| import torch |
| import torch.nn.functional as F |
| import tqdm |
| import os, argparse |
| import cv2 |
|
|
| from utils.dataset import MedDataset |
| from lib.Network_Res2Net_GRA_NCD import Network |
| os.environ["CUDA_VISIBLE_DEVICES"] = "5" |
|
|
| parser = argparse.ArgumentParser() |
| parser.add_argument('--test_file', type=str, default='/data2/sod_data/test_sample_half.lst', help='test_lst') |
| parser.add_argument('--testsize', type=int, default=256, help='testing size') |
| parser.add_argument('--pth_path', type=str, default='/data2/zhouhan/SINet/sinet_ori_resize/save_path/entro_75_1_half/weight/Net_epoch59_bestdice0.9342.pth') |
| parser.add_argument('--mode', type=str, default='entro') |
| parser.add_argument('--ratio_list', type=list, default=[0.75,0.75,1], help='the path to save model, figure and log') |
| opt = parser.parse_args() |
|
|
| save_path = os.path.join(opt.pth_path.split('/weight')[0], "image_pred_new") |
| print(save_path) |
|
|
| os.makedirs(save_path, exist_ok=True) |
|
|
| model = Network(mode = opt.mode, ratio_list = opt.ratio_list) |
| model.load_state_dict(torch.load(opt.pth_path)) |
| model.cuda() |
| model.eval() |
| test_loader = MedDataset(trainsize = opt.testsize, file=opt.test_file, mode='test') |
| for i, (image, shape, name) in enumerate(tqdm.tqdm(test_loader)): |
| |
| |
| H, W = shape |
| s = max(H, W) |
| h_pad_0 = (s - H) // 2 |
| w_pad_0 = (s - W) // 2 |
| h_pad_1 = s - H - h_pad_0 |
| w_pad_1 = s - W - w_pad_0 |
| image = image.cuda() |
| image = torch.unsqueeze(image, 0).float() |
|
|
| res4, res3, res2, res1 = model(image) |
| res = res1 |
| res = F.interpolate(res, (s, s), mode='bilinear', align_corners=True) |
| res = res[0, 0][h_pad_0:(s-h_pad_1), w_pad_0:(s-w_pad_1)] |
| res = res.sigmoid().data.cpu().numpy() |
| |
| res = (res - res.min()) / (res.max() - res.min() + 1e-8) |
| res = (res >= 0.5) |
| cv2.imwrite(os.path.join(save_path, name), res * 255) |
| |
|
|
