def build_cls_test_loader(cfg, dataset_name, mapper=None, **kwargs):
cfg = cfg.clone()
dataset = DATASET_REGISTRY.get(dataset_name)(root=_root, **kwargs)
if comm.is_main_process():
dataset.show_test()
test_items = dataset.query
if mapper is not None:
transforms = mapper
else:
transforms = build_transforms(cfg, is_train=False)
test_set = CommDataset(test_items, transforms, relabel=False)
mini_batch_size = cfg.TEST.IMS_PER_BATCH // comm.get_world_size()
data_sampler = samplers.InferenceSampler(len(test_set))
batch_sampler = torch.utils.data.BatchSampler(data_sampler,
mini_batch_size, False)
test_loader = DataLoader(
test_set,
batch_sampler=batch_sampler,
num_workers=4, # save some memory
collate_fn=fast_batch_collator,
pin_memory=True,
)
return test_loader
def build_test_loader(cls, cfg, test_set):
logger = logging.getLogger('fastreid')
logger.info("Prepare testing loader")
# test_loader = DataLoader(
# # Preprocessor(test_set),
# test_set,
# batch_size=cfg.TEST.IMS_PER_BATCH,
# num_workers=cfg.DATALOADER.NUM_WORKERS,
# shuffle=False,
# pin_memory=True,
# )
test_batch_size = cfg.TEST.IMS_PER_BATCH
mini_batch_size = test_batch_size // comm.get_world_size()
num_workers = cfg.DATALOADER.NUM_WORKERS
data_sampler = samplers.InferenceSampler(len(test_set))
batch_sampler = BatchSampler(data_sampler, mini_batch_size, False)
test_loader = DataLoaderX(
comm.get_local_rank(),
dataset=test_set,
batch_sampler=batch_sampler,
num_workers=num_workers, # save some memory
collate_fn=fast_batch_collator,
pin_memory=True,
)
return test_loader
def build_attr_test_loader(cfg, dataset_name):
cfg = cfg.clone()
cfg.defrost()
dataset = DATASET_REGISTRY.get(dataset_name)(
root=_root, combineall=cfg.DATASETS.COMBINEALL)
if comm.is_main_process():
dataset.show_test()
test_items = dataset.test
test_transforms = build_transforms(cfg, is_train=False)
test_set = AttrDataset(test_items, dataset.attr_dict, test_transforms)
mini_batch_size = cfg.TEST.IMS_PER_BATCH // comm.get_world_size()
data_sampler = samplers.InferenceSampler(len(test_set))
batch_sampler = torch.utils.data.BatchSampler(data_sampler,
mini_batch_size, False)
test_loader = DataLoader(
test_set,
batch_sampler=batch_sampler,
num_workers=4, # save some memory
collate_fn=fast_batch_collator,
pin_memory=True,
)
return test_loader
def build_face_test_loader(cfg, dataset_name, **kwargs):
dataset = DATASET_REGISTRY.get(dataset_name)(root=_root, **kwargs)
if comm.is_main_process():
dataset.show_test()
test_set = FaceCommDataset(dataset.carray, dataset.is_same)
mini_batch_size = cfg.TEST.IMS_PER_BATCH // comm.get_world_size()
data_sampler = samplers.InferenceSampler(len(test_set))
batch_sampler = torch.utils.data.BatchSampler(data_sampler,
mini_batch_size, False)
test_loader = DataLoader(
test_set,
batch_sampler=batch_sampler,
num_workers=4, # save some memory
collate_fn=fast_batch_collator,
pin_memory=True,
)
return test_loader, test_set.labels