def test(args, saved_model_path, noise, famous_path, testset_path=None): """Run predictable test """ torch.manual_seed(7) model = restore_model(args, saved_model_path) if USE_CUDA: model = model.cuda() norm_noise = common.normilize(noise, 255) padding = 20 if testset_path is not None and os.path.isdir(testset_path): testset = create_test_dataset(testset_path, noise, padding) test_loader = DataLoader(testset) ours_psnr, bm3d_psnr = avarge_psnr_testset(model, test_loader, padding, norm_noise) else: print('testset path was not provided or does not exsist on machine' +' skipping to famouse images testset') ours_psnr = bm3d_psnr = 0 testset = create_famous_dataset(famous_path, noise, padding) file_names = testset.image_filenames famous_loader = DataLoader(testset) fam_psnrs, fam_res_array =\ famous_images_teset( model, famous_loader, file_names, padding, norm_noise) return fam_psnrs, fam_res_array, file_names, ours_psnr, bm3d_psnr
def input_process_fn(_x): return gaussian(_x, is_training=True, mean=0, stddev=normilize(noise, 255))
def pre_process_fn(_x): return normilize(nhwc_to_nchw(_x), 255)
def pre_process_fn(_x): return normilize(_x, 255)