662 lines
23 KiB
Python
662 lines
23 KiB
Python
# copyright (c) 2021 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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from cmath import nan
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import numpy as np
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from sklearn.metrics import hamming_loss
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from sklearn.metrics import accuracy_score as accuracy_metric
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from sklearn.metrics import multilabel_confusion_matrix
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from sklearn.preprocessing import binarize
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from easydict import EasyDict
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from ppcls.metric.avg_metrics import AvgMetrics
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from ppcls.utils.misc import AverageMeter, AttrMeter
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from ppcls.utils import logger
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class TopkAcc(AvgMetrics):
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def __init__(self, topk=(1, 5)):
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super().__init__()
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assert isinstance(topk, (int, list, tuple))
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if isinstance(topk, int):
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topk = [topk]
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self.topk = topk
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self.reset()
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self.warned = False
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def reset(self):
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self.avg_meters = {
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f"top{k}": AverageMeter(f"top{k}")
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for k in self.topk
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}
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def forward(self, x, label):
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if isinstance(x, dict):
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x = x["logits"]
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output_dims = x.shape[-1]
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metric_dict = dict()
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for idx, k in enumerate(self.topk):
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if output_dims < k:
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if not self.warned:
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msg = f"The output dims({output_dims}) is less than k({k}), so the Top-{k} metric is meaningless."
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logger.warning(msg)
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self.warned = True
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metric_dict[f"top{k}"] = 1
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else:
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metric_dict[f"top{k}"] = paddle.metric.accuracy(x, label, k=k)
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self.avg_meters[f"top{k}"].update(metric_dict[f"top{k}"],
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x.shape[0])
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return metric_dict
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class mAP(nn.Layer):
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def __init__(self, descending=True):
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super().__init__()
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self.descending = descending
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def forward(self, similarities_matrix, query_img_id, gallery_img_id,
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keep_mask):
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metric_dict = dict()
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choosen_indices = paddle.argsort(
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similarities_matrix, axis=1, descending=self.descending)
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gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
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gallery_labels_transpose = paddle.broadcast_to(
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gallery_labels_transpose,
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shape=[
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choosen_indices.shape[0], gallery_labels_transpose.shape[1]
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])
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choosen_label = paddle.index_sample(gallery_labels_transpose,
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choosen_indices)
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equal_flag = paddle.equal(choosen_label, query_img_id)
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if keep_mask is not None:
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keep_mask = paddle.index_sample(
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keep_mask.astype('float32'), choosen_indices)
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equal_flag = paddle.logical_and(equal_flag,
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keep_mask.astype('bool'))
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equal_flag = paddle.cast(equal_flag, 'float32')
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num_rel = paddle.sum(equal_flag, axis=1)
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num_rel = paddle.greater_than(num_rel, paddle.to_tensor(0.))
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num_rel_index = paddle.nonzero(num_rel.astype("int"))
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num_rel_index = paddle.reshape(num_rel_index, [num_rel_index.shape[0]])
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if paddle.numel(num_rel_index).item() == 0:
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metric_dict["mAP"] = np.nan
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return metric_dict
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equal_flag = paddle.index_select(equal_flag, num_rel_index, axis=0)
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acc_sum = paddle.cumsum(equal_flag, axis=1)
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div = paddle.arange(acc_sum.shape[1]).astype("float32") + 1
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precision = paddle.divide(acc_sum, div)
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#calc map
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precision_mask = paddle.multiply(equal_flag, precision)
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ap = paddle.sum(precision_mask, axis=1) / paddle.sum(equal_flag,
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axis=1)
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metric_dict["mAP"] = float(paddle.mean(ap))
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return metric_dict
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class mINP(nn.Layer):
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def __init__(self, descending=True):
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super().__init__()
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self.descending = descending
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def forward(self, similarities_matrix, query_img_id, gallery_img_id,
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keep_mask):
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metric_dict = dict()
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choosen_indices = paddle.argsort(
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similarities_matrix, axis=1, descending=self.descending)
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gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
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gallery_labels_transpose = paddle.broadcast_to(
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gallery_labels_transpose,
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shape=[
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choosen_indices.shape[0], gallery_labels_transpose.shape[1]
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])
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choosen_label = paddle.index_sample(gallery_labels_transpose,
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choosen_indices)
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equal_flag = paddle.equal(choosen_label, query_img_id)
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if keep_mask is not None:
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keep_mask = paddle.indechmx_sample(
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keep_mask.astype('float32'), choosen_indices)
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equal_flag = paddle.logical_and(equal_flag,
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keep_mask.astype('bool'))
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equal_flag = paddle.cast(equal_flag, 'float32')
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num_rel = paddle.sum(equal_flag, axis=1)
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num_rel = paddle.greater_than(num_rel, paddle.to_tensor(0.))
