parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, help='model checkpoints path')
parser.add_argument('--weights',
type=str,
default='./checkpoints/1/model/3-regular.pth',
help='the weights file you want to test') # 修改点
args = parser.parse_args()
config_path = os.path.join(args.path, 'config.yml')
# load config file
config = Config(config_path)
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(e) for e in config.GPU)
if torch.cuda.is_available():
config.DEVICE = torch.device("cuda")
print('\nGPU IS AVAILABLE')
torch.backends.cudnn.benchmark = True
else:
config.DEVICE = torch.device("cpu")
net = VGG16().to(config.DEVICE)
test_set = ImageFolder(config.TEST_PATH, transform=test_tf)
test_data = torch.utils.data.DataLoader(test_set,
batch_size=config.BATCH_SIZE,
shuffle=True)
pth_path = args.weights
net.load_state_dict(torch.load(pth_path), config.DEVICE)
##print(net)
def load_config(mode=None):
r"""loads model config
Args:
mode (int): 1: train, 2: test, 3: eval, reads from config file if not specified
"""
parser = argparse.ArgumentParser()
parser.add_argument('--path',
'--checkpoints',
type=str,
default='./checkpoints',
help='model checkpoints path (default: ./checkpoints)')
parser.add_argument('--output',
type=str,
default='./output',
help='path to the output directory')
# test mode
if mode >= 2:
parser.add_argument(
'--input',
type=str,
help='path to the input images directory or an input image')
parser.add_argument('--mask',
type=str,
help='path to the masks directory or a mask file')
args = parser.parse_args()
config_path = os.path.join(args.path, 'config.yml')
# create checkpoints path if does't exist
create_dir(args.path)
# copy config template if does't exist
if not os.path.exists(config_path):
copyfile('./config.yml.example', config_path)
# load config file
config = Config(config_path)
config.print()
# train mode
if mode == 1:
config.MODE = 1
# test mode
elif mode == 2:
config.MODE = 2
# config.INPUT_SIZE = 0 Set to 0 for one to one mapping
if args.input is not None:
config.TEST_FLIST = args.input
if args.mask is not None:
config.TEST_MASK_FLIST = args.mask
if args.output is not None:
config.RESULTS = args.output
# eval mode
elif mode == 3:
config.MODE = 3
# set cuda visble devices from config file
# Initialization
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(str(e) for e in config.GPU)
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# init device
if torch.cuda.is_available():
config.DEVICE = torch.device("cuda")
torch.backends.cudnn.benchmark = True # cudnn auto-tuner
else:
config.DEVICE = torch.device("cpu")
# set cv2 running threads to 1 (prevents deadlocks with pytorch dataloader)
cv2.setNumThreads(0)
# initialize random seed
torch.manual_seed(config.SEED)
torch.cuda.manual_seed_all(config.SEED)
np.random.seed(config.SEED)
random.seed(config.SEED)
return config
