def multilabel_idxcount_v2_val(args):
image_information = loadpickle(args.val_file)
dataset = ImageRelLists(image_paths=image_information,
image_root=args.data_dir,
transform=get_val_simple_transform(),
target_transform=multilabelidxcount2KL(
args.num_classes))
return dataset
# if __name__ == '__main__':
# # x_transform = multilabel2multihot(500)
# # x = x_transform([4, 10])
# # print("DEB")
# from argparse import Namespace
# from CNNs.dataloaders.utils import none_collate
#
# args = Namespace(num_classes=742)
# annotation_file = '/home/zwei/Dev/AttributeNet3/AdobeStockSelection/RetrieveSelected778/data_v2/CNNsplit_{}.pkl'
# data_dir = '/home/zwei/datasets/stockimage_742/images-256'
# dataset = multilabel_val(args, annotation_file, data_dir)
# val_loader = torch.utils.data.DataLoader(dataset,
# batch_size=10, shuffle=False,
# num_workers=4, pin_memory=True, collate_fn=none_collate)
# import tqdm
#
# for s_images, s_labels in tqdm.tqdm(val_loader):
# pass
# print("Done")
def deepsentiment_s_test(args):
image_information = loadpickle(args.test_file)
dataset = ImageRelLists(image_paths=image_information,
image_root=args.data_dir,
transform=get_val_simple_transform(),
target_transform=None)
return dataset
def deepsentiment_m_val(args):
image_information = loadpickle(args.val_files[args.ind])
dataset = ImageRelLists(
image_paths=image_information, #[:n_samples],
image_root=args.data_dirs[args.ind],
transform=get_val_simple_transform(),
target_transform=None)
return dataset
def multilabel_idxcount_v2_val(args):
image_information = loadpickle(args.val_file)
dataset = ImageRelLists(image_paths=image_information,
image_root=args.data_dir,
transform=get_val_simple_transform(),
target_transform=multilabelidxcount2multihot(
args.num_classes))
return dataset
def multilabel_idxcount_v2_train(args):
image_information = loadpickle(args.train_file)
dataset = ImageRelLists(image_paths=image_information,
image_root=args.data_dir,
transform=get_train_fix_size_transform(),
target_transform=multilabelidxcount2KL(
args.num_classes))
return dataset
def singlelabel_v2_val(args):
#FIXME:
image_information = loadpickle(args.val_file)
dataset = ImageRelLists(image_paths=image_information,
image_root=args.data_dir,
transform=get_val_simple_transform(),
target_transform=None)
return dataset
def singlelabel_test(args, annotation_file, data_dir):
#FIXME:
annotation_file = annotation_file.format('test')
image_information = loadpickle(annotation_file)
dataset = ImageRelLists(image_paths=image_information,
image_root=data_dir,
transform=get_val_simple_transform(),
target_transform=None)
return dataset
def simple_multilabel_val(args):
#FIXME:
# annotation_file = annotation_file.format('train')
image_information = loadpickle(args.val_file)
dataset = ImageRelLists(image_paths=image_information,
image_root=args.data_dir,
transform=get_val_simple_transform(),
target_transform=simple_multitrans())
return dataset
def multilabel_v2_val(args):
#FIXME:
# annotation_file = annotation_file.format('val')
image_information = loadpickle(args.val_file)
dataset = ImageRelLists(image_paths=image_information,
image_root=args.data_dir,
transform=get_val_simple_transform(),
target_transform=multilabel2multihot(
args.num_classes))
return dataset
def multilabel_BCE_test(args, annotation_file, data_dir):
#FIXME:
annotation_file = annotation_file.format('test')
image_information = loadpickle(annotation_file)
dataset = ImageRelLists(image_paths=image_information,
image_root=data_dir,
transform=get_val_simple_transform(),
target_transform=multilabel2multi1(
args.num_classes))
return dataset
