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108 lines
3.5 KiB
108 lines
3.5 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 rescale op in DE
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"""
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import mindspore.dataset as ds
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import mindspore.dataset.vision.c_transforms as vision
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from mindspore import log as logger
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from util import visualize_image, diff_mse, save_and_check_md5
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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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GENERATE_GOLDEN = False
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def rescale_np(image):
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"""
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Apply the rescale
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"""
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image = image / 255.0
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image = image - 1.0
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return image
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def get_rescaled(image_id):
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"""
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Reads the image using DE ops and then rescales using Numpy
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"""
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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decode_op = vision.Decode()
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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num_iter = 0
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for item in data1.create_dict_iterator(num_epochs=1):
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image = item["image"].asnumpy()
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if num_iter == image_id:
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return rescale_np(image)
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num_iter += 1
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return None
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def test_rescale_op(plot=False):
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"""
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Test rescale
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"""
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logger.info("Test rescale")
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# define map operations
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decode_op = vision.Decode()
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rescale_op = vision.Rescale(1.0 / 255.0, -1.0)
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# apply map operations on images
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data1 = data1.map(operations=decode_op, input_columns=["image"])
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data2 = data1.map(operations=rescale_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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image_original = item1["image"]
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image_de_rescaled = item2["image"]
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image_np_rescaled = get_rescaled(num_iter)
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mse = diff_mse(image_de_rescaled, image_np_rescaled)
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assert mse < 0.001 # rounding error
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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_original, image_de_rescaled, mse, image_np_rescaled)
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def test_rescale_md5():
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"""
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Test Rescale with md5 comparison
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"""
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logger.info("Test Rescale with md5 comparison")
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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 = vision.Decode()
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rescale_op = vision.Rescale(1.0 / 255.0, -1.0)
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# apply map operations on images
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data = data.map(operations=decode_op, input_columns=["image"])
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data = data.map(operations=rescale_op, input_columns=["image"])
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# check results with md5 comparison
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filename = "rescale_01_result.npz"
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save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
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if __name__ == "__main__":
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test_rescale_op(plot=True)
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test_rescale_md5()
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