Source code for paddle.fluid.metrics

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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"""
Fluid Metrics
"""

from __future__ import print_function

import numpy as np
import copy
import warnings
import six

from .layer_helper import LayerHelper
from .initializer import Constant
from . import unique_name
from .framework import Program, Variable, program_guard
from . import layers

__all__ = [
    'MetricBase',
    'CompositeMetric',
    'Precision',
    'Recall',
    'Accuracy',
    'ChunkEvaluator',
    'EditDistance',
    'DetectionMAP',
    'Auc',
]


def _is_numpy_(var):
    return isinstance(var, (np.ndarray, np.generic))


def _is_number_(var):
    return isinstance(var, int) or isinstance(var, np.int64) or isinstance(
        var, float) or (isinstance(var, np.ndarray) and var.shape == (1, ))


def _is_number_or_matrix_(var):
    return _is_number_(var) or isinstance(var, np.ndarray)


[docs]class MetricBase(object): """ Base Class for all Metrics. MetricBase define a group of interfaces for the model evaluation methods. Metrics accumulate metric states between consecutive minibatches, at every minibatch, use update interface to add current minibatch value to global states. Use eval to compute accumative metric value from last reset() or from scratch on. If you need to custom a new metric, please inherit from MetricBase and custom implementation. Args: name(str): The name of metric instance. such as, "accuracy". It needed if you want to distinct different metrics in a model. """ def __init__(self, name): self._name = str(name) if name != None else self.__class__.__name__ def __str__(self): return self._name
[docs] def reset(self): """ reset clear the states of metrics. By default, the states are the members who do not has _ prefix, reset set them to inital states. If you violate the implicit name rule, please also custom the reset interface. """ states = { attr: value for attr, value in six.iteritems(self.__dict__) if not attr.startswith("_") } for attr, value in six.iteritems(states): if isinstance(value, int): setattr(self, attr, 0) elif isinstance(value, float): setattr(self, attr, .0) elif isinstance(value, (np.ndarray, np.generic)): setattr(self, attr, np.zeros_like(value)) else: setattr(self, attr, None)
[docs] def get_config(self): """ Get the metric and current states. The states are the members who do not has "_" prefix. Args: None Returns: dict: a dict of metric and states """ states = { attr: value for attr, value in six.iteritems(self.__dict__) if not attr.startswith("_") } config = {} config.update({"name": self._name, "states": copy.deepcopy(states)}) return config
[docs] def update(self, preds, labels): """ Updates the metric states at every minibatch. One user can compute the minibatch metric via pure Python, or via a c++ operator. Args: preds(numpy.array): the predictions of current minibatch labels(numpy.array): the labels of current minibatch, if the label is one-hot or soft-label, should custom the corresponding update rule. """ raise NotImplementedError( "Should not use it directly, please extend it.")
[docs] def eval(self): """ Evalute the current metrics based the accumulated states. Returns: float|list(float)|numpy.array: the metrics via Python. """ raise NotImplementedError( "Should not use it directly, please extend it.")
