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import os
import math
from collections import abc
from loguru import logger
from torch.utils.data.dataset import Dataset
from tqdm import tqdm
from os import path as osp
from pathlib import Path
from joblib import Parallel, delayed
import pytorch_lightning as pl
from torch import distributed as dist
from torch.utils.data import (
Dataset,
DataLoader,
ConcatDataset,
DistributedSampler,
RandomSampler,
dataloader,
)
from src.utils.augment import build_augmentor
from src.utils.dataloader import get_local_split
from src.utils.misc import tqdm_joblib
from src.utils import comm
from src.datasets.megadepth import MegaDepthDataset
from src.datasets.scannet import ScanNetDataset
from src.datasets.sampler import RandomConcatSampler
class MultiSceneDataModule(pl.LightningDataModule):
"""
For distributed training, each training process is assgined
only a part of the training scenes to reduce memory overhead.
"""
def __init__(self, args, config):
super().__init__()
# 1. data config
# Train and Val should from the same data source
self.trainval_data_source = config.DATASET.TRAINVAL_DATA_SOURCE
self.test_data_source = config.DATASET.TEST_DATA_SOURCE
# training and validating
self.train_data_root = config.DATASET.TRAIN_DATA_ROOT
self.train_pose_root = config.DATASET.TRAIN_POSE_ROOT # (optional)
self.train_npz_root = config.DATASET.TRAIN_NPZ_ROOT
self.train_list_path = config.DATASET.TRAIN_LIST_PATH
self.train_intrinsic_path = config.DATASET.TRAIN_INTRINSIC_PATH
self.val_data_root = config.DATASET.VAL_DATA_ROOT
self.val_pose_root = config.DATASET.VAL_POSE_ROOT # (optional)
self.val_npz_root = config.DATASET.VAL_NPZ_ROOT
self.val_list_path = config.DATASET.VAL_LIST_PATH
self.val_intrinsic_path = config.DATASET.VAL_INTRINSIC_PATH
# testing
self.test_data_root = config.DATASET.TEST_DATA_ROOT
self.test_pose_root = config.DATASET.TEST_POSE_ROOT # (optional)
self.test_npz_root = config.DATASET.TEST_NPZ_ROOT
self.test_list_path = config.DATASET.TEST_LIST_PATH
self.test_intrinsic_path = config.DATASET.TEST_INTRINSIC_PATH
# 2. dataset config
# general options
self.min_overlap_score_test = (
config.DATASET.MIN_OVERLAP_SCORE_TEST
) # 0.4, omit data with overlap_score < min_overlap_score
self.min_overlap_score_train = config.DATASET.MIN_OVERLAP_SCORE_TRAIN
self.augment_fn = build_augmentor(
config.DATASET.AUGMENTATION_TYPE
) # None, options: [None, 'dark', 'mobile']
# MegaDepth options
self.mgdpt_img_resize = config.DATASET.MGDPT_IMG_RESIZE # 840
self.mgdpt_img_pad = config.DATASET.MGDPT_IMG_PAD # True
self.mgdpt_depth_pad = config.DATASET.MGDPT_DEPTH_PAD # True
self.mgdpt_df = config.DATASET.MGDPT_DF # 8
self.coarse_scale = 1 / config.ASPAN.RESOLUTION[0] # 0.125. for training loftr.
# 3.loader parameters
self.train_loader_params = {
"batch_size": args.batch_size,
"num_workers": args.num_workers,
"pin_memory": getattr(args, "pin_memory", True),
}
self.val_loader_params = {
"batch_size": 1,
"shuffle": False,
"num_workers": args.num_workers,
"pin_memory": getattr(args, "pin_memory", True),
}
self.test_loader_params = {
"batch_size": 1,
"shuffle": False,
"num_workers": args.num_workers,
"pin_memory": True,
}
# 4. sampler
self.data_sampler = config.TRAINER.DATA_SAMPLER
self.n_samples_per_subset = config.TRAINER.N_SAMPLES_PER_SUBSET
self.subset_replacement = config.TRAINER.SB_SUBSET_SAMPLE_REPLACEMENT
self.shuffle = config.TRAINER.SB_SUBSET_SHUFFLE
self.repeat = config.TRAINER.SB_REPEAT
# (optional) RandomSampler for debugging
# misc configurations
self.parallel_load_data = getattr(args, "parallel_load_data", False)
self.seed = config.TRAINER.SEED # 66
def setup(self, stage=None):
"""
Setup train / val / test dataset. This method will be called by PL automatically.
