Source code for mindspore.nn.metrics.metric

# 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.
# ============================================================================
"""Metric base class."""
from abc import ABCMeta, abstractmethod
import numpy as np
from mindspore.common.tensor import Tensor


[docs]class Metric(metaclass=ABCMeta): """ Base class of metric. Note: For examples of subclasses, please refer to the definition of class `MAE`, 'Recall' etc. """ def __init__(self): pass def _convert_data(self, data): """ Convert data type to numpy array. Args: data (Object): Input data. Returns: Ndarray, data with `np.ndarray` type. """ if isinstance(data, Tensor): data = data.asnumpy() elif isinstance(data, list): data = np.array(data) elif isinstance(data, np.ndarray): pass else: raise TypeError('Input data type must be tensor, list or numpy.ndarray') return data def _check_onehot_data(self, data): """ Whether input data are one-hot encoding. Args: data (numpy.array): Input data. Returns: bool, return true, if input data are one-hot encoding. """ if data.ndim > 1 and np.equal(data ** 2, data).all(): shp = (data.shape[0],) + data.shape[2:] if np.equal(np.ones(shp), data.sum(axis=1)).all(): return True return False def __call__(self, *inputs): """ Evaluate input data once. Args: inputs (tuple): The first item is predict array, the second item is target array. Returns: Float, compute result. """ self.clear() self.update(*inputs) return self.eval()
[docs] @abstractmethod def clear(self): """ An interface describes the behavior of clearing the internal evaluation result. Note: All subclasses must override this interface. """ raise NotImplementedError('Must define clear function to use this base class')
[docs] @abstractmethod def eval(self): """ An interface describes the behavior of computing the evaluation result. Note: All subclasses must override this interface. """ raise NotImplementedError('Must define eval function to use this base class')
[docs] @abstractmethod def update(self, *inputs): """ An interface describes the behavior of updating the internal evaluation result. Note: All subclasses must override this interface. Args: inputs: A variable-length input argument list. """ raise NotImplementedError('Must define update function to use this base class')