[docs]class CompositeMetric(MetricBase): """ Composite multiple metrics in one instance. for example, merge F1, accuracy, recall into one Metric. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np preds = [[0.1], [0.7], [0.8], [0.9], [0.2], [0.2], [0.3], [0.5], [0.8], [0.6]] labels = [[0], [1], [1], [1], [1], [0], [0], [0], [0], [0]] preds = np.array(preds) labels = np.array(labels) comp = fluid.metrics.CompositeMetric() precision = fluid.metrics.Precision() recall = fluid.metrics.Recall() comp.add_metric(precision) comp.add_metric(recall) comp.update(preds=preds, labels=labels) numpy_precision, numpy_recall = comp.eval() print("expect precision: %.2f, got %.2f" % ( 3. / 5, numpy_precision ) ) print("expect recall: %.2f, got %.2f" % (3. / 4, numpy_recall ) ) """ def __init__(self, name=None): super(CompositeMetric, self).__init__(name) self._metrics = []
[docs] def add_metric(self, metric): """ add one metric instance to CompositeMetric. Args: metric: a instance of MetricBase. """ if not isinstance(metric, MetricBase): raise ValueError("SubMetric should be inherit from MetricBase.") self._metrics.append(metric)
[docs] def update(self, preds, labels): """ Update every metrics in sequence. Args: preds(numpy.array): the predictions of current minibatch labels(numpy.array): the labels of current minibatch, if the label is one-hot or soft-label, should custom the corresponding update rule. """ for m in self._metrics: m.update(preds, labels)
[docs] def eval(self): """ Evaluate every metrics in sequence. Returns: list(float|numpy.array): a list of metrics value in Python. """ ans = [] for m in self._metrics: ans.append(m.eval()) return ans
[docs]class Precision(MetricBase): """ Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers This class mangages the precision score for binary classification task. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np metric = fluid.metrics.Precision() # generate the preds and labels preds = [[0.1], [0.7], [0.8], [0.9], [0.2], [0.2], [0.3], [0.5], [0.8], [0.6]] labels = [[0], [1], [1], [1], [1], [0], [0], [0], [0], [0]] preds = np.array(preds) labels = np.array(labels) metric.update(preds=preds, labels=labels) numpy_precision = metric.eval() print("expct precision: %.2f and got %.2f" % ( 3.0 / 5.0, numpy_precision)) """ def __init__(self, name=None): super(Precision, self).__init__(name) self.tp = 0 # true positive self.fp = 0 # false positive def update(self, preds, labels): if not _is_numpy_(preds): raise ValueError("The 'preds' must be a numpy ndarray.") if not _is_numpy_(labels): raise ValueError("The 'labels' must be a numpy ndarray.") sample_num = labels.shape[0] preds = np.rint(preds).astype("int32") for i in range(sample_num): pred = preds[i] label = labels[i] if pred == 1: if pred == label: self.tp += 1 else: self.fp += 1 def eval(self): ap = self.tp + self.fp return float(self.tp) / ap if ap != 0 else .0
[docs]class Recall(MetricBase): """ Recall (also known as sensitivity) is the fraction of relevant instances that have been retrieved over the total amount of relevant instances https://en.wikipedia.org/wiki/Precision_and_recall This class mangages the recall score for binary classification task. Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np metric = fluid.metrics.Recall() # generate the preds and labels preds = [[0.1], [0.7], [0.8], [0.9], [0.2], [0.2], [0.3], [0.5], [0.8], [0.6]] labels = [[0], [1], [1], [1], [1], [0], [0], [0], [0], [0]] preds = np.array(preds) labels = np.array(labels) metric.update(preds=preds, labels=labels) numpy_precision = metric.eval() print("expct precision: %.2f and got %.2f" % ( 3.0 / 4.0, numpy_precision)) """ def __init__(self, name=None): super(Recall, self).__init__(name) self.tp = 0 # true positive self.fn = 0 # false negtive def update(self, preds, labels): if not _is_numpy_(preds): raise ValueError("The 'preds' must be a numpy ndarray.") if not _is_numpy_(labels): raise ValueError("The 'labels' must be a numpy ndarray.") sample_num = labels.shape[0] preds = np.rint(preds).astype("int32") for i in range(sample_num): pred = preds[i] label = labels[i] if label == 1: if pred == label: self.tp += 1 else: self.fn += 1 def eval(self): recall = self.tp + self.fn return float(self.tp) / recall if recall != 0 else .0
