decompose_data = torch.cat([y_, High_noise, Low_noise], dim=1)
# x = torch.cat([High_origin.unsqueeze(1), Low_origin.unsqueeze(1)], dim=1)
model = model.cpu()
decom_output = decompose_model(decompose_data.float()).squeeze(
0) # inference
dncnn_output = model(High_noise[:, 0].unsqueeze(1)).squeeze(0)
# dncnn_high, dncnn_low = Decomposition(dncnn_output, 0.10)
# x_ = output[0].cpu().detach().numpy().astype(np.float32)
output = ComplexTensor(dncnn_output[1] + decom_output[3],
decom_output[2] +
decom_output[4]).abs()
# output = ComplexTensor(dncnn_high[0,0] + decom_output[3], decom_output[2] + decom_output[4]).abs()
x_ = output.cpu().detach().numpy().astype(np.float32)
# x_ = ComplexTensor(output[:, 0] + output[:, 2], Low_noise[:, 0] + Low_noise[:, 1]).abs().squeeze(0)
# x_ = ComplexTensor(output[:, 0] + output[:, 2], output[:, 1] + output[:, 3]).abs().squeeze(0)
# x_ = torch.add(output[:,0], output[:,1]).squeeze(0)
# x_ = x_.cpu().detach().numpy().astype(np.float32)
# x_ = x_.view(y.shape[0], y.shape[1])
# x_ = x_.cpu()
# x_ = x_.detach().numpy().astype(np.float32)
elapsed_time = time.time() - start_time
psnr_x_ = compare_psnr(x, x_)
ssim_x_ = compare_ssim(x, x_)
# 0.14
High_noise, Low_noise = Decomposition(y_.squeeze(0), 0.125)
High_noise = High_noise.cuda()
Low_noise = Low_noise.cuda()
y_ = y_.cuda()
y_ = torch.cat([High_noise, Low_noise], dim=1)
# x = torch.cat([High_origin.unsqueeze(1), Low_origin.unsqueeze(1)], dim=1)
output = model(y_.cuda().float()) # inference
# x_ = ComplexTensor(output[:, 0] + output[:, 2], Low_noise[:, 0] + Low_noise[:, 1]).abs().squeeze(0)
x_ = ComplexTensor(output[:, 0] + output[:, 2], output[:, 1] + output[:, 3]).abs().squeeze(0)
# x_ = torch.add(output[:,0], output[:,1]).squeeze(0)
x_ = x_.cpu().detach().numpy().astype(np.float32)
# x_ = x_.view(y.shape[0], y.shape[1])
# x_ = x_.cpu()
# x_ = x_.detach().numpy().astype(np.float32)
torch.cuda.synchronize()
elapsed_time = time.time() - start_time
psnr_x_ = compare_psnr(x, x_)
ssim_x_ = compare_ssim(x, x_)
print('%10s : %10s : %2.4f second %2.2f PSNR' % (set_cur, im, elapsed_time, psnr_x_))
if args.save_result:
name, ext = os.path.splitext(im)
show(np.hstack((y, x_))) # show the image
