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import os
import torch
import pytorch_lightning as pl
from tqdm import tqdm
from joblib import Parallel, delayed
from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader, ConcatDataset
from datasets.augment import build_augmentor
from tools.misc import tqdm_joblib
from .gl3d.gl3d import GL3DDataset
from .gtasfm.gtasfm import GTASfMDataset
from .multifov.multifov import MultiFoVDataset
from .gl3d.gl3d import GL3DDataset as BlendedMVSDataset
from .iclnuim.iclnuim import ICLNUIMDataset
from .scenenet.scenenet import SceneNetDataset
from .eth3d.eth3d import ETH3DDataset
from .kitti.kitti import KITTIDataset
from .robotcar.robotcar import RobotcarDataset
Benchmarks = dict(
GL3D = GL3DDataset,
GTASfM = GTASfMDataset,
MultiFoV = MultiFoVDataset,
BlendedMVS = BlendedMVSDataset,
ICLNUIM = ICLNUIMDataset,
SceneNet = SceneNetDataset,
ETH3DO = ETH3DDataset,
ETH3DI = ETH3DDataset,
KITTI = KITTIDataset,
RobotcarNight = RobotcarDataset,
RobotcarSeason = RobotcarDataset,
RobotcarWeather = RobotcarDataset,
)
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, dcfg):
"""
Args:
args: (ArgumentParser) The only useful args is args.trains and args.valids
each one is a list, which contain like [PhotoTourism, MegaDepth,...]
We should traverse each item in args.trains and args.valids to build
self.train_datasets and self.valid_datasets
dcfg: (yacs) It contain all configs for each benchmark in args.trains and
args.valids
"""
super().__init__()
self.args = args
self.dcfg = dcfg
self.train_loader_params = {'batch_size': args.batch_size,
'shuffle': True,
'num_workers': args.threads,
'pin_memory': True,
'drop_last': True}
self.valid_loader_params = {'batch_size': args.batch_size,
'shuffle': False,
'num_workers': args.threads,
'pin_memory': True,
'drop_last': False}
self.tests_loader_params = {'batch_size': args.batch_size,
'shuffle': False,
'num_workers': args.threads,
'pin_memory': True,
'drop_last': False}
def setup(self, stage=None):
"""
Setup train/valid/test dataset. This method will be called by PL automatically.
Args:
stage (str): 'fit' in training phase, and 'test' in testing phase.
"""
self.gpus = self.trainer.gpus
self.gpuid = self.trainer.global_rank
self.train_datasets = None
self.valid_datasets = None
self.tests_datasets = None
# TRAIN
if stage == 'fit':
train_datasets = []
for benchmark in self.args.trains:
dcfg = self.dcfg.get(benchmark, None)
assert dcfg is not None, "Training dcfg is None"
datasets = self._setup_dataset(
benchmark=benchmark,
data_root=dcfg.DATASET.TRAIN.DATA_ROOT,
npz_root=dcfg.DATASET.TRAIN.NPZ_ROOT,
scene_list_path=dcfg.DATASET.TRAIN.LIST_PATH,
df=self.dcfg.DF,
padding=dcfg.DATASET.TRAIN.PADDING,
min_overlap_score=dcfg.DATASET.TRAIN.MIN_OVERLAP_SCORE,
max_overlap_score=dcfg.DATASET.TRAIN.MAX_OVERLAP_SCORE,
max_resize=self.args.img_size,
augment_fn=build_augmentor(dcfg.DATASET.TRAIN.AUGMENTATION_TYPE),
max_samples=dcfg.DATASET.TRAIN.MAX_SAMPLES,
mode='train',
njobs=dcfg.NJOBS,
cfg=dcfg.DATASET.TRAIN,
)
train_datasets += datasets
self.train_datasets = ConcatDataset(train_datasets)
os.environ['TOTAL_TRAIN_SAMPLES'] = str(len(self.train_datasets))
# VALID
valid_datasets = []
for benchmark in self.args.valids:
dcfg = self.dcfg.get(benchmark, None)
