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from pathlib import Path
from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
import pytorch3d
import torch
from torch.utils.data import SequentialSampler
from omegaconf import DictConfig
from pytorch3d.implicitron.dataset.data_loader_map_provider import \
SequenceDataLoaderMapProvider
from pytorch3d.implicitron.dataset.dataset_base import FrameData
from pytorch3d.implicitron.dataset.json_index_dataset import JsonIndexDataset
from pytorch3d.implicitron.dataset.json_index_dataset_map_provider_v2 import (
JsonIndexDatasetMapProviderV2, registry)
from pytorch3d.implicitron.tools.config import expand_args_fields
from pytorch3d.renderer.cameras import CamerasBase
from torch.utils.data import DataLoader
from pytorch3d.datasets import R2N2, collate_batched_meshes
from configs.structured import CO3DConfig, DataloaderConfig, ProjectConfig, Optional
from .utils import DatasetMap
def get_dataset(cfg: ProjectConfig):
if cfg.dataset.type == 'co3dv2':
from .exclude_sequence import EXCLUDE_SEQUENCE, LOW_QUALITY_SEQUENCE
dataset_cfg: CO3DConfig = cfg.dataset
dataloader_cfg: DataloaderConfig = cfg.dataloader
# Exclude bad and low-quality sequences, XH: why this is needed?
exclude_sequence = []
exclude_sequence.extend(EXCLUDE_SEQUENCE.get(dataset_cfg.category, []))
exclude_sequence.extend(LOW_QUALITY_SEQUENCE.get(dataset_cfg.category, []))
# Whether to load pointclouds
kwargs = dict(
remove_empty_masks=True,
n_frames_per_sequence=1,
load_point_clouds=True,
max_points=dataset_cfg.max_points,
image_height=dataset_cfg.image_size,
image_width=dataset_cfg.image_size,
mask_images=dataset_cfg.mask_images,
exclude_sequence=exclude_sequence,
pick_sequence=() if dataset_cfg.restrict_model_ids is None else dataset_cfg.restrict_model_ids,
)
# Get dataset mapper
dataset_map_provider_type = registry.get(JsonIndexDatasetMapProviderV2, "JsonIndexDatasetMapProviderV2")
expand_args_fields(dataset_map_provider_type)
dataset_map_provider = dataset_map_provider_type(
category=dataset_cfg.category,
subset_name=dataset_cfg.subset_name,
dataset_root=dataset_cfg.root,
test_on_train=False,
only_test_set=False,
load_eval_batches=True,
dataset_JsonIndexDataset_args=DictConfig(kwargs),
)
# Get datasets
datasets = dataset_map_provider.get_dataset_map() # how to select specific frames??
# PATCH BUG WITH POINT CLOUD LOCATIONS!
for dataset in (datasets["train"], datasets["val"]):
# print(dataset.seq_annots.items())
for key, ann in dataset.seq_annots.items():
correct_point_cloud_path = Path(dataset.dataset_root) / Path(*Path(ann.point_cloud.path).parts[-3:])
assert correct_point_cloud_path.is_file(), correct_point_cloud_path
ann.point_cloud.path = str(correct_point_cloud_path)
# Get dataloader mapper
data_loader_map_provider_type = registry.get(SequenceDataLoaderMapProvider, "SequenceDataLoaderMapProvider")
expand_args_fields(data_loader_map_provider_type)
data_loader_map_provider = data_loader_map_provider_type(
batch_size=dataloader_cfg.batch_size,
num_workers=dataloader_cfg.num_workers,
)
# QUICK HACK: Patch the train dataset because it is not used but it throws an error
if (len(datasets['train']) == 0 and len(datasets[dataset_cfg.eval_split]) > 0 and
dataset_cfg.restrict_model_ids is not None and cfg.run.job == 'sample'):
datasets = DatasetMap(train=datasets[dataset_cfg.eval_split], val=datasets[dataset_cfg.eval_split],
test=datasets[dataset_cfg.eval_split])
# XH: why all eval split?
print('Note: You used restrict_model_ids and there were no ids in the train set.')
