a5ded997d5ba19dff88502b0b661e3852bf9f38189640111811aedd47dc37068

Generation_Pipeline_filter_all2/Atlas_X_1k.txt

@@ -0,0 +1,956 @@
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Generation_Pipeline_filter_all2/best_metric_model_classification3d_dict.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c435f0e8efa89f02ac5fb0bd1092608332f2de6f8f7aabbe17fcc6d1a83d35c6
+size 45594067

Generation_Pipeline_filter_all2/get_syn_list.py

@@ -0,0 +1,26 @@
+import os
+
+organ = 'colon'
+real_organ = []
+with open(f'real_set/{organ}.txt', 'r') as f:
+    real_organ=f.readlines()
+real_organ = [i.split('\n')[0] for i in real_organ]
+
+
+total_case = []
+with open(f'real_total.txt', 'r') as f:
+    total_case=f.readlines()
+total_case = [i.split('\n')[0] for i in total_case]
+
+
+absence2= list(set(total_case) - set(real_organ))
+absence2 = [i for i in absence2]
+# breakpoint()
+
+filename = open(f'syn_{organ}/healthy_{organ}_1k.txt','a+')#dict转txt
+for i in absence2:
+    filename.write(i)
+    filename.write('\n')
+filename.close()
+
+

Generation_Pipeline_filter_all2/get_training_list.py

@@ -0,0 +1,45 @@
+import os
+
+total_case = []
+with open(f'real_total.txt', 'r') as f:
+    total_case=f.readlines()
+total_case = [i.split('\n')[0] for i in total_case]
+
+
+real_organ = []
+with open(f'val_set/bodymap_liver.txt', 'r') as f:
+    real_organ=f.readlines()
+real_organ = [i.split('\n')[0] for i in real_organ]
+total_case= list(set(total_case) - set(real_organ))
+total_case = [i for i in total_case]
+
+real_organ = []
+with open(f'val_set/bodymap_pancreas.txt', 'r') as f:
+    real_organ=f.readlines()
+real_organ = [i.split('\n')[0] for i in real_organ]
+total_case= list(set(total_case) - set(real_organ))
+total_case = [i for i in total_case]
+
+real_organ = []
+with open(f'val_set/bodymap_kidney.txt', 'r') as f:
+    real_organ=f.readlines()
+real_organ = [i.split('\n')[0] for i in real_organ]
+total_case= list(set(total_case) - set(real_organ))
+total_case = [i for i in total_case]
+
+real_organ = []
+with open(f'val_set/bodymap_colon.txt', 'r') as f:
+    real_organ=f.readlines()
+real_organ = [i.split('\n')[0] for i in real_organ]
+total_case= list(set(total_case) - set(real_organ))
+total_case = [i for i in total_case]
+
+
+
+filename = open(f'Atlas_X_1k.txt','a+')#dict转txt
+for i in total_case:
+    filename.write(i)
+    filename.write('\n')
+filename.close()
+
+

Generation_Pipeline_filter_all2/real_set/colon.txt

@@ -0,0 +1,126 @@
+BDMAP_00001078
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Generation_Pipeline_filter_all2/real_set/kidney.txt

@@ -0,0 +1,489 @@
+BDMAP_00000245
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Generation_Pipeline_filter_all2/real_set/liver.txt

@@ -0,0 +1,159 @@
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Generation_Pipeline_filter_all2/real_set/pancreas.txt

@@ -0,0 +1,281 @@
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Generation_Pipeline_filter_all2/real_total.txt

@@ -0,0 +1,1054 @@
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Generation_Pipeline_filter_all2/resample.py