assert dcfg is not None, "Validing dcfg is None"
datasets = self._setup_dataset(
benchmark=benchmark,
data_root=dcfg.DATASET.VALID.DATA_ROOT,
npz_root=dcfg.DATASET.VALID.NPZ_ROOT,
scene_list_path=dcfg.DATASET.VALID.LIST_PATH,
df=self.dcfg.DF,
padding=dcfg.DATASET.VALID.PADDING,
min_overlap_score=dcfg.DATASET.VALID.MIN_OVERLAP_SCORE,
max_overlap_score=dcfg.DATASET.VALID.MAX_OVERLAP_SCORE,
max_resize=self.args.img_size,
augment_fn=build_augmentor(dcfg.DATASET.VALID.AUGMENTATION_TYPE),
max_samples=dcfg.DATASET.VALID.MAX_SAMPLES,
mode='valid',
njobs=dcfg.NJOBS,
cfg=dcfg.DATASET.VALID,
)
valid_datasets += datasets
self.valid_datasets = ConcatDataset(valid_datasets)
os.environ['TOTAL_VALID_SAMPLES'] = str(len(self.valid_datasets))
# TEST
if stage == 'test':
tests_datasets = []
for benchmark in [self.args.tests]:
dcfg = self.dcfg.get(benchmark, None)
assert dcfg is not None, "Validing dcfg is None"
datasets = self._setup_dataset(
benchmark=benchmark,
data_root=dcfg.DATASET.TESTS.DATA_ROOT,
npz_root=dcfg.DATASET.TESTS.NPZ_ROOT,
scene_list_path=dcfg.DATASET.TESTS.LIST_PATH,
df=self.dcfg.DF,
padding=dcfg.DATASET.TESTS.PADDING,
min_overlap_score=dcfg.DATASET.TESTS.MIN_OVERLAP_SCORE,
max_overlap_score=dcfg.DATASET.TESTS.MAX_OVERLAP_SCORE,
max_resize=self.args.img_size,
augment_fn=build_augmentor(dcfg.DATASET.TESTS.AUGMENTATION_TYPE),
max_samples=dcfg.DATASET.TESTS.MAX_SAMPLES,
mode='test',
njobs=dcfg.NJOBS,
cfg=dcfg.DATASET.TESTS,
)
tests_datasets += datasets
self.tests_datasets = ConcatDataset(tests_datasets)
os.environ['TOTAL_TESTS_SAMPLES'] = str(len(self.tests_datasets))
if self.gpuid == 0: print('TOTAL_TESTS_SAMPLES:', len(self.tests_datasets))
def _setup_dataset(self, benchmark, data_root, npz_root, scene_list_path, df, padding,
min_overlap_score, max_overlap_score, max_resize, augment_fn,
max_samples, mode, njobs, cfg):
seq_names = [benchmark.lower()]
with tqdm_joblib(tqdm(bar_format="{l_bar}{bar:3}{r_bar}", ncols=100,
desc=f'[GPU {self.gpuid}] load {mode} {benchmark:14} data',
total=len(seq_names), disable=int(self.gpuid) != 0)):
datasets = Parallel(n_jobs=njobs)(
delayed(lambda x: _build_dataset(
Benchmarks.get(benchmark),
root_dir=data_root,
npz_root=npz_root,
seq_name=x,
mode=mode,
min_overlap_score=min_overlap_score,
max_overlap_score=max_overlap_score,
max_resize=max_resize,
df=df,
padding=padding,
augment_fn=augment_fn,
max_samples=max_samples,
**cfg
))(seqname) for seqname in seq_names)
return datasets
def train_dataloader(self, *args, **kwargs):
return DataLoader(self.train_datasets, collate_fn=collate_fn, **self.train_loader_params)
def valid_dataloader(self, *args, **kwargs):
return DataLoader(self.valid_datasets, collate_fn=collate_fn, **self.valid_loader_params)
def val_dataloader(self, *args, **kwargs):
return self.valid_dataloader(*args, **kwargs)
def test_dataloader(self, *args, **kwargs):
return DataLoader(self.tests_datasets, collate_fn=collate_fn, **self.tests_loader_params)
def collate_fn(batch):
batch = list(filter(lambda x: x is not None, batch))
return torch.utils.data.dataloader.default_collate(batch)
def _build_dataset(dataset: Dataset, *args, **kwargs):
# noinspection PyCallingNonCallable
return dataset(*args, **kwargs)
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