# Copyright 2020 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. # ============================================================================ import numpy as np import mindspore.context as context import mindspore.nn as nn from mindspore import Tensor from mindspore.ops import operations as P context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") class EditDistance(nn.Cell): def __init__(self, hypothesis_shape, truth_shape, normalize=True): super(EditDistance, self).__init__() self.edit_distance = P.EditDistance(normalize) self.hypothesis_shape = hypothesis_shape self.truth_shape = truth_shape def construct(self, hypothesis_indices, hypothesis_values, truth_indices, truth_values): return self.edit_distance(hypothesis_indices, hypothesis_values, self.hypothesis_shape, truth_indices, truth_values, self.truth_shape) def test_edit_distance(): h1, h2, h3 = np.array([[0, 0, 0], [1, 0, 1], [1, 1, 1]]), np.array([1, 2, 3]), np.array([2, 2, 2]) t1, t2, t3 = np.array([[0, 1, 0], [0, 0, 1], [1, 1, 0], [1, 0, 1]]), np.array([1, 2, 3, 1]), np.array([2, 2, 2]) hypothesis_indices = Tensor(h1.astype(np.int64)) hypothesis_values = Tensor(h2.astype(np.int64)) hypothesis_shape = Tensor(h3.astype(np.int64)) truth_indices = Tensor(t1.astype(np.int64)) truth_values = Tensor(t2.astype(np.int64)) truth_shape = Tensor(t3.astype(np.int64)) edit_distance = EditDistance(hypothesis_shape, truth_shape) out = edit_distance(hypothesis_indices, hypothesis_values, truth_indices, truth_values) print(out)