# Get dataloaders
dataloaders = data_loader_map_provider.get_data_loader_map(datasets)
dataloader_train = dataloaders['train']
dataloader_val = dataloader_vis = dataloaders[dataset_cfg.eval_split]
# Replace validation dataloader sampler with SequentialSampler
# seems to be randomly sampled? with a fixed random seed? but one cannot control which image is being sampled??
dataloader_val.batch_sampler.sampler = SequentialSampler(dataloader_val.batch_sampler.sampler.data_source)
# Modify for accelerate
dataloader_train.batch_sampler.drop_last = True
dataloader_val.batch_sampler.drop_last = False
elif cfg.dataset.type == 'shapenet_r2n2':
# from ..configs.structured import ShapeNetR2N2Config
from .r2n2_my import R2N2Sample
dataset_cfg: ShapeNetR2N2Config = cfg.dataset
# for k in dataset_cfg:
# print(k)
datasets = [R2N2Sample(dataset_cfg.max_points, dataset_cfg.fix_sample,
dataset_cfg.image_size, cfg.augmentations,
s, dataset_cfg.shapenet_dir,
dataset_cfg.r2n2_dir, dataset_cfg.splits_file,
load_textures=False, return_all_views=True) for s in ['train', 'val', 'test']]
dataloader_train = DataLoader(datasets[0], batch_size=cfg.dataloader.batch_size,
collate_fn=collate_batched_meshes,
num_workers=cfg.dataloader.num_workers, shuffle=True)
dataloader_val = DataLoader(datasets[1], batch_size=cfg.dataloader.batch_size,
collate_fn=collate_batched_meshes,
num_workers=cfg.dataloader.num_workers, shuffle=False)
dataloader_vis = DataLoader(datasets[2], batch_size=cfg.dataloader.batch_size,
collate_fn=collate_batched_meshes,
num_workers=cfg.dataloader.num_workers, shuffle=False)
elif cfg.dataset.type in ['behave', 'behave-objonly', 'behave-humonly', 'behave-dtransl',
'behave-objonly-segm', 'behave-humonly-segm', 'behave-attn',
'behave-test', 'behave-attn-test', 'behave-hum-pe', 'behave-hum-noscale',
'behave-hum-surf', 'behave-objv2v']:
from .behave_dataset import BehaveDataset, NTUDataset, BehaveObjOnly, BehaveHumanOnly, BehaveHumanOnlyPosEnc
from .behave_dataset import BehaveHumanOnlySegmInput, BehaveObjOnlySegmInput, BehaveTestOnly, BehaveHumNoscale
from .behave_dataset import BehaveHumanOnlySurfSample
from .dtransl_dataset import DirectTranslDataset
from .behave_paths import DataPaths
from configs.structured import BehaveDatasetConfig
from .behave_crossattn import BehaveCrossAttnDataset, BehaveCrossAttnTest
from .behave_dataset import BehaveObjOnlyV2V
dataset_cfg: BehaveDatasetConfig = cfg.dataset
# print(dataset_cfg.behave_dir)
train_paths, val_paths = DataPaths.load_splits(dataset_cfg.split_file, dataset_cfg.behave_dir)
# exit(0)
# split validation paths to only consider the selected batches
bs = cfg.dataloader.batch_size
num_batches_total = int(np.ceil(len(val_paths)/cfg.dataloader.batch_size))
end_idx = cfg.run.batch_end if cfg.run.batch_end is not None else num_batches_total
# print(cfg.run.batch_end, cfg.run.batch_start, end_idx)
val_paths = val_paths[cfg.run.batch_start*bs:end_idx*bs]
if cfg.dataset.type == 'behave':
train_type = BehaveDataset
val_datatype = BehaveDataset if 'ntu' not in dataset_cfg.split_file else NTUDataset
elif cfg.dataset.type == 'behave-test':
train_type = BehaveDataset
val_datatype = BehaveTestOnly
elif cfg.dataset.type == 'behave-objonly':
train_type = BehaveObjOnly
val_datatype = BehaveObjOnly
assert 'ntu' not in dataset_cfg.split_file, 'ntu not implemented!'
elif cfg.dataset.type == 'behave-humonly':
train_type = BehaveHumanOnly
val_datatype = BehaveHumanOnly
assert 'ntu' not in dataset_cfg.split_file, 'ntu not implemented!'
