forked from mindspore-Ecosystem/mindspore
66 lines
2.0 KiB
Python
66 lines
2.0 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 the one_hot op in DE
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"""
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import mindspore.dataset.transforms.vision.c_transforms as vision
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import mindspore.dataset.transforms.c_transforms as data_trans
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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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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 one_hot(index, depth):
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"""
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Apply the one_hot
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"""
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arr = np.zeros([1, depth], dtype=np.int32)
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arr[0, index] = 1
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return arr
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def test_one_hot():
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"""
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Test one_hot
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"""
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logger.info("Test one_hot")
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depth = 10
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# First dataset
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data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
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one_hot_op = data_trans.OneHot(depth)
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data1 = data1.map(input_columns=["label"], operations=one_hot_op, columns_order=["label"])
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# Second dataset
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data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["label"], shuffle=False)
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num_iter = 0
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for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()):
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assert len(item1) == len(item2)
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label1 = item1["label"]
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label2 = one_hot(item2["label"][0], depth)
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mse = np.sum(label1 - label2)
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logger.info("DE one_hot: {}, Numpy one_hot: {}, diff: {}".format(label1, label2, mse))
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num_iter += 1
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if __name__ == "__main__":
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test_one_hot()
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