deep-person-reid/torchreid/data_manager.py

from __future__ import absolute_import
from __future__ import print_function

from torch.utils.data import DataLoader

from .dataset_loader import ImageDataset, VideoDataset
from .datasets import init_imgreid_dataset, init_vidreid_dataset
from .transforms import build_transforms
from .samplers import build_train_sampler


class BaseDataManager(object):

    @property
    def num_train_pids(self):
        return self._num_train_pids

    @property
    def num_train_cams(self):
        return self._num_train_cams

    def return_dataloaders(self):
        """
        Return trainloader and testloader dictionary
        """
        return self.trainloader, self.testloader_dict

    def return_testdataset_by_name(self, name):
        """
        Return query and gallery, each containing a list of (img_path, pid, camid).
        """
        return self.testdataset_dict[name]['query'], self.testdataset_dict[name]['gallery']


class ImageDataManager(BaseDataManager):
    """
    Image-ReID data manager
    """

    def __init__(self,
                 use_gpu,
                 source_names,
                 target_names,
                 root='data',
                 split_id=0,
                 height=256,
                 width=128,
                 train_batch_size=32,
                 test_batch_size=100,
                 workers=4,
                 train_sampler='',
                 augdata_re=False, # use random erasing for data augmentation
                 num_instances=4, # number of instances per identity (for RandomIdentitySampler)
                 cuhk03_labeled=False, # use cuhk03's labeled or detected images
                 cuhk03_classic_split=False, # use cuhk03's classic split or 767/700 split
                 market1501_500k=False, # add 500k distractors to the gallery set for market1501
                 ):
        super(ImageDataManager, self).__init__()

        print('=> Initializing TRAIN (source) datasets')
        train = []
        self._num_train_pids = 0
        self._num_train_cams = 0

        for name in source_names:
            dataset = init_imgreid_dataset(
                root=root, name=name, split_id=split_id, cuhk03_labeled=cuhk03_labeled,
                cuhk03_classic_split=cuhk03_classic_split, market1501_500k=market1501_500k
            )

            for img_path, pid, camid in dataset.train:
                pid += self._num_train_pids
                camid += self._num_train_cams
                train.append((img_path, pid, camid))

            self._num_train_pids += dataset.num_train_pids
            self._num_train_cams += dataset.num_train_cams

        transform_train, transform_test = build_transforms(height, width, augdata_re=augdata_re)
        
        train_sampler = build_train_sampler(
            train, train_sampler,
            train_batch_size=train_batch_size,
            num_instances=num_instances,
        )

        self.trainloader = DataLoader(
            ImageDataset(train, transform=transform_train), sampler=train_sampler,
            batch_size=train_batch_size, shuffle=False, num_workers=workers,
            pin_memory=use_gpu, drop_last=True
        )

        print('=> Initializing TEST (target) datasets')
        self.testloader_dict = {name: {'query': None, 'gallery': None} for name in target_names}
        self.testdataset_dict = {name: {'query': None, 'gallery': None} for name in target_names}
        
        for name in target_names:
            dataset = init_imgreid_dataset(
                root=root, name=name, split_id=split_id, cuhk03_labeled=cuhk03_labeled,
                cuhk03_classic_split=cuhk03_classic_split, market1501_500k=market1501_500k
            )

            self.testloader_dict[name]['query'] = DataLoader(
                ImageDataset(dataset.query, transform=transform_test),
                batch_size=test_batch_size, shuffle=False, num_workers=workers,
                pin_memory=use_gpu, drop_last=False
            )

            self.testloader_dict[name]['gallery'] = DataLoader(
                ImageDataset(dataset.gallery, transform=transform_test),
                batch_size=test_batch_size, shuffle=False, num_workers=workers,
                pin_memory=use_gpu, drop_last=False
            )

            self.testdataset_dict[name]['query'] = dataset.query
            self.testdataset_dict[name]['gallery'] = dataset.gallery