Args:
stage (str): 'fit' in training phase, and 'test' in testing phase.
"""
assert stage in ["fit", "test"], "stage must be either fit or test"
try:
self.world_size = dist.get_world_size()
self.rank = dist.get_rank()
logger.info(f"[rank:{self.rank}] world_size: {self.world_size}")
except AssertionError as ae:
self.world_size = 1
self.rank = 0
logger.warning(str(ae) + " (set wolrd_size=1 and rank=0)")
if stage == "fit":
self.train_dataset = self._setup_dataset(
self.train_data_root,
self.train_npz_root,
self.train_list_path,
self.train_intrinsic_path,
mode="train",
min_overlap_score=self.min_overlap_score_train,
pose_dir=self.train_pose_root,
)
# setup multiple (optional) validation subsets
if isinstance(self.val_list_path, (list, tuple)):
self.val_dataset = []
if not isinstance(self.val_npz_root, (list, tuple)):
self.val_npz_root = [
self.val_npz_root for _ in range(len(self.val_list_path))
]
for npz_list, npz_root in zip(self.val_list_path, self.val_npz_root):
self.val_dataset.append(
self._setup_dataset(
self.val_data_root,
npz_root,
npz_list,
self.val_intrinsic_path,
mode="val",
min_overlap_score=self.min_overlap_score_test,
pose_dir=self.val_pose_root,
)
)
else:
self.val_dataset = self._setup_dataset(
self.val_data_root,
self.val_npz_root,
self.val_list_path,
self.val_intrinsic_path,
mode="val",
min_overlap_score=self.min_overlap_score_test,
pose_dir=self.val_pose_root,
)
logger.info(f"[rank:{self.rank}] Train & Val Dataset loaded!")
else: # stage == 'test
self.test_dataset = self._setup_dataset(
self.test_data_root,
self.test_npz_root,
self.test_list_path,
self.test_intrinsic_path,
mode="test",
min_overlap_score=self.min_overlap_score_test,
pose_dir=self.test_pose_root,
)
logger.info(f"[rank:{self.rank}]: Test Dataset loaded!")
def _setup_dataset(
self,
data_root,
split_npz_root,
scene_list_path,
intri_path,
mode="train",
min_overlap_score=0.0,
pose_dir=None,
):
"""Setup train / val / test set"""
with open(scene_list_path, "r") as f:
npz_names = [name.split()[0] for name in f.readlines()]
if mode == "train":
local_npz_names = get_local_split(
npz_names, self.world_size, self.rank, self.seed
)
else:
local_npz_names = npz_names
logger.info(f"[rank {self.rank}]: {len(local_npz_names)} scene(s) assigned.")
dataset_builder = (
self._build_concat_dataset_parallel
if self.parallel_load_data
else self._build_concat_dataset
)
return dataset_builder(
data_root,
local_npz_names,
split_npz_root,
intri_path,
mode=mode,
min_overlap_score=min_overlap_score,
pose_dir=pose_dir,
)
def _build_concat_dataset(
self,
data_root,
npz_names,
npz_dir,
intrinsic_path,
mode,
min_overlap_score=0.0,
pose_dir=None,
):
datasets = []
augment_fn = self.augment_fn if mode == "train" else None
data_source = (
self.trainval_data_source
if mode in ["train", "val"]
else self.test_data_source
)
if data_source == "GL3D" and mode == "val":
data_source = "MegaDepth"
if str(data_source).lower() == "megadepth":
npz_names = [f"{n}.npz" for n in npz_names]
if str(data_source).lower() == "gl3d":
npz_names = [f"{n}.txt" for n in npz_names]
# npz_names=npz_names[:8]
for npz_name in tqdm(
npz_names,
desc=f"[rank:{self.rank}] loading {mode} datasets",
disable=int(self.rank) != 0,
):
