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28e6f98 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | import os
import sys
from tqdm import tqdm
import shutil
import argparse
import logging
import numpy as np
from skimage import io
from scipy.ndimage import zoom
import torch
import torch.nn as nn
from torchvision import transforms
from torch.utils.data import DataLoader
from networks.compare_models import build_model_from_name
from dataloaders.BRATS_dataloader_new import Hybrid as MyDataset
from dataloaders.BRATS_dataloader_new import ToTensor
from networks.mynet import TwoBranch
from utils import bright, trunc
from skimage.metrics import mean_squared_error, peak_signal_noise_ratio, structural_similarity
parser = argparse.ArgumentParser()
parser.add_argument('--root_path', type=str, default='/home/xiaohan/datasets/BRATS_dataset/BRATS_2020_images/selected_images/')
parser.add_argument('--MRIDOWN', type=str, default='4X', help='MRI down-sampling rate')
parser.add_argument('--low_field_SNR', type=int, default=15, help='SNR of the simulated low-field image')
parser.add_argument('--phase', type=str, default='test', help='Name of phase')
parser.add_argument('--gpu', type=str, default='0', help='GPU to use')
parser.add_argument('--exp', type=str, default='msl_model', help='model_name')
parser.add_argument('--seed', type=int, default=1337, help='random seed')
parser.add_argument('--base_lr', type=float, default=0.0002, help='maximum epoch numaber to train')
parser.add_argument('--model_name', type=str, default='unet_single', help='model_name')
parser.add_argument('--relation_consistency', type=str, default='False', help='regularize the consistency of feature relation')
parser.add_argument('--norm', type=str, default='False', help='Norm Layer between UNet and Transformer')
parser.add_argument('--input_normalize', type=str, default='mean_std', help='choose from [min_max, mean_std, divide]')
# args = parser.parse_args()
from option import args
test_data_path = args.root_path
snapshot_path = "model/" + args.exp + "/"
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
def normalize_output(out_img):
out_img = (out_img - out_img.min())/(out_img.max() - out_img.min() + 1e-8)
return out_img
if __name__ == "__main__":
## make logger file
if not os.path.exists(snapshot_path):
os.makedirs(snapshot_path)
logging.basicConfig(filename=snapshot_path + "/log.txt", level=logging.INFO,
format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S')
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
logging.info(str(args))
network = TwoBranch(args).cuda()
device = torch.device('cuda')
network.to(device)
if len(args.gpu.split(',')) > 1:
network = nn.DataParallel(network)
db_test = MyDataset(split='test', MRIDOWN=args.MRIDOWN, SNR=args.low_field_SNR,
transform=transforms.Compose([ToTensor()]),
base_dir=test_data_path, input_normalize = args.input_normalize)
testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=2, pin_memory=True)
if args.phase == 'test':
save_mode_path = os.path.join(snapshot_path, 'best_checkpoint.pth')
print('load weights from ' + save_mode_path)
checkpoint = torch.load(save_mode_path)
network.load_state_dict(checkpoint['network'])
network.eval()
cnt = 0
save_path = snapshot_path + '/result_case/'
feature_save_path = snapshot_path + '/feature_visualization/'
if not os.path.exists(save_path):
os.makedirs(save_path)
if not os.path.exists(feature_save_path):
os.makedirs(feature_save_path)
t1_MSE_all, t1_PSNR_all, t1_SSIM_all = [], [], []
t2_MSE_all, t2_PSNR_all, t2_SSIM_all = [], [], []
for (sampled_batch, sample_stats) in tqdm(testloader, ncols=70):
cnt += 1
print('processing ' + str(cnt) + ' image')
t1_in, t1, t2_in, t2 = sampled_batch['image_in'].cuda(), sampled_batch['image'].cuda(), \
sampled_batch['target_in'].cuda(), sampled_batch['target'].cuda()
t1_krecon, t2_krecon = sampled_batch['image_krecon'].cuda(), sampled_batch['target_krecon'].cuda()
t2_mean = sample_stats['t2_mean'].data.cpu().numpy()[0]
t2_std = sample_stats['t2_std'].data.cpu().numpy()[0]
t1_out, t2_out = None, None
t2_out = network(t2_in, t1_in)['img_out']
t2_out_2 = network(t2_in, t1_in)['img_out']
t1_mean = sample_stats['t1_mean'].data.cpu().numpy()[0]
t1_std = sample_stats['t1_std'].data.cpu().numpy()[0]
t2_mean = sample_stats['t2_mean'].data.cpu().numpy()[0]
t2_std = sample_stats['t2_std'].data.cpu().numpy()[0]
if t1_out is not None:
t1_img = (np.clip(t1.data.cpu().numpy()[0, 0] * t1_std + t1_mean, 0, 1) * 255).astype(np.uint8)
t1_out_img = (np.clip(t1_out.data.cpu().numpy()[0, 0] * t1_std + t1_mean, 0, 1) * 255).astype(np.uint8)
t1_krecon_img = (np.clip(t1_krecon.data.cpu().numpy()[0, 0] * t1_std + t1_mean, 0, 1) * 255).astype(np.uint8)
t1_img = (np.clip(t1.data.cpu().numpy()[0, 0] * t1_std + t1_mean, 0, 1) * 255).astype(np.uint8)
t2_in_img = (np.clip(t2_in.data.cpu().numpy()[0, 0] * t2_std + t2_mean, 0, 1) * 255).astype(np.uint8)
t2_img = (np.clip(t2.data.cpu().numpy()[0, 0] * t2_std + t2_mean, 0, 1) * 255).astype(np.uint8)
t2_out_img = (np.clip(t2_out.data.cpu().numpy()[0, 0] * t2_std + t2_mean, 0, 1) * 255).astype(np.uint8)
t2_krecon_img = (np.clip(t2_krecon.data.cpu().numpy()[0, 0] * t2_std + t2_mean, 0, 1) * 255).astype(np.uint8)
t2_out_2_img = (np.clip(t2_out_2.data.cpu().numpy()[0, 0] * t2_std + t2_mean, 0, 1) * 255).astype(np.uint8)
io.imsave(save_path + str(cnt) + '_t1.png', bright(t1_img,0,0.8))
io.imsave(save_path + str(cnt) + '_t2.png', bright(t2_img,0,0.8))
io.imsave(save_path + str(cnt) + '_t2_in.png', bright(t2_in_img,0,0.8))
io.imsave(save_path + str(cnt) + '_t2_out.png', bright(t2_out_img,0,0.8))
io.imsave(save_path + str(cnt) + '_t2_out2.png', bright(t2_out_2_img,0,0.8))
if t2_out is not None:
t2_out_img[t2_out_img < 0.0] = 0.0
t2_img[t2_img < 0.0] = 0.0
MSE = mean_squared_error(t2_img, t2_out_img)
PSNR = peak_signal_noise_ratio(t2_img, t2_out_img)
SSIM = structural_similarity(t2_img, t2_out_img)
t2_MSE_all.append(MSE)
t2_PSNR_all.append(PSNR)
t2_SSIM_all.append(SSIM)
print("[t2 MRI] MSE:", MSE, "PSNR:", PSNR, "SSIM:", SSIM)
# if cnt > 20:
# break
print("[T2 MRI:] average MSE:", np.array(t2_MSE_all).mean(), "average PSNR:", np.array(t2_PSNR_all).mean(), "average SSIM:", np.array(t2_SSIM_all).mean())
print("[T2 MRI:] average MSE:", np.array(t2_MSE_all).std(), "average PSNR:", np.array(t2_PSNR_all).std(), "average SSIM:", np.array(t2_SSIM_all).std())
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