[docs]class Accuracy(MetricBase): """ Calculate the mean accuracy over multiple batches. https://en.wikipedia.org/wiki/Accuracy_and_precision Args: name: the metrics name Examples: .. code-block:: python import paddle.fluid as fluid #suppose we have batch_size = 128 batch_size=128 accuracy_manager = fluid.metrics.Accuracy() #suppose the accuracy is 0.9 for the 1st batch batch1_acc = 0.9 accuracy_manager.update(value = batch1_acc, weight = batch_size) print("expect accuracy: %.2f, get accuracy: %.2f" % (batch1_acc, accuracy_manager.eval())) #suppose the accuracy is 0.8 for the 2nd batch batch2_acc = 0.8 accuracy_manager.update(value = batch2_acc, weight = batch_size) #the joint acc for batch1 and batch2 is (batch1_acc * batch_size + batch2_acc * batch_size) / batch_size / 2 print("expect accuracy: %.2f, get accuracy: %.2f" % ((batch1_acc * batch_size + batch2_acc * batch_size) / batch_size / 2, accuracy_manager.eval())) #reset the accuracy_manager accuracy_manager.reset() #suppose the accuracy is 0.8 for the 3rd batch batch3_acc = 0.8 accuracy_manager.update(value = batch3_acc, weight = batch_size) print("expect accuracy: %.2f, get accuracy: %.2f" % (batch3_acc, accuracy_manager.eval())) """ def __init__(self, name=None): super(Accuracy, self).__init__(name) self.value = .0 self.weight = .0
[docs] def update(self, value, weight): """ Update minibatch states. Args: value(float|numpy.array): accuracy of one minibatch. weight(int|float): batch size. """ if not _is_number_or_matrix_(value): raise ValueError( "The 'value' must be a number(int, float) or a numpy ndarray.") if not _is_number_(weight): raise ValueError("The 'weight' must be a number(int, float).") if _is_number_(weight) and weight < 0: raise ValueError("The 'weight' can not be negative") self.value += value * weight self.weight += weight
[docs] def eval(self): """ Return the mean accuracy (float or numpy.array) for all accumulated batches. """ if self.weight == 0: raise ValueError("There is no data in Accuracy Metrics. \ Please check layers.accuracy output has added to Accuracy.") return self.value / self.weight
[docs]class ChunkEvaluator(MetricBase): """ Accumulate counter numbers output by chunk_eval from mini-batches and compute the precision recall and F1-score using the accumulated counter numbers. For some basics of chunking, please refer to `Chunking with Support Vector Machines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>`_ . ChunkEvalEvaluator computes the precision, recall, and F1-score of chunk detection, and supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes. Examples: .. code-block:: python import paddle.fluid as fluid # init the chunck-level evaluation manager metric = fluid.metrics.ChunkEvaluator() # suppose the model predict 10 chuncks, while 8 ones are correct and the ground truth has 9 chuncks. num_infer_chunks = 10 num_label_chunks = 9 num_correct_chunks = 8 metric.update(num_infer_chunks, num_label_chunks, num_correct_chunks) numpy_precision, numpy_recall, numpy_f1 = metric.eval() print("precision: %.2f, recall: %.2f, f1: %.2f" % (numpy_precision, numpy_recall, numpy_f1)) # the next batch, predicting 3 prefectly correct chuncks. num_infer_chunks = 3 num_label_chunks = 3 num_correct_chunks = 3 metric.update(num_infer_chunks, num_label_chunks, num_correct_chunks) numpy_precision, numpy_recall, numpy_f1 = metric.eval() print("precision: %.2f, recall: %.2f, f1: %.2f" % (numpy_precision, numpy_recall, numpy_f1)) """ def __init__(self, name=None): super(ChunkEvaluator, self).__init__(name) self.num_infer_chunks = 0 self.num_label_chunks = 0 self.num_correct_chunks = 0
[docs] def update(self, num_infer_chunks, num_label_chunks, num_correct_chunks): """ Update the states based on the layers.chunk_eval() ouputs. Args: num_infer_chunks(int|numpy.array): The number of chunks in Inference on the given minibatch. num_label_chunks(int|numpy.array): The number of chunks in Label on the given mini-batch. num_correct_chunks(int|float|numpy.array): The number of chunks both in Inference and Label on the given mini-batch. """ if not _is_number_or_matrix_(num_infer_chunks): raise ValueError( "The 'num_infer_chunks' must be a number(int) or a numpy ndarray." ) if not _is_number_or_matrix_(num_label_chunks): raise ValueError( "The 'num_label_chunks' must be a number(int, float) or a numpy ndarray." ) if not _is_number_or_matrix_(num_correct_chunks): raise ValueError( "The 'num_correct_chunks' must be a number(int, float) or a numpy ndarray." ) self.num_infer_chunks += num_infer_chunks self.num_label_chunks += num_label_chunks self.num_correct_chunks += num_correct_chunks
