def test_normalizepad_op_c(plot=False): """ Test NormalizePad in cpp transformations """ logger.info("Test Normalize in cpp") mean = [121.0, 115.0, 100.0] std = [70.0, 68.0, 71.0] # define map operations decode_op = c_vision.Decode() normalizepad_op = c_vision.NormalizePad(mean, std) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(operations=decode_op, input_columns=["image"]) data1 = data1.map(operations=normalizepad_op, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(operations=decode_op, input_columns=["image"]) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): image_de_normalized = item1["image"] image_original = item2["image"] image_np_normalized = normalizepad_np(image_original, mean, std) mse = diff_mse(image_de_normalized, image_np_normalized) logger.info("image_{}, mse: {}".format(num_iter + 1, mse)) assert mse < 0.01 if plot: visualize_image(image_original, image_de_normalized, mse, image_np_normalized) num_iter += 1
def test_rescale_op(plot=False): """ Test rescale """ logger.info("Test rescale") data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) # define map operations decode_op = vision.Decode() rescale_op = vision.Rescale(1.0 / 255.0, -1.0) # apply map operations on images data1 = data1.map(operations=decode_op, input_columns=["image"]) data2 = data1.map(operations=rescale_op, input_columns=["image"]) num_iter = 0 for item1, item2 in zip( data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): image_original = item1["image"] image_de_rescaled = item2["image"] image_np_rescaled = get_rescaled(num_iter) mse = diff_mse(image_de_rescaled, image_np_rescaled) assert mse < 0.001 # rounding error logger.info("image_{}, mse: {}".format(num_iter + 1, mse)) num_iter += 1 if plot: visualize_image(image_original, image_de_rescaled, mse, image_np_rescaled)
def test_random_vertical_op(plot=False): """ Test random_vertical with default probability """ logger.info("Test random_vertical") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() random_vertical_op = c_vision.RandomVerticalFlip(1.0) data1 = data1.map(input_columns=["image"], operations=decode_op) data1 = data1.map(input_columns=["image"], operations=random_vertical_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=decode_op) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): # with the seed value, we can only guarantee the first number generated if num_iter > 0: break image_v_flipped = item1["image"] image = item2["image"] image_v_flipped_2 = v_flip(image) mse = diff_mse(image_v_flipped, image_v_flipped_2) assert mse == 0 logger.info("image_{}, mse: {}".format(num_iter + 1, mse)) num_iter += 1 if plot: visualize_image(image, image_v_flipped, mse, image_v_flipped_2)
def test_soft_dvpp_decode_resize_jpeg_supplement(plot=False): """ Test SoftDvppDecodeResizeJpeg op """ logger.info("test_random_decode_resize_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = vision.Decode() resize_op = vision.Resize(1134) data1 = data1.map(operations=[decode_op, resize_op], input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) soft_dvpp_decode_resize_op = vision.SoftDvppDecodeResizeJpeg(1134) data2 = data2.map(operations=soft_dvpp_decode_resize_op, input_columns=["image"]) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): if num_iter > 0: break image1 = item1["image"] image2 = item2["image"] mse = diff_mse(image1, image2) assert mse <= 0.02 logger.info("random_crop_decode_resize_op_{}, mse: {}".format(num_iter + 1, mse)) if plot: visualize_image(image1, image2, mse) num_iter += 1
def test_soft_dvpp_decode_random_crop_resize_jpeg(plot=False): """ Test SoftDvppDecodeRandomCropResizeJpeg op """ logger.info("test_random_decode_resize_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) random_crop_decode_resize_op = vision.RandomCropDecodeResize((256, 512), (1, 1), (0.5, 0.5)) data1 = data1.map(input_columns=["image"], operations=random_crop_decode_resize_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) soft_dvpp_random_crop_decode_resize_op = vision.SoftDvppDecodeRandomCropResizeJpeg((256, 512), (1, 1), (0.5, 0.5)) data2 = data2.map(input_columns=["image"], operations=soft_dvpp_random_crop_decode_resize_op) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): if num_iter > 0: break image1 = item1["image"] image2 = item2["image"] mse = diff_mse(image1, image2) assert mse <= 0.06 logger.info("random_crop_decode_resize_op_{}, mse: {}".format(num_iter + 1, mse)) if plot: visualize_image(image1, image2, mse) num_iter += 1
