head ** -0.5
+ self.heads = heads
+ self.causal = causal
+ self.mask = mask
+
+ inner_dim = dim_head * heads
+
+ self.to_q = nn.Linear(dim, inner_dim, bias=False)
+ self.to_k = nn.Linear(dim, inner_dim, bias=False)
+ self.to_v = nn.Linear(dim, inner_dim, bias=False)
+ self.dropout = nn.Dropout(dropout)
+
+ # talking heads
+ self.talking_heads = talking_heads
+ if talking_heads:
+ self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+ self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+
+ # explicit topk sparse attention
+ self.sparse_topk = sparse_topk
+
+ # entmax
+ #self.attn_fn = entmax15 if use_entmax15 else F.softmax
+ self.attn_fn = F.softmax
+
+ # add memory key / values
+ self.num_mem_kv = num_mem_kv
+ if num_mem_kv > 0:
+ self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+ self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+
+ # attention on attention
+ self.attn_on_attn = on_attn
+ self.to_out = nn.Sequential(nn.Linear(inner_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(inner_dim, dim)
+
+ def forward(
+ self,
+ x,
+ context=None,
+ mask=None,
+ context_mask=None,
+ rel_pos=None,
+ sinusoidal_emb=None,
+ prev_attn=None,
+ mem=None
+ ):
+ b, n, _, h, talking_heads, device = *x.shape, self.heads, self.talking_heads, x.device
+ kv_input = default(context, x)
+
+ q_input = x
+ k_input = kv_input
+ v_input = kv_input
+
+ if exists(mem):
+ k_input = torch.cat((mem, k_input), dim=-2)
+ v_input = torch.cat((mem, v_input), dim=-2)
+
+ if exists(sinusoidal_emb):
+ # in shortformer, the query would start at a position offset depending on the past cached memory
+ offset = k_input.shape[-2] - q_input.shape[-2]
+ q_input = q_input + sinusoidal_emb(q_input, offset=offset)
+ k_input = k_input + sinusoidal_emb(k_input)
+
+ q = self.to_q(q_input)
+ k = self.to_k(k_input)
+ v = self.to_v(v_input)
+
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
+
+ input_mask = None
+ if any(map(exists, (mask, context_mask))):
+ q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
+ k_mask = q_mask if not exists(context) else context_mask
+ k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
+ q_mask = rearrange(q_mask, 'b i -> b () i ()')
+ k_mask = rearrange(k_mask, 'b j -> b () () j')
+ input_mask = q_mask * k_mask
+
+ if self.num_mem_kv > 0:
+ mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
+ k = torch.cat((mem_k, k), dim=-2)
+ v = torch.cat((mem_v, v), dim=-2)
+ if exists(input_mask):
+ input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
+
+ dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+ mask_value = max_neg_value(dots)
+
+ if exists(prev_attn):
+ dots = dots + prev_attn
+
+ pre_softmax_attn = dots
+
+ if talking_heads:
+ dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
+
+ if exists(rel_pos):
+ dots = rel_pos(dots)
+
+ if exists(input_mask):
+ dots.masked_fill_(~input_mask, mask_value)
+ del input_mask
+
+ if self.causal:
+ i, j = dots.shape[-2:]
+ r = torch.arange(i, device=device)
+ mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
+ mask = F.pad(mask, (j - i, 0), value=False)
+ dots.masked_fill_(mask, mask_value)
+ del mask
+
+ if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
+ top, _ = dots.topk(self.sparse_topk, dim=-1)
+ vk = top[..., -1].unsqueeze(-1).expand_as(dots)
+ mask = dots < vk
+ dots.masked_fill_(mask, mask_value)
+ del mask
+
+ attn = self.attn_fn(dots, dim=-1)
+ post_softmax_attn = attn
+
+ attn = self.dropout(attn)
+
+ if talking_heads:
+ attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
+
+ out = einsum('b h i j, b h j d -> b h i d', attn, v)
+ out = rearrange(out, 'b h n d -> b n (h d)')
+
+ intermediates = Intermediates(
+ pre_softmax_attn=pre_softmax_attn,
+ post_softmax_attn=post_softmax_attn
+ )
+
+ return self.to_out(out), intermediates
+
+
+class AttentionLayers(nn.Module):
+ def __init__(
+ self,
+ dim,
+ depth,
+ heads=8,
+ causal=False,
+ cross_attend=False,
+ only_cross=False,
+ use_scalenorm=False,
+ use_rmsnorm=False,
+ use_rezero=False,
+ rel_pos_num_buckets=32,
+ rel_pos_max_distance=128,
+ position_infused_attn=False,
+ custom_layers=None,
+ sandwich_coef=None,
+ par_ratio=None,
+ residual_attn=False,
+ cross_residual_attn=False,
+ macaron=False,
+ pre_norm=True,
+ gate_residual=False,
+ **kwargs
+ ):
+ super().__init__()
+ ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
+ attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
+
+ dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
+
+ self.dim = dim
+ self.depth = depth
+ self.layers = nn.ModuleList([])
+
+ self.has_pos_emb = position_infused_attn
+ self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
+ self.rotary_pos_emb = always(None)
+
+ assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
+ self.rel_pos = None
+
+ self.pre_norm = pre_norm
+
+ self.residual_attn = residual_attn
+ self.cross_residual_attn = cross_residual_attn
+
+ norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
+ norm_class = RMSNorm if use_rmsnorm else norm_class
+ norm_fn = partial(norm_class, dim)
+
+ norm_fn = nn.Identity if use_rezero else norm_fn
+ branch_fn = Rezero if use_rezero else None
+
+ if cross_attend and not only_cross:
+ default_block = ('a', 'c', 'f')
+ elif cross_attend and only_cross:
+ default_block = ('c', 'f')
+ else:
+ default_block = ('a', 'f')
+
+ if macaron:
+ default_block = ('f',) + default_block
+
+ if exists(custom_layers):
+ layer_types = custom_layers
+ elif exists(par_ratio):
+ par_depth = depth * len(default_block)
+ assert 1 < par_ratio <= par_depth, 'par ratio out of range'
+ default_block = tuple(filter(not_equals('f'), default_block))
+ par_attn = par_depth // par_ratio
+ depth_cut = par_depth * 2 // 3 # 2 / 3 attention layer cutoff suggested by PAR paper
+ par_width = (depth_cut + depth_cut // par_attn) // par_attn
+ assert len(default_block) <= par_width, 'default block is too large for par_ratio'
+ par_block = default_block + ('f',) * (par_width - len(default_block))
+ par_head = par_block * par_attn
+ layer_types = par_head + ('f',) * (par_depth - len(par_head))
+ elif exists(sandwich_coef):
+ assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
+ layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
+ else:
+ layer_types = default_block * depth
+
+ self.layer_types = layer_types
+ self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
+
+ for layer_type in self.layer_types:
+ if layer_type == 'a':
+ layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
+ elif layer_type == 'c':
+ layer = Attention(dim, heads=heads, **attn_kwargs)
+ elif layer_type == 'f':
+ layer = FeedForward(dim, **ff_kwargs)
+ layer = layer if not macaron else Scale(0.5, layer)
+ else:
+ raise Exception(f'invalid layer type {layer_type}')
+
+ if isinstance(layer, Attention) and exists(branch_fn):
+ layer = branch_fn(layer)
+
+ if gate_residual:
+ residual_fn = GRUGating(dim)
+ else:
+ residual_fn = Residual()
+
+ self.layers.append(nn.ModuleList([
+ norm_fn(),
+ layer,
+ residual_fn
+ ]))
+
+ def forward(
+ self,
+ x,
+ context=None,
+ mask=None,
+ context_mask=None,
+ mems=None,
+ return_hiddens=False
+ ):
+ hiddens = []
+ intermediates = []
+ prev_attn = None
+ prev_cross_attn = None
+
+ mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
+
+ for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
+ is_last = ind == (len(self.layers) - 1)
+
+ if layer_type == 'a':
+ hiddens.append(x)
+ layer_mem = mems.pop(0)
+
+ residual = x
+
+ if self.pre_norm:
+ x = norm(x)
+
+ if layer_type == 'a':
+ out, inter = block(x, mask=mask, sinusoidal_emb=self.pia_pos_emb, rel_pos=self.rel_pos,
+ prev_attn=prev_attn, mem=layer_mem)
+ elif layer_type == 'c':
+ out, inter = block(x, context=context, mask=mask, context_mask=context_mask, prev_attn=prev_cross_attn)
+ elif layer_type == 'f':
+ out = block(x)
+
+ x = residual_fn(out, residual)
+
+ if layer_type in ('a', 'c'):
+ intermediates.append(inter)
+
+ if layer_type == 'a' and self.residual_attn:
+ prev_attn = inter.pre_softmax_attn
+ elif layer_type == 'c' and self.cross_residual_attn:
+ prev_cross_attn = inter.pre_softmax_attn
+
+ if not self.pre_norm and not is_last:
+ x = norm(x)
+
+ if return_hiddens:
+ intermediates = LayerIntermediates(
+ hiddens=hiddens,
+ attn_intermediates=intermediates
+ )
+
+ return x, intermediates
+
+ return x
+
+
+class Encoder(AttentionLayers):
+ def __init__(self, **kwargs):
+ assert 'causal' not in kwargs, 'cannot set causality on encoder'
+ super().__init__(causal=False, **kwargs)
+
+
+
+class TransformerWrapper(nn.Module):
+ def __init__(
+ self,
+ *,
+ num_tokens,
+ max_seq_len,
+ attn_layers,
+ emb_dim=None,
+ max_mem_len=0.,
+ emb_dropout=0.,
+ num_memory_tokens=None,
+ tie_embedding=False,
+ use_pos_emb=True
+ ):
+ super().__init__()
+ assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
+
+ dim = attn_layers.dim
+ emb_dim = default(emb_dim, dim)
+
+ self.max_seq_len = max_seq_len
+ self.max_mem_len = max_mem_len
+ self.num_tokens = num_tokens
+
+ self.token_emb = nn.Embedding(num_tokens, emb_dim)
+ self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
+ use_pos_emb and not attn_layers.has_pos_emb) else always(0)
+ self.emb_dropout = nn.Dropout(emb_dropout)
+
+ self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
+ self.attn_layers = attn_layers
+ self.norm = nn.LayerNorm(dim)
+
+ self.init_()
+
+ self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
+
+ # memory tokens (like [cls]) from Memory Transformers paper
+ num_memory_tokens = default(num_memory_tokens, 0)
+ self.num_memory_tokens = num_memory_tokens
+ if num_memory_tokens > 0:
+ self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
+
+ # let funnel encoder know number of memory tokens, if specified
+ if hasattr(attn_layers, 'num_memory_tokens'):
+ attn_layers.num_memory_tokens = num_memory_tokens
+
+ def init_(self):
+ nn.init.normal_(self.token_emb.weight, std=0.02)
+
+ def forward(
+ self,
+ x,
+ return_embeddings=False,
+ mask=None,
+ return_mems=False,
+ return_attn=False,
+ mems=None,
+ **kwargs
+ ):
+ b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
+ x = self.token_emb(x)
+ x += self.pos_emb(x)
+ x = self.emb_dropout(x)
+
+ x = self.project_emb(x)
+
+ if num_mem > 0:
+ mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
+ x = torch.cat((mem, x), dim=1)
+
+ # auto-handle masking after appending memory tokens
+ if exists(mask):
+ mask = F.pad(mask, (num_mem, 0), value=True)
+
+ x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
+ x = self.norm(x)
+
+ mem, x = x[:, :num_mem], x[:, num_mem:]
+
+ out = self.to_logits(x) if not return_embeddings else x
+
+ if return_mems:
+ hiddens = intermediates.hiddens
+ new_mems = list(map(lambda pair: torch.cat(pair, dim=-2), zip(mems, hiddens))) if exists(mems) else hiddens
+ new_mems = list(map(lambda t: t[..., -self.max_mem_len:, :].detach(), new_mems))
+ return out, new_mems
+
+ if return_attn:
+ attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+ return out, attn_maps
+
+ return out
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/util.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..45cb050ece6f401a22dde098ce3f1ff663c5eb6a
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/ldm/util.py
@@ -0,0 +1,197 @@
+import importlib
+
+import torch
+from torch import optim
+import numpy as np
+
+from inspect import isfunction
+from PIL import Image, ImageDraw, ImageFont
+
+
+def log_txt_as_img(wh, xc, size=10):
+ # wh a tuple of (width, height)
+ # xc a list of captions to plot
+ b = len(xc)
+ txts = list()
+ for bi in range(b):
+ txt = Image.new("RGB", wh, color="white")
+ draw = ImageDraw.Draw(txt)
+ font = ImageFont.truetype('font/DejaVuSans.ttf', size=size)
+ nc = int(40 * (wh[0] / 256))
+ lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
+
+ try:
+ draw.text((0, 0), lines, fill="black", font=font)
+ except UnicodeEncodeError:
+ print("Cant encode string for logging. Skipping.")
+
+ txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
+ txts.append(txt)
+ txts = np.stack(txts)
+ txts = torch.tensor(txts)
+ return txts
+
+
+def ismap(x):
+ if not isinstance(x, torch.Tensor):
+ return False
+ return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+
+def isimage(x):
+ if not isinstance(x,torch.Tensor):
+ return False
+ return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+
+def exists(x):
+ return x is not None
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+
+def mean_flat(tensor):
+ """
+ https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
+ Take the mean over all non-batch dimensions.
+ """
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def count_params(model, verbose=False):
+ total_params = sum(p.numel() for p in model.parameters())
+ if verbose:
+ print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
+ return total_params
+
+
+def instantiate_from_config(config):
+ if not "target" in config:
+ if config == '__is_first_stage__':
+ return None
+ elif config == "__is_unconditional__":
+ return None
+ raise KeyError("Expected key `target` to instantiate.")
+ return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False):
+ module, cls = string.rsplit(".", 1)
+ if reload:
+ module_imp = importlib.import_module(module)
+ importlib.reload(module_imp)
+ return getattr(importlib.import_module(module, package=None), cls)
+
+
+class AdamWwithEMAandWings(optim.Optimizer):
+ # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
+ def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using
+ weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code
+ ema_power=1., param_names=()):
+ """AdamW that saves EMA versions of the parameters."""
+ if not 0.0 <= lr:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if not 0.0 <= eps:
+ raise ValueError("Invalid epsilon value: {}".format(eps))
+ if not 0.0 <= betas[0] < 1.0:
+ raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
+ if not 0.0 <= betas[1] < 1.0:
+ raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
+ if not 0.0 <= weight_decay:
+ raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+ if not 0.0 <= ema_decay <= 1.0:
+ raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
+ defaults = dict(lr=lr, betas=betas, eps=eps,
+ weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
+ ema_power=ema_power, param_names=param_names)
+ super().__init__(params, defaults)
+
+ def __setstate__(self, state):
+ super().__setstate__(state)
+ for group in self.param_groups:
+ group.setdefault('amsgrad', False)
+
+ @torch.no_grad()
+ def step(self, closure=None):
+ """Performs a single optimization step.
+ Args:
+ closure (callable, optional): A closure that reevaluates the model
+ and returns the loss.
+ """
+ loss = None
+ if closure is not None:
+ with torch.enable_grad():
+ loss = closure()
+
+ for group in self.param_groups:
+ params_with_grad = []
+ grads = []
+ exp_avgs = []
+ exp_avg_sqs = []
+ ema_params_with_grad = []
+ state_sums = []
+ max_exp_avg_sqs = []
+ state_steps = []
+ amsgrad = group['amsgrad']
+ beta1, beta2 = group['betas']
+ ema_decay = group['ema_decay']
+ ema_power = group['ema_power']
+
+ for p in group['params']:
+ if p.grad is None:
+ continue
+ params_with_grad.append(p)
+ if p.grad.is_sparse:
+ raise RuntimeError('AdamW does not support sparse gradients')
+ grads.append(p.grad)
+
+ state = self.state[p]
+
+ # State initialization
+ if len(state) == 0:
+ state['step'] = 0
+ # Exponential moving average of gradient values
+ state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ # Exponential moving average of squared gradient values
+ state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ if amsgrad:
+ # Maintains max of all exp. moving avg. of sq. grad. values
+ state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ # Exponential moving average of parameter values
+ state['param_exp_avg'] = p.detach().float().clone()
+
+ exp_avgs.append(state['exp_avg'])
+ exp_avg_sqs.append(state['exp_avg_sq'])
+ ema_params_with_grad.append(state['param_exp_avg'])
+
+ if amsgrad:
+ max_exp_avg_sqs.append(state['max_exp_avg_sq'])
+
+ # update the steps for each param group update
+ state['step'] += 1
+ # record the step after step update
+ state_steps.append(state['step'])
+
+ optim._functional.adamw(params_with_grad,
+ grads,
+ exp_avgs,
+ exp_avg_sqs,
+ max_exp_avg_sqs,
+ state_steps,
+ amsgrad=amsgrad,
+ beta1=beta1,
+ beta2=beta2,
+ lr=group['lr'],
+ weight_decay=group['weight_decay'],
+ eps=group['eps'],
+ maximize=False)
+
+ cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
+ for param, ema_param in zip(params_with_grad, ema_params_with_grad):
+ ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
+
+ return loss
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000723.ckpt b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000723.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..1b6f5dec5f587c0636b283c13f6c4b8b6acb87d0
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000723.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e80356e7e689dde5669b42aeda58676985d967b17ac06cf8749ffc1178d0f55f
+size 7325151006
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000847.ckpt b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000847.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..5f8ae131557a6fffa1cd50f1828f29d4cfd99b44
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000847.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21a82d682b3452c522bd202f3eac5e0e0602cfe09d75e9a03414655e7d951a3e
+size 7325151006
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000931.ckpt b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000931.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..0d427a5122614fa819140c7c05c71a53bb7b252e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000931.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed30f6c4649b39308b03bfbeb8ba8d1a07029ab02d2c5120544a632d464376a4
+size 7325158367
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000999.ckpt b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000999.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..22cc28daea3f6a6d82cdae7c89070a62adb83d40
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000999.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2be78b7c1e808649d1f56377ed269dcd452e2105c5cd7458ddee0933829bb04
+size 7325158558
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/last.ckpt b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/last.ckpt
new file mode 100644
index 0000000000000000000000000000000000000000..22cc28daea3f6a6d82cdae7c89070a62adb83d40
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/checkpoints/last.ckpt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c2be78b7c1e808649d1f56377ed269dcd452e2105c5cd7458ddee0933829bb04
+size 7325158558
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T15-52-32-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T15-52-32-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9b88d453925a3fab5830877d511e0665c0ac8e7b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T15-52-32-lightning.yaml
@@ -0,0 +1,12 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T15-52-32-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T15-52-32-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0818df4c2ab0bb444ad6babe59b9bce57001c083
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T15-52-32-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-52-29-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-52-29-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9b88d453925a3fab5830877d511e0665c0ac8e7b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-52-29-lightning.yaml
@@ -0,0 +1,12 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-52-29-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-52-29-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0818df4c2ab0bb444ad6babe59b9bce57001c083
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-52-29-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-59-11-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-59-11-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9b88d453925a3fab5830877d511e0665c0ac8e7b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-59-11-lightning.yaml
@@ -0,0 +1,12 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-59-11-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-59-11-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0818df4c2ab0bb444ad6babe59b9bce57001c083
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T17-59-11-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T18-34-00-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T18-34-00-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ae4e23f6ed7bf2ea789aa6df326117a0e04c9f0e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T18-34-00-lightning.yaml
@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_2d_with_body_mask/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T18-34-00-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T18-34-00-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0818df4c2ab0bb444ad6babe59b9bce57001c083
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-17T18-34-00-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-22T21-36-50-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-22T21-36-50-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ae4e23f6ed7bf2ea789aa6df326117a0e04c9f0e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-22T21-36-50-lightning.yaml
@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_2d_with_body_mask/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-22T21-36-50-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-22T21-36-50-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0818df4c2ab0bb444ad6babe59b9bce57001c083
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-22T21-36-50-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-23T23-44-47-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-23T23-44-47-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ae4e23f6ed7bf2ea789aa6df326117a0e04c9f0e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-23T23-44-47-lightning.yaml
@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_2d_with_body_mask/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-23T23-44-47-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-23T23-44-47-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0818df4c2ab0bb444ad6babe59b9bce57001c083
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-23T23-44-47-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-24T02-55-56-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-24T02-55-56-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ae4e23f6ed7bf2ea789aa6df326117a0e04c9f0e
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-24T02-55-56-lightning.yaml
@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_2d_with_body_mask/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-24T02-55-56-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-24T02-55-56-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0818df4c2ab0bb444ad6babe59b9bce57001c083
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-24T02-55-56-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-28T21-58-24-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-28T21-58-24-lightning.yaml
