def main():
root = r"/media/root/文档/wqr/my_spixel_fcn/data_preprocessing/up_facet"
dataset = Data_api_rec(root=root)
from data_api.get_dataset_mean_std import get_mean_std
a, b, _ = dataset[0]
mean, std = get_mean_std(dataset, ratio=0.1)
# print(mean)
# print(std)
# exit()
# print(a.shape)
# print(b.shape)
from torchvision import transforms
tran = transforms.ToPILImage()
imga = tran(a)
imgb = tran(b)
imgaa = cv2.cvtColor(np.array(imga), cv2.COLOR_RGB2BGR)
cv2.imshow("a", np.uint8(imgaa))
cv2.imshow("b", np.uint8(imgb))
cv2.waitKey()
test_parser.add_argument("--continue_train",
"-ct",
default=False,
type=bool,
help="continue train using trained model")
test_parser.add_argument(
"--state_path",
"-sp",
default="checkpoints/network_state/acc94.74_model.pth",
help="the p500ath of trained model")
opt = test_parser.parse_args()
print(opt.device)
# 获取数据集的均值、方差
Norm, [mean,
std] = get_mean_std(mean_std_path=os.path.join(base_path, "data_api"))
test_data = Data_image_label(root=opt.data_root,
updata_txt=True,
train_test_ratio=0.0,
phase="test",
Norm=Norm)
test_loader = DataLoader(test_data, batch_size=opt.batch_size, shuffle=False)
# model
model = Res18_UNet(n_classes=2, layer=4).to(opt.device)
# device_ids = [0]
# model = torch.nn.DataParallel(model, device_ids=device_ids)
# model = model.cuda(device=device_ids[0])
model.eval()
train_parser.add_argument("--state_path",
"-sp",
default="checkpoints/network_state/network_epo1.pth",
help="the path of trained model")
opt = train_parser.parse_args()
print(opt.device)
train_data = Data_api_rec(root=opt.data_root,
gen_label=False,
updata_txt=False,
train_test_ratio=0.8,
phase="train",
use_relative_path=False)
Norm, [mean,
std] = get_mean_std(mean_std_path=os.path.join(base_path, "data_api"),
dataset=train_data)
train_data = Data_api_rec(root=opt.data_root,
gen_label=False,
updata_txt=False,
train_test_ratio=0.8,
phase="train",
use_relative_path=False,
Norm=Norm)
val_data = Data_api_rec(root=opt.data_root,
gen_label=False,
updata_txt=False,
train_test_ratio=0.8,
phase="val",
Norm=Norm)