# `ScanNetDataset`/`MegaDepthDataset` load all data from npz_path when initialized, which might take time.
npz_path = osp.join(npz_dir, npz_name)
if data_source == "ScanNet":
datasets.append(
ScanNetDataset(
data_root,
npz_path,
intrinsic_path,
mode=mode,
min_overlap_score=min_overlap_score,
augment_fn=augment_fn,
pose_dir=pose_dir,
)
)
elif data_source == "MegaDepth":
datasets.append(
MegaDepthDataset(
data_root,
npz_path,
mode=mode,
min_overlap_score=min_overlap_score,
img_resize=self.mgdpt_img_resize,
df=self.mgdpt_df,
img_padding=self.mgdpt_img_pad,
depth_padding=self.mgdpt_depth_pad,
augment_fn=augment_fn,
coarse_scale=self.coarse_scale,
)
)
else:
raise NotImplementedError()
return ConcatDataset(datasets)
def _build_concat_dataset_parallel(
self,
data_root,
npz_names,
npz_dir,
intrinsic_path,
mode,
min_overlap_score=0.0,
pose_dir=None,
):
augment_fn = self.augment_fn if mode == "train" else None
data_source = (
self.trainval_data_source
if mode in ["train", "val"]
else self.test_data_source
)
if str(data_source).lower() == "megadepth":
npz_names = [f"{n}.npz" for n in npz_names]
# npz_names=npz_names[:8]
with tqdm_joblib(
tqdm(
desc=f"[rank:{self.rank}] loading {mode} datasets",
total=len(npz_names),
disable=int(self.rank) != 0,
)
):
if data_source == "ScanNet":
datasets = Parallel(
n_jobs=math.floor(
len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()
)
)(
delayed(
lambda x: _build_dataset(
ScanNetDataset,
data_root,
osp.join(npz_dir, x),
intrinsic_path,
mode=mode,
min_overlap_score=min_overlap_score,
augment_fn=augment_fn,
pose_dir=pose_dir,
)
)(name)
for name in npz_names
)
elif data_source == "MegaDepth":
# TODO: _pickle.PicklingError: Could not pickle the task to send it to the workers.
raise NotImplementedError()
datasets = Parallel(
n_jobs=math.floor(
len(os.sched_getaffinity(0)) * 0.9 / comm.get_local_size()
)
)(
delayed(
lambda x: _build_dataset(
MegaDepthDataset,
data_root,
osp.join(npz_dir, x),
mode=mode,
min_overlap_score=min_overlap_score,
img_resize=self.mgdpt_img_resize,
df=self.mgdpt_df,
img_padding=self.mgdpt_img_pad,
depth_padding=self.mgdpt_depth_pad,
augment_fn=augment_fn,
coarse_scale=self.coarse_scale,
)
)(name)
for name in npz_names
)
else:
raise ValueError(f"Unknown dataset: {data_source}")
return ConcatDataset(datasets)
def train_dataloader(self):
"""Build training dataloader for ScanNet / MegaDepth."""
assert self.data_sampler in ["scene_balance"]
logger.info(
f"[rank:{self.rank}/{self.world_size}]: Train Sampler and DataLoader re-init (should not re-init between epochs!)."
)
if self.data_sampler == "scene_balance":
sampler = RandomConcatSampler(
self.train_dataset,
self.n_samples_per_subset,
self.subset_replacement,
self.shuffle,
self.repeat,
self.seed,
)
else:
sampler = None
dataloader = DataLoader(
self.train_dataset, sampler=sampler, **self.train_loader_params
)
return dataloader
def val_dataloader(self):
"""Build validation dataloader for ScanNet / MegaDepth."""
logger.info(
f"[rank:{self.rank}/{self.world_size}]: Val Sampler and DataLoader re-init."
)
if not isinstance(self.val_dataset, abc.Sequence):
sampler = DistributedSampler(self.val_dataset, shuffle=False)
return DataLoader(
self.val_dataset, sampler=sampler, **self.val_loader_params
)
else:
dataloaders = []
for dataset in self.val_dataset:
sampler = DistributedSampler(dataset, shuffle=False)
dataloaders.append(
DataLoader(dataset, sampler=sampler, **self.val_loader_params)
)
return dataloaders
def test_dataloader(self, *args, **kwargs):
logger.info(
f"[rank:{self.rank}/{self.world_size}]: Test Sampler and DataLoader re-init."
)
sampler = DistributedSampler(self.test_dataset, shuffle=False)
return DataLoader(self.test_dataset, sampler=sampler, **self.test_loader_params)
def _build_dataset(dataset: Dataset, *args, **kwargs):
return dataset(*args, **kwargs)
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