@@ -0,0 +1,120 @@
+import os, time, csv
+import numpy as np
+import torch
+from sklearn.metrics import confusion_matrix
+from scipy import ndimage
+from scipy.ndimage import label
+from functools import partial
+import monai
+from monai.transforms import AsDiscrete,AsDiscreted,Compose,Invertd,SaveImaged
+from monai import transforms, data
+import nibabel as nib
+
+import warnings
+warnings.filterwarnings("ignore")
+
+import argparse
+parser = argparse.ArgumentParser(description='colon tumor validation')
+
+# file dir
+parser.add_argument('--data_root', default=None, type=str)
+parser.add_argument('--organ_type', default='colon', type=str)
+parser.add_argument('--save_dir', default='out', type=str)
+parser.add_argument('--data_file', default='out', type=str)
+parser.add_argument('--ddim_ts', default=50, type=int)
+parser.add_argument('--fg_thresh', default=30, type=int)
+parser.add_argument('--start', default=0, type=int)
+parser.add_argument('--end', default=1000, type=int)
+
+def voxel2R(A):
+    return (np.array(A)/4*3/np.pi)**(1/3)
+    
+def _get_loader(args):
+    # val_data_dir = args.val_dir
+    # datalist_json = args.json_dir 
+    val_org_transform = transforms.Compose(
+        [
+            transforms.LoadImaged(keys=["image"], dtype=np.int16),
+            transforms.AddChanneld(keys=["image"]),
+            transforms.Orientationd(keys=["image"], axcodes="RAS"),
+            transforms.Spacingd(keys=["image"], pixdim=(1.0, 1.0, 1.0), mode=("bilinear")),
+            # transforms.ScaleIntensityRanged(keys=["image"], a_min=-175, a_max=250, b_min=0.0, b_max=1.0, clip=True),
+            transforms.ToTensord(keys=["image"]),
+        ]
+    )
+
+    val_img=[]
+    val_name=[]
+    
+    for line in open(args.data_file):
+        # name = line.strip().split()[1].split('.')[0]
+        # val_img.append(args.data_root + line.strip().split()[0])
+        # val_lbl.append(args.data_root + line.strip().split()[1])
+        # breakpoint()
+        name = line.strip()
+        val_img.append(os.path.join(args.data_root, name, 'ct.nii.gz'))
+        val_name.append(name)
+    data_dicts_val = [{'image': image, 'name': name}
+                for image, name in zip(val_img, val_name)]
+    
+    if args.end < len(data_dicts_val):
+        data_dicts_val = data_dicts_val[args.start:args.end]
+    else:
+        data_dicts_val = data_dicts_val[args.start:]
+    print('val len {}'.format(len(data_dicts_val)))
+    val_org_ds = data.Dataset(data_dicts_val, transform=val_org_transform)
+    val_org_loader = data.DataLoader(val_org_ds, batch_size=1, shuffle=False, num_workers=4, sampler=None, pin_memory=True)
+
+    post_transforms = Compose([
+        Invertd(
+            keys=['image'],
+            transform=val_org_transform,
+            orig_keys="image",
+            nearest_interp=False,
+            # nearest_interp=True,
+            to_tensor=True,
+        )
+    ])
+    return val_org_loader, post_transforms
+
+def main():
+    args = parser.parse_args()
+    output_dir = args.save_dir
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    print("MAIN Argument values:")
+    for k, v in vars(args).items():
+        print(k, '=>', v)
+    print('-----------------')
+
+    ## loader and post_transform
+    val_loader, post_transforms = _get_loader(args)
+
+    start_time=0
+    with torch.no_grad():
+        for idx, val_data in enumerate(val_loader):
+            print('idx',idx)
+            if idx == 0:
+                start_time = time.time()
+
+            data_names = val_data['name']
+            case_name = data_names[0].split('/')[-1]
+            print('case_name', case_name)
+            original_affine = val_data["image_meta_dict"]["original_affine"][0].numpy()
+
+            val_data = [post_transforms(i) for i in data.decollate_batch(val_data)]
+            synt_data = val_data[0]['image'][0]
+            final_data = synt_data.cpu().numpy()
+
+            # synt_data = val_data['image']
+            # final_data = synt_data.cpu().numpy()[0,0]
+            # breakpoint()
+            os.makedirs(os.path.join(output_dir, f'{case_name}'), exist_ok=True)
+            nib.save(nib.Nifti1Image(final_data, original_affine), os.path.join(output_dir, f'{case_name}/ct.nii.gz'))
+            # breakpoint()
+            print('time = ', time.time()-start_time)
+            start_time = time.time()
+
+
+if __name__ == "__main__":
+    main()

