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build.py
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build.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
import numpy as np
import operator
import json
import torch.utils.data
from detectron2.utils.comm import get_world_size
from detectron2.data.common import (
DatasetFromList,
MapDataset,
)
from detectron2.data.dataset_mapper import DatasetMapper
from detectron2.data.samplers import (
InferenceSampler,
RepeatFactorTrainingSampler,
TrainingSampler,
)
from detectron2.data.build import (
trivial_batch_collator,
worker_init_reset_seed,
get_detection_dataset_dicts,
build_batch_data_loader,
)
from ubteacher.data.common import (
AspectRatioGroupedSemiSupDatasetTwoCrop,
)
"""
This file contains the default logic to build a dataloader for training or testing.
"""
def divide_label_unlabel(
dataset_dicts, SupPercent, random_data_seed, random_data_seed_path
):
num_all = len(dataset_dicts)
num_label = int(SupPercent / 100.0 * num_all)
# read from pre-generated data seed
with open(random_data_seed_path) as COCO_sup_file:
coco_random_idx = json.load(COCO_sup_file)
labeled_idx = np.array(coco_random_idx[str(SupPercent)][str(random_data_seed)])
assert labeled_idx.shape[0] == num_label, "Number of READ_DATA is mismatched."
label_dicts = []
unlabel_dicts = []
labeled_idx = set(labeled_idx)
for i in range(len(dataset_dicts)):
if i in labeled_idx:
label_dicts.append(dataset_dicts[i])
else:
unlabel_dicts.append(dataset_dicts[i])
return label_dicts, unlabel_dicts
# uesed by supervised-only baseline trainer
def build_detection_semisup_train_loader(cfg, mapper=None):
dataset_dicts = get_detection_dataset_dicts(
cfg.DATASETS.TRAIN,
filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
if cfg.MODEL.KEYPOINT_ON
else 0,
proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN
if cfg.MODEL.LOAD_PROPOSALS
else None,
)
# Divide into labeled and unlabeled sets according to supervision percentage
label_dicts, unlabel_dicts = divide_label_unlabel(
dataset_dicts,
cfg.DATALOADER.SUP_PERCENT,
cfg.DATALOADER.RANDOM_DATA_SEED,
cfg.DATALOADER.RANDOM_DATA_SEED_PATH,
)
dataset = DatasetFromList(label_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(
label_dicts, cfg.DATALOADER.REPEAT_THRESHOLD
)
)
sampler = RepeatFactorTrainingSampler(repeat_factors)
else:
raise ValueError("Unknown training sampler: {}".format(sampler_name))
# list num of labeled and unlabeled
logger.info("Number of training samples " + str(len(dataset)))
logger.info("Supervision percentage " + str(cfg.DATALOADER.SUP_PERCENT))
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,
)
# uesed by evaluation
def build_detection_test_loader(cfg, dataset_name, mapper=None):
dataset_dicts = get_detection_dataset_dicts(
[dataset_name],
filter_empty=False,
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))
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
# uesed by unbiased teacher trainer
def build_detection_semisup_train_loader_two_crops(cfg, mapper=None):
if cfg.DATASETS.CROSS_DATASET: # cross-dataset (e.g., coco-additional)
label_dicts = get_detection_dataset_dicts(
cfg.DATASETS.TRAIN_LABEL,
filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
if cfg.MODEL.KEYPOINT_ON
else 0,
proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN
if cfg.MODEL.LOAD_PROPOSALS
else None,
)
unlabel_dicts = get_detection_dataset_dicts(
cfg.DATASETS.TRAIN_UNLABEL,
filter_empty=False,
min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
if cfg.MODEL.KEYPOINT_ON
