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RE-OWOD/projects/DensePose/densepose/data/build.py

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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import itertools
import logging
import numpy as np
from collections import UserDict
from typing import Any, Callable, Collection, Dict, Iterable, List, Optional, Sequence
import torch
from torch.utils.data.dataset import Dataset
from detectron2.config import CfgNode
from detectron2.data.build import (
build_batch_data_loader,
load_proposals_into_dataset,
print_instances_class_histogram,
trivial_batch_collator,
)
from detectron2.data.catalog import DatasetCatalog, Metadata, MetadataCatalog
from detectron2.data.common import DatasetFromList, MapDataset
from detectron2.data.samplers import InferenceSampler, RepeatFactorTrainingSampler, TrainingSampler
from detectron2.utils.comm import get_world_size
from densepose.config import get_bootstrap_dataset_config
from .combined_loader import CombinedDataLoader, Loader
from .dataset_mapper import DatasetMapper
from .datasets.coco import DENSEPOSE_KEYS_WITHOUT_MASK as DENSEPOSE_COCO_KEYS_WITHOUT_MASK
from .datasets.coco import DENSEPOSE_MASK_KEY as DENSEPOSE_COCO_MASK_KEY
from .datasets.dataset_type import DatasetType
from .inference_based_loader import InferenceBasedLoader, ScoreBasedFilter
from .samplers import (
DensePoseConfidenceBasedSampler,
DensePoseUniformSampler,
MaskFromDensePoseSampler,
PredictionToGroundTruthSampler,
)
from .transform import ImageResizeTransform
from .video import (
FirstKFramesSelector,
FrameSelectionStrategy,
LastKFramesSelector,
RandomKFramesSelector,
VideoKeyframeDataset,
video_list_from_file,
)
__all__ = ["build_detection_train_loader", "build_detection_test_loader"]
Instance = Dict[str, Any]
InstancePredicate = Callable[[Instance], bool]
def _compute_num_images_per_worker(cfg: CfgNode):
num_workers = get_world_size()
images_per_batch = cfg.SOLVER.IMS_PER_BATCH
assert (
images_per_batch % num_workers == 0
), "SOLVER.IMS_PER_BATCH ({}) must be divisible by the number of workers ({}).".format(
images_per_batch, num_workers
)
assert (
images_per_batch >= num_workers
), "SOLVER.IMS_PER_BATCH ({}) must be larger than the number of workers ({}).".format(
images_per_batch, num_workers
)
images_per_worker = images_per_batch // num_workers
return images_per_worker
def _map_category_id_to_contiguous_id(dataset_name: str, dataset_dicts: Iterable[Instance]):
meta = MetadataCatalog.get(dataset_name)
for dataset_dict in dataset_dicts:
for ann in dataset_dict["annotations"]:
ann["category_id"] = meta.thing_dataset_id_to_contiguous_id[ann["category_id"]]
def _add_category_id_to_contiguous_id_maps_to_metadata(dataset_names: Iterable[str]):
# merge categories for all datasets
merged_categories = {}
for dataset_name in dataset_names:
meta = MetadataCatalog.get(dataset_name)
for cat_id, cat_name in meta.categories.items():
if cat_id not in merged_categories:
merged_categories[cat_id] = (cat_name, dataset_name)
continue
cat_name_other, dataset_name_other = merged_categories[cat_id]
if cat_name_other != cat_name:
raise ValueError(
f"Incompatible categories for category ID {cat_id}: "
f'dataset {dataset_name} value "{cat_name}", '
f'dataset {dataset_name_other} value "{cat_name_other}"'
)
merged_cat_id_to_cont_id = {}
for i, cat_id in enumerate(sorted(merged_categories.keys())):
merged_cat_id_to_cont_id[cat_id] = i
# add category maps to metadata
for dataset_name in dataset_names:
meta = MetadataCatalog.get(dataset_name)
categories = meta.get("categories")
meta.thing_classes = [categories[cat_id] for cat_id in sorted(categories.keys())]
meta.thing_dataset_id_to_contiguous_id = {
cat_id: merged_cat_id_to_cont_id[cat_id] for cat_id in sorted(categories.keys())
}
meta.thing_contiguous_id_to_dataset_id = {
merged_cat_id_to_cont_id[cat_id]: cat_id for cat_id in sorted(categories.keys())
}
def _maybe_create_general_keep_instance_predicate(cfg: CfgNode) -> Optional[InstancePredicate]:
def has_annotations(instance: Instance) -> bool:
return "annotations" in instance
def has_only_crowd_anotations(instance: Instance) -> bool:
for ann in instance["annotations"]:
if ann.get("is_crowd", 0) == 0:
return False
return True
def general_keep_instance_predicate(instance: Instance) -> bool:
return has_annotations(instance) and not has_only_crowd_anotations(instance)
if not cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS:
return None
return general_keep_instance_predicate
def _maybe_create_keypoints_keep_instance_predicate(cfg: CfgNode) -> Optional[InstancePredicate]:
min_num_keypoints = cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