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@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_2d_with_body_mask/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-28T21-58-24-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/configs/2025-10-28T21-58-24-project.yaml
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+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/testtube/version_6/tf/events.out.tfevents.1761274618.node-0.4798.0 b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/testtube/version_6/tf/events.out.tfevents.1761274618.node-0.4798.0
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/testtube/version_7/meta.experiment b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/testtube/version_7/meta.experiment
new file mode 100644
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/testtube/version_7/metrics.csv b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask/testtube/version_7/metrics.csv
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-28T21-59-42-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-28T21-59-42-lightning.yaml
new file mode 100644
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+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-28T21-59-42-lightning.yaml
@@ -0,0 +1,12 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-28T21-59-42-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-28T21-59-42-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7a7dab1599480e5a6916b98ec6bfe915d873b0d5
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-28T21-59-42-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.5
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T03-06-33-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T03-06-33-lightning.yaml
new file mode 100644
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--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T03-06-33-lightning.yaml
@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_2d_with_body_mask2/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T03-06-33-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T03-06-33-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7a7dab1599480e5a6916b98ec6bfe915d873b0d5
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T03-06-33-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.5
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-26-38-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-26-38-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1b0338aead6c797df75ee541d54de45b0d63accf
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-26-38-lightning.yaml
@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_2d_with_body_mask2/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-26-38-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-26-38-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7a7dab1599480e5a6916b98ec6bfe915d873b0d5
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-26-38-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.5
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-50-09-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-50-09-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1b0338aead6c797df75ee541d54de45b0d63accf
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-50-09-lightning.yaml
@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_2d_with_body_mask2/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-50-09-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-50-09-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7a7dab1599480e5a6916b98ec6bfe915d873b0d5
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/configs/2025-10-29T06-50-09-project.yaml
@@ -0,0 +1,58 @@
+model:
+ base_learning_rate: 5.0e-07
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.5
+ use_ema: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 8
+ num_workers: 12
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/testtube/version_0/meta.experiment b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/testtube/version_0/meta.experiment
new file mode 100644
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/testtube/version_0/meta_tags.csv b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/testtube/version_0/meta_tags.csv
new file mode 100644
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/testtube/version_0/metrics.csv b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_2d_with_body_mask2/testtube/version_0/metrics.csv
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask/configs/2025-10-27T05-24-24-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask/configs/2025-10-27T05-24-24-lightning.yaml
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+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask/configs/2025-10-27T05-24-24-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask/configs/2025-10-27T05-24-24-project.yaml
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+model:
+ base_learning_rate: 5.0e-06
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: false
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae-retrain/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000999.ckpt
+ load_only_unet: true
+ fix_t: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask/testtube/version_0/meta.experiment b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask/testtube/version_0/meta.experiment
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+{"name": "testtube", "version": 0, "tags_path": "logs/full_ct_3d_with_body_mask/testtube/version_0/meta_tags.csv", "metrics_path": "logs/full_ct_3d_with_body_mask/testtube/version_0/metrics.csv", "autosave": false, "description": null, "created_at": "2025-10-27 05:24:55.199695", "exp_hash": "testtube_v0"}
\ No newline at end of file
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+key,value
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask/testtube/version_0/metrics.csv b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask/testtube/version_0/metrics.csv
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask/testtube/version_0/tf/events.out.tfevents.1761542695.node-0.1923.0 b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask/testtube/version_0/tf/events.out.tfevents.1761542695.node-0.1923.0
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-29T14-12-09-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-29T14-12-09-lightning.yaml
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+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-29T14-12-09-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-29T14-12-09-project.yaml
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--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-29T14-12-09-project.yaml
@@ -0,0 +1,61 @@
+model:
+ base_learning_rate: 5.0e-06
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: false
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae-retrain/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000999.ckpt
+ load_only_unet: true
+ fix_t: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-30T17-42-15-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-30T17-42-15-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..230511a02424ed10f059b373a04513b0024480d8
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-30T17-42-15-lightning.yaml
@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_3d_with_body_mask2/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-30T17-42-15-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-30T17-42-15-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c40b7c4cc710a034b88c8dbafbbcc2248bb880ee
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-30T17-42-15-project.yaml
@@ -0,0 +1,61 @@
+model:
+ base_learning_rate: 5.0e-06
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: false
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae-retrain/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000999.ckpt
+ load_only_unet: true
+ fix_t: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-03-07-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-03-07-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..230511a02424ed10f059b373a04513b0024480d8
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-03-07-lightning.yaml
@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_3d_with_body_mask2/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-03-07-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-03-07-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c40b7c4cc710a034b88c8dbafbbcc2248bb880ee
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-03-07-project.yaml
@@ -0,0 +1,61 @@
+model:
+ base_learning_rate: 5.0e-06
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: false
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae-retrain/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000999.ckpt
+ load_only_unet: true
+ fix_t: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-18-06-lightning.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-18-06-lightning.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..230511a02424ed10f059b373a04513b0024480d8
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-18-06-lightning.yaml
@@ -0,0 +1,13 @@
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 100000000
+ max_images: 16
+ increase_log_steps: false
+ trainer:
+ benchmark: true
+ accelerator: ddp
+ gpus: 0,1,2,3,
+ resume_from_checkpoint: ./logs/full_ct_3d_with_body_mask2/checkpoints/last.ckpt
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-18-06-project.yaml b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-18-06-project.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c40b7c4cc710a034b88c8dbafbbcc2248bb880ee
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/configs/2025-10-31T06-18-06-project.yaml
@@ -0,0 +1,61 @@
+model:
+ base_learning_rate: 5.0e-06
+ target: ldm.models.diffusion.ddpm_pseudo3D.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.012
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: volume_data
+ cond_stage_key: volume_seg_and_text
+ conditioning_key: crossattn
+ text_enc: custom
+ image_size: 64
+ channels: 16
+ monitor: val/loss_simple_ema
+ scale_factor: 1.52
+ use_ema: false
+ ckpt_path: /sd/qichen/full_ct_gen/GEM-3D-ct-text4-newdata-newvae-retrain/logs/full_ct_2d_with_body_mask/checkpoints/epoch=000999.ckpt
+ load_only_unet: true
+ fix_t: true
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel_pseudo3D.UNetModel
+ params:
+ image_size: 64
+ in_channels: 32
+ out_channels: 16
+ model_channels: 224
+ attention_resolutions:
+ - 8
+ - 4
+ - 2
+ num_res_blocks: 2
+ channel_mult:
+ - 1
+ - 2
+ - 3
+ - 4
+ num_head_channels: 32
+ use_spatial_transformer: true
+ context_dim: 768
+ transformer_depth: 1
+ use_checkpoint: true
+ legacy: false
+data:
+ target: main.DataModuleFromConfig
+ params:
+ batch_size: 4
+ num_workers: 16
+ wrap: false
+ train:
+ target: ldm.data.ct_clip_data_train.CTReportDataset
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/train_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/train_reports.csv
+ validation:
+ target: ldm.data.ct_clip_data_inference.CTReportDatasetinfer
+ params:
+ data_folder: /sd/shuhan/CT-RATE/dataset/valid_fixed
+ csv_file: /sd/shuhan/CT-RATE/radiology_text_reports/valid_reports.csv
+ labels: /sd/shuhan/CT-RATE/multi_abnormality_labels/valid_predicted_labels.csv
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/meta.experiment b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/meta.experiment
new file mode 100644
index 0000000000000000000000000000000000000000..97981c85b19356b7b98588f6cdd047bd3b902ea0
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/meta.experiment
@@ -0,0 +1 @@