        print('\n')
        print('  **************** Summary ****************')
        print('  train names      : {}'.format(source_names))
        print('  # train datasets : {}'.format(len(source_names)))
        print('  # train ids      : {}'.format(self.num_train_pids))
        print('  # train images   : {}'.format(len(train)))
        print('  # train cameras  : {}'.format(self.num_train_cams))
        print('  test names       : {}'.format(target_names))
        print('  *****************************************')
        print('\n')


class VideoDataManager(BaseDataManager):
    """
    Video-ReID data manager
    """

    def __init__(self,
                 use_gpu,
                 source_names,
                 target_names,
                 root='data',
                 split_id=0,
                 height=256,
                 width=128,
                 train_batch_size=32,
                 test_batch_size=100,
                 workers=4,
                 train_sampler='',
                 augdata_re=False, # use random erasing for data augmentation
                 num_instances=4,
                 seq_len=15,
                 sample_method='evenly',
                 image_training=True # train the video-reid model with images rather than tracklets
                 ):
        super(VideoDataManager, self).__init__()

        print('=> Initializing TRAIN (source) datasets')
        train = []
        self._num_train_pids = 0
        self._num_train_cams = 0

        for name in source_names:
            dataset = init_vidreid_dataset(root=root, name=name, split_id=split_id)

            for img_paths, pid, camid in dataset.train:
                pid += self._num_train_pids
                camid += self._num_train_cams
                if image_training:
                    # decompose tracklets into images
                    for img_path in img_paths:
                        train.append((img_path, pid, camid))
                else:
                    train.append((img_paths, pid, camid))

            self._num_train_pids += dataset.num_train_pids
            self._num_train_cams += dataset.num_train_cams

        transform_train, transform_test = build_transforms(height, width, augdata_re=augdata_re)
        train_sampler = build_train_sampler(
            train, train_sampler,
            train_batch_size=train_batch_size,
            num_instances=num_instances,
        )

        if image_training:
            # each batch has image data of shape (batch, channel, height, width)
            self.trainloader = DataLoader(
                ImageDataset(train, transform=transform_train), sampler=train_sampler,
                batch_size=train_batch_size, shuffle=False, num_workers=workers,
                pin_memory=use_gpu, drop_last=True
            )
        
        else:
            # each batch has image data of shape (batch, seq_len, channel, height, width)
            # note: this requires new training scripts
            self.trainloader = DataLoader(
                VideoDataset(train, seq_len=seq_len, sample_method=sample_method, transform=transform_train),
                batch_size=train_batch_size, shuffle=True, num_workers=workers,
                pin_memory=use_gpu, drop_last=True
            )

        print('=> Initializing TEST (target) datasets')
        self.testloader_dict = {name: {'query': None, 'gallery': None} for name in target_names}
        self.testdataset_dict = {name: {'query': None, 'gallery': None} for name in target_names}

        for name in target_names:
            dataset = init_vidreid_dataset(root=root, name=name, split_id=split_id)

            self.testloader_dict[name]['query'] = DataLoader(
                VideoDataset(dataset.query, seq_len=seq_len, sample_method=sample_method, transform=transform_test),
                batch_size=test_batch_size, shuffle=False, num_workers=workers,
                pin_memory=use_gpu, drop_last=False,
            )

            self.testloader_dict[name]['gallery'] = DataLoader(
                VideoDataset(dataset.gallery, seq_len=seq_len, sample_method=sample_method, transform=transform_test),
                batch_size=test_batch_size, shuffle=False, num_workers=workers,
                pin_memory=use_gpu, drop_last=False,
            )

            self.testdataset_dict[name]['query'] = dataset.query
            self.testdataset_dict[name]['gallery'] = dataset.gallery

        print('\n')
        print('  **************** Summary ****************')
        print('  train names       : {}'.format(source_names))
        print('  # train datasets  : {}'.format(len(source_names)))
        print('  # train ids       : {}'.format(self.num_train_pids))
        if image_training:
            print('  # train images   : {}'.format(len(train)))
        else:
            print('  # train tracklets: {}'.format(len(train)))
        print('  # train cameras   : {}'.format(self.num_train_cams))
        print('  test names        : {}'.format(target_names))
        print('  *****************************************')
        print('\n')