Generation_Pipeline_filter_all2/syn_colon/CT_syn_colon_data_new.py

@@ -0,0 +1,227 @@
+import os, time, csv
+import numpy as np
+import torch
+from sklearn.metrics import confusion_matrix
+from scipy import ndimage
+from scipy.ndimage import label
+from functools import partial
+import monai
+from monai.transforms import AsDiscrete,AsDiscreted,Compose,Invertd,SaveImaged
+from monai import transforms, data
+from TumorGeneration.utils import synthesize_colon_tumor, synt_model_prepare
+import nibabel as nib
+
+import warnings
+warnings.filterwarnings("ignore")
+
+import argparse
+parser = argparse.ArgumentParser(description='colon tumor validation')
+
+# file dir
+parser.add_argument('--data_root', default=None, type=str)
+parser.add_argument('--organ_type', default='colon', type=str)
+parser.add_argument('--save_dir', default='out', type=str)
+parser.add_argument('--data_file', default='out', type=str)
+parser.add_argument('--ddim_ts', default=50, type=int)
+parser.add_argument('--fg_thresh', default=30, type=int)
+parser.add_argument('--start', default=0, type=int)
+parser.add_argument('--end', default=1000, type=int)
+
+def voxel2R(A):
+    return (np.array(A)/4*3/np.pi)**(1/3)
+
+class RandCropByPosNegLabeld_select(transforms.RandCropByPosNegLabeld):
+    def __init__(self, keys, label_key, spatial_size, 
+                 pos=1.0, neg=1.0, num_samples=1, 
+                 image_key=None, image_threshold=0.0, allow_missing_keys=True,
+                   fg_thresh=0):
+        super().__init__(keys=keys, label_key=label_key, spatial_size=spatial_size, 
+                 pos=pos, neg=neg, num_samples=num_samples, 
+                 image_key=image_key, image_threshold=image_threshold, allow_missing_keys=allow_missing_keys)
+        self.fg_thresh = fg_thresh
+
+    def R2voxel(self,R):
+        return (4/3*np.pi)*(R)**(3)
+
+    def __call__(self, data):
+        d = dict(data)
+        data_name = d['name']
+        d.pop('name')
+
+        if '10_Decathlon' in data_name or '05_KiTS' in data_name:
+            d_crop = super().__call__(d)
+
+        else:
+            flag=0
+            while 1:
+                flag+=1
+
+                d_crop = super().__call__(d)
+                pixel_num = (d_crop[0]['label']>0).sum()
+                
+                if pixel_num > self.R2voxel(self.fg_thresh):
+                    break
+                if flag>5 and pixel_num > self.R2voxel(max(self.fg_thresh-5, 5)):
+                    break
+                if flag>10 and pixel_num > self.R2voxel(max(self.fg_thresh-10, 5)):
+                    break
+                if flag>15 and pixel_num > self.R2voxel(max(self.fg_thresh-15, 5)):
+                    break
+                if flag>20 and pixel_num > self.R2voxel(max(self.fg_thresh-20, 5)):
+                    break
+                if flag>25 and pixel_num > self.R2voxel(max(self.fg_thresh-25, 5)):
+                    break
+                if flag>50:
+                    break
+            
+        d_crop[0]['name'] = data_name
+        
+        return d_crop
+    
+def _get_loader(args):
+    # val_data_dir = args.val_dir
+    # datalist_json = args.json_dir 
+    val_org_transform = transforms.Compose(
+        [
+            transforms.LoadImaged(keys=["image", "label", "raw_image"]),
+            transforms.AddChanneld(keys=["image", "label", "raw_image"]),
+            transforms.Orientationd(keys=["image", "label"], axcodes="RAS"),
+            transforms.Spacingd(keys=["image", "label"], pixdim=(1.0, 1.0, 1.0), mode=("bilinear", "bilinear")),
+            transforms.ScaleIntensityRanged(keys=["image"], a_min=-175, a_max=250, b_min=0.0, b_max=1.0, clip=True),
+            transforms.SpatialPadd(keys=["image", "label"], mode=["minimum", "constant"], spatial_size=[96, 96, 96]),
+            RandCropByPosNegLabeld_select(
+                keys=["image", "label", "name"],
+                label_key="label",
+                spatial_size=(96, 96, 96),
+                pos=1,
+                neg=0,
+                num_samples=1,
+                image_key="image",
+                image_threshold=0,
+                fg_thresh = args.fg_thresh,
+            ),
+            transforms.ToTensord(keys=["image", "label", "raw_image"]),
+        ]
+    )
+
+    val_img=[]
+    val_lbl=[]
+    val_name=[]
+    
+    for line in open(args.data_file):
+        # name = line.strip().split()[1].split('.')[0]
+        # val_img.append(args.data_root + line.strip().split()[0])
+        # val_lbl.append(args.data_root + line.strip().split()[1])
+        # breakpoint()
+        name = line.strip()
+        val_img.append(os.path.join(args.data_root, name, 'ct.nii.gz'))
+        val_lbl.append(os.path.join(args.data_root, name, 'segmentations/colon.nii.gz'))
