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219 lines
8.2 KiB
219 lines
8.2 KiB
# Copyright 2019 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Testing the random vertical flip op in DE
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"""
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import numpy as np
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.py_transforms
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import mindspore.dataset.vision.c_transforms as c_vision
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import mindspore.dataset.vision.py_transforms as py_vision
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from mindspore import log as logger
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from util import save_and_check_md5, visualize_list, visualize_image, diff_mse, \
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config_get_set_seed, config_get_set_num_parallel_workers
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GENERATE_GOLDEN = False
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DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
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SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
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def v_flip(image):
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"""
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Apply the random_vertical
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"""
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# with the seed provided in this test case, it will always flip.
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# that's why we flip here too
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image = image[::-1, :, :]
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return image
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def test_random_vertical_op(plot=False):
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"""
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Test random_vertical with default probability
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"""
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logger.info("Test random_vertical")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = c_vision.Decode()
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random_vertical_op = c_vision.RandomVerticalFlip(1.0)
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=random_vertical_op, input_columns=["image"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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data2 = data2.map(operations=decode_op, input_columns=["image"])
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num_iter = 0
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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# with the seed value, we can only guarantee the first number generated
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if num_iter > 0:
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break
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image_v_flipped = item1["image"]
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image = item2["image"]
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image_v_flipped_2 = v_flip(image)
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mse = diff_mse(image_v_flipped, image_v_flipped_2)
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assert mse == 0
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logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
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num_iter += 1
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if plot:
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visualize_image(image, image_v_flipped, mse, image_v_flipped_2)
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def test_random_vertical_valid_prob_c():
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"""
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Test RandomVerticalFlip op with c_transforms: valid non-default input, expect to pass
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"""
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logger.info("test_random_vertical_valid_prob_c")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = c_vision.Decode()
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random_horizontal_op = c_vision.RandomVerticalFlip(0.8)
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=random_horizontal_op, input_columns=["image"])
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filename = "random_vertical_01_c_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_vertical_valid_prob_py():
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"""
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Test RandomVerticalFlip op with py_transforms: valid non-default input, expect to pass
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"""
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logger.info("test_random_vertical_valid_prob_py")
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original_seed = config_get_set_seed(0)
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original_num_parallel_workers = config_get_set_num_parallel_workers(1)
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms = [
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py_vision.Decode(),
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py_vision.RandomVerticalFlip(0.8),
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py_vision.ToTensor()
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]
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transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
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data = data.map(operations=transform, input_columns=["image"])
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filename = "random_vertical_01_py_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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# Restore config setting
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ds.config.set_seed(original_seed)
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ds.config.set_num_parallel_workers(original_num_parallel_workers)
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def test_random_vertical_invalid_prob_c():
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"""
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Test RandomVerticalFlip op in c_transforms: invalid input, expect to raise error
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"""
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logger.info("test_random_vertical_invalid_prob_c")
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = c_vision.Decode()
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try:
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# Note: Valid range of prob should be [0.0, 1.0]
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random_horizontal_op = c_vision.RandomVerticalFlip(1.5)
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=random_horizontal_op, input_columns=["image"])
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert 'Input prob is not within the required interval of (0.0 to 1.0).' in str(e)
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def test_random_vertical_invalid_prob_py():
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"""
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Test RandomVerticalFlip op in py_transforms: invalid input, expect to raise error
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"""
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logger.info("test_random_vertical_invalid_prob_py")
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# Generate dataset
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data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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try:
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transforms = [
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py_vision.Decode(),
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# Note: Valid range of prob should be [0.0, 1.0]
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py_vision.RandomVerticalFlip(1.5),
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py_vision.ToTensor()
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]
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transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
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data = data.map(operations=transform, input_columns=["image"])
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except ValueError as e:
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logger.info("Got an exception in DE: {}".format(str(e)))
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assert 'Input prob is not within the required interval of (0.0 to 1.0).' in str(e)
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def test_random_vertical_comp(plot=False):
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"""
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Test test_random_vertical_flip and compare between python and c image augmentation ops
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"""
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logger.info("test_random_vertical_comp")
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = c_vision.Decode()
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# Note: The image must be flipped if prob is set to be 1
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random_horizontal_op = c_vision.RandomVerticalFlip(1)
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data1 = data1.map(operations=random_horizontal_op, input_columns=["image"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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transforms = [
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py_vision.Decode(),
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# Note: The image must be flipped if prob is set to be 1
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py_vision.RandomVerticalFlip(1),
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py_vision.ToTensor()
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]
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transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
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data2 = data2.map(operations=transform, input_columns=["image"])
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images_list_c = []
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images_list_py = []
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for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
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data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
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image_c = item1["image"]
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image_py = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
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images_list_c.append(image_c)
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images_list_py.append(image_py)
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# Check if the output images are the same
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mse = diff_mse(image_c, image_py)
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assert mse < 0.001
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if plot:
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visualize_list(images_list_c, images_list_py, visualize_mode=2)
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if __name__ == "__main__":
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test_random_vertical_op(plot=True)
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test_random_vertical_valid_prob_c()
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test_random_vertical_valid_prob_py()
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test_random_vertical_invalid_prob_c()
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test_random_vertical_invalid_prob_py()
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test_random_vertical_comp(plot=True)
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