+{"name": "testtube", "version": 0, "tags_path": "logs/full_ct_3d_with_body_mask2/testtube/version_0/meta_tags.csv", "metrics_path": "logs/full_ct_3d_with_body_mask2/testtube/version_0/metrics.csv", "autosave": false, "description": null, "created_at": "2025-10-29 14:13:17.969983", "exp_hash": "testtube_v0"}
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/meta_tags.csv b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/meta_tags.csv
new file mode 100644
index 0000000000000000000000000000000000000000..942a47139f86778b1f3e5ad923104dd038b1ce88
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/meta_tags.csv
@@ -0,0 +1 @@
+key,value
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/metrics.csv b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/metrics.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e330c7ab3b8304529c64f11158abe27fc124cbad
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/metrics.csv
@@ -0,0 +1,444 @@
+train/loss_simple_step,train/loss_vlb_step,train/loss_step,global_step,epoch,created_at,val/loss_simple,val/loss_vlb,val/loss,val/loss_simple_ema,val/loss_vlb_ema,val/loss_ema,train/loss_simple_epoch,train/loss_vlb_epoch,train/loss_epoch
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/tf/events.out.tfevents.1761747200.node-0.1948.0 b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_0/tf/events.out.tfevents.1761747200.node-0.1948.0
new file mode 100644
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--- /dev/null
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_1/meta.experiment b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_1/meta.experiment
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diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_1/meta_tags.csv b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_1/meta_tags.csv
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+0.29835590720176697,0.002196893561631441,0.29835590720176697,24699.0,77,2025-10-31 06:40:46.257806,,,,,,,,,
+0.6672286987304688,0.12987467646598816,0.6672286987304688,24749.0,77,2025-10-31 06:45:03.366070,,,,,,,,,
+0.131357803940773,0.000526526418980211,0.131357803940773,24799.0,77,2025-10-31 06:49:16.560747,,,,,,,,,
+,,,,77,2025-10-31 06:52:03.117988,0.28831642866134644,0.002885563997551799,0.28831642866134644,0.27810245752334595,0.0051479279063642025,0.27810245752334595,,,
+,,,,77,2025-10-31 06:52:54.205030,,,,,,,0.28482744097709656,0.004544297233223915,0.28482744097709656
+0.29872941970825195,0.002703337464481592,0.29872941970825195,24849.0,78,2025-10-31 06:57:45.349744,,,,,,,,,
+0.3147170841693878,0.006378016900271177,0.3147170841693878,24899.0,78,2025-10-31 07:01:59.606373,,,,,,,,,
+0.24917656183242798,0.0009646091493777931,0.24917656183242798,24949.0,78,2025-10-31 07:06:16.011276,,,,,,,,,
+0.2608265280723572,0.0012748067965731025,0.2608265280723572,24999.0,78,2025-10-31 07:10:30.443870,,,,,,,,,
+0.2963445484638214,0.0013238799292594194,0.2963445484638214,25049.0,78,2025-10-31 07:14:46.832359,,,,,,,,,
+0.05422722548246384,0.0002059258404187858,0.05422722548246384,25099.0,78,2025-10-31 07:19:01.128892,,,,,,,,,
+,,,,78,2025-10-31 07:23:03.280382,0.3215169608592987,0.004063682164996862,0.3215169608592987,0.3016454875469208,0.0030227508395910263,0.3016454875469208,,,
+,,,,78,2025-10-31 07:23:50.595417,,,,,,,0.2850608229637146,0.0034778970293700695,0.2850608229637146
+0.28421592712402344,0.0010798118310049176,0.28421592712402344,25149.0,79,2025-10-31 07:27:32.408833,,,,,,,,,
+0.03173404932022095,0.00013555384066421539,0.03173404932022095,25199.0,79,2025-10-31 07:31:48.835135,,,,,,,,,
+0.3687518835067749,0.008241117000579834,0.3687518835067749,25249.0,79,2025-10-31 07:36:05.946930,,,,,,,,,
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_3/tf/events.out.tfevents.1761891594.node-0.2049.0 b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_3/tf/events.out.tfevents.1761891594.node-0.2049.0
new file mode 100644
index 0000000000000000000000000000000000000000..7426b4d287dfeece532a025a691df3ab2db29c62
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/logs/full_ct_3d_with_body_mask2/testtube/version_3/tf/events.out.tfevents.1761891594.node-0.2049.0
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cb581c31ef62eccc55c363fe31196626c5b494eb081bb0ca7e1957742ea75b9a
+size 5622
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/main.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..8775f89b0b4a1f417e7f5c9855236c046aa0cb13
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/main.py
@@ -0,0 +1,774 @@
+import argparse, os, sys, datetime, glob, importlib, csv
+import numpy as np
+import time
+import torch
+import torchvision
+import enum
+
+# 保存原始实现（可选，用于调试或回滚）
+_enum_format_orig = enum.Enum.__format__
+
+def _enum_format_value(self, format_spec: str) -> str:
+ # 如果指定了 format 规范，就交给 value 本身的格式化；否则直接返回 value
+ return format(self.value, format_spec) if format_spec else str(self.value)
+
+# 全局替换 Enum.__format__
+enum.Enum.__format__ = _enum_format_value
+
+import pytorch_lightning as pl
+
+from packaging import version
+from omegaconf import OmegaConf
+from torch.utils.data import random_split, DataLoader, Dataset, Subset
+from functools import partial
+from PIL import Image
+
+from pytorch_lightning import seed_everything
+from pytorch_lightning.trainer import Trainer
+from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from pytorch_lightning.utilities import rank_zero_info
+
+from ldm.data.base import Txt2ImgIterableBaseDataset
+from ldm.util import instantiate_from_config
+
+
+def get_parser(**parser_kwargs):
+ def str2bool(v):
+ if isinstance(v, bool):
+ return v
+ if v.lower() in ("yes", "true", "t", "y", "1"):
+ return True
+ elif v.lower() in ("no", "false", "f", "n", "0"):
+ return False
+ else:
+ raise argparse.ArgumentTypeError("Boolean value expected.")
+
+ parser = argparse.ArgumentParser(**parser_kwargs)
+ parser.add_argument(
+ "-n",
+ "--name",
+ type=str,
+ const=True,
+ default="",
+ nargs="?",
+ help="postfix for logdir",
+ )
+ parser.add_argument(
+ "-r",
+ "--resume",
+ type=str,
+ const=True,
+ default="",
+ nargs="?",
+ help="resume from logdir or checkpoint in logdir",
+ )
+ parser.add_argument(
+ "-b",
+ "--base",
+ nargs="*",
+ metavar="base_config.yaml",
+ help="paths to base configs. Loaded from left-to-right. "
+ "Parameters can be overwritten or added with command-line options of the form `--key value`.",
+ default=list(),
+ )
+ parser.add_argument(
+ "-t",
+ "--train",
+ type=str2bool,
+ const=True,
+ default=False,
+ nargs="?",
+ help="train",
+ )
+ parser.add_argument(
+ "--no-test",
+ type=str2bool,
+ const=True,
+ default=False,
+ nargs="?",
+ help="disable test",
+ )
+ parser.add_argument(
+ "-p",
+ "--project",
+ help="name of new or path to existing project"
+ )
+ parser.add_argument(
+ "-d",
+ "--debug",
+ type=str2bool,
+ nargs="?",
+ const=True,
+ default=False,
+ help="enable post-mortem debugging",
+ )
+ parser.add_argument(
+ "-s",
+ "--seed",
+ type=int,
+ default=23,
+ help="seed for seed_everything",
+ )
+ parser.add_argument(
+ "-f",
+ "--postfix",
+ type=str,
+ default="",
+ help="post-postfix for default name",
+ )
+ parser.add_argument(
+ "-l",
+ "--logdir",
+ type=str,
+ default="logs",
+ help="directory for logging dat shit",
+ )
+ parser.add_argument(
+ "--scale_lr",
+ type=str2bool,
+ nargs="?",
+ const=True,
+ default=True,
+ help="scale base-lr by ngpu * batch_size * n_accumulate",
+ )
+ return parser
+
+
+def nondefault_trainer_args(opt):
+ parser = argparse.ArgumentParser()
+ parser = Trainer.add_argparse_args(parser)
+ args = parser.parse_args([])
+ return sorted(k for k in vars(args) if getattr(opt, k) != getattr(args, k))
+
+
+class WrappedDataset(Dataset):
+ """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
+
+ def __init__(self, dataset):
+ self.data = dataset
+
+ def __len__(self):
+ return len(self.data)
+
+ def __getitem__(self, idx):
+ return self.data[idx]
+
+
+def worker_init_fn(_):
+ worker_info = torch.utils.data.get_worker_info()
+
+ dataset = worker_info.dataset
+ worker_id = worker_info.id
+
+ if isinstance(dataset, Txt2ImgIterableBaseDataset):
+ split_size = dataset.num_records // worker_info.num_workers
+ # reset num_records to the true number to retain reliable length information
+ dataset.sample_ids = dataset.valid_ids[worker_id * split_size:(worker_id + 1) * split_size]
+ current_id = np.random.choice(len(np.random.get_state()[1]), 1)
+ return np.random.seed(np.random.get_state()[1][current_id] + worker_id)
+ else:
+ return np.random.seed(np.random.get_state()[1][0] + worker_id)
+
+
+class DataModuleFromConfig(pl.LightningDataModule):
+ def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,
+ wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
+ shuffle_val_dataloader=False):
+ super().__init__()
+ self.batch_size = batch_size
+ self.dataset_configs = dict()
+ self.num_workers = num_workers if num_workers is not None else batch_size * 2
+ self.use_worker_init_fn = use_worker_init_fn
+ if train is not None:
+ self.dataset_configs["train"] = train
+ self.train_dataloader = self._train_dataloader
+ if validation is not None:
+ self.dataset_configs["validation"] = validation
+ self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
+ if test is not None:
+ self.dataset_configs["test"] = test
+ self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
+ if predict is not None:
+ self.dataset_configs["predict"] = predict
+ self.predict_dataloader = self._predict_dataloader
+ self.wrap = wrap
+
+ def prepare_data(self):
+ for data_cfg in self.dataset_configs.values():
+ instantiate_from_config(data_cfg)
+
+ def setup(self, stage=None):
+ self.datasets = dict(
+ (k, instantiate_from_config(self.dataset_configs[k]))
+ for k in self.dataset_configs)
+ if self.wrap:
+ for k in self.datasets:
+ self.datasets[k] = WrappedDataset(self.datasets[k])
+
+ def _train_dataloader(self):
+ is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
+ if is_iterable_dataset or self.use_worker_init_fn:
+ init_fn = worker_init_fn
+ else:
+ init_fn = None
+ return DataLoader(self.datasets["train"], batch_size=self.batch_size,
+ num_workers=self.num_workers, shuffle=False if is_iterable_dataset else True,
+ worker_init_fn=init_fn)
+
+ def _val_dataloader(self, shuffle=False):
+ if isinstance(self.datasets['validation'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
+ init_fn = worker_init_fn
+ else:
+ init_fn = None
+ return DataLoader(self.datasets["validation"],
+ batch_size=self.batch_size,
+ num_workers=self.num_workers,
+ worker_init_fn=init_fn,
+ shuffle=shuffle)
+
+ def _test_dataloader(self, shuffle=False):
+ is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
+ if is_iterable_dataset or self.use_worker_init_fn:
+ init_fn = worker_init_fn
+ else:
+ init_fn = None
+
+ # do not shuffle dataloader for iterable dataset
+ shuffle = shuffle and (not is_iterable_dataset)
+
+ return DataLoader(self.datasets["test"], batch_size=self.batch_size,
+ num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle)
+
+ def _predict_dataloader(self, shuffle=False):
+ if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
+ init_fn = worker_init_fn
+ else:
+ init_fn = None
+ return DataLoader(self.datasets["predict"], batch_size=self.batch_size,
+ num_workers=self.num_workers, worker_init_fn=init_fn)
+
+
+class SetupCallback(Callback):
+ def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
+ super().__init__()
+ self.resume = resume
+ self.now = now
+ self.logdir = logdir
+ self.ckptdir = ckptdir
+ self.cfgdir = cfgdir
+ self.config = config
+ self.lightning_config = lightning_config
+
+ def on_keyboard_interrupt(self, trainer, pl_module):
+ if trainer.global_rank == 0:
+ print("Summoning checkpoint.")
+ ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
+ trainer.save_checkpoint(ckpt_path)
+
+ def on_pretrain_routine_start(self, trainer, pl_module):
+ if trainer.global_rank == 0:
+ # Create logdirs and save configs
+ os.makedirs(self.logdir, exist_ok=True)
+ os.makedirs(self.ckptdir, exist_ok=True)
+ os.makedirs(self.cfgdir, exist_ok=True)
+
+ if "callbacks" in self.lightning_config:
+ if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']:
+ os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)
+ print("Project config")
+ print(OmegaConf.to_yaml(self.config))
+ OmegaConf.save(self.config,
+ os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
+
+ print("Lightning config")
+ print(OmegaConf.to_yaml(self.lightning_config))
+ OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
+ os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))
+
+ else:
+ # ModelCheckpoint callback created log directory --- remove it
+ if not self.resume and os.path.exists(self.logdir):
+ dst, name = os.path.split(self.logdir)
+ dst = os.path.join(dst, "child_runs", name)
+ os.makedirs(os.path.split(dst)[0], exist_ok=True)
+ try:
+ os.rename(self.logdir, dst)
+ except FileNotFoundError:
+ pass
+
+
+class ImageLogger(Callback):
+ def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True,
+ rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
+ log_images_kwargs=None):
+ super().__init__()
+ self.rescale = rescale
+ self.batch_freq = batch_frequency
+ self.max_images = max_images
+ self.logger_log_images = {
+ pl.loggers.TestTubeLogger: self._testtube,
+ }
+ self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
+ if not increase_log_steps:
+ self.log_steps = [self.batch_freq]
+ self.clamp = clamp
+ self.disabled = disabled
+ self.log_on_batch_idx = log_on_batch_idx
+ self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
+ self.log_first_step = log_first_step
+
+ @rank_zero_only
+ def _testtube(self, pl_module, images, batch_idx, split):
+ for k in images:
+ grid = torchvision.utils.make_grid(images[k])
+ grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
+
+ tag = f"{split}/{k}"
+ pl_module.logger.experiment.add_image(
+ tag, grid,
+ global_step=pl_module.global_step)
+
+ @rank_zero_only
+ def log_local(self, save_dir, split, images,
+ global_step, current_epoch, batch_idx):
+ root = os.path.join(save_dir, "images", split)
+ for k in images:
+ grid = torchvision.utils.make_grid(images[k], nrow=4)
+ if self.rescale:
+ grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
+ grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
+ grid = grid.numpy()
+ grid = (grid * 255).astype(np.uint8)
+ filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
+ k,
+ global_step,
+ current_epoch,
+ batch_idx)
+ path = os.path.join(root, filename)
+ os.makedirs(os.path.split(path)[0], exist_ok=True)
+ Image.fromarray(grid).save(path)
+
+ def log_img(self, pl_module, batch, batch_idx, split="train"):
+ check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
+ if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0
+ hasattr(pl_module, "log_images") and
+ callable(pl_module.log_images) and
+ self.max_images > 0):
+ logger = type(pl_module.logger)
+
+ is_train = pl_module.training
+ if is_train:
+ pl_module.eval()
+
+ with torch.no_grad():
+ images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
+
+ for k in images:
+ N = min(images[k].shape[0], self.max_images)
+ images[k] = images[k][:N]
+ if isinstance(images[k], torch.Tensor):
+ images[k] = images[k].detach().cpu()
+ if self.clamp:
+ images[k] = torch.clamp(images[k], -1., 1.)
+
+ self.log_local(pl_module.logger.save_dir, split, images,
+ pl_module.global_step, pl_module.current_epoch, batch_idx)
+
+ logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
+ logger_log_images(pl_module, images, pl_module.global_step, split)
+
+ if is_train:
+ pl_module.train()
+
+ def check_frequency(self, check_idx):
+ if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
+ check_idx > 0 or self.log_first_step):
+ try:
+ self.log_steps.pop(0)
+ except IndexError as e:
+ print(e)
+ pass
+ return True
+ return False
+
+ def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+ if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
+ self.log_img(pl_module, batch, batch_idx, split="train")
+
+ def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+ # if not self.disabled and pl_module.global_step > 0:
+ # self.log_img(pl_module, batch, batch_idx, split="val")
+ if hasattr(pl_module, 'calibrate_grad_norm'):
+ if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
+ self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
+
+
+class CUDACallback(Callback):
+ # see https://github.com/SeanNaren/minGPT/blob/master/mingpt/callback.py
+ def on_train_epoch_start(self, trainer, pl_module):
+ # Reset the memory use counter
+ torch.cuda.reset_peak_memory_stats(trainer.root_gpu)
+ torch.cuda.synchronize(trainer.root_gpu)
+ self.start_time = time.time()
+
+ def on_train_epoch_end(self, trainer, pl_module, outputs):
+ torch.cuda.synchronize(trainer.root_gpu)
+ max_memory = torch.cuda.max_memory_allocated(trainer.root_gpu) / 2 ** 20
+ epoch_time = time.time() - self.start_time
+
+ try:
+ max_memory = trainer.training_type_plugin.reduce(max_memory)
+ epoch_time = trainer.training_type_plugin.reduce(epoch_time)
+
+ rank_zero_info(f"Average Epoch time: {epoch_time:.2f} seconds")
+ rank_zero_info(f"Average Peak memory {max_memory:.2f}MiB")
+ except AttributeError:
+ pass
+
+
+if __name__ == "__main__":
+ # custom parser to specify config files, train, test and debug mode,
+ # postfix, resume.
+ # `--key value` arguments are interpreted as arguments to the trainer.
+ # `nested.key=value` arguments are interpreted as config parameters.
+ # configs are merged from left-to-right followed by command line parameters.
+
+ # model:
+ # base_learning_rate: float
+ # target: path to lightning module
+ # params:
+ # key: value
+ # data:
+ # target: main.DataModuleFromConfig
+ # params:
+ # batch_size: int
+ # wrap: bool
+ # train:
+ # target: path to train dataset
+ # params:
+ # key: value
+ # validation:
+ # target: path to validation dataset
+ # params:
+ # key: value
+ # test:
+ # target: path to test dataset
+ # params:
+ # key: value
+ # lightning: (optional, has sane defaults and can be specified on cmdline)
+ # trainer:
+ # additional arguments to trainer
+ # logger:
+ # logger to instantiate
+ # modelcheckpoint:
+ # modelcheckpoint to instantiate
+ # callbacks:
+ # callback1:
+ # target: importpath
+ # params:
+ # key: value
+
+ now = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+
+ # add cwd for convenience and to make classes in this file available when
+ # running as `python main.py`
+ # (in particular `main.DataModuleFromConfig`)
+ sys.path.append(os.getcwd())
+
+ parser = get_parser()
+ parser = Trainer.add_argparse_args(parser)
+
+ opt, unknown = parser.parse_known_args()
+ if opt.name and opt.resume:
+ raise ValueError(
+ "-n/--name and -r/--resume cannot be specified both."