+        val_name.append(name)
+    data_dicts_val = [{'image': image, 'raw_image':image, 'label': label, 'name': name}
+                for image, label, name in zip(val_img, val_lbl, val_name)]
+    
+    if args.end < len(data_dicts_val):
+        data_dicts_val = data_dicts_val[args.start:args.end]
+    else:
+        data_dicts_val = data_dicts_val[args.start:]
+    print('val len {}'.format(len(data_dicts_val)))
+    val_org_ds = data.Dataset(data_dicts_val, transform=val_org_transform)
+    val_org_loader = data.DataLoader(val_org_ds, batch_size=1, shuffle=False, num_workers=4, sampler=None, pin_memory=True)
+
+    post_transforms = Compose([
+        Invertd(
+            keys=['image'],
+            transform=val_org_transform,
+            orig_keys="image",
+            nearest_interp=False,
+            # nearest_interp=True,
+            to_tensor=True,
+        ),
+        Invertd(
+            keys=['label'],
+            transform=val_org_transform,
+            orig_keys="label",
+            nearest_interp=False,
+            # nearest_interp=True,
+            to_tensor=True,
+        )
+    ])
+    return val_org_loader, post_transforms
+
+def main():
+    args = parser.parse_args()
+    output_dir = args.save_dir
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    print("MAIN Argument values:")
+    for k, v in vars(args).items():
+        print(k, '=>', v)
+    print('-----------------')
+
+    ## loader and post_transform
+    val_loader, post_transforms = _get_loader(args)
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = monai.networks.nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=2).to(device)
+    model.load_state_dict(torch.load("../best_metric_model_classification3d_dict.pth"))
+    model.eval()
+    
+    start_time=0
+    with torch.no_grad():
+        for idx, val_data in enumerate(val_loader):
+            print('idx',idx)
+            if idx == 0:
+                start_time = time.time()
+            # val_inputs = val_data["image"]
+            # name = val_data['label_meta_dict']['filename_or_obj'][0].split('/')[-1].split('.')[0]
+            
+            vqgan, noearly_sampler= synt_model_prepare(device = torch.device("cuda"), fold=0, organ=args.organ_type)
+            
+            healthy_data, healthy_target, data_names, raw_data = val_data['image'], val_data['label'], val_data['name'], val_data['raw_image']
+            case_name = data_names[0].split('/')[-1]
+            print('case_name', case_name)
+            original_affine = val_data["label_meta_dict"]["original_affine"][0].numpy()
+
+            if healthy_target.sum() == 0:
+                val_data = [post_transforms(i) for i in data.decollate_batch(val_data)]
+                tumor_mask = val_data[0]['label'][0].cpu().numpy().astype(np.uint8)
+                tumor_mask_ = np.zeros_like(tumor_mask)
+                nib.save(nib.Nifti1Image(tumor_mask_, original_affine), os.path.join(output_dir, f'{case_name}/segmentations/colon_tumor.nii.gz'))
+                continue
+            
+            healthy_data, healthy_target = healthy_data.cuda(), healthy_target.cuda()
+            healthy_target = (healthy_target>0).to(healthy_target)
+
+            flag=0
+            while 1:
+                synt_data, synt_target = synthesize_colon_tumor(healthy_data, healthy_target, vqgan, noearly_sampler, ddim_ts=args.ddim_ts)
+                syn_confidence = model(synt_data).sigmoid()[:,1]
+                flag+=1
+                if syn_confidence>0.005:
+                    break
+                elif flag > 20 and syn_confidence>0.001:
+                    break
+            val_data['image'] = synt_data.detach()
+            val_data['label'] = synt_target.detach()
+
+            val_data = [post_transforms(i) for i in data.decollate_batch(val_data)]
+            synt_data = val_data[0]['image'][0]
+            synt_target = val_data[0]['label'][0]
+            final_data = raw_data[0,0]
+
+            synt_data = (synt_data*(250+175)-175)
+            final_data[synt_target>1] = synt_data[synt_target>1]
+            final_data = final_data.cpu().numpy()
+            final_label = (synt_target>=1.5).cpu().numpy().astype(np.uint8)
+
+            os.makedirs(os.path.join(output_dir, f'{case_name}'), exist_ok=True)
+            os.makedirs(os.path.join(output_dir, f'{case_name}/segmentations'), exist_ok=True)
+            nib.save(nib.Nifti1Image(final_data, original_affine), os.path.join(output_dir, f'{case_name}/ct.nii.gz'))
+            nib.save(nib.Nifti1Image(final_label, original_affine), os.path.join(output_dir, f'{case_name}/segmentations/colon_tumor.nii.gz'))
+
+            print('time = ', time.time()-start_time)
+            start_time = time.time()
+
+
+if __name__ == "__main__":
+    main()