elif cfg.dataset.type == 'behave-hum-noscale':
train_type = BehaveHumNoscale
val_datatype = BehaveHumNoscale
elif cfg.dataset.type == 'behave-hum-pe':
train_type = BehaveHumanOnlyPosEnc
val_datatype = BehaveHumanOnlyPosEnc
elif cfg.dataset.type == 'behave-hum-surf':
train_type = BehaveHumanOnlySurfSample
val_datatype = BehaveHumanOnlySurfSample
elif cfg.dataset.type == 'behave-humonly-segm':
assert cfg.dataset.ho_segm_pred_path is not None, 'please specify predicted HO segmentation!'
train_type = BehaveHumanOnly
val_datatype = BehaveHumanOnlySegmInput
assert 'ntu' not in dataset_cfg.split_file, 'ntu not implemented!'
elif cfg.dataset.type == 'behave-objonly-segm':
assert cfg.dataset.ho_segm_pred_path is not None, 'please specify predicted HO segmentation!'
train_type = BehaveObjOnly
val_datatype = BehaveObjOnlySegmInput
assert 'ntu' not in dataset_cfg.split_file, 'ntu not implemented!'
elif cfg.dataset.type == 'behave-dtransl':
train_type = DirectTranslDataset
val_datatype = DirectTranslDataset
elif cfg.dataset.type == 'behave-attn':
train_type = BehaveCrossAttnDataset
val_datatype = BehaveCrossAttnDataset
elif cfg.dataset.type == 'behave-attn-test':
train_type = BehaveCrossAttnDataset
val_datatype = BehaveCrossAttnTest
elif cfg.dataset.type == 'behave-objv2v':
train_type = BehaveObjOnlyV2V
val_datatype = BehaveObjOnlyV2V
else:
raise NotImplementedError
dataset_train = train_type(train_paths, dataset_cfg.max_points, dataset_cfg.fix_sample,
(dataset_cfg.image_size, dataset_cfg.image_size),
split='train', sample_ratio_hum=dataset_cfg.sample_ratio_hum,
normalize_type=dataset_cfg.normalize_type, smpl_type='gt',
load_corr_points=dataset_cfg.load_corr_points,
uniform_obj_sample=dataset_cfg.uniform_obj_sample,
bkg_type=dataset_cfg.bkg_type,
bbox_params=dataset_cfg.bbox_params,
pred_binary=cfg.model.predict_binary,
ho_segm_pred_path=cfg.dataset.ho_segm_pred_path,
compute_closest_points=cfg.model.model_name=='pc2-diff-ho-tune-newloss',
use_gt_transl=cfg.dataset.use_gt_transl,
cam_noise_std=cfg.dataset.cam_noise_std,
sep_same_crop=cfg.dataset.sep_same_crop,
aug_blur=cfg.dataset.aug_blur,
std_coverage=cfg.dataset.std_coverage,
v2v_path=cfg.dataset.v2v_path)
dataset_val = val_datatype(val_paths, dataset_cfg.max_points, dataset_cfg.fix_sample,
(dataset_cfg.image_size, dataset_cfg.image_size),
split='val', sample_ratio_hum=dataset_cfg.sample_ratio_hum,
normalize_type=dataset_cfg.normalize_type, smpl_type=dataset_cfg.smpl_type,
load_corr_points=dataset_cfg.load_corr_points,
test_transl_type=dataset_cfg.test_transl_type,
uniform_obj_sample=dataset_cfg.uniform_obj_sample,
bkg_type=dataset_cfg.bkg_type,
bbox_params=dataset_cfg.bbox_params,
pred_binary=cfg.model.predict_binary,
ho_segm_pred_path=cfg.dataset.ho_segm_pred_path,
compute_closest_points=cfg.model.model_name=='pc2-diff-ho-tune-newloss',
use_gt_transl=cfg.dataset.use_gt_transl,
sep_same_crop=cfg.dataset.sep_same_crop,
std_coverage=cfg.dataset.std_coverage,
v2v_path=cfg.dataset.v2v_path)
# dataset_test = val_datatype(val_paths, dataset_cfg.max_points, dataset_cfg.fix_sample,
# (dataset_cfg.image_size, dataset_cfg.image_size),
# split='test', sample_ratio_hum=dataset_cfg.sample_ratio_hum,
# normalize_type=dataset_cfg.normalize_type, smpl_type=dataset_cfg.smpl_type,
# load_corr_points=dataset_cfg.load_corr_points,
# test_transl_type=dataset_cfg.test_transl_type,
# uniform_obj_sample=dataset_cfg.uniform_obj_sample,
# bkg_type=dataset_cfg.bkg_type,
# bbox_params=dataset_cfg.bbox_params,
# pred_binary=cfg.model.predict_binary,
# ho_segm_pred_path=cfg.dataset.ho_segm_pred_path,
# compute_closest_points=cfg.model.model_name=='pc2-diff-ho-tune-newloss',
# use_gt_transl=cfg.dataset.use_gt_transl,
# sep_same_crop=cfg.dataset.sep_same_crop)
dataloader_train = DataLoader(dataset_train, batch_size=cfg.dataloader.batch_size,
