def __getitem__(self, idx): (image, mask) = self.pair[idx] x_tensor, y_tensor = self.ToTensor( image, MyFunctions.ImageRescale(mask, [0, 1])) return x_tensor, y_tensor
def __getitem__(self, idx): (image, mask) = self.pair[idx] # only load the original noisy image channel x_tensor, y_tensor = self.ToTensor( image[0, :, :], MyFunctions.ImageRescale(mask, [0, 1])) return x_tensor, y_tensor
volume_sf = np.zeros([486, 1024, 500], dtype=np.float32) volume_dn = np.zeros([486, 1024, 500], dtype=np.float32) for step, [img, mask] in enumerate(test_loader): model.eval() with torch.no_grad(): x = Variable(img).to(device) pred_y = model(x) pred_y = pred_y.detach().cpu().numpy() x = img.numpy() y = mask.numpy() volume_x[step, :, :] = x[0, 0, :, :500] volume_y[step, :, :] = MyFunctions.ImageRescale( y[0, 0, :, :500], [0, 255]) volume_sf[step, :, :] = x[0, 2, :, :500] volume_dn[step, :, :] = MyFunctions.ImageRescale( pred_y[0, 0, :, :500], [0, 255]) if step % 20 == 0: plt.figure(figsize=(18, 15)) plt.subplot(1, 4, 1), plt.axis('off'), plt.imshow(x[0, 0, :, :], cmap='gray'), plt.title('noisy input') plt.subplot(1, 4, 2), plt.axis('off'), plt.imshow(x[0, 2, :, :], cmap='gray'), plt.title('self-fusion') plt.subplot(1, 4, 4), plt.axis('off'), plt.imshow(y[0, 0, :, :], cmap='gray'), plt.title('5-average') plt.subplot(1, 4, 3), plt.axis('off'), plt.imshow(pred_y[0, 0, :, :], cmap='gray'), plt.title('denoise') plt.show()
G_error.item(), D_x, D_G_z1, D_G_z2)) print( 'BCE_loss: %.4f\tL1_loss: %.4f\tL2_loss: %.4f' % (BCE_loss(output, label).item(), alpha * L1_loss(fake_y, y).item(), beta * MSE_loss(fake_y, y).item())) netG.eval() if step == 0: with torch.no_grad(): # Train data denoising denoise_train = netG(x).detach().cpu().numpy() noise_train = train_x.numpy() avg_train = train_y.numpy() img_x = MyFunctions.ImageRescale(noise_train[0, 0, :, :500], [0, 255]) img_y = MyFunctions.ImageRescale(avg_train[0, 0, :, :500], [0, 255]) img_sf = MyFunctions.ImageRescale(noise_train[0, 2, :, :500], [0, 255]) img_dn = MyFunctions.ImageRescale(denoise_train[0, 0, :, :500], [0, 255]) plt.figure(figsize=(18, 15)) plt.subplot(1, 4, 1), plt.axis('off'), plt.imshow(img_x, cmap='gray'), plt.title('noisy') plt.subplot(1, 4, 2), plt.axis('off'), plt.imshow(img_sf, cmap='gray'), plt.title('self-fused') plt.subplot(1, 4, 3), plt.axis('off'), plt.imshow(img_dn, cmap='gray'), plt.title('denoised') plt.subplot(1, 4, 4), plt.axis('off'),