Generation_Pipeline_filter_all2/syn_colon/TumorGeneration/README.md

@@ -0,0 +1,5 @@
+```bash
+wget https://www.dropbox.com/scl/fi/k856fhk60kck8uqxtxazw/model_weight.tar.gz?rlkey=hrcn4cbt690dzern1bkbfejll
+mv model_weight.tar.gz?rlkey=hrcn4cbt690dzern1bkbfejll model_weight.tar.gz
+tar -xzvf model_weight.tar.gz
+```

Generation_Pipeline_filter_all2/syn_colon/TumorGeneration/TumorGenerated.py

@@ -0,0 +1,39 @@
+import random
+from typing import Hashable, Mapping, Dict
+
+from monai.config import KeysCollection
+from monai.config.type_definitions import NdarrayOrTensor
+from monai.transforms.transform import MapTransform, RandomizableTransform
+
+from .utils_ import SynthesisTumor
+import numpy as np
+
+class TumorGenerated(RandomizableTransform, MapTransform):
+    def __init__(self, 
+    keys: KeysCollection, 
+    prob: float = 0.1,
+    tumor_prob = [0.2, 0.2, 0.2, 0.2, 0.2],
+    allow_missing_keys: bool = False
+    ) -> None:
+        MapTransform.__init__(self, keys, allow_missing_keys)
+        RandomizableTransform.__init__(self, prob)
+        random.seed(0)
+        np.random.seed(0)
+
+        self.tumor_types = ['tiny', 'small', 'medium', 'large', 'mix']
+        
+        assert len(tumor_prob) == 5
+        self.tumor_prob = np.array(tumor_prob)
+
+
+
+    def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
+        d = dict(data)
+        self.randomize(None)
+
+        if self._do_transform and (np.max(d['label']) <= 1):  
+            tumor_type = np.random.choice(self.tumor_types, p=self.tumor_prob.ravel())
+
+            d['image'][0], d['label'][0] = SynthesisTumor(d['image'][0], d['label'][0], tumor_type)
+            # print(tumor_type, d['image'].shape, np.max(d['label']))
+        return d