else 0,
proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN
if cfg.MODEL.LOAD_PROPOSALS
else None,
)
else: # different degree of supervision (e.g., COCO-supervision)
dataset_dicts = get_detection_dataset_dicts(
cfg.DATASETS.TRAIN,
filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
if cfg.MODEL.KEYPOINT_ON
else 0,
proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN
if cfg.MODEL.LOAD_PROPOSALS
else None,
)
# Divide into labeled and unlabeled sets according to supervision percentage
label_dicts, unlabel_dicts = divide_label_unlabel(
dataset_dicts,
cfg.DATALOADER.SUP_PERCENT,
cfg.DATALOADER.RANDOM_DATA_SEED,
cfg.DATALOADER.RANDOM_DATA_SEED_PATH,
)
label_dataset = DatasetFromList(label_dicts, copy=False)
# exclude the labeled set from unlabeled dataset
unlabel_dataset = DatasetFromList(unlabel_dicts, copy=False)
# include the labeled set in unlabel dataset
# unlabel_dataset = DatasetFromList(dataset_dicts, copy=False)
if mapper is None:
mapper = DatasetMapper(cfg, True)
label_dataset = MapDataset(label_dataset, mapper)
unlabel_dataset = MapDataset(unlabel_dataset, mapper)
sampler_name = cfg.DATALOADER.SAMPLER_TRAIN
logger = logging.getLogger(__name__)
logger.info("Using training sampler {}".format(sampler_name))
if sampler_name == "TrainingSampler":
label_sampler = TrainingSampler(len(label_dataset))
unlabel_sampler = TrainingSampler(len(unlabel_dataset))
elif sampler_name == "RepeatFactorTrainingSampler":
raise NotImplementedError("{} not yet supported.".format(sampler_name))
else:
raise ValueError("Unknown training sampler: {}".format(sampler_name))
return build_semisup_batch_data_loader_two_crop(
(label_dataset, unlabel_dataset),
(label_sampler, unlabel_sampler),
cfg.SOLVER.IMG_PER_BATCH_LABEL,
cfg.SOLVER.IMG_PER_BATCH_UNLABEL,
aspect_ratio_grouping=cfg.DATALOADER.ASPECT_RATIO_GROUPING,
num_workers=cfg.DATALOADER.NUM_WORKERS,
)
# batch data loader
def build_semisup_batch_data_loader_two_crop(
dataset,
sampler,
total_batch_size_label,
total_batch_size_unlabel,
*,
aspect_ratio_grouping=False,
num_workers=0
):
world_size = get_world_size()
assert (
total_batch_size_label > 0 and total_batch_size_label % world_size == 0
), "Total label batch size ({}) must be divisible by the number of gpus ({}).".format(
total_batch_size_label, world_size
)
assert (
total_batch_size_unlabel > 0 and total_batch_size_unlabel % world_size == 0
), "Total unlabel batch size ({}) must be divisible by the number of gpus ({}).".format(
total_batch_size_label, world_size
)
batch_size_label = total_batch_size_label // world_size
batch_size_unlabel = total_batch_size_unlabel // world_size
label_dataset, unlabel_dataset = dataset
label_sampler, unlabel_sampler = sampler
if aspect_ratio_grouping:
label_data_loader = torch.utils.data.DataLoader(
label_dataset,
sampler=label_sampler,
num_workers=num_workers,
batch_sampler=None,
collate_fn=operator.itemgetter(
0
), # don't batch, but yield individual elements
worker_init_fn=worker_init_reset_seed,
) # yield individual mapped dict
unlabel_data_loader = torch.utils.data.DataLoader(
unlabel_dataset,
sampler=unlabel_sampler,
num_workers=num_workers,
batch_sampler=None,
collate_fn=operator.itemgetter(
0
), # don't batch, but yield individual elements
worker_init_fn=worker_init_reset_seed,
) # yield individual mapped dict
return AspectRatioGroupedSemiSupDatasetTwoCrop(
(label_data_loader, unlabel_data_loader),
(batch_size_label, batch_size_unlabel),
)
else:
raise NotImplementedError("ASPECT_RATIO_GROUPING = False is not supported yet")