def build_dataset(dataset_list,
transform=None,
target_transform=None,
is_train=True):
assert len(dataset_list) > 0
datasets = []
for dataset_name in dataset_list:
data = DatasetCatalog.get(dataset_name)
args = data['args']
factory = _DATASETS[data['factory']]
args['transform'] = transform
args['target_transform'] = target_transform
if factory == VOCDataset:
args['keep_difficult'] = not is_train
elif factory == COCODataset:
args['remove_empty'] = is_train
elif factory == MyDataset:
args['remove_empty'] = is_train
dataset = factory(**args)
datasets.append(dataset)
# for testing, return a list of datasets
if not is_train:
return datasets
dataset = datasets[0]
if len(datasets) > 1:
dataset = ConcatDataset(datasets)
return [dataset]
def build_dataset(dataset_list,
transform=None,
target_transform=None,
is_train=True) -> Dataset:
"""
returns: a torch.data.dataset.Dataset instance
"""
assert dataset_list, "dataset_list should not be empty"
datasets = []
for dataset_name in dataset_list:
data = DatasetCatalog.get(dataset_name)
args = data["args"]
factory = _DATASETS[data["factory"]]
args["transform"] = transform
args["target_transform"] = target_transform
if factory == VOCDataset:
args["keep_difficult"] = not is_train
elif factory == XVIEWCOCODataset:
args["remove_empty"] = is_train
elif factory == UCBCOCODataset:
args["remove_empty"] = is_train
elif factory == COCODataset:
args["remove_empty"] = is_train
dataset = factory(**args)
datasets.append(dataset)
# for testing, return a list of datasets
if not is_train:
return datasets
dataset = datasets[0]
if len(datasets) > 1:
dataset = ConcatDataset(datasets)
return [dataset]
def build_dataset(dataset_list,
transform=None,
target_transform=None,
is_test=False,
split=False,
split_val_size=10):
assert len(dataset_list) > 0
datasets = []
for dataset_name in dataset_list:
data = DatasetCatalog.get(dataset_name)
args = data['args']
factory = _DATASETS[data['factory']]
args['transform'] = transform
args['target_transform'] = target_transform
if factory == VOCDataset or factory == VOCModelDataset:
args['keep_difficult'] = is_test
elif factory == COCODataset:
args['remove_empty'] = not is_test
dataset = factory(**args)
datasets.append(dataset)
# for testing, return a list of datasets
if is_test:
return datasets
if len(datasets) > 1:
dataset = DetectionConcatDataset(datasets)
if split:
return get_train_val_splits(dataset, split_val_size)
else:
dataset = datasets[0]
if split:
return get_train_val_splits(dataset, split_val_size)
return dataset
def build_dataset(dataset_list,
transform=None,
target_transform=None,
is_test=False):
assert len(dataset_list) > 0
datasets = []
for dataset_name in dataset_list:
if dataset_name == 'voc_2012_trainval': continue
data = DatasetCatalog.get(dataset_name)
args = data['args']
factory = _DATASETS[data['factory']]
args['transform'] = transform
args['target_transform'] = target_transform
if factory == VOCDataset:
args['keep_difficult'] = is_test
elif factory == COCODataset:
args['remove_empty'] = not is_test
dataset = factory(**args)
datasets.append(dataset)
# for testing, return a list of datasets
if is_test:
return datasets
if len(datasets) > 1:
dataset = ConcatDataset(datasets)
else:
dataset = datasets[0]
return dataset
def build_dataset(dataset_list, transform=None, target_transform=None, is_test=False):
assert len(dataset_list) > 0
datasets = []
for dataset_name in dataset_list:
data = DatasetCatalog.get(dataset_name)
args = data['args']
factory = globals()[data['factory']]
args['transform'] = transform
args['target_transform'] = target_transform
dataset = factory(**args)
datasets.append(dataset)
# for testing, return a list of datasets
if is_test:
return datasets
if len(datasets) > 1:
dataset = ConcatDataset(datasets)
else:
dataset = datasets[0]
return dataset
def build_dataset(base_path: str,
dataset_list,
transform=None,
target_transform=None,
is_train=True):
assert len(dataset_list) > 0
datasets = []
for dataset_name in dataset_list:
data = DatasetCatalog.get(base_path, dataset_name)
args = data['args']
factory = _DATASETS[data['factory']]
args['transform'] = transform
args['target_transform'] = target_transform
dataset = factory(**args)
datasets.append(dataset)
# for testing, return a list of datasets
if not is_train:
return datasets
dataset = datasets[0]
if len(datasets) > 1:
dataset = ConcatDataset(datasets)
#get_data_stats(dataset)
return [dataset]
