def build_train_loader(cls, cfg):
"""
Returns:
iterable
It now calls :func:`fastreid.data.build_reid_train_loader`.
Overwrite it if you'd like a different data loader.
"""
logger = logging.getLogger("fastreid.clas_dataset")
logger.info("Prepare training set")
train_items = list()
for d in cfg.DATASETS.NAMES:
data = DATASET_REGISTRY.get(d)(root=_root)
if comm.is_main_process():
data.show_train()
train_items.extend(data.train)
transforms = build_transforms(cfg, is_train=True)
train_set = ClasDataset(train_items, transforms)
data_loader = build_reid_train_loader(cfg, train_set=train_set)
# Save index to class dictionary
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
path = os.path.join(output_dir, "idx2class.json")
with PathManager.open(path, "w") as f:
json.dump(train_set.idx_to_class, f)
return data_loader
def build_train_loader(cls, cfg):
path_imgrec = cfg.DATASETS.REC_PATH
if path_imgrec != "":
transforms = build_transforms(cfg, is_train=True)
train_set = MXFaceDataset(path_imgrec, transforms)
return build_reid_train_loader(cfg, train_set=train_set)
else:
return DefaultTrainer.build_train_loader(cfg)
def build_train_loader(cls, cfg):
"""
Returns:
iterable
It now calls :func:`fastreid.data.build_reid_train_loader`.
Overwrite it if you'd like a different data loader.
"""
logger = logging.getLogger("fastreid.clas_dataset")
logger.info("Prepare training set")
data_loader = build_reid_train_loader(cfg, Dataset=ClasDataset)
# Save index to class dictionary
output_dir = cfg.OUTPUT_DIR
if comm.is_main_process() and output_dir:
path = os.path.join(output_dir, "idx2class.json")
with PathManager.open(path, "w") as f:
json.dump(data_loader.dataset.idx_to_class, f)
return data_loader
def build_train_loader(cls, cfg):
logger = logging.getLogger("fastreid.attr_dataset")
train_items = list()
attr_dict = None
for d in cfg.DATASETS.NAMES:
dataset = DATASET_REGISTRY.get(d)(
root=_root, combineall=cfg.DATASETS.COMBINEALL)
if comm.is_main_process():
dataset.show_train()
if attr_dict is not None:
assert attr_dict == dataset.attr_dict, f"attr_dict in {d} does not match with previous ones"
else:
attr_dict = dataset.attr_dict
train_items.extend(dataset.train)
train_transforms = build_transforms(cfg, is_train=True)
train_set = AttrDataset(train_items, train_transforms, attr_dict)
data_loader = build_reid_train_loader(cfg, train_set=train_set)
AttrTrainer.sample_weights = data_loader.dataset.sample_weights
return data_loader
def build_train_loader(cls, cfg):
"""
Returns:
iterable
It now calls :func:`fastreid.data.build_reid_train_loader`.
Overwrite it if you'd like a different data loader.
"""
logger = logging.getLogger("fastreid.clas_dataset")
logger.info("Prepare training set")
train_items = list()
for d in cfg.DATASETS.NAMES:
data = DATASET_REGISTRY.get(d)(root=_root)
if comm.is_main_process():
data.show_train()
train_items.extend(data.train)
transforms = build_transforms(cfg, is_train=True)
train_set = ClasDataset(train_items, transforms)
cls.idx2class = train_set.idx_to_class
data_loader = build_reid_train_loader(cfg, train_set=train_set)
return data_loader