def eval(self): precision = float( self.num_correct_chunks ) / self.num_infer_chunks if self.num_infer_chunks else 0 recall = float(self.num_correct_chunks ) / self.num_label_chunks if self.num_label_chunks else 0 f1_score = float(2 * precision * recall) / ( precision + recall) if self.num_correct_chunks else 0 return precision, recall, f1_score
[docs]class EditDistance(MetricBase): """ Edit distance is a way of quantifying how dissimilar two strings (e.g., words) are to each another by counting the minimum number of edit operations (add, remove or replace) required to transform one string into the other. Refer to https://en.wikipedia.org/wiki/Edit_distance This EditDistance class takes two inputs by using update function: 1. distances: a (batch_size, 1) numpy.array, each element represents the edit distance between two sequences. 2. seq_num: a int|float value, standing for the number of sequence pairs. and returns the overall edit distance of multiple sequence-pairs. Args: name: the metrics name Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # suppose that batch_size is 128 batch_size = 128 # init the edit distance manager distance_evaluator = fluid.metrics.EditDistance("EditDistance") # generate the edit distance across 128 sequence pairs, the max distance is 10 here edit_distances_batch0 = np.random.randint(low = 0, high = 10, size = (batch_size, 1)) seq_num_batch0 = batch_size distance_evaluator.update(edit_distances_batch0, seq_num_batch0) avg_distance, wrong_instance_ratio = distance_evaluator.eval() print("the average edit distance for batch0 is %.2f and the wrong instance ratio is %.2f " % (avg_distance, wrong_instance_ratio)) edit_distances_batch1 = np.random.randint(low = 0, high = 10, size = (batch_size, 1)) seq_num_batch1 = batch_size distance_evaluator.update(edit_distances_batch1, seq_num_batch1) avg_distance, wrong_instance_ratio = distance_evaluator.eval() print("the average edit distance for batch0 and batch1 is %.2f and the wrong instance ratio is %.2f " % (avg_distance, wrong_instance_ratio)) distance_evaluator.reset() edit_distances_batch2 = np.random.randint(low = 0, high = 10, size = (batch_size, 1)) seq_num_batch2 = batch_size distance_evaluator.update(edit_distances_batch2, seq_num_batch2) avg_distance, wrong_instance_ratio = distance_evaluator.eval() print("the average edit distance for batch2 is %.2f and the wrong instance ratio is %.2f " % (avg_distance, wrong_instance_ratio)) """ def __init__(self, name): super(EditDistance, self).__init__(name) self.total_distance = .0 self.seq_num = 0 self.instance_error = 0
[docs] def update(self, distances, seq_num): """ Update the overall edit distance Args: distances: a (batch_size, 1) numpy.array, each element represents the edit distance between two sequences. seq_num: a int|float value, standing for the number of sequence pairs. """ if not _is_numpy_(distances): raise ValueError("The 'distances' must be a numpy ndarray.") if not _is_number_(seq_num): raise ValueError("The 'seq_num' must be a number(int, float).") seq_right_count = np.sum(distances == 0) total_distance = np.sum(distances) self.seq_num += seq_num self.instance_error += seq_num - seq_right_count self.total_distance += total_distance
[docs] def eval(self): """ Return two floats: avg_distance: the average distance for all sequence pairs updated using the update function. avg_instance_error: the ratio of sequence pairs whose edit distance is not zero. """ if self.seq_num == 0: raise ValueError( "There is no data in EditDistance Metric. Please check layers.edit_distance output has been added to EditDistance." ) avg_distance = self.total_distance / self.seq_num avg_instance_error = self.instance_error / float(self.seq_num) return avg_distance, avg_instance_error