def has_sufficient_num_keypoints(instance: Instance) -> bool:
num_kpts = sum(
(np.array(ann["keypoints"][2::3]) > 0).sum()
for ann in instance["annotations"]
if "keypoints" in ann
)
return num_kpts >= min_num_keypoints
if cfg.MODEL.KEYPOINT_ON and (min_num_keypoints > 0):
return has_sufficient_num_keypoints
return None
def _maybe_create_mask_keep_instance_predicate(cfg: CfgNode) -> Optional[InstancePredicate]:
if not cfg.MODEL.MASK_ON:
return None
def has_mask_annotations(instance: Instance) -> bool:
return any("segmentation" in ann for ann in instance["annotations"])
return has_mask_annotations
def _maybe_create_densepose_keep_instance_predicate(cfg: CfgNode) -> Optional[InstancePredicate]:
if not cfg.MODEL.DENSEPOSE_ON:
return None
use_masks = cfg.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS
def has_densepose_annotations(instance: Instance) -> bool:
for ann in instance["annotations"]:
if all(key in ann for key in DENSEPOSE_COCO_KEYS_WITHOUT_MASK) and (
(DENSEPOSE_COCO_MASK_KEY in ann) or ("segmentation" in ann)
):
return True
if use_masks and "segmentation" in ann:
return True
return False
return has_densepose_annotations
def _maybe_create_specific_keep_instance_predicate(cfg: CfgNode) -> Optional[InstancePredicate]:
specific_predicate_creators = [
_maybe_create_keypoints_keep_instance_predicate,
_maybe_create_mask_keep_instance_predicate,
_maybe_create_densepose_keep_instance_predicate,
]
predicates = [creator(cfg) for creator in specific_predicate_creators]
predicates = [p for p in predicates if p is not None]
if not predicates:
return None
def combined_predicate(instance: Instance) -> bool:
return any(p(instance) for p in predicates)
return combined_predicate
def _get_train_keep_instance_predicate(cfg: CfgNode):
general_keep_predicate = _maybe_create_general_keep_instance_predicate(cfg)
combined_specific_keep_predicate = _maybe_create_specific_keep_instance_predicate(cfg)
def combined_general_specific_keep_predicate(instance: Instance) -> bool:
return general_keep_predicate(instance) and combined_specific_keep_predicate(instance)
if (general_keep_predicate is None) and (combined_specific_keep_predicate is None):
return None
if general_keep_predicate is None:
return combined_specific_keep_predicate
if combined_specific_keep_predicate is None:
return general_keep_predicate
return combined_general_specific_keep_predicate
def _get_test_keep_instance_predicate(cfg: CfgNode):
general_keep_predicate = _maybe_create_general_keep_instance_predicate(cfg)
return general_keep_predicate
def _maybe_filter_and_map_categories(
dataset_name: str, dataset_dicts: List[Instance]
) -> List[Instance]:
meta = MetadataCatalog.get(dataset_name)
whitelisted_categories = meta.get("whitelisted_categories")
category_map = meta.get("category_map", {})
if whitelisted_categories is None and not category_map:
return dataset_dicts
filtered_dataset_dicts = []
for dataset_dict in dataset_dicts:
anns = []
for ann in dataset_dict["annotations"]:
cat_id = ann["category_id"]
if whitelisted_categories is not None and cat_id not in whitelisted_categories:
continue
ann["category_id"] = category_map.get(cat_id, cat_id)
anns.append(ann)
dataset_dict["annotations"] = anns
filtered_dataset_dicts.append(dataset_dict)
return filtered_dataset_dicts
def _add_category_whitelists_to_metadata(cfg: CfgNode):
for dataset_name, whitelisted_cat_ids in cfg.DATASETS.WHITELISTED_CATEGORIES.items():
meta = MetadataCatalog.get(dataset_name)
meta.whitelisted_categories = whitelisted_cat_ids
logger = logging.getLogger(__name__)
logger.info(
"Whitelisted categories for dataset {}: {}".format(
dataset_name, meta.whitelisted_categories
)
)
def _add_category_maps_to_metadata(cfg: CfgNode):
for dataset_name, category_map in cfg.DATASETS.CATEGORY_MAPS.items():
category_map = {
int(cat_id_src): int(cat_id_dst) for cat_id_src, cat_id_dst in category_map.items()
}
meta = MetadataCatalog.get(dataset_name)
meta.category_map = category_map
logger = logging.getLogger(__name__)
logger.info("Category maps for dataset {}: {}".format(dataset_name, meta.category_map))
def combine_detection_dataset_dicts(
dataset_names: Collection[str],
keep_instance_predicate: Optional[InstancePredicate] = None,
proposal_files: Optional[Collection[str]] = None,
) -> List[Instance]:
"""
Load and prepare dataset dicts for training / testing
Args:
dataset_names (Collection[str]): a list of dataset names
keep_instance_predicate (Callable: Dict[str, Any] -> bool): predicate
applied to instance dicts which defines whether to keep the instance
proposal_files (Collection[str]): if given, a list of object proposal files
that match each dataset in `dataset_names`.
