from lib.PraNet_Res2Net import PraNet
from utils.dataloader import test_dataset
parser = argparse.ArgumentParser()
parser.add_argument('--testsize', type=int, default=352, help='testing size')
parser.add_argument('--pth_path',
type=str,
default='./snapshots/PraNet_Res2Net/PraNet-19.pth')
for _data_name in [
'CVC-300', 'CVC-ClinicDB', 'Kvasir', 'CVC-ColonDB', 'ETIS-LaribPolypDB'
]:
data_path = './data/TestDataset/{}/'.format(_data_name)
save_path = './results/PraNet/{}/'.format(_data_name)
opt = parser.parse_args()
model = PraNet()
model.load_state_dict(torch.load(opt.pth_path))
model.cuda()
model.eval()
os.makedirs(save_path, exist_ok=True)
image_root = '{}/images/'.format(data_path)
gt_root = '{}/masks/'.format(data_path)
test_loader = test_dataset(image_root, gt_root, opt.testsize)
for i in range(test_loader.size):
image, gt, name = test_loader.load_data()
gt = np.asarray(gt, np.float32)
gt /= (gt.max() + 1e-8)
image = image.cuda()
default=0.1,
help='decay rate of learning rate')
parser.add_argument('--decay_epoch',
type=int,
default=50,
help='every n epochs decay learning rate')
parser.add_argument('--train_path',
type=str,
default='。/data/TrainDataset',
help='path to train dataset')
parser.add_argument('--train_save', type=str, default='PraNet_Res2Net')
opt = parser.parse_args()
# ---- build models ----
# torch.cuda.set_device(0) # set your gpu device
model = PraNet().cuda()
# ---- flops and params ----
# from utils.utils import CalParams
# x = torch.randn(1, 3, 352, 352).cuda()
# CalParams(lib, x)
params = model.parameters()
optimizer = torch.optim.Adam(params, opt.lr)
image_root = '{}/images/'.format(opt.train_path)
gt_root = '{}/masks/'.format(opt.train_path)
train_loader = get_loader(image_root,
gt_root,
batchsize=opt.batchsize,
default='./data/ValDataset/',
help='test dataset path')
opt = parser.parse_args()
dataset_path = opt.test_path
# set device for test
if opt.gpu_id == '0':
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
print('USE GPU 0')
elif opt.gpu_id == '1':
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
print('USE GPU 1')
# load the model
model = PraNet(channel=32).cuda()
model.load_state_dict(torch.load(opt.snapshot))
model.cuda()
model.eval()
# test
save_path = 'data/mask/'
dataset_path = 'data/medico2020/'
if not os.path.exists(save_path):
os.makedirs(save_path)
time_taken = []
test_loader = test_dataset(dataset_path, dataset_path, opt.testsize)
for i in range(test_loader.size):
image, gt, name, image_for_post = test_loader.load_data()
from lib.PraNet_Res2Net import PraNet
from utils.dataloader import test_dataset
jt.flags.use_cuda = 1
parser = argparse.ArgumentParser()
parser.add_argument('--testsize', type=int, default=352, help='testing size')
parser.add_argument('--pth_path', type=str, default='./snapshots/PraNet-ori.pth')
opt = parser.parse_args()
for _data_name in ['CVC-300', 'CVC-ClinicDB', 'CVC-ColonDB', 'ETIS-LaribPolypDB', 'Kvasir']:
data_path = './data/TestDataset/{}/'.format(_data_name)
save_path = './results/PraNet/{}/'.format(_data_name)
model = PraNet()
model.load(opt.pth_path)
model.eval()
os.makedirs(save_path, exist_ok=True)
image_root = '{}/images/'.format(data_path)
gt_root = '{}/masks/'.format(data_path)
test_loader = test_dataset(image_root, gt_root, opt.testsize) \
.set_attrs(batch_size=1, shuffle=False)
for image, gt, name in test_loader:
gt /= (gt.max() + 1e-08)
(res5, res4, res3, res2) = model(image)
res = res2
c, h, w = gt.shape
type=str,
default='./snapshot/',
help='the path to save model and log')
opt = parser.parse_args()
# set the device for training
if opt.gpu_id == '0':
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
print('USE GPU 0')
elif opt.gpu_id == '1':
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
print('USE GPU 1')
cudnn.benchmark = True
# build the model
model = PraNet(channel=32).cuda()
if opt.load is not None:
model.load_state_dict(torch.load(opt.load))
print('load model from ', opt.load)
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
save_path = opt.save_path
if not os.path.exists(save_path):
os.makedirs(save_path)
# load data
print('load data...')
train_loader = get_loader(image_root=opt.train_root + 'images/',
gt_root=opt.train_root + 'masks/',