# Copyright 2021 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.
# ============================================================================
"""CosineSimilarity."""
import numpy as np
from mindspore._checkparam import Validator as validator
from .metric import Metric, rearrange_inputs
[docs]class CosineSimilarity(Metric):
"""
Computes representation similarity
Args:
similarity (str): 'dot' or 'cosine'. Default: 'cosine'
reduction (str): 'none', 'sum', 'mean' (all along dim -1). Default: 'none'
zero_diagonal (bool): If true, the diagonals are set to zero. Default: True
Return:
A square matrix (input1, input1) with the similarity scores between all elements.
If sum or mean is used, then returns (b, 1) with the reduced value for each row.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Example:
>>> import numpy as np
>>> from mindspore import nn
>>>
>>> test_data = np.array([[1, 3, 4, 7], [2, 4, 2, 5], [3, 1, 5, 8]])
>>> metric = nn.CosineSimilarity()
>>> metric.clear()
>>> metric.update(test_data)
>>> square_matrix = metric.eval()
>>> print(square_matrix)
[[0. 0.94025615 0.95162452]
[0.94025615 0. 0.86146098]
[0.95162452 0.86146098 0.]]
"""
def __init__(self, similarity='cosine', reduction='none', zero_diagonal=True):
super().__init__()
similarity_list = ['dot', 'cosine']
reduction_list = ['none', 'sum', 'mean']
similarity = validator.check_value_type("similarity", similarity, [str])
self.similarity = validator.check_string(similarity, similarity_list, "similarity")
reduction = validator.check_value_type("reduction", reduction, [str])
self.reduction = validator.check_string(reduction, reduction_list, "reduction")
self.zero_diagonal = validator.check_value_type("zero_diagonal", zero_diagonal, [bool])
self.clear()
[docs] def clear(self):
"""Clears the internal evaluation result."""
self.sqr_mtx_res = 0
self._is_update = False
[docs] @rearrange_inputs
def update(self, inputs):
"""
Updates the internal evaluation result with 'input1'.
Args:
inputs: input_data `input1`. The input_data is a `Tensor` or an array.
"""
input_data = self._convert_data(inputs)
if self.similarity == 'cosine':
data = np.linalg.norm(input_data, ord=2, axis=1)
input_data = input_data / np.expand_dims(data, 1)
self.sqr_mtx_res = np.dot(input_data, input_data.transpose(1, 0))
self._is_update = True
[docs] def eval(self):
"""
Computes the Cosine_Similarity square matrix.
Returns:
A square matrix.
Raises:
RuntimeError: If the update method is not called first, an error will be reported.
"""
if not self._is_update:
raise RuntimeError('Call the update method before calling eval.')
if self.zero_diagonal:
np.fill_diagonal(self.sqr_mtx_res, 0)
if self.reduction == 'mean':
self.sqr_mtx_res = np.mean(self.sqr_mtx_res, axis=-1)
if self.reduction == 'sum':
self.sqr_mtx_res = np.sum(self.sqr_mtx_res, axis=-1)
return self.sqr_mtx_res