mindspore.train.metrics.metric 源代码

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"""Metric base class."""
from __future__ import absolute_import

from abc import ABCMeta, abstractmethod
import functools
import numpy as np

from mindspore.common.tensor import Tensor

_eval_types = {'classification', 'multilabel'}


[文档]def rearrange_inputs(func): """ This decorator is used to rearrange the inputs according to its `indexes` attribute of the class. This decorator is currently applied on the `update` of :class:`mindspore.train.Metric`. Args: func (Callable): A candidate function to be wrapped whose input will be rearranged. Returns: Callable, used to exchange metadata between functions. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> from mindspore.nn import rearrange_inputs >>> class RearrangeInputsExample: ... def __init__(self): ... self._indexes = None ... ... @property ... def indexes(self): ... return getattr(self, '_indexes', None) ... ... def set_indexes(self, indexes): ... self._indexes = indexes ... return self ... ... @rearrange_inputs ... def update(self, *inputs): ... return inputs >>> >>> rearrange_inputs_example = RearrangeInputsExample().set_indexes([1, 0]) >>> outs = rearrange_inputs_example.update(5, 9) >>> print(outs) (9, 5) """ @functools.wraps(func) def wrapper(self, *inputs): indexes = self.indexes inputs = inputs if not indexes else [inputs[i] for i in indexes] return func(self, *inputs) return wrapper
[文档]class Metric(metaclass=ABCMeta): """ Base class of metric, which is used to evaluate metrics. The `clear`, `update`, and `eval` should be called when evaluating metric, and they should be overridden by subclasse. `update` will accumulate intermediate results in the evaluation process, `eval` will evaluate the final result, and `clear` will reinitialize the intermediate results. Never use this class directly, but instantiate one of its subclasses instead, for examples, :class:`mindspore.train.MAE`, :class:`mindspore.train.Recall` etc. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` """ def __init__(self): self._indexes = None 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(f"For 'Metric' and its derived classes, the input data type must be tensor, list or " f"numpy.ndarray, but got {type(data)}.") return data @property def indexes(self): """Get the current indexes value. The default value is None and can be changed by `set_indexes`. """ return getattr(self, '_indexes', None)
[文档] def set_indexes(self, indexes): """ This interface is to rearrange the inputs of `update`. Given (label0, label1, logits), set the `indexes` to [2, 1] then the (logits, label1) will be the actually inputs of `update`. Note: When customize a metric, decorate the `update` function with the decorator :func:`mindspore.train.rearrange_inputs` for the `indexes` to take effect. Args: indexes (List(int)): The order of logits and labels to be rearranged. Outputs: :class:`Metric`, its original Class instance. Raises: ValueError: If the type of input 'indexes' is not a list or its elements are not all int. Examples: >>> import numpy as np >>> from mindspore import Tensor >>> from mindspore.train import Accuracy >>> >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]])) >>> y = Tensor(np.array([1, 0, 1])) >>> y2 = Tensor(np.array([0, 0, 1])) >>> metric = Accuracy('classification').set_indexes([0, 2]) >>> metric.clear() >>> # indexes is [0, 2], using x as logits, y2 as label. >>> metric.update(x, y, y2) >>> accuracy = metric.eval() >>> print(accuracy) 0.3333333333333333 """ if not isinstance(indexes, list) or not all(isinstance(i, int) for i in indexes): raise ValueError("For 'set_indexes', the argument 'indexes' must be a list and all its elements must " "be int, please check whether it is correct.") self._indexes = indexes return self
def __call__(self, *inputs): """ Evaluate input data once. Args: inputs (tuple): The first item is a predict array, the second item is a target array. Returns: Float, compute result. """ self.clear() self.update(*inputs) return self.eval()
[文档] @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')
[文档] @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')
