def test_cutmix_batch_success2(plot=False): """ Test CutMixBatch op with default values for alpha and prob on a batch of rescaled HWC images """ logger.info("test_cutmix_batch_success2") # Original Images ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False) ds_original = ds_original.batch(5, drop_remainder=True) images_original = None for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = image else: images_original = np.append(images_original, image, axis=0) # CutMix Images data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False) one_hot_op = data_trans.OneHot(num_classes=10) data1 = data1.map(input_columns=["label"], operations=one_hot_op) rescale_op = vision.Rescale((1.0/255.0), 0.0) data1 = data1.map(input_columns=["image"], operations=rescale_op) cutmix_batch_op = vision.CutMixBatch(mode.ImageBatchFormat.NHWC) data1 = data1.batch(5, drop_remainder=True) data1 = data1.map(input_columns=["image", "label"], operations=cutmix_batch_op) images_cutmix = None for idx, (image, _) in enumerate(data1): if idx == 0: images_cutmix = image else: images_cutmix = np.append(images_cutmix, image, axis=0) if plot: visualize_list(images_original, images_cutmix) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_cutmix[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse))))
def test_cutmix_batch_success1(plot=False): """ Test CutMixBatch op with specified alpha and prob parameters on a batch of CHW images """ logger.info("test_cutmix_batch_success1") # Original Images ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False) ds_original = ds_original.batch(5, drop_remainder=True) images_original = None for idx, (image, _) in enumerate(ds_original): if idx == 0: images_original = image else: images_original = np.append(images_original, image, axis=0) # CutMix Images data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False) hwc2chw_op = vision.HWC2CHW() data1 = data1.map(input_columns=["image"], operations=hwc2chw_op) one_hot_op = data_trans.OneHot(num_classes=10) data1 = data1.map(input_columns=["label"], operations=one_hot_op) cutmix_batch_op = vision.CutMixBatch(mode.ImageBatchFormat.NCHW, 2.0, 0.5) data1 = data1.batch(5, drop_remainder=True) data1 = data1.map(input_columns=["image", "label"], operations=cutmix_batch_op) images_cutmix = None for idx, (image, _) in enumerate(data1): if idx == 0: images_cutmix = image.transpose(0, 2, 3, 1) else: images_cutmix = np.append(images_cutmix, image.transpose(0, 2, 3, 1), axis=0) if plot: visualize_list(images_original, images_cutmix) num_samples = images_original.shape[0] mse = np.zeros(num_samples) for i in range(num_samples): mse[i] = diff_mse(images_cutmix[i], images_original[i]) logger.info("MSE= {}".format(str(np.mean(mse))))
def test_cutmix_batch_fail1(): """ Test CutMixBatch Fail 1 We expect this to fail because the images and labels are not batched """ logger.info("test_cutmix_batch_fail1") # CutMixBatch Images data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False) one_hot_op = data_trans.OneHot(num_classes=10) data1 = data1.map(input_columns=["label"], operations=one_hot_op) cutmix_batch_op = vision.CutMixBatch(mode.ImageBatchFormat.NHWC) with pytest.raises(RuntimeError) as error: data1 = data1.map(input_columns=["image", "label"], operations=cutmix_batch_op) for idx, (image, _) in enumerate(data1): if idx == 0: images_cutmix = image else: images_cutmix = np.append(images_cutmix, image, axis=0) error_message = "You must make sure images are HWC or CHW and batch " assert error_message in str(error.value)
def test_cutmix_batch_fail7(): """ Test CutMixBatch op We expect this to fail because labels are not in one-hot format """ logger.info("test_cutmix_batch_fail7") # CutMixBatch Images data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False) cutmix_batch_op = vision.CutMixBatch(mode.ImageBatchFormat.NHWC) data1 = data1.batch(5, drop_remainder=True) data1 = data1.map(input_columns=["image", "label"], operations=cutmix_batch_op) with pytest.raises(RuntimeError) as error: images_cutmix = np.array([]) for idx, (image, _) in enumerate(data1): if idx == 0: images_cutmix = image else: images_cutmix = np.append(images_cutmix, image, axis=0) error_message = "CutMixBatch: Wrong labels shape. The second column (labels) must have a shape of NC or NLC" assert error_message in str(error.value)
def test_cutmix_batch_nhwc_md5(): """ Test CutMixBatch on a batch of HWC images with MD5: """ logger.info("test_cutmix_batch_nhwc_md5") original_seed = config_get_set_seed(0) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # CutMixBatch Images data = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False) one_hot_op = data_trans.OneHot(num_classes=10) data = data.map(input_columns=["label"], operations=one_hot_op) cutmix_batch_op = vision.CutMixBatch(mode.ImageBatchFormat.NHWC) data = data.batch(5, drop_remainder=True) data = data.map(input_columns=["image", "label"], operations=cutmix_batch_op) filename = "cutmix_batch_c_nhwc_result.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN) # Restore config setting ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)