# Copyright 2019 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 the one_hot op in DE """ import mindspore.dataset.transforms.vision.c_transforms as vision import mindspore.dataset.transforms.c_transforms as data_trans import numpy as np import mindspore.dataset as ds from mindspore import log as logger 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 one_hot(index, depth): """ Apply the one_hot """ arr = np.zeros([1, depth], dtype=np.int32) arr[0, index] = 1 return arr def test_one_hot(): """ Test one_hot """ logger.info("Test one_hot") depth = 10 # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False) one_hot_op = data_trans.OneHot(depth) data1 = data1.map(input_columns=["label"], operations=one_hot_op, columns_order=["label"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["label"], shuffle=False) num_iter = 0 for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): assert len(item1) == len(item2) label1 = item1["label"] label2 = one_hot(item2["label"][0], depth) mse = np.sum(label1 - label2) logger.info("DE one_hot: {}, Numpy one_hot: {}, diff: {}".format(label1, label2, mse)) num_iter += 1 if __name__ == "__main__": test_one_hot()