1# Copyright 2021 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15"""CosineSimilarity.""" 16import numpy as np 17from mindspore._checkparam import Validator as validator 18from .metric import Metric, rearrange_inputs 19 20 21class CosineSimilarity(Metric): 22 """ 23 Computes representation similarity 24 25 Args: 26 similarity (str): 'dot' or 'cosine'. Default: 'cosine' 27 reduction (str): 'none', 'sum', 'mean' (all along dim -1). Default: 'none' 28 zero_diagonal (bool): If true, the diagonals are set to zero. Default: True 29 30 Return: 31 A square matrix (input1, input1) with the similarity scores between all elements. 32 If sum or mean is used, then returns (b, 1) with the reduced value for each row. 33 34 Supported Platforms: 35 ``Ascend`` ``GPU`` ``CPU`` 36 37 Example: 38 >>> import numpy as np 39 >>> from mindspore import nn 40 >>> 41 >>> test_data = np.array([[1, 3, 4, 7], [2, 4, 2, 5], [3, 1, 5, 8]]) 42 >>> metric = nn.CosineSimilarity() 43 >>> metric.clear() 44 >>> metric.update(test_data) 45 >>> square_matrix = metric.eval() 46 >>> print(square_matrix) 47 [[0. 0.94025615 0.95162452] 48 [0.94025615 0. 0.86146098] 49 [0.95162452 0.86146098 0.]] 50 """ 51 def __init__(self, similarity='cosine', reduction='none', zero_diagonal=True): 52 super().__init__() 53 similarity_list = ['dot', 'cosine'] 54 reduction_list = ['none', 'sum', 'mean'] 55 similarity = validator.check_value_type("similarity", similarity, [str]) 56 self.similarity = validator.check_string(similarity, similarity_list, "similarity") 57 reduction = validator.check_value_type("reduction", reduction, [str]) 58 self.reduction = validator.check_string(reduction, reduction_list, "reduction") 59 self.zero_diagonal = validator.check_value_type("zero_diagonal", zero_diagonal, [bool]) 60 self.clear() 61 62 def clear(self): 63 """Clears the internal evaluation result.""" 64 self.sqr_mtx_res = 0 65 self._is_update = False 66 67 @rearrange_inputs 68 def update(self, inputs): 69 """ 70 Updates the internal evaluation result with 'input1'. 71 72 Args: 73 inputs: input_data `input1`. The input_data is a `Tensor` or an array. 74 """ 75 input_data = self._convert_data(inputs) 76 77 if self.similarity == 'cosine': 78 data = np.linalg.norm(input_data, ord=2, axis=1) 79 input_data = input_data / np.expand_dims(data, 1) 80 81 self.sqr_mtx_res = np.dot(input_data, input_data.transpose(1, 0)) 82 self._is_update = True 83 84 def eval(self): 85 """ 86 Computes the Cosine_Similarity square matrix. 87 88 Returns: 89 A square matrix. 90 91 Raises: 92 RuntimeError: If the update method is not called first, an error will be reported. 93 94 """ 95 if not self._is_update: 96 raise RuntimeError('Call the update method before calling eval.') 97 98 if self.zero_diagonal: 99 np.fill_diagonal(self.sqr_mtx_res, 0) 100 101 if self.reduction == 'mean': 102 self.sqr_mtx_res = np.mean(self.sqr_mtx_res, axis=-1) 103 104 if self.reduction == 'sum': 105 self.sqr_mtx_res = np.sum(self.sqr_mtx_res, axis=-1) 106 107 return self.sqr_mtx_res 108