# Copyright 2020 Huawei Technologies Co., Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing TenCrop in DE """ import pytest import numpy as np import mindspore.dataset as ds import mindspore.dataset.transforms.vision.py_transforms as vision from mindspore import log as logger from util import visualize_list, save_and_check_md5 GENERATE_GOLDEN = False DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"] SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json" def util_test_ten_crop(crop_size, vertical_flip=False, plot=False): """ Utility function for testing TenCrop. Input arguments are given by other tests """ data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_1 = [ vision.Decode(), vision.ToTensor(), ] 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.TenCrop(crop_size, use_vertical_flip=vertical_flip), lambda images: np.stack([vision.ToTensor()(image) for image in images]) # 4D stack of 10 images ] 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"] 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)) if plot: visualize_list(np.array([image_1]*10), (image_2 * 255).astype(np.uint8).transpose(0, 2, 3, 1)) # The output data should be of a 4D tensor shape, a stack of 10 images. assert len(image_2.shape) == 4 assert image_2.shape[0] == 10 def test_ten_crop_op_square(plot=False): """ Tests TenCrop for a square crop """ logger.info("test_ten_crop_op_square") util_test_ten_crop(200, plot=plot) def test_ten_crop_op_rectangle(plot=False): """ Tests TenCrop for a rectangle crop """ logger.info("test_ten_crop_op_rectangle") util_test_ten_crop((200, 150), plot=plot) def test_ten_crop_op_vertical_flip(plot=False): """ Tests TenCrop with vertical flip set to True """ logger.info("test_ten_crop_op_vertical_flip") util_test_ten_crop(200, vertical_flip=True, plot=plot) def test_ten_crop_md5(): """ Tests TenCrops for giving the same results in multiple runs. Since TenCrop is a deterministic function, we expect it to return the same result for a specific input every time """ logger.info("test_ten_crop_md5") data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms_2 = [ vision.Decode(), vision.TenCrop((200, 100), use_vertical_flip=True), lambda images: np.stack([vision.ToTensor()(image) for image in images]) # 4D stack of 10 images ] transform_2 = vision.ComposeOp(transforms_2) data2 = data2.map(input_columns=["image"], operations=transform_2()) # Compare with expected md5 from images filename = "ten_crop_01_result.npz" save_and_check_md5(data2, filename, generate_golden=GENERATE_GOLDEN) def test_ten_crop_list_size_error_msg(): """ Tests TenCrop error message when the size arg has more than 2 elements """ logger.info("test_ten_crop_list_size_error_msg") with pytest.raises(TypeError) as info: _ = [ vision.Decode(), vision.TenCrop([200, 200, 200]), lambda images: np.stack([vision.ToTensor()(image) for image in images]) # 4D stack of 10 images ] error_msg = "Size should be a single integer or a list/tuple (h, w) of length 2." assert error_msg == str(info.value) def test_ten_crop_invalid_size_error_msg(): """ Tests TenCrop error message when the size arg is not positive """ logger.info("test_ten_crop_invalid_size_error_msg") with pytest.raises(ValueError) as info: _ = [ vision.Decode(), vision.TenCrop(0), lambda images: np.stack([vision.ToTensor()(image) for image in images]) # 4D stack of 10 images ] error_msg = "Input is not within the required range" assert error_msg == str(info.value) with pytest.raises(ValueError) as info: _ = [ vision.Decode(), vision.TenCrop(-10), lambda images: np.stack([vision.ToTensor()(image) for image in images]) # 4D stack of 10 images ] assert error_msg == str(info.value) def test_ten_crop_wrong_img_error_msg(): """ Tests TenCrop error message when the image is not in the correct format. """ logger.info("test_ten_crop_wrong_img_error_msg") data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) transforms = [ vision.Decode(), vision.TenCrop(200), vision.ToTensor() ] transform = vision.ComposeOp(transforms) data = data.map(input_columns=["image"], operations=transform()) with pytest.raises(RuntimeError) as info: data.create_tuple_iterator().get_next() error_msg = "TypeError: img should be PIL Image or Numpy array. Got " # error msg comes from ToTensor() assert error_msg in str(info.value) if __name__ == "__main__": test_ten_crop_op_square(plot=True) test_ten_crop_op_rectangle(plot=True) test_ten_crop_op_vertical_flip(plot=True) test_ten_crop_md5() test_ten_crop_list_size_error_msg() test_ten_crop_invalid_size_error_msg() test_ten_crop_wrong_img_error_msg()