"""
assert len(dataset_names)
if proposal_files is None:
proposal_files = [None] * len(dataset_names)
assert len(dataset_names) == len(proposal_files)
# load annotations and dataset metadata
dataset_map = {}
for dataset_name in dataset_names:
dataset_dicts = DatasetCatalog.get(dataset_name)
dataset_map[dataset_name] = dataset_dicts
# initialize category maps
_add_category_id_to_contiguous_id_maps_to_metadata(dataset_names)
# apply category maps
all_datasets_dicts = []
for dataset_name, proposal_file in zip(dataset_names, proposal_files):
dataset_dicts = dataset_map[dataset_name]
assert len(dataset_dicts), f"Dataset '{dataset_name}' is empty!"
if proposal_file is not None:
dataset_dicts = load_proposals_into_dataset(dataset_dicts, proposal_file)
dataset_dicts = _maybe_filter_and_map_categories(dataset_name, dataset_dicts)
_map_category_id_to_contiguous_id(dataset_name, dataset_dicts)
print_instances_class_histogram(
dataset_dicts, MetadataCatalog.get(dataset_name).thing_classes
)
all_datasets_dicts.append(dataset_dicts)
if keep_instance_predicate is not None:
all_datasets_dicts_plain = [
d
for d in itertools.chain.from_iterable(all_datasets_dicts)
if keep_instance_predicate(d)
]
else:
all_datasets_dicts_plain = list(itertools.chain.from_iterable(all_datasets_dicts))
return all_datasets_dicts_plain
def build_detection_train_loader(cfg: CfgNode, mapper=None):
"""
A data loader is created in a way similar to that of Detectron2.
The main differences are:
- it allows to combine datasets with different but compatible object category sets
The data loader is created by the following steps:
1. Use the dataset names in config to query :class:`DatasetCatalog`, and obtain a list of dicts.
2. Start workers to work on the dicts. Each worker will:
* Map each metadata dict into another format to be consumed by the model.
* Batch them by simply putting dicts into a list.
The batched ``list[mapped_dict]`` is what this dataloader will return.
Args:
cfg (CfgNode): the config
mapper (callable): a callable which takes a sample (dict) from dataset and
returns the format to be consumed by the model.
By default it will be `DatasetMapper(cfg, True)`.
Returns:
an infinite iterator of training data
"""
_add_category_whitelists_to_metadata(cfg)
_add_category_maps_to_metadata(cfg)
dataset_dicts = combine_detection_dataset_dicts(
cfg.DATASETS.TRAIN,
keep_instance_predicate=_get_train_keep_instance_predicate(cfg),
proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
)
dataset = DatasetFromList(dataset_dicts, copy=False)
if mapper is None:
mapper = DatasetMapper(cfg, True)
dataset = MapDataset(dataset, mapper)
sampler_name = cfg.DATALOADER.SAMPLER_TRAIN
logger = logging.getLogger(__name__)
logger.info("Using training sampler {}".format(sampler_name))
if sampler_name == "TrainingSampler":
sampler = TrainingSampler(len(dataset))
elif sampler_name == "RepeatFactorTrainingSampler":
repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
dataset_dicts, cfg.DATALOADER.REPEAT_THRESHOLD
)
sampler = RepeatFactorTrainingSampler(repeat_factors)
else:
raise ValueError("Unknown training sampler: {}".format(sampler_name))
return build_batch_data_loader(
dataset,
sampler,
cfg.SOLVER.IMS_PER_BATCH,
aspect_ratio_grouping=cfg.DATALOADER.ASPECT_RATIO_GROUPING,
num_workers=cfg.DATALOADER.NUM_WORKERS,
)
def build_detection_test_loader(cfg, dataset_name, mapper=None):
"""
Similar to `build_detection_train_loader`.
