forked from mindspore-Ecosystem/mindspore
82 lines
2.9 KiB
Python
82 lines
2.9 KiB
Python
# 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 Decode op in DE
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"""
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import cv2
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import mindspore.dataset.transforms.vision.c_transforms as vision
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import numpy as np
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import mindspore.dataset as ds
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from mindspore import log as logger
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from util import diff_mse
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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 test_decode_op():
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"""
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Test Decode op
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"""
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logger.info("test_decode_op")
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# Decode with rgb format set to True
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
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# Serialize and Load dataset requires using vision.Decode instead of vision.Decode().
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data1 = data1.map(input_columns=["image"], operations=[vision.Decode(True)])
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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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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
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actual = item1["image"]
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expected = cv2.imdecode(item2["image"], cv2.IMREAD_COLOR)
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expected = cv2.cvtColor(expected, cv2.COLOR_BGR2RGB)
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assert actual.shape == expected.shape
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diff = actual - expected
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mse = np.sum(np.power(diff, 2))
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assert mse == 0
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def test_decode_op_tf_file_dataset():
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"""
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Test Decode op with tf_file dataset
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"""
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logger.info("test_decode_op_tf_file_dataset")
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# Decode with rgb format set to True
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=ds.Shuffle.FILES)
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data1 = data1.map(input_columns=["image"], operations=vision.Decode(True))
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for item in data1.create_dict_iterator():
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logger.info('decode == {}'.format(item['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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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
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actual = item1["image"]
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expected = cv2.imdecode(item2["image"], cv2.IMREAD_COLOR)
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expected = cv2.cvtColor(expected, cv2.COLOR_BGR2RGB)
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assert actual.shape == expected.shape
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diff = actual - expected
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mse = np.sum(np.power(diff, 2))
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assert mse == 0
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if __name__ == "__main__":
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test_decode_op()
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test_decode_op_tf_file_dataset()
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