def get_inplace_data_set_val(args, net_name):
from dataset import InplaceDataset
from create_dataset import create_pnet_data_txt_parser, landmark_dataset_txt_parser
img_faces = create_pnet_data_txt_parser(args.class_val_data_txt_path, args.class_val_data_dir)
img_face_landmark = landmark_dataset_txt_parser(args.landmark_data_txt_path, args.landmark_data_dir)
if net_name == 'pnet':
IDS = InplaceDataset(img_face_landmark, img_faces, cropsize=12)
elif net_name == 'rnet':
IDS = InplaceDataset(img_face_landmark, img_faces, cropsize=24, pnet=load_net(args, 'pnet').to('cpu'))
elif net_name == 'onet':
IDS = InplaceDataset(img_face_landmark, img_faces, cropsize=48,
pnet=load_net(args, 'pnet').to('cpu'), rnet=load_net(args, 'rnet').to('cpu'))
return DataLoader(IDS,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=False)
crop_img, label, offset, ldmk = self.get_crop_img_label_offset_ldmk(img, faces, ldmk, index)
if crop_img is None: return self.__getitem__(random.randint(0, self.__len__()))
img_tensor = transforms.ToTensor()(crop_img)
# label = torch.tensor([1.0]) if item[1] in ['p', 'pf', 'l'] else torch.tensor([0.0])
landmark_flag = torch.FloatTensor([1.0 if label == 'l' else 0.0])
label = torch.FloatTensor([1.0 if label in ['p', 'pf', 'l'] else 0.0])
offset = torch.FloatTensor(offset if 4 == len(offset) else 4 * [0.0])
landmark = torch.FloatTensor(ldmk if 10 == len(ldmk) else 10 * [0.0])
# print('label type:', label.type())
# print('data_imformation:', label, offset, landmark_flag, landmark)
return (img_tensor, label, offset, landmark_flag, landmark)
def __len__(self):
# self.ct += 1
# return self.ct
return len(self.img_faces)
if __name__ == '__main__':
from create_dataset import create_pnet_data_txt_parser, landmark_dataset_txt_parser, dataset_config
args = dataset_config()
img_faces = create_pnet_data_txt_parser(args.class_data_txt_path, args.class_data_dir)
img_face_landmark = landmark_dataset_txt_parser(args.landmark_data_txt_path, args.landmark_data_dir)
IDS = InplaceDataset(img_face_landmark, img_faces, cropsize=48,
pnet=load_net(args, 'pnet'), rnet=load_net(args, 'rnet'))
for i, (img_tensor, label, offset, landmark_flag, landmark) in enumerate(IDS):
print(label, offset, landmark_flag, landmark)
print(i)
pass