def test_random_crop_decode_resize_op(plot=False): """ Test RandomCropDecodeResize op """ logger.info("test_random_decode_resize_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = vision.Decode() random_crop_decode_resize_op = vision.RandomCropDecodeResize((256, 512), (1, 1), (0.5, 0.5)) data1 = data1.map(operations=random_crop_decode_resize_op, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) random_crop_resize_op = vision.RandomResizedCrop((256, 512), (1, 1), (0.5, 0.5)) data2 = data2.map(operations=decode_op, input_columns=["image"]) data2 = data2.map(operations=random_crop_resize_op, input_columns=["image"]) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): if num_iter > 0: break image1 = item1["image"] image2 = item2["image"] mse = diff_mse(image1, image2) assert mse == 0 logger.info("random_crop_decode_resize_op_{}, mse: {}".format(num_iter + 1, mse)) if plot: visualize_image(image1, image2, mse) num_iter += 1
def util_test_random_color_adjust_op(brightness=(1, 1), contrast=(1, 1), saturation=(1, 1), hue=(0, 0), plot=False): """ Util function that tests RandomColorAdjust for a specific argument """ # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() random_adjust_op = c_vision.RandomColorAdjust(brightness=brightness, contrast=contrast, saturation=saturation, hue=hue) ctrans = [decode_op, random_adjust_op, ] data1 = data1.map(operations=ctrans, input_columns=["image"]) # Second dataset transforms = [ py_vision.Decode(), py_vision.RandomColorAdjust(brightness=brightness, contrast=contrast, saturation=saturation, hue=hue), py_vision.ToTensor() ] transform = mindspore.dataset.transforms.py_transforms.Compose(transforms) data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(operations=transform, input_columns=["image"]) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): num_iter += 1 c_image = item1["image"] py_image = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) logger.info("shape of c_image: {}".format(c_image.shape)) logger.info("shape of py_image: {}".format(py_image.shape)) logger.info("dtype of c_image: {}".format(c_image.dtype)) logger.info("dtype of py_image: {}".format(py_image.dtype)) mse = diff_mse(c_image, py_image) logger.info("mse is {}".format(mse)) logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse)) assert mse < 0.01 if plot: visualize_image(c_image, py_image, mse)
def test_cut_out_op(plot=False): """ Test Cutout """ logger.info("test_cut_out") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_1 = [f.Decode(), f.ToTensor(), f.RandomErasing(value='random')] transform_1 = mindspore.dataset.transforms.py_transforms.Compose( transforms_1) data1 = data1.map(operations=transform_1, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c.Decode() cut_out_op = c.CutOut(80) transforms_2 = [decode_op, cut_out_op] data2 = data2.map(operations=transforms_2, input_columns=["image"]) num_iter = 0 for item1, item2 in zip( data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): num_iter += 1 image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) # C image doesn't require transpose image_2 = item2["image"] logger.info("shape of image_1: {}".format(image_1.shape)) logger.info("shape of image_2: {}".format(image_2.shape)) logger.info("dtype of image_1: {}".format(image_1.dtype)) logger.info("dtype of image_2: {}".format(image_2.dtype)) mse = diff_mse(image_1, image_2) if plot: visualize_image(image_1, image_2, mse)
def test_normalize_op_py(plot=False): """ Test Normalize in python transformations """ logger.info("Test Normalize in python") mean = [0.475, 0.45, 0.392] std = [0.275, 0.267, 0.278] # define map operations transforms = [py_vision.Decode(), py_vision.ToTensor()] transform = mindspore.dataset.transforms.py_transforms.Compose(transforms) normalize_op = py_vision.Normalize(mean, std) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(operations=transform, input_columns=["image"]) data1 = data1.map(operations=normalize_op, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(operations=transform, input_columns=["image"]) num_iter = 0 for item1, item2 in zip( data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): image_de_normalized = (item1["image"].transpose(1, 2, 0) * 255).astype( np.uint8) image_np_normalized = ( normalize_np(item2["image"].transpose(1, 2, 0), mean, std) * 255).astype(np.uint8) image_original = (item2["image"].transpose(1, 2, 0) * 255).astype( np.uint8) mse = diff_mse(image_de_normalized, image_np_normalized) logger.info("image_{}, mse: {}".format(num_iter + 1, mse)) assert mse < 0.01 if plot: visualize_image(image_original, image_de_normalized, mse, image_np_normalized) num_iter += 1
