# Copyright (c) Facebook, Inc. and its affiliates. # Modified by Jialian Wu from https://github.com/facebookresearch/Detic/blob/main/detic/data/custom_dataset_dataloader.py import operator import torch import torch.utils.data from detectron2.utils.comm import get_world_size from detectron2.config import configurable from torch.utils.data.sampler import BatchSampler, Sampler from detectron2.data.common import DatasetFromList, MapDataset from detectron2.data.dataset_mapper import DatasetMapper from detectron2.data.build import get_detection_dataset_dicts, build_batch_data_loader from detectron2.data.samplers import TrainingSampler from detectron2.data.build import worker_init_reset_seed, print_instances_class_histogram from detectron2.data.build import filter_images_with_only_crowd_annotations from detectron2.data.build import filter_images_with_few_keypoints from detectron2.data.build import check_metadata_consistency from detectron2.data.catalog import MetadataCatalog, DatasetCatalog from detectron2.utils import comm import itertools from typing import Optional def _custom_train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None): sampler_name = cfg.DATALOADER.SAMPLER_TRAIN if 'MultiDataset' in sampler_name: dataset_dicts = get_detection_dataset_dicts_with_source( 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, ) else: 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, ) if mapper is None: mapper = DatasetMapper(cfg, True) if sampler is not None: pass elif sampler_name == "TrainingSampler": sampler = TrainingSampler(len(dataset)) elif sampler_name == "MultiDatasetSampler": sampler = MultiDatasetSampler( dataset_dicts, dataset_ratio=cfg.DATALOADER.DATASET_RATIO, ) else: raise ValueError("Unknown training sampler: {}".format(sampler_name)) return { "dataset": dataset_dicts, "sampler": sampler, "mapper": mapper, "total_batch_size": cfg.SOLVER.IMS_PER_BATCH, "num_workers": cfg.DATALOADER.NUM_WORKERS, 'dataset_bs': cfg.DATALOADER.DATASET_BS, 'num_datasets': len(cfg.DATASETS.TRAIN) } @configurable(from_config=_custom_train_loader_from_config) def build_custom_train_loader( dataset, *, mapper, sampler, total_batch_size=16, num_workers=0, num_datasets=1, dataset_bs=1 ): if isinstance(dataset, list): dataset = DatasetFromList(dataset, copy=False) if mapper is not None: dataset = MapDataset(dataset, mapper) if sampler is None: sampler = TrainingSampler(len(dataset)) assert isinstance(sampler, torch.utils.data.sampler.Sampler) return build_dataset_batch_data_loader( dataset_bs, dataset, sampler, total_batch_size, num_datasets=num_datasets, num_workers=num_workers, ) def build_dataset_batch_data_loader( dataset_bs, dataset, sampler, total_batch_size, num_datasets, num_workers=0 ): world_size = get_world_size() assert ( total_batch_size > 0 and total_batch_size % world_size == 0 ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format( total_batch_size, world_size ) data_loader = torch.utils.data.DataLoader( dataset, sampler=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, ) if num_datasets > 1: return MultiDatasets(data_loader, dataset_bs, num_datasets) else: return SingleDataset(data_loader, dataset_bs) def get_detection_dataset_dicts_with_source( dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None ): assert len(dataset_names) dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names] for dataset_name, dicts in zip(dataset_names, dataset_dicts): assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) for source_id, (dataset_name, dicts) in \ enumerate(zip(dataset_names, dataset_dicts)): assert len(dicts), "Dataset '{}' is empty!".format(dataset_name) for d in dicts: d['dataset_source'] = source_id if "annotations" in dicts[0]: try: class_names = MetadataCatalog.get(dataset_name).thing_classes check_metadata_consistency("thing_classes", dataset_name) print_instances_class_histogram(dicts, class_names) except AttributeError: # class names are not available for this dataset pass assert proposal_files is None dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts)) has_instances = "annotations" in dataset_dicts[0] if filter_empty and has_instances: dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts) if min_keypoints > 0 and has_instances: dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints) return dataset_dicts class MultiDatasetSampler(Sampler): def __init__( self, dataset_dicts, dataset_ratio, seed: Optional[int] = None, ): sizes = [0 for _ in range(len(dataset_ratio))] for d in dataset_dicts: sizes[d['dataset_source']] += 1 print('dataset sizes', sizes) self.sizes = sizes assert len(dataset_ratio) == len(sizes), \ 'length of dataset ratio {} should be equal to number if dataset {}'.format( len(dataset_ratio), len(sizes) ) if seed is None: seed = comm.shared_random_seed() self._seed = int(seed) self._rank = comm.get_rank() self._world_size = comm.get_world_size() self.dataset_ids = torch.tensor( [d['dataset_source'] for d in dataset_dicts], dtype=torch.long) self.dataset_ratio = dataset_ratio dataset_weight = [torch.ones(s) * max(sizes) / s * r / sum(dataset_ratio) \ for i, (r, s) in enumerate(zip(dataset_ratio, sizes))] dataset_weight = torch.cat(dataset_weight) self.weights = dataset_weight self.sample_epoch_size = len(self.weights) def __iter__(self): start = self._rank yield from itertools.islice( self._infinite_indices(), start, None, self._world_size) def _infinite_indices(self): g = torch.Generator() g.manual_seed(self._seed) while True: if len(self.dataset_ratio) > 1: # multiple datasets ids = torch.multinomial( self.weights, self.sample_epoch_size, generator=g, replacement=True) nums = [(self.dataset_ids[ids] == i).sum().int().item() \ for i in range(len(self.sizes))] yield from ids else: # single dataset yield from torch.randperm(self.sizes[0], generator=g).tolist() class SingleDataset(torch.utils.data.IterableDataset): def __init__(self, dataset, batch_sizes): self.dataset = dataset self.batch_sizes = batch_sizes self._buckets = [[] for _ in range(2)] def __iter__(self): for d in self.dataset: w, h = d["width"], d["height"] aspect_ratio_bucket_id = 0 if w > h else 1 bucket_id = aspect_ratio_bucket_id bucket = self._buckets[bucket_id] bucket.append(d) if len(bucket) == self.batch_sizes: yield bucket[:] del bucket[:] class MultiDatasets(torch.utils.data.IterableDataset): def __init__(self, dataset, batch_sizes, num_datasets): self.dataset = dataset self.batch_sizes = batch_sizes self._buckets = [[] for _ in range(2 * num_datasets)] self.iter_idx = 0 self.num_datasets = num_datasets def __iter__(self): for d in self.dataset: w, h = d["width"], d["height"] aspect_ratio_bucket_id = 0 if w > h else 1 bucket_id = d['dataset_source'] * 2 + aspect_ratio_bucket_id bucket = self._buckets[bucket_id] if len(bucket) < self.batch_sizes: bucket.append(d) selected_dataset = self.iter_idx % self.num_datasets if len(bucket) == self.batch_sizes and selected_dataset == d['dataset_source']: self.iter_idx += 1 yield bucket[:] del bucket[:]