| import torch |
| import torch.nn.functional as F |
| import numpy as np |
| import os, argparse |
| import cv2 |
| import tqdm |
|
|
| from utils.dataset import MedDataset |
| from lib.Network import Network |
|
|
| parser = argparse.ArgumentParser() |
| parser.add_argument('--test_file', type=str, default='/data/liulian/Med_Seg/dataset/dice_choose/hard', help='test list') |
| parser.add_argument('--testsize', type=int, default=256, help='testing size') |
| parser.add_argument('--checkpoint_path', type=str, default='/data/liulian/Med_Seg/train_output/unet_tem/20230217-221311_qulvent_24cat/weight/Net_epoch68_bestdice0.8961.pth') |
| parser.add_argument('--pred_save_dir', type=str, default='/data/liulian/Med_Seg/save_preds/unet_tem/20230217-221311_qulvent_24cat/hard/image_pred') |
| opt = parser.parse_args() |
| os.makedirs(opt.pred_save_dir, exist_ok=True) |
|
|
| model = Network() |
| 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() |
|
|
| res = model(image) |
| 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(opt.pred_save_dir, name), res * 255) |
| |
|
|