+ "If you want to resume training in a new log folder, "
+ "use -n/--name in combination with --resume_from_checkpoint"
+ )
+ if opt.resume:
+ # if not os.path.exists(opt.resume):
+ # raise ValueError("Cannot find {}".format(opt.resume))
+ if os.path.isfile(opt.resume):
+ paths = opt.resume.split("/")
+ # idx = len(paths)-paths[::-1].index("logs")+1
+ # logdir = "/".join(paths[:idx])
+ logdir = "/".join(paths[:-2])
+ ckpt = opt.resume
+ else:
+ # assert os.path.isdir(opt.resume), opt.resume
+ logdir = opt.resume.rstrip("/")
+ ckpt = os.path.join(logdir, "checkpoints", "last.ckpt")
+
+ if os.path.exists(ckpt):
+ opt.resume_from_checkpoint = ckpt
+ base_configs = sorted(glob.glob(os.path.join(logdir, "configs/*.yaml")))
+ opt.base = base_configs + opt.base
+ _tmp = logdir.split("/")
+ nowname = _tmp[-1]
+ else:
+ if opt.name:
+ name = opt.name
+ elif opt.base:
+ cfg_fname = os.path.split(opt.base[0])[-1]
+ cfg_name = os.path.splitext(cfg_fname)[0]
+ name = cfg_name
+ else:
+ name = ""
+ nowname = name + opt.postfix
+ logdir = os.path.join(opt.logdir, nowname)
+ else:
+ if opt.name:
+ # name = "_" + opt.name
+ name = opt.name
+ elif opt.base:
+ cfg_fname = os.path.split(opt.base[0])[-1]
+ cfg_name = os.path.splitext(cfg_fname)[0]
+ # name = "_" + cfg_name
+ name = cfg_name
+ else:
+ name = ""
+ # nowname = now + name + opt.postfix
+ nowname = name + opt.postfix
+ logdir = os.path.join(opt.logdir, nowname)
+
+ ckptdir = os.path.join(logdir, "checkpoints")
+ cfgdir = os.path.join(logdir, "configs")
+ seed_everything(opt.seed)
+
+ try:
+ # init and save configs
+ configs = [OmegaConf.load(cfg) for cfg in opt.base]
+ cli = OmegaConf.from_dotlist(unknown)
+ config = OmegaConf.merge(*configs, cli)
+ lightning_config = config.pop("lightning", OmegaConf.create())
+ # merge trainer cli with config
+ trainer_config = lightning_config.get("trainer", OmegaConf.create())
+ # default to ddp
+ trainer_config["accelerator"] = "ddp"
+ for k in nondefault_trainer_args(opt):
+ trainer_config[k] = getattr(opt, k)
+ if not "gpus" in trainer_config:
+ del trainer_config["accelerator"]
+ cpu = True
+ else:
+ gpuinfo = trainer_config["gpus"]
+ print(f"Running on GPUs {gpuinfo}")
+ cpu = False
+ trainer_opt = argparse.Namespace(**trainer_config)
+ lightning_config.trainer = trainer_config
+
+ # model
+ model = instantiate_from_config(config.model)
+
+ # trainer and callbacks
+ trainer_kwargs = dict()
+
+ # default logger configs
+ default_logger_cfgs = {
+ "wandb": {
+ "target": "pytorch_lightning.loggers.WandbLogger",
+ "params": {
+ "name": nowname,
+ "save_dir": logdir,
+ "offline": opt.debug,
+ "id": nowname,
+ }
+ },
+ "testtube": {
+ "target": "pytorch_lightning.loggers.TestTubeLogger",
+ "params": {
+ "name": "testtube",
+ "save_dir": logdir,
+ }
+ },
+ }
+ default_logger_cfg = default_logger_cfgs["testtube"]
+ if "logger" in lightning_config:
+ logger_cfg = lightning_config.logger
+ else:
+ logger_cfg = OmegaConf.create()
+ logger_cfg = OmegaConf.merge(default_logger_cfg, logger_cfg)
+ trainer_kwargs["logger"] = instantiate_from_config(logger_cfg)
+
+ # modelcheckpoint - use TrainResult/EvalResult(checkpoint_on=metric) to
+ # specify which metric is used to determine best models
+ default_modelckpt_cfg = {
+ "target": "pytorch_lightning.callbacks.ModelCheckpoint",
+ "params": {
+ "dirpath": ckptdir,
+ "filename": "{epoch:06}",
+ "verbose": True,
+ "save_last": True,
+ }
+ }
+ if hasattr(model, "monitor"):
+ print(f"Monitoring {model.monitor} as checkpoint metric.")
+ default_modelckpt_cfg["params"]["monitor"] = model.monitor
+ default_modelckpt_cfg["params"]["save_top_k"] = 3
+
+ if "modelcheckpoint" in lightning_config:
+ modelckpt_cfg = lightning_config.modelcheckpoint
+ else:
+ modelckpt_cfg = OmegaConf.create()
+ modelckpt_cfg = OmegaConf.merge(default_modelckpt_cfg, modelckpt_cfg)
+ print(f"Merged modelckpt-cfg: \n{modelckpt_cfg}")
+ if version.parse(pl.__version__) < version.parse('1.4.0'):
+ trainer_kwargs["checkpoint_callback"] = instantiate_from_config(modelckpt_cfg)
+
+ # add callback which sets up log directory
+ default_callbacks_cfg = {
+ "setup_callback": {
+ "target": "main.SetupCallback",
+ "params": {
+ "resume": opt.resume,
+ "now": now,
+ "logdir": logdir,
+ "ckptdir": ckptdir,
+ "cfgdir": cfgdir,
+ "config": config,
+ "lightning_config": lightning_config,
+ }
+ },
+ "image_logger": {
+ "target": "main.ImageLogger",
+ "params": {
+ "batch_frequency": 750,
+ "max_images": 4,
+ "clamp": True
+ }
+ },
+ "learning_rate_logger": {
+ "target": "main.LearningRateMonitor",
+ "params": {
+ "logging_interval": "step",
+ # "log_momentum": True
+ }
+ },
+ "cuda_callback": {
+ "target": "main.CUDACallback"
+ },
+ }
+ if version.parse(pl.__version__) >= version.parse('1.4.0'):
+ default_callbacks_cfg.update({'checkpoint_callback': modelckpt_cfg})
+
+ if "callbacks" in lightning_config:
+ callbacks_cfg = lightning_config.callbacks
+ else:
+ callbacks_cfg = OmegaConf.create()
+
+ if 'metrics_over_trainsteps_checkpoint' in callbacks_cfg:
+ print(
+ 'Caution: Saving checkpoints every n train steps without deleting. This might require some free space.')
+ default_metrics_over_trainsteps_ckpt_dict = {
+ 'metrics_over_trainsteps_checkpoint':
+ {"target": 'pytorch_lightning.callbacks.ModelCheckpoint',
+ 'params': {
+ "dirpath": os.path.join(ckptdir, 'trainstep_checkpoints'),
+ "filename": "{epoch:06}-{step:09}",
+ "verbose": True,
+ 'save_top_k': -1,
+ 'every_n_train_steps': 10000,
+ 'save_weights_only': True
+ }
+ }
+ }
+ default_callbacks_cfg.update(default_metrics_over_trainsteps_ckpt_dict)
+
+ callbacks_cfg = OmegaConf.merge(default_callbacks_cfg, callbacks_cfg)
+ if 'ignore_keys_callback' in callbacks_cfg and hasattr(trainer_opt, 'resume_from_checkpoint'):
+ callbacks_cfg.ignore_keys_callback.params['ckpt_path'] = trainer_opt.resume_from_checkpoint
+ elif 'ignore_keys_callback' in callbacks_cfg:
+ del callbacks_cfg['ignore_keys_callback']
+
+ trainer_kwargs["callbacks"] = [instantiate_from_config(callbacks_cfg[k]) for k in callbacks_cfg]
+
+ trainer = Trainer.from_argparse_args(trainer_opt, **trainer_kwargs)
+ trainer.logdir = logdir ###
+
+ # data
+ data = instantiate_from_config(config.data)
+ # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
+ # calling these ourselves should not be necessary but it is.
+ # lightning still takes care of proper multiprocessing though
+ # breakpoint()
+ data.prepare_data()
+ data.setup()
+
+ # breakpoint()
+ # CTReportDataset(data_folder='train', csv_file='/home/v-qichen3/blob/ct_rate/CT-RATE/radiology_text_reports/train_reports.csv')
+
+ print("#### Data #####")
+ for k in data.datasets:
+ print(f"{k}, {data.datasets[k].__class__.__name__}, {len(data.datasets[k])}")
+
+ # configure learning rate
+ bs, base_lr = config.data.params.batch_size, config.model.base_learning_rate
+ # breakpoint()
+ if not cpu:
+ ngpu = len(lightning_config.trainer.gpus.strip(",").split(','))
+ else:
+ ngpu = 1
+ if 'accumulate_grad_batches' in lightning_config.trainer:
+ accumulate_grad_batches = lightning_config.trainer.accumulate_grad_batches
+ else:
+ accumulate_grad_batches = 1
+ print(f"accumulate_grad_batches = {accumulate_grad_batches}")
+ lightning_config.trainer.accumulate_grad_batches = accumulate_grad_batches
+ if opt.scale_lr:
+ model.learning_rate = accumulate_grad_batches * ngpu * bs * base_lr
+ print(
+ "Setting learning rate to {:.2e} = {} (accumulate_grad_batches) * {} (num_gpus) * {} (batchsize) * {:.2e} (base_lr)".format(
+ model.learning_rate, accumulate_grad_batches, ngpu, bs, base_lr))
+ else:
+ model.learning_rate = base_lr
+ print("++++ NOT USING LR SCALING ++++")
+ print(f"Setting learning rate to {model.learning_rate:.2e}")
+
+
+ # allow checkpointing via USR1
+ def melk(*args, **kwargs):
+ # run all checkpoint hooks
+ if trainer.global_rank == 0:
+ print("Summoning checkpoint.")
+ ckpt_path = os.path.join(ckptdir, "last.ckpt")
+ trainer.save_checkpoint(ckpt_path)
+
+
+ def divein(*args, **kwargs):
+ if trainer.global_rank == 0:
+ import pudb;
+ pudb.set_trace()
+
+
+ import signal
+
+ signal.signal(signal.SIGUSR1, melk)
+ signal.signal(signal.SIGUSR2, divein)
+
+ # run
+ if opt.train:
+ try:
+ trainer.fit(model, data)
+ except Exception:
+ melk()
+ raise
+ if not opt.no_test and not trainer.interrupted:
+ trainer.test(model, data)
+ except Exception:
+ if opt.debug and trainer.global_rank == 0:
+ try:
+ import pudb as debugger
+ except ImportError:
+ import pdb as debugger
+ debugger.post_mortem()
+ raise
+ finally:
+ # move newly created debug project to debug_runs
+ if opt.debug and not opt.resume and trainer.global_rank == 0:
+ dst, name = os.path.split(logdir)
+ dst = os.path.join(dst, "debug_runs", name)
+ os.makedirs(os.path.split(dst)[0], exist_ok=True)
+ os.rename(logdir, dst)
+ if trainer.global_rank == 0:
+ print(trainer.profiler.summary())
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/mask_generation.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/mask_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..982545ca730f3ef8f7d0373e7b9a44c7fefb887a
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/mask_generation.py
@@ -0,0 +1,292 @@
+### Tumor Generateion
+import random
+import cv2
+import elasticdeform
+import numpy as np
+from scipy.ndimage import gaussian_filter
+
+
+def generate_prob_function(mask_shape):
+ sigma = np.random.uniform(3,15)
+ # uniform noise generate
+ a = np.random.uniform(0, 1, size=(mask_shape[0],mask_shape[1],mask_shape[2]))
+
+ # Gaussian filter
+ # this taks some time
+ a_2 = gaussian_filter(a, sigma=sigma)
+
+ scale = np.random.uniform(0.19, 0.21)
+ base = np.random.uniform(0.04, 0.06)
+ a = scale * (a_2 - np.min(a_2)) / (np.max(a_2) - np.min(a_2)) + base
+
+ return a
+
+# Step 1: Random select (numbers) location for tumor.
