156 lines
5.1 KiB
Python
156 lines
5.1 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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__all__ = ['AverageMeter']
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class AverageMeter(object):
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"""
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Computes and stores the average and current value
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Code was based on https://github.com/pytorch/examples/blob/master/imagenet/main.py
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"""
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def __init__(self, name='', fmt='f', postfix="", need_avg=True):
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self.name = name
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self.fmt = fmt
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self.postfix = postfix
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self.need_avg = need_avg
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self.reset()
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def reset(self):
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""" reset """
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self.val = 0
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self.avg = 0
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self.sum = 0
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self.count = 0
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def update(self, val, n=1):
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""" update """
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self.val = val
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self.sum += val * n
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self.count += n
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self.avg = self.sum / self.count
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@property
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def avg_info(self):
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if isinstance(self.avg, paddle.Tensor):
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self.avg = float(self.avg)
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return "{}: {:.5f}".format(self.name, self.avg)
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@property
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def total(self):
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return '{self.name}_sum: {self.sum:{self.fmt}}{self.postfix}'.format(
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self=self)
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@property
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def total_minute(self):
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return '{self.name} {s:{self.fmt}}{self.postfix} min'.format(
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s=self.sum / 60, self=self)
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@property
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def mean(self):
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return '{self.name}: {self.avg:{self.fmt}}{self.postfix}'.format(
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self=self) if self.need_avg else ''
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@property
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def value(self):
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return '{self.name}: {self.val:{self.fmt}}{self.postfix}'.format(
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self=self)
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class AttrMeter(object):
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"""
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Computes and stores the average and current value
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Code was based on https://github.com/pytorch/examples/blob/master/imagenet/main.py
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"""
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def __init__(self, threshold=0.5):
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self.threshold = threshold
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self.reset()
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def reset(self):
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self.gt_pos = 0
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self.gt_neg = 0
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self.true_pos = 0
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self.true_neg = 0
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self.false_pos = 0
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self.false_neg = 0
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self.gt_pos_ins = []
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self.true_pos_ins = []
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self.intersect_pos = []
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self.union_pos = []
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def update(self, metric_dict):
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self.gt_pos += metric_dict['gt_pos']
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self.gt_neg += metric_dict['gt_neg']
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self.true_pos += metric_dict['true_pos']
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self.true_neg += metric_dict['true_neg']
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self.false_pos += metric_dict['false_pos']
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self.false_neg += metric_dict['false_neg']
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self.gt_pos_ins += metric_dict['gt_pos_ins'].tolist()
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self.true_pos_ins += metric_dict['true_pos_ins'].tolist()
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self.intersect_pos += metric_dict['intersect_pos'].tolist()
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self.union_pos += metric_dict['union_pos'].tolist()
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def res(self):
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import numpy as np
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eps = 1e-20
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label_pos_recall = 1.0 * self.true_pos / (
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self.gt_pos + eps) # true positive
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label_neg_recall = 1.0 * self.true_neg / (
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self.gt_neg + eps) # true negative
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# mean accuracy
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label_ma = (label_pos_recall + label_neg_recall) / 2
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label_pos_recall = np.mean(label_pos_recall)
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label_neg_recall = np.mean(label_neg_recall)
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label_prec = (self.true_pos / (self.true_pos + self.false_pos + eps))
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label_acc = (self.true_pos /
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(self.true_pos + self.false_pos + self.false_neg + eps))
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label_f1 = np.mean(2 * label_prec * label_pos_recall /
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(label_prec + label_pos_recall + eps))
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ma = (np.mean(label_ma))
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self.gt_pos_ins = np.array(self.gt_pos_ins)
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self.true_pos_ins = np.array(self.true_pos_ins)
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self.intersect_pos = np.array(self.intersect_pos)
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self.union_pos = np.array(self.union_pos)
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instance_acc = self.intersect_pos / (self.union_pos + eps)
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instance_prec = self.intersect_pos / (self.true_pos_ins + eps)
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instance_recall = self.intersect_pos / (self.gt_pos_ins + eps)
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instance_f1 = 2 * instance_prec * instance_recall / (
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instance_prec + instance_recall + eps)
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instance_acc = np.mean(instance_acc)
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instance_prec = np.mean(instance_prec)
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instance_recall = np.mean(instance_recall)
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instance_f1 = 2 * instance_prec * instance_recall / (
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instance_prec + instance_recall + eps)
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instance_acc = np.mean(instance_acc)
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instance_prec = np.mean(instance_prec)
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instance_recall = np.mean(instance_recall)
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instance_f1 = np.mean(instance_f1)
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res = [
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ma, label_f1, label_pos_recall, label_neg_recall, instance_f1,
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instance_acc, instance_prec, instance_recall
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]
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return res
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