[文档] @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, usually are the logits and the corresponding labels. """ raise NotImplementedError('Must define update function to use this base class')
class EvaluationBase(Metric): """ Base class of evaluation. Note: Please refer to the definition of class `Accuracy`. Args: eval_type (str): Type of evaluation must be in {'classification', 'multilabel'}. Raises: TypeError: If the input type is not classification or multilabel. """ def __init__(self, eval_type): super(EvaluationBase, self).__init__() if eval_type not in _eval_types: raise TypeError("The argument 'eval_type' must be in {}, but got {}".format(_eval_types, eval_type)) self._type = eval_type def _check_shape(self, y_pred, y): """ Checks the shapes of y_pred and y. Args: y_pred (Tensor): Predict array. y (Tensor): Target array. """ if self._type == 'classification': if y_pred.ndim != y.ndim + 1: raise ValueError("In classification case, the dimension of y_pred (predicted value) should equal to " "the dimension of y (true value) add 1, but got y_pred dimension: {} and y " "dimension: {}.".format(y_pred.ndim, y.ndim)) if y.shape != (y_pred.shape[0],) + y_pred.shape[2:]: raise ValueError("In classification case, y_pred (predicted value) shape and y (true value) shape " "can not match, y shape should be equal to y_pred shape that the value at index 1 " "is deleted. Such as y_pred shape (1, 2, 3), then y shape should be (1, 3). " "But got y_pred shape {} and y shape {}".format(y_pred.shape, y.shape)) else: if y_pred.ndim != y.ndim: raise ValueError("In {} case, the dimension of y_pred (predicted value) should equal to the dimension" " of y (true value), but got y_pred dimension: {} and y dimension: {}." .format(self._type, y_pred.ndim, y.ndim)) if y_pred.shape != y.shape: raise ValueError("In {} case, the shape of y_pred (predicted value) should equal to the shape of y " "(true value), but got y_pred shape: {} and y shape: {}." .format(self._type, y_pred.shape, y.shape)) def _check_value(self, y_pred, y): """ Checks the values of y_pred and y. Args: y_pred (Tensor): Predict array. y (Tensor): Target array. """ if self._type != 'classification' and not (np.equal(y_pred ** 2, y_pred).all() and np.equal(y ** 2, y).all()): raise ValueError("In multilabel case, all elements in y_pred (predicted value) and y (true value) should " "be 0 or 1.Please check whether your inputs y_pred and y are correct.") def clear(self): """ A interface describes the behavior of clearing the internal evaluation result. Note: All subclasses must override this interface. """ raise NotImplementedError def update(self, *inputs): """ A interface describes the behavior of updating the internal evaluation result. Note: All subclasses must override this interface. Args: inputs: The first item is a predicted array and the second item is a target array. """ raise NotImplementedError def eval(self): """ A interface describes the behavior of computing the evaluation result. Note: All subclasses must override this interface. """ raise NotImplementedError def _check_onehot_data(data): """ Whether input data is one-hot encoding. Args: data (numpy.array): Input data. Returns: bool, return true, if input data is 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 _binary_clf_curve(preds, target, sample_weights=None, pos_label=1): """Calculate True Positives and False Positives per binary classification threshold.""" if sample_weights is not None and not isinstance(sample_weights, np.ndarray): sample_weights = np.array(sample_weights) if preds.ndim > target.ndim: preds = preds[:, 0] desc_score_indices = np.argsort(-preds) preds = preds[desc_score_indices] target = target[desc_score_indices] if sample_weights is not None: weight = sample_weights[desc_score_indices] else: weight = 1. distinct_value_indices = np.where(preds[1:] - preds[:-1])[0] threshold_idxs = np.pad(distinct_value_indices, (0, 1), constant_values=target.shape[0] - 1) target = np.array(target == pos_label).astype(np.int64) tps = np.cumsum(target * weight, axis=0)[threshold_idxs] if sample_weights is not None: fps = np.cumsum((1 - target) * weight, axis=0)[threshold_idxs] else: fps = 1 + threshold_idxs - tps return fps, tps, preds[threshold_idxs]