+def random_select(mask_scan):
+ # we first find z index and then sample point with z slice
+ z_start, z_end = np.where(np.any(mask_scan, axis=(0, 1)))[0][[0, -1]]
+
+ # we need to strict number z's position (0.3 - 0.7 in the middle of liver)
+ z = round(random.uniform(0.3, 0.7) * (z_end - z_start)) + z_start
+
+ liver_mask = mask_scan[..., z]
+
+ # erode the mask (we don't want the edge points)
+ kernel = np.ones((5,5), dtype=np.uint8)
+ liver_mask = cv2.erode(liver_mask, kernel, iterations=1)
+
+ coordinates = np.argwhere(liver_mask == 1)
+ random_index = np.random.randint(0, len(coordinates))
+ xyz = coordinates[random_index].tolist() # get x,y
+ xyz.append(z)
+ potential_points = xyz
+
+ return potential_points
+
+# Step 2 : generate the ellipsoid
+def get_ellipsoid(x, y, z):
+ """"
+ x, y, z is the radius of this ellipsoid in x, y, z direction respectly.
+ """
+ sh = (4*x, 4*y, 4*z)
+ out = np.zeros(sh, int)
+ aux = np.zeros(sh)
+ radii = np.array([x, y, z])
+ com = np.array([2*x, 2*y, 2*z]) # center point
+
+ # calculate the ellipsoid
+ bboxl = np.floor(com-radii).clip(0,None).astype(int)
+ bboxh = (np.ceil(com+radii)+1).clip(None, sh).astype(int)
+ roi = out[tuple(map(slice,bboxl,bboxh))]
+ roiaux = aux[tuple(map(slice,bboxl,bboxh))]
+ logrid = *map(np.square,np.ogrid[tuple(
+ map(slice,(bboxl-com)/radii,(bboxh-com-1)/radii,1j*(bboxh-bboxl)))]),
+ dst = (1-sum(logrid)).clip(0,None)
+ mask = dst>roiaux
+ roi[mask] = 1
+ np.copyto(roiaux,dst,where=mask)
+
+ return out
+
+def get_fixed_geo(mask_scan, tumor_type):
+
+ enlarge_x, enlarge_y, enlarge_z = 160, 160, 160
+ geo_mask = np.zeros((mask_scan.shape[0] + enlarge_x, mask_scan.shape[1] + enlarge_y, mask_scan.shape[2] + enlarge_z), dtype=np.int8)
+ # texture_map = np.zeros((mask_scan.shape[0] + enlarge_x, mask_scan.shape[1] + enlarge_y, mask_scan.shape[2] + enlarge_z), dtype=np.float16)
+ tiny_radius, small_radius, medium_radius, large_radius = 4, 8, 16, 32
+
+ if tumor_type == 'tiny':
+ num_tumor = random.randint(3,10)
+ for _ in range(num_tumor):
+ # Tiny tumor
+ x = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
+ y = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
+ z = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
+ sigma = random.uniform(0.5,1)
+
+ geo = get_ellipsoid(x, y, z)
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
+ point = random_select(mask_scan)
+ new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
+ x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
+ y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
+ z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
+
+ # paste small tumor geo into test sample
+ geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
+
+ if tumor_type == 'small':
+ num_tumor = random.randint(3,10)
+ for _ in range(num_tumor):
+ # Small tumor
+ x = random.randint(int(0.75*small_radius), int(1.25*small_radius))
+ y = random.randint(int(0.75*small_radius), int(1.25*small_radius))
+ z = random.randint(int(0.75*small_radius), int(1.25*small_radius))
+ sigma = random.randint(1, 2)
+
+ geo = get_ellipsoid(x, y, z)
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
+ # texture = get_texture((4*x, 4*y, 4*z))
+ point = random_select(mask_scan)
+ new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
+ x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
+ y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
+ z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
+
+ # paste small tumor geo into test sample
+ geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
+ # texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
+
+ if tumor_type == 'medium':
+ num_tumor = random.randint(2, 5)
+ for _ in range(num_tumor):
+ # medium tumor
+ x = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
+ y = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
+ z = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
+ sigma = random.randint(3, 6)
+
+ geo = get_ellipsoid(x, y, z)
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
+ # texture = get_texture((4*x, 4*y, 4*z))
+ point = random_select(mask_scan)
+ new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
+ x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
+ y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
+ z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
+
+ # paste medium tumor geo into test sample
+ geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
+ # texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
+
+ if tumor_type == 'large':
+ num_tumor = random.randint(1,3)
+ for _ in range(num_tumor):
+ # Large tumor
+ x = random.randint(int(0.75*large_radius), int(1.25*large_radius))
+ y = random.randint(int(0.75*large_radius), int(1.25*large_radius))
+ z = random.randint(int(0.75*large_radius), int(1.25*large_radius))
+ sigma = random.randint(5, 10)
+
+ geo = get_ellipsoid(x, y, z)
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
+ # texture = get_texture((4*x, 4*y, 4*z))
+ point = random_select(mask_scan)
+ new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
+ x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
+ y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
+ z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
+
+ # paste small tumor geo into test sample
+ geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
+ # texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
+
+ if tumor_type == "mix":
+ # tiny
+ num_tumor = random.randint(3,10)
+ for _ in range(num_tumor):
+ # Tiny tumor
+ x = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
+ y = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
+ z = random.randint(int(0.75*tiny_radius), int(1.25*tiny_radius))
+ sigma = random.uniform(0.5,1)
+
+ geo = get_ellipsoid(x, y, z)
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
+ point = random_select(mask_scan)
+ new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
+ x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
+ y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
+ z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
+
+ # paste small tumor geo into test sample
+ geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
+
+ # small
+ num_tumor = random.randint(5,10)
+ for _ in range(num_tumor):
+ # Small tumor
+ x = random.randint(int(0.75*small_radius), int(1.25*small_radius))
+ y = random.randint(int(0.75*small_radius), int(1.25*small_radius))
+ z = random.randint(int(0.75*small_radius), int(1.25*small_radius))
+ sigma = random.randint(1, 2)
+
+ geo = get_ellipsoid(x, y, z)
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
+ # texture = get_texture((4*x, 4*y, 4*z))
+ point = random_select(mask_scan)
+ new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
+ x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
+ y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
+ z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
+
+ # paste small tumor geo into test sample
+ geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
+ # texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
+
+ # medium
+ num_tumor = random.randint(2, 5)
+ for _ in range(num_tumor):
+ # medium tumor
+ x = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
+ y = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
+ z = random.randint(int(0.75*medium_radius), int(1.25*medium_radius))
+ sigma = random.randint(3, 6)
+
+ geo = get_ellipsoid(x, y, z)
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
+ # texture = get_texture((4*x, 4*y, 4*z))
+ point = random_select(mask_scan)
+ new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
+ x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
+ y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
+ z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
+
+ # paste medium tumor geo into test sample
+ geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
+ # texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
+
+ # large
+ num_tumor = random.randint(1,3)
+ for _ in range(num_tumor):
+ # Large tumor
+ x = random.randint(int(0.75*large_radius), int(1.25*large_radius))
+ y = random.randint(int(0.75*large_radius), int(1.25*large_radius))
+ z = random.randint(int(0.75*large_radius), int(1.25*large_radius))
+ sigma = random.randint(5, 10)
+ geo = get_ellipsoid(x, y, z)
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,1))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(1,2))
+ geo = elasticdeform.deform_random_grid(geo, sigma=sigma, points=3, order=0, axis=(0,2))
+ # texture = get_texture((4*x, 4*y, 4*z))
+ point = random_select(mask_scan)
+ new_point = [point[0] + enlarge_x//2, point[1] + enlarge_y//2, point[2] + enlarge_z//2]
+ x_low, x_high = new_point[0] - geo.shape[0]//2, new_point[0] + geo.shape[0]//2
+ y_low, y_high = new_point[1] - geo.shape[1]//2, new_point[1] + geo.shape[1]//2
+ z_low, z_high = new_point[2] - geo.shape[2]//2, new_point[2] + geo.shape[2]//2