collate_fn=collate_batched_meshes,
num_workers=cfg.dataloader.num_workers, shuffle=True)
shuffle = cfg.run.job == 'train'
dataloader_val = DataLoader(dataset_val, batch_size=cfg.dataloader.batch_size,
collate_fn=collate_batched_meshes,
num_workers=cfg.dataloader.num_workers, shuffle=shuffle)
dataloader_vis = DataLoader(dataset_val, batch_size=cfg.dataloader.batch_size,
collate_fn=collate_batched_meshes,
num_workers=cfg.dataloader.num_workers, shuffle=shuffle)
# datasets = [BehaveDataset(p, dataset_cfg.max_points, dataset_cfg.fix_sample,
# (dataset_cfg.image_size, dataset_cfg.image_size),
# split=s, sample_ratio_hum=dataset_cfg.sample_ratio_hum,
# normalize_type=dataset_cfg.normalize_type) for p, s in zip([train_paths, val_paths, val_paths],
# ['train', 'val', 'test'])]
# dataloader_train = DataLoader(datasets[0], batch_size=cfg.dataloader.batch_size,
# collate_fn=collate_batched_meshes,
# num_workers=cfg.dataloader.num_workers, shuffle=True)
# dataloader_val = DataLoader(datasets[1], batch_size=cfg.dataloader.batch_size,
# collate_fn=collate_batched_meshes,
# num_workers=cfg.dataloader.num_workers, shuffle=False)
# dataloader_vis = DataLoader(datasets[2], batch_size=cfg.dataloader.batch_size,
# collate_fn=collate_batched_meshes,
# num_workers=cfg.dataloader.num_workers, shuffle=False)
elif cfg.dataset.type in ['shape']:
from .shape_dataset import ShapeDataset
from .behave_paths import DataPaths
from configs.structured import ShapeDatasetConfig
dataset_cfg: ShapeDatasetConfig = cfg.dataset
train_paths, _ = DataPaths.load_splits(dataset_cfg.split_file, dataset_cfg.behave_dir)
val_paths = train_paths # same as training, this is for overfitting
# split validation paths to only consider the selected batches
bs = cfg.dataloader.batch_size
num_batches_total = int(np.ceil(len(val_paths) / cfg.dataloader.batch_size))
end_idx = cfg.run.batch_end if cfg.run.batch_end is not None else num_batches_total
# print(cfg.run.batch_end, cfg.run.batch_start, end_idx)
val_paths = val_paths[cfg.run.batch_start * bs:end_idx * bs]
dataset_train = ShapeDataset(train_paths, dataset_cfg.max_points, dataset_cfg.fix_sample,
(dataset_cfg.image_size, dataset_cfg.image_size),
split='train', )
dataset_val = ShapeDataset(val_paths, dataset_cfg.max_points, dataset_cfg.fix_sample,
(dataset_cfg.image_size, dataset_cfg.image_size),
split='train', )
dataloader_train = DataLoader(dataset_train, batch_size=cfg.dataloader.batch_size,
collate_fn=collate_batched_meshes,
num_workers=cfg.dataloader.num_workers, shuffle=True)
shuffle = cfg.run.job == 'train'
dataloader_val = DataLoader(dataset_val, batch_size=cfg.dataloader.batch_size,
collate_fn=collate_batched_meshes,
num_workers=cfg.dataloader.num_workers, shuffle=shuffle)
dataloader_vis = DataLoader(dataset_val, batch_size=cfg.dataloader.batch_size,
collate_fn=collate_batched_meshes,
num_workers=cfg.dataloader.num_workers, shuffle=shuffle)
else:
raise NotImplementedError(cfg.dataset.type)
return dataloader_train, dataloader_val, dataloader_vis
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