But this function uses the given `dataset_name` argument (instead of the names in cfg),
and uses batch size 1.
Args:
cfg: a detectron2 CfgNode
dataset_name (str): a name of the dataset that's available in the DatasetCatalog
mapper (callable): a callable which takes a sample (dict) from dataset
and returns the format to be consumed by the model.
By default it will be `DatasetMapper(cfg, False)`.
Returns:
DataLoader: a torch DataLoader, that loads the given detection
dataset, with test-time transformation and batching.
"""
_add_category_whitelists_to_metadata(cfg)
_add_category_maps_to_metadata(cfg)
dataset_dicts = combine_detection_dataset_dicts(
[dataset_name],
keep_instance_predicate=_get_test_keep_instance_predicate(cfg),
proposal_files=[
cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(dataset_name)]
]
if cfg.MODEL.LOAD_PROPOSALS
else None,
)
dataset = DatasetFromList(dataset_dicts)
if mapper is None:
mapper = DatasetMapper(cfg, False)
dataset = MapDataset(dataset, mapper)
sampler = InferenceSampler(len(dataset))
# Always use 1 image per worker during inference since this is the
# standard when reporting inference time in papers.
batch_sampler = torch.utils.data.sampler.BatchSampler(sampler, 1, drop_last=False)
data_loader = torch.utils.data.DataLoader(
dataset,
num_workers=cfg.DATALOADER.NUM_WORKERS,
batch_sampler=batch_sampler,
collate_fn=trivial_batch_collator,
)
return data_loader
def build_frame_selector(cfg: CfgNode):
strategy = FrameSelectionStrategy(cfg.STRATEGY)
if strategy == FrameSelectionStrategy.RANDOM_K:
frame_selector = RandomKFramesSelector(cfg.NUM_IMAGES)
elif strategy == FrameSelectionStrategy.FIRST_K:
frame_selector = FirstKFramesSelector(cfg.NUM_IMAGES)
elif strategy == FrameSelectionStrategy.LAST_K:
frame_selector = LastKFramesSelector(cfg.NUM_IMAGES)
elif strategy == FrameSelectionStrategy.ALL:
frame_selector = None
return frame_selector
def build_transform(cfg: CfgNode, data_type: str):
if cfg.TYPE == "resize":
if data_type == "image":
return ImageResizeTransform(cfg.MIN_SIZE, cfg.MAX_SIZE)
raise ValueError(f"Unknown transform {cfg.TYPE} for data type {data_type}")
def build_combined_loader(cfg: CfgNode, loaders: Collection[Loader], ratios: Sequence[float]):
images_per_worker = _compute_num_images_per_worker(cfg)
return CombinedDataLoader(loaders, images_per_worker, ratios)
def build_bootstrap_dataset(dataset_name: str, cfg: CfgNode) -> Sequence[torch.Tensor]:
"""
Build dataset that provides data to bootstrap on
Args:
dataset_name (str): Name of the dataset, needs to have associated metadata
to load the data
cfg (CfgNode): bootstrapping config
Returns:
Sequence[Tensor] - dataset that provides image batches, Tensors of size
[N, C, H, W] of type float32
"""
logger = logging.getLogger(__name__)
meta = MetadataCatalog.get(dataset_name)
factory = BootstrapDatasetFactoryCatalog.get(meta.dataset_type)
dataset = None
if factory is not None:
dataset = factory(meta, cfg)
if dataset is None:
logger.warning(f"Failed to create dataset {dataset_name} of type {meta.dataset_type}")
return dataset
def build_data_sampler(cfg: CfgNode):
if cfg.TYPE == "densepose_uniform":
data_sampler = PredictionToGroundTruthSampler()
# transform densepose pred -> gt
data_sampler.register_sampler(
"pred_densepose",
"gt_densepose",
DensePoseUniformSampler(count_per_class=cfg.COUNT_PER_CLASS),
)
data_sampler.register_sampler("pred_densepose", "gt_masks", MaskFromDensePoseSampler())
return data_sampler
elif cfg.TYPE == "densepose_UV_confidence":
data_sampler = PredictionToGroundTruthSampler()
# transform densepose pred -> gt
data_sampler.register_sampler(
"pred_densepose",
"gt_densepose",
DensePoseConfidenceBasedSampler(
confidence_channel="sigma_2",
count_per_class=cfg.COUNT_PER_CLASS,
search_proportion=0.5,
),
)
data_sampler.register_sampler("pred_densepose", "gt_masks", MaskFromDensePoseSampler())