def test_random_erasing_op(plot=False): """ Test RandomErasing and Cutout """ logger.info("test_random_erasing") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_1 = [ vision.Decode(), vision.ToTensor(), vision.RandomErasing(value='random') ] transform_1 = vision.ComposeOp(transforms_1) data1 = data1.map(input_columns=["image"], operations=transform_1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_2 = [vision.Decode(), vision.ToTensor(), vision.Cutout(80)] transform_2 = vision.ComposeOp(transforms_2) data2 = data2.map(input_columns=["image"], operations=transform_2()) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): num_iter += 1 image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) logger.info("shape of image_1: {}".format(image_1.shape)) logger.info("shape of image_2: {}".format(image_2.shape)) logger.info("dtype of image_1: {}".format(image_1.dtype)) logger.info("dtype of image_2: {}".format(image_2.dtype)) mse = diff_mse(image_1, image_2) if plot: visualize_image(image_1, image_2, mse)
def test_random_rotation_op_py(plot=False): """ Test RandomRotation in python transformations op """ logger.info("test_random_rotation_op_py") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) # use [90, 90] to force rotate 90 degrees, expand is set to be True to match output size transform1 = mindspore.dataset.transforms.py_transforms.Compose([ py_vision.Decode(), py_vision.RandomRotation((90, 90), expand=True), py_vision.ToTensor() ]) data1 = data1.map(operations=transform1, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transform2 = mindspore.dataset.transforms.py_transforms.Compose( [py_vision.Decode(), py_vision.ToTensor()]) data2 = data2.map(operations=transform2, input_columns=["image"]) num_iter = 0 for item1, item2 in zip( data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): if num_iter > 0: break rotation_de = (item1["image"].transpose(1, 2, 0) * 255).astype( np.uint8) original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) logger.info("shape before rotate: {}".format(original.shape)) rotation_cv = cv2.rotate(original, cv2.ROTATE_90_COUNTERCLOCKWISE) mse = diff_mse(rotation_de, rotation_cv) logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse)) assert mse == 0 num_iter += 1 if plot: visualize_image(original, rotation_de, mse, rotation_cv)
def test_random_horizontal_op(plot=False): """ Test RandomHorizontalFlip op """ logger.info("test_random_horizontal_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = c_vision.Decode() random_horizontal_op = c_vision.RandomHorizontalFlip(1.0) data1 = data1.map(operations=decode_op, input_columns=["image"]) data1 = data1.map(operations=random_horizontal_op, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(operations=decode_op, input_columns=["image"]) num_iter = 0 for item1, item2 in zip( data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): # with the seed value, we can only guarantee the first number generated if num_iter > 0: break image_h_flipped = item1["image"] image = item2["image"] image_h_flipped_2 = h_flip(image) mse = diff_mse(image_h_flipped, image_h_flipped_2) assert mse == 0 logger.info("image_{}, mse: {}".format(num_iter + 1, mse)) num_iter += 1 if plot: visualize_image(image, image_h_flipped, mse, image_h_flipped_2)
def test_random_crop_decode_resize_op(plot=False): """ Test RandomCropDecodeResize op """ logger.info("test_random_decode_resize_op") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) decode_op = vision.Decode() random_crop_decode_resize_op = vision.RandomCropDecodeResize( (256, 512), (1, 1), (0.5, 0.5)) data1 = data1.map(input_columns=["image"], operations=random_crop_decode_resize_op) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=decode_op) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): if num_iter > 0: break crop_and_resize_de = item1["image"] original = item2["image"] crop_and_resize_cv = cv2.resize(original, (512, 256)) mse = diff_mse(crop_and_resize_de, crop_and_resize_cv) logger.info("random_crop_decode_resize_op_{}, mse: {}".format( num_iter + 1, mse)) if plot: visualize_image(original, crop_and_resize_de, mse, crop_and_resize_cv) num_iter += 1
def test_random_rotation_op_c(plot=False): """ Test RandomRotation in c++ transformations op """ logger.info("test_random_rotation_op_c") # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) decode_op = c_vision.Decode() # use [90, 90] to force rotate 90 degrees, expand is set to be True to match output size random_rotation_op = c_vision.RandomRotation((90, 90), expand=True) data1 = data1.map(operations=decode_op, input_columns=["image"]) data1 = data1.map(operations=random_rotation_op, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(operations=decode_op, input_columns=["image"]) num_iter = 0 for item1, item2 in zip( data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): if num_iter > 0: break rotation_de = item1["image"] original = item2["image"] logger.info("shape before rotate: {}".format(original.shape)) rotation_cv = cv2.rotate(original, cv2.ROTATE_90_COUNTERCLOCKWISE) mse = diff_mse(rotation_de, rotation_cv) logger.info("random_rotation_op_{}, mse: {}".format(num_iter + 1, mse)) assert mse == 0 num_iter += 1 if plot: visualize_image(original, rotation_de, mse, rotation_cv)