+
+ # paste small tumor geo into test sample
+ geo_mask[x_low:x_high, y_low:y_high, z_low:z_high] += geo
+ # texture_map[x_low:x_high, y_low:y_high, z_low:z_high] = texture
+
+ geo_mask = geo_mask[enlarge_x//2:-enlarge_x//2, enlarge_y//2:-enlarge_y//2, enlarge_z//2:-enlarge_z//2]
+ # texture_map = texture_map[enlarge_x//2:-enlarge_x//2, enlarge_y//2:-enlarge_y//2, enlarge_z//2:-enlarge_z//2]
+ geo_mask = (geo_mask * mask_scan) >=1
+
+ return geo_mask
+
+
+def SynthesisTumor(mask_scan, tumor_type, texture):
+ # for speed_generate_tumor, we only send the liver part into the generate program
+ x_start, x_end = np.where(np.any(mask_scan, axis=(1, 2)))[0][[0, -1]]
+ y_start, y_end = np.where(np.any(mask_scan, axis=(0, 2)))[0][[0, -1]]
+ z_start, z_end = np.where(np.any(mask_scan, axis=(0, 1)))[0][[0, -1]]
+
+ # shrink the boundary
+ x_start, x_end = max(0, x_start+1), min(mask_scan.shape[0], x_end-1)
+ y_start, y_end = max(0, y_start+1), min(mask_scan.shape[1], y_end-1)
+ z_start, z_end = max(0, z_start+1), min(mask_scan.shape[2], z_end-1)
+
+ liver_mask = get_fixed_geo(mask_scan, tumor_type)
+
+ mask_scan[x_start:x_end, y_start:y_end, z_start:z_end] = liver_mask
+
+ return mask_scan
+
+if __name__ == 'main':
+ geo_mask = get_fixed_geo(mask_scan, tumor_type)
+
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/mask_generation_pipeline.py b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/mask_generation_pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..79ec5941dea8f6d790313e38918b79e02bc3597b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/mask_generation_pipeline.py
@@ -0,0 +1,127 @@
+import glob
+import nibabel as nib
+import numpy as np
+import os
+import torch
+
+from einops import rearrange
+from omegaconf import OmegaConf
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from ldm.util import instantiate_from_config
+import argparse
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+ "-s",
+ "--save_path",
+ type=str,
+ default=None,
+)
+
+args = parser.parse_args()
+ddim_steps=args.time_steps
+# breakpoint()
+
+config = OmegaConf.load('./configs/latent-diffusion/mask_generation.yaml')
+data = instantiate_from_config(config.data)
+data.prepare_data()
+data.setup()
+
+
+save_path = args.save_path
+if not os.path.exists(save_path):
+ os.makedirs(save_path)
+
+val_dataset = data.datasets['validation']
+batch_size = 1
+valloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2, pin_memory=True)
+val_num = len(val_dataset)
+save_gt = True
+
+for idx, data in tqdm(enumerate(valloader)):
+
+ if idx >= val_num:
+ break
+
+ name = data['name'][0]
+ volume_data = data['volume_data']
+
+
+ window_length = 16
+ h = 1
+ slice_num =volume_data.shape[1]
+ result = torch.zeros((batch_size, slice_num, 4, 64, 64)).cuda()
+
+
+ upper_iters = (slice_num-h) // (window_length-h)+1 if (slice_num-h)%(window_length-h) != 0 else (slice_num-h) // (window_length-h)
+ print('upper_iters', upper_iters)
+ # breakpoint()
+ for i in range(upper_iters):
+ print('i', i)
+ input_data={}
+ if i == upper_iters-1:
+ input_data['name'] = data['name']
+ input_data['volume_data'] = data['volume_data'][:,-window_length:].to(device)
+ input_data['masked_data'] = data['masked_data'][:,-window_length:].to(device)
+ input_data['tumor_mask'] = data['tumor_mask'][:,-window_length:].to(device)
+ else:
+ input_data['volume_data'] = data['volume_data'][:, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['masked_data'] = data['masked_data'][:, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+ input_data['tumor_mask'] = data['tumor_mask'][:, i*window_length-i*h:(i+1)*window_length-i*h].to(device)
+
+ with torch.no_grad():
+ _, c = model.get_input(input_data, model.first_stage_key)
+
+ if i == 0:
+ samples_i, _ = model.sample_log(cond=c, batch_size=window_length, ddim=True, eta=1., ddim_steps=ddim_steps)
+ else:
+ samples_i, _ = model.sample_log(cond=c, batch_size=window_length, ddim=True, eta=1., ddim_steps=ddim_steps, previous=x_minus1)
+ # breakpoint()
+ samples_i = rearrange(samples_i, '(b z) c h w -> b z c h w', z=window_length)
+
+ if i == upper_iters-1:
+ result[:, -window_length+h:] = samples_i[:,h:,...]
+ else:
+ if i == 0:
+ result[:, :window_length] = samples_i
+ else:
+ result[:, i*window_length-i*h+h:(i+1)*window_length-i*h] = samples_i[:, h:]
+ x_minus1 = samples_i[:, -h:,...]
+ # breakpoint()
+ result = rearrange(result, 'b z c h w -> (b z) c h w')
+ x_result = torch.zeros((result.shape[0],3,512,512))
+ # breakpoint()
+ dec_unit = 16
+ num_dec_iter = slice_num // dec_unit + 1 if slice_num % dec_unit != 0 else slice_num // dec_unit
+ for i in range(num_dec_iter):
+ if i == num_dec_iter - 1:
+ x_result[-dec_unit:] = model.decode_first_stage(result[-dec_unit:])
+ x_result[i*dec_unit:(i+1)*dec_unit] = model.decode_first_stage(result[i*dec_unit:(i+1)*dec_unit])
+ x_result[x_result>1.0] = 1.0
+ x_result[x_result<-1.0] = -1.0
+ x_result = (x_result+1)/2
+ x_result = rearrange(x_result, '(b z) c h w -> b z c h w', z=slice_num)
+ x_result_ = x_result[0].mean(axis=1).detach().cpu().numpy()
+ # x_result = x_result[0,:,0,...].detach().cpu().numpy()
+
+ x_result = x_result_.transpose(2,1,0)
+ # x_result = np.rot90(x_result, k=1, axes=(0,1))
+ # x_result = np.flip(x_result,axis=(0,1))
+ # import imageio as io
+ # io.imsave('exp.png', (x_result[:,:,400]*255).astype(np.uint8))
+
+ # breakpoint()
+ ref_root = '/storage/chenqi/data/CT/Task03_Liver/imagesTr'
+ ref_nii = os.path.join(ref_root, name+'.nii.gz')
+ affine = nib.load(ref_nii).affine
+
+ x_result = x_result*425.0 - 175.0
+ data_path = os.path.join(save_path, str(f'{name}.nii.gz'))
+ data_nii = nib.Nifti1Image(x_result.astype(np.int16), affine)
+
+ nib.save(data_nii, data_path)
+
+ # breakpoint()
+
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/misc/overview.jpg b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/misc/overview.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8f470dcf462f73d7e9d9d0c5af065b006e01f43b
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/misc/overview.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d22a8ed27837d92fc7a7583cbf898c6442ab127dc914cb9d02dd0cfa05ff7802
+size 122928
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/recon_test.txt b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/recon_test.txt
new file mode 100644
index 0000000000000000000000000000000000000000..e5645cc3c82b8252fa9c75c20f6227c10e8ed8ac
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/recon_test.txt
@@ -0,0 +1,5 @@
+kl-f8
+Test average MSE: 229.57958816063248 average PSNR: 25.01263807051269 average SSIM: 0.7587649705198748
+
+kl-f4
+Test average MSE: 229.5796621685892 average PSNR: 25.01263423093404 average SSIM: 0.7587650323867727
\ No newline at end of file
diff --git a/GEM-3D-ct-text4-newdata-newvae-retrain-disease/requirements.txt b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..432c8e6ef057b8d911781b09493f63e5d2f8e0ce
--- /dev/null
+++ b/GEM-3D-ct-text4-newdata-newvae-retrain-disease/requirements.txt
@@ -0,0 +1,101 @@
+absl-py==2.3.0
+aiohappyeyeballs==2.6.1
+aiohttp==3.12.12
+aiosignal==1.3.2
+antlr4-python3-runtime==4.8
+async-timeout==5.0.1
+attrs==25.3.0
+blosc2==2.5.1
+Brotli==1.0.9
+cachetools==5.5.2
+certifi==2025.4.26
+charset-normalizer==3.3.2
+contourpy==1.3.0
+cycler==0.12.1
+easydict==1.11
+einops==0.7.0
+filelock==3.17.0
+fonttools==4.58.2
+frozenlist==1.7.0
+fsspec==2025.5.1
+ftfy==6.3.1
+future==1.0.0
+gmpy2==2.2.1
+google-auth==2.40.3
+google-auth-oauthlib==1.2.2
+grpcio==1.73.0
+hf-xet==1.1.3
+huggingface-hub==0.33.0
+idna==3.7
+imageio==2.31.6
+importlib_metadata==8.7.0
+importlib_resources==6.5.2
+Jinja2==3.1.6
+joblib==1.5.1
+kiwisolver==1.4.7
+lazy_loader==0.4
+lightning-utilities==0.9.0
+lpips==0.1.4
+Markdown==3.8
+MarkupSafe==3.0.2
+matplotlib==3.9.4
+mkl_fft==1.3.11
+mkl_random==1.2.8
+mkl-service==2.4.1
+mpmath==1.3.0
+msgpack==1.1.0
+multidict==6.4.4
+ndindex==1.10.0
+networkx==3.2.1
+nibabel==5.2.0
+numpy
+oauthlib==3.2.2
+omegaconf==2.1.1
+open-clip-torch==2.7.0
+opencv-python==4.8.1.78
+packaging==25.0
+pandas==2.1.2
+Pillow==10.0.1
+pip==22.3.1
+propcache==0.3.2
+protobuf==4.23.4
+py-cpuinfo==9.0.0
+pyasn1==0.6.1
+pyasn1_modules==0.4.2
+pyDeprecate==0.3.1
+pyparsing==3.1.1
+PySocks==1.7.1
+python-dateutil==2.9.0.post0
+pytorch-lightning==1.4.2
+pytz==2025.2
+PyYAML==6.0.2
+regex==2024.11.6
+requests==2.32.3
+requests-oauthlib==2.0.0
+rsa==4.9.1
+scikit-image==0.22.0
+scikit-learn==1.3.2
+scipy==1.11.3
+seaborn==0.13.0
+setuptools==78.1.1
+six==1.17.0
+sympy==1.13.3
+tensorboard==2.15.1
+tensorboard-data-server==0.7.2
+tensorboardX==2.6.2.2
+test_tube==0.7.5
+threadpoolctl==3.6.0
+tifffile==2024.8.30
+tokenizers==0.13.3
+tqdm==4.66.1
+transformers==4.29.2
+triton==2.1.0
+typing_extensions==4.12.2
+tzdata==2025.2
+urllib3==2.3.0
+volumentations-3D==1.0.4
+wcwidth==0.2.13
+Werkzeug==3.1.3
+wheel==0.45.1
+yarl==1.20.1
+zipp==3.23.0