return data_sampler
elif cfg.TYPE == "densepose_fine_segm_confidence":
data_sampler = PredictionToGroundTruthSampler()
# transform densepose pred -> gt
data_sampler.register_sampler(
"pred_densepose",
"gt_densepose",
DensePoseConfidenceBasedSampler(
confidence_channel="fine_segm_confidence",
count_per_class=cfg.COUNT_PER_CLASS,
search_proportion=0.5,
),
)
data_sampler.register_sampler("pred_densepose", "gt_masks", MaskFromDensePoseSampler())
return data_sampler
elif cfg.TYPE == "densepose_coarse_segm_confidence":
data_sampler = PredictionToGroundTruthSampler()
# transform densepose pred -> gt
data_sampler.register_sampler(
"pred_densepose",
"gt_densepose",
DensePoseConfidenceBasedSampler(
confidence_channel="coarse_segm_confidence",
count_per_class=cfg.COUNT_PER_CLASS,
search_proportion=0.5,
),
)
data_sampler.register_sampler("pred_densepose", "gt_masks", MaskFromDensePoseSampler())
return data_sampler
raise ValueError(f"Unknown data sampler type {cfg.TYPE}")
def build_data_filter(cfg: CfgNode):
if cfg.TYPE == "detection_score":
min_score = cfg.MIN_VALUE
return ScoreBasedFilter(min_score=min_score)
raise ValueError(f"Unknown data filter type {cfg.TYPE}")
def build_inference_based_loader(
cfg: CfgNode, dataset_cfg: CfgNode, model: torch.nn.Module
) -> InferenceBasedLoader:
"""
Constructs data loader based on inference results of a model.
"""
dataset = build_bootstrap_dataset(dataset_cfg.DATASET, dataset_cfg.IMAGE_LOADER)
training_sampler = TrainingSampler(len(dataset))
data_loader = torch.utils.data.DataLoader(
dataset,
batch_size=dataset_cfg.IMAGE_LOADER.BATCH_SIZE,
sampler=training_sampler,
num_workers=dataset_cfg.IMAGE_LOADER.NUM_WORKERS,
collate_fn=trivial_batch_collator,
)
return InferenceBasedLoader(
model,
data_loader=data_loader,
data_sampler=build_data_sampler(dataset_cfg.DATA_SAMPLER),
data_filter=build_data_filter(dataset_cfg.FILTER),
shuffle=True,
batch_size=dataset_cfg.INFERENCE.OUTPUT_BATCH_SIZE,
inference_batch_size=dataset_cfg.INFERENCE.INPUT_BATCH_SIZE,
)
def has_inference_based_loaders(cfg: CfgNode) -> bool:
"""
Returns True, if at least one inferense-based loader must
be instantiated for training
"""
return len(cfg.BOOTSTRAP_DATASETS) > 0
def build_inference_based_loaders(
cfg: CfgNode, model: torch.nn.Module
) -> List[InferenceBasedLoader]:
loaders = []
ratios = []
for dataset_spec in cfg.BOOTSTRAP_DATASETS:
dataset_cfg = get_bootstrap_dataset_config().clone()
dataset_cfg.merge_from_other_cfg(CfgNode(dataset_spec))
loader = build_inference_based_loader(cfg, dataset_cfg, model)
loaders.append(loader)
ratios.append(dataset_cfg.RATIO)
return loaders, ratios
def build_video_list_dataset(meta: Metadata, cfg: CfgNode):
video_list_fpath = meta.video_list_fpath
video_base_path = meta.video_base_path
if cfg.TYPE == "video_keyframe":
frame_selector = build_frame_selector(cfg.SELECT)
transform = build_transform(cfg.TRANSFORM, data_type="image")
video_list = video_list_from_file(video_list_fpath, video_base_path)
return VideoKeyframeDataset(video_list, frame_selector, transform)
class _BootstrapDatasetFactoryCatalog(UserDict):
"""
A global dictionary that stores information about bootstrapped datasets creation functions
from metadata and config, for diverse DatasetType
"""
def register(self, dataset_type: DatasetType, factory: Callable[[Metadata, CfgNode], Dataset]):
"""
Args:
dataset_type (DatasetType): a DatasetType e.g. DatasetType.VIDEO_LIST
factory (Callable[Metadata, CfgNode]): a callable which takes Metadata and cfg
arguments and returns a dataset object.
"""
assert dataset_type not in self, "Dataset '{}' is already registered!".format(dataset_type)
self[dataset_type] = factory
BootstrapDatasetFactoryCatalog = _BootstrapDatasetFactoryCatalog()
BootstrapDatasetFactoryCatalog.register(DatasetType.VIDEO_LIST, build_video_list_dataset)
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