[docs]class Auc(MetricBase): """ The auc metric is for binary classification. Refer to https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve Please notice that the auc metric is implemented with python, which may be a little bit slow. If you concern the speed, please use the fluid.layers.auc instead. The `auc` function creates four local variables, `true_positives`, `true_negatives`, `false_positives` and `false_negatives` that are used to compute the AUC. To discretize the AUC curve, a linearly spaced set of thresholds is used to compute pairs of recall and precision values. The area under the ROC-curve is therefore computed using the height of the recall values by the false positive rate, while the area under the PR-curve is the computed using the height of the precision values by the recall. Args: name: metric name curve: Specifies the name of the curve to be computed, 'ROC' [default] or 'PR' for the Precision-Recall-curve. "NOTE: only implement the ROC curve type via Python now." Examples: .. code-block:: python import paddle.fluid as fluid import numpy as np # init the auc metric auc_metric = fluid.metrics.Auc("ROC") # suppose that batch_size is 128 batch_num = 100 batch_size = 128 for batch_id in range(batch_num): class0_preds = np.random.random(size = (batch_size, 1)) class1_preds = 1 - class0_preds preds = np.concatenate((class0_preds, class1_preds), axis=1) labels = np.random.randint(2, size = (batch_size, 1)) auc_metric.update(preds = preds, labels = labels) # shall be some score closing to 0.5 as the preds are randomly assigned print("auc for iteration %d is %.2f" % (batch_id, auc_metric.eval())) """ def __init__(self, name, curve='ROC', num_thresholds=4095): super(Auc, self).__init__(name=name) self._curve = curve self._num_thresholds = num_thresholds _num_pred_buckets = num_thresholds + 1 self._stat_pos = [0] * _num_pred_buckets self._stat_neg = [0] * _num_pred_buckets
[docs] def update(self, preds, labels): """ Update the auc curve with the given predictions and labels Args: preds: an numpy array in the shape of (batch_size, 2), preds[i][j] denotes the probability of classifying the instance i into the class j. labels: an numpy array in the shape of (batch_size, 1), labels[i] is either o or 1, representing the label of the instance i. """ if not _is_numpy_(labels): raise ValueError("The 'labels' must be a numpy ndarray.") if not _is_numpy_(preds): raise ValueError("The 'predictions' must be a numpy ndarray.") for i, lbl in enumerate(labels): value = preds[i, 1] bin_idx = int(value * self._num_thresholds) assert bin_idx <= self._num_thresholds if lbl: self._stat_pos[bin_idx] += 1.0 else: self._stat_neg[bin_idx] += 1.0
@staticmethod def trapezoid_area(x1, x2, y1, y2): return abs(x1 - x2) * (y1 + y2) / 2.0
[docs] def eval(self): """ Return the area (a float score) under auc curve """ tot_pos = 0.0 tot_neg = 0.0 auc = 0.0 idx = self._num_thresholds while idx >= 0: tot_pos_prev = tot_pos tot_neg_prev = tot_neg tot_pos += self._stat_pos[idx] tot_neg += self._stat_neg[idx] auc += self.trapezoid_area(tot_neg, tot_neg_prev, tot_pos, tot_pos_prev) idx -= 1 return auc / tot_pos / tot_neg if tot_pos > 0.0 and tot_neg > 0.0 else 0.0
[docs]class DetectionMAP(object): """ Calculate the detection mean average precision (mAP). The general steps are as follows: 1. calculate the true positive and false positive according to the input of detection and labels. 2. calculate mAP value, support two versions: '11 point' and 'integral'. Please get more information from the following articles: https://sanchom.wordpress.com/tag/average-precision/ https://arxiv.org/abs/1512.02325 Args: input (Variable): The detection results, which is a LoDTensor with shape [M, 6]. The layout is [label, confidence, xmin, ymin, xmax, ymax]. gt_label (Variable): The ground truth label index, which is a LoDTensor with shape [N, 1]. gt_box (Variable): The ground truth bounding box (bbox), which is a LoDTensor with shape [N, 4]. The layout is [xmin, ymin, xmax, ymax]. gt_difficult (Variable|None): Whether this ground truth is a difficult bounding bbox, which can be a LoDTensor [N, 1] or not set. If None, it means all the ground truth labels are not difficult bbox. class_num (int): The class number. background_label (int): The index of background label, the background label will be ignored. If set to -1, then all categories will be considered, 0 by default. overlap_threshold (float): The threshold for deciding true/false positive, 0.5 by default. evaluate_difficult (bool): Whether to consider difficult ground truth for evaluation, True by default. This argument does not work when gt_difficult is None. ap_version (string): The average precision calculation ways, it must be 'integral' or '11point'. Please check https://sanchom.wordpress.com/tag/average-precision/ for details. - 11point: the 11-point interpolated average precision. - integral: the natural integral of the precision-recall curve. Examples: .. code-block:: python import paddle.fluid as fluid import paddle.fluid.layers as layers batch_size = -1 # can be any size image_boxs_num = 10 bounding_bboxes_num = 21 pb = layers.data(name='prior_box', shape=[image_boxs_num, 4], append_batch_size=False, dtype='float32') pbv = layers.data(name='prior_box_var', shape=[image_boxs_num, 4], append_batch_size=False, dtype='float32') loc = layers.data(name='target_box', shape=[batch_size, bounding_bboxes_num, 4], append_batch_size=False, dtype='float32') scores = layers.data(name='scores', shape=[batch_size, bounding_bboxes_num, image_boxs_num], append_batch_size=False, dtype='float32') nmsed_outs = fluid.layers.detection_output(scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv) gt_box = fluid.layers.data(name="gt_box", shape=[batch_size, 4], dtype="float32") gt_label = fluid.layers.data(name="gt_label", shape=[batch_size, 1], dtype="float32") difficult = fluid.layers.data(name="difficult", shape=[batch_size, 1], dtype="float32") exe = fluid.Executor(fluid.CUDAPlace(0)) map_evaluator = fluid.metrics.DetectionMAP(nmsed_outs, gt_label, gt_box, difficult, class_num = 3) cur_map, accum_map = map_evaluator.get_map_var() # see detailed examples at https://github.com/PaddlePaddle/models/blob/43cdafbb97e52e6d93cc5bbdc6e7486f27665fc8/PaddleCV/object_detection """ def __init__(self, input, gt_label, gt_box, gt_difficult=None, class_num=None, background_label=0, overlap_threshold=0.5, evaluate_difficult=True, ap_version='integral'): self.helper = LayerHelper('map_eval') gt_label = layers.cast(x=gt_label, dtype=gt_box.dtype) if gt_difficult: gt_difficult = layers.cast(x=gt_difficult, dtype=gt_box.dtype) label = layers.concat([gt_label, gt_difficult, gt_box], axis=1) else: label = layers.concat([gt_label, gt_box], axis=1) # calculate mean average precision (mAP) of current mini-batch map = layers.detection_map( input, label, class_num, background_label, overlap_threshold=overlap_threshold, evaluate_difficult=evaluate_difficult, ap_version=ap_version) states = [] states.append( self._create_state( dtype='int32', shape=None, suffix='accum_pos_count')) states.append( self._create_state( dtype='float32', shape=None, suffix='accum_true_pos')) states.append( self._create_state( dtype='float32', shape=None, suffix='accum_false_pos')) var = self._create_state(dtype='int32', shape=[1], suffix='has_state') self.helper.set_variable_initializer( var, initializer=Constant(value=int(0))) self.has_state = var # calculate accumulative mAP accum_map = layers.detection_map( input, label, class_num, background_label, overlap_threshold=overlap_threshold, evaluate_difficult=evaluate_difficult, has_state=self.has_state, input_states=states, out_states=states, ap_version=ap_version) layers.fill_constant( shape=self.has_state.shape, value=1, dtype=self.has_state.dtype, out=self.has_state) self.cur_map = map self.accum_map = accum_map def _create_state(self, suffix, dtype, shape): """ Create state variable. Args: suffix(str): the state suffix. dtype(str|core.VarDesc.VarType): the state data type shape(tuple|list): the shape of state Returns: State variable """ state = self.helper.create_variable( name="_".join([unique_name.generate(self.helper.name), suffix]), persistable=True, dtype=dtype, shape=shape) return state
[docs] def get_map_var(self): """ Returns: mAP variable of current mini-batch and accumulative mAP variable cross mini-batches. """ return self.cur_map, self.accum_map
[docs] def reset(self, executor, reset_program=None): """ Reset metric states at the begin of each pass/user specified batch. Args: executor(Executor): a executor for executing the reset_program. reset_program(Program|None): a single Program for reset process. If None, will create a Program. """ def _clone_var_(block, var): assert isinstance(var, Variable) return block.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, lod_level=var.lod_level, persistable=var.persistable) if reset_program is None: reset_program = Program() with program_guard(main_program=reset_program): var = _clone_var_(reset_program.current_block(), self.has_state) layers.fill_constant( shape=var.shape, value=0, dtype=var.dtype, out=var) executor.run(reset_program)