def __init__(self, split, args, transform=None, target_transform=None):
assert args.dataset in ['PETA', 'PETA_dataset', 'PA100k', 'RAP', 'RAP2'], \
f'dataset name {args.dataset} is not exist'
data_path = get_pkl_rootpath(args.dataset)
dataset_info = pickle.load(open(data_path, 'rb+'))
img_id = dataset_info.image_name
attr_label = dataset_info.label
assert split in dataset_info.partition.keys(), f'split {split} is not exist'
self.dataset = args.dataset
self.transform = transform
self.target_transform = target_transform
self.root_path = dataset_info.root
self.attr_id = dataset_info.attr_name
self.attr_num = len(self.attr_id)
self.img_idx = dataset_info.partition[split]
if isinstance(self.img_idx, list):
self.img_idx = self.img_idx[0] # default partition 0
self.img_num = self.img_idx.shape[0]
self.img_id = [img_id[i] for i in self.img_idx]
self.label = attr_label[self.img_idx]
def __init__(self, split, args, transform=None, target_transform=None):
assert args.dataset in ['PETA', 'PETA_dataset', 'PA100k', 'RAP', 'RAP2'], \
f'dataset name {args.dataset} is not exist'
self.data_name = args.dataset
data_path = get_pkl_rootpath(args.dataset)
dataset_info = pickle.load(open(data_path, 'rb+'))
img_id = dataset_info.image_name
attr_label = dataset_info.label
#pdb.set_trace()
assert split in dataset_info.partition.keys(
), f'split {split} is not exist'
self.dataset = args.dataset
self.transform = transform
self.depth_transform = T.Compose([
T.Resize((height, width)),
T.ToTensor(),
normalize_peta,
])
#self.depth_transform = transform
self.target_transform = target_transform
self.root_path = dataset_info.root
self.attr_id = dataset_info.attr_name
self.attr_num = len(self.attr_id)
self.attr_name = dataset_info.attr_name
self.img_idx = dataset_info.partition[split]
if isinstance(self.img_idx, list):
self.img_idx = self.img_idx[0] # default partition 0
self.img_num = self.img_idx.shape[0]
self.img_id = [img_id[i] for i in self.img_idx]
self.label = attr_label[self.img_idx]