Generation_Pipeline_filter_all2/syn_colon/TumorGeneration/__init__.py

@@ -0,0 +1,5 @@
+### Online Version TumorGeneration ###
+
+from .TumorGenerated import TumorGenerated
+
+# from .utils import synthesize_early_tumor, synthesize_medium_tumor, synthesize_large_tumor

Generation_Pipeline_filter_all2/syn_colon/TumorGeneration/diffusion_config/ddpm.yaml

@@ -0,0 +1,29 @@
+vqgan_ckpt: None
+
+# Have to be derived from VQ-GAN Latent space dimensions
+diffusion_img_size: 24
+diffusion_depth_size: 24
+diffusion_num_channels: 17 # 17
+out_dim: 8
+dim_mults: [1,2,4,8]
+results_folder: checkpoints/ddpm/
+results_folder_postfix: 'own_dataset_t2'
+load_milestone: False # False
+
+batch_size: 2 # 40
+num_workers: 20
+logger: wandb
+objective: pred_x0
+save_and_sample_every: 1000
+denoising_fn: Unet3D
+train_lr: 1e-4
+timesteps: 2 # number of steps
+sampling_timesteps: 250 # number of sampling timesteps (using ddim for faster inference [see citation for ddim paper])
+loss_type: l1 # L1 or L2
+train_num_steps: 700000 # total training steps
+gradient_accumulate_every: 2 # gradient accumulation steps
+ema_decay: 0.995 # exponential moving average decay
+amp: False # turn on mixed precision
+num_sample_rows: 1
+gpus: 0
+

Generation_Pipeline_filter_all2/syn_colon/TumorGeneration/diffusion_config/vq_gan_3d.yaml

@@ -0,0 +1,37 @@
+seed: 1234
+batch_size: 2 # 30
+num_workers: 32 # 30
+
+gpus: 1
+accumulate_grad_batches: 1
+default_root_dir: checkpoints/vq_gan/
+default_root_dir_postfix: 'flair'
+resume_from_checkpoint:
+max_steps: -1
+max_epochs: -1
+precision: 16
+gradient_clip_val: 1.0
+
+
+embedding_dim: 8 # 256
+n_codes: 16384 # 2048
+n_hiddens: 16
+lr: 3e-4
+downsample: [2, 2, 2] # [4, 4, 4]
+disc_channels: 64
+disc_layers: 3
+discriminator_iter_start: 10000 # 50000
+disc_loss_type: hinge
+image_gan_weight: 1.0
+video_gan_weight: 1.0
+l1_weight: 4.0
+gan_feat_weight: 4.0 # 0.0
+perceptual_weight: 4.0 # 0.0
+i3d_feat: False
+restart_thres: 1.0
+no_random_restart: False
+norm_type: group
+padding_type: replicate
+num_groups: 32
+
+

Generation_Pipeline_filter_all2/syn_colon/TumorGeneration/ldm/ddpm/__init__.py

@@ -0,0 +1 @@
+from .diffusion import Unet3D, GaussianDiffusion, Tester

Generation_Pipeline_filter_all2/syn_colon/TumorGeneration/ldm/ddpm/ddim.py

@@ -0,0 +1,206 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+from functools import partial
+
+from .util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
+
+
+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): # "uniform" 'quad'
+        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)))
+        # breakpoint()
+        # 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, ...
+               **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, T, H, W = shape
+        # breakpoint()
+        size = (batch_size, C, T, H, W)
+        # print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+        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,
+                                                    unconditional_guidance_scale=unconditional_guidance_scale,
+                                                    unconditional_conditioning=unconditional_conditioning,
+                                                    )
+        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,):
+        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 = 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(time_range):
+            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)  # TODO: deterministic forward pass?
+                img = img_orig * mask + (1. - mask) * img
+
+            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)
+            img, pred_x0 = outs
+            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_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):
+        b, *_, device = *x.shape, x.device
+
+        if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+            # breakpoint()
+            e_t = self.model.denoise_fn(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.denoise_fn(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)
+
+        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.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
+        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)
+        # 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