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num_rel_index = paddle.nonzero(num_rel.astype("int"))
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num_rel_index = paddle.reshape(num_rel_index, [num_rel_index.shape[0]])
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equal_flag = paddle.index_select(equal_flag, num_rel_index, axis=0)
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#do accumulative sum
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div = paddle.arange(equal_flag.shape[1]).astype("float32") + 2
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minus = paddle.divide(equal_flag, div)
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auxilary = paddle.subtract(equal_flag, minus)
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hard_index = paddle.argmax(auxilary, axis=1).astype("float32")
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all_INP = paddle.divide(paddle.sum(equal_flag, axis=1), hard_index)
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mINP = paddle.mean(all_INP)
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metric_dict["mINP"] = float(mINP)
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return metric_dict
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class TprAtFpr(nn.Layer):
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def __init__(self, max_fpr=1 / 1000.):
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super().__init__()
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self.gt_pos_score_list = []
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self.gt_neg_score_list = []
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self.softmax = nn.Softmax(axis=-1)
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self.max_fpr = max_fpr
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self.max_tpr = 0.
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def forward(self, x, label):
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if isinstance(x, dict):
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x = x["logits"]
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x = self.softmax(x)
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for i, label_i in enumerate(label):
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if label_i[0] == 0:
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self.gt_neg_score_list.append(x[i][1].numpy())
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else:
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self.gt_pos_score_list.append(x[i][1].numpy())
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return {}
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def reset(self):
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self.gt_pos_score_list = []
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self.gt_neg_score_list = []
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self.max_tpr = 0.
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@property
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def avg(self):
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return self.max_tpr
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@property
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def avg_info(self):
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max_tpr = 0.
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result = ""
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gt_pos_score_list = np.array(self.gt_pos_score_list)
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gt_neg_score_list = np.array(self.gt_neg_score_list)
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for i in range(0, 10000):
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threshold = i / 10000.
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if len(gt_pos_score_list) == 0:
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continue
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tpr = np.sum(
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gt_pos_score_list > threshold) / len(gt_pos_score_list)
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if len(gt_neg_score_list) == 0 and tpr > max_tpr:
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max_tpr = tpr
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result = "threshold: {}, fpr: 0.0, tpr: {:.5f}".format(
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threshold, tpr)
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msg = f"The number of negative samples is 0, please add negative samples."
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logger.warning(msg)
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fpr = np.sum(
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gt_neg_score_list > threshold) / len(gt_neg_score_list)
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if fpr <= self.max_fpr and tpr > max_tpr:
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max_tpr = tpr
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result = "threshold: {}, fpr: {}, tpr: {:.5f}".format(
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threshold, fpr, tpr)
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self.max_tpr = max_tpr
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return result
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class MultilabelMeanAccuracy(nn.Layer):
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def __init__(self,
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start_threshold=0.4,
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num_iterations=10,
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end_threshold=0.9):
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super().__init__()
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self.start_threshold = start_threshold
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self.num_iterations = num_iterations
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self.end_threshold = end_threshold
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self.gt_all_score_list = []
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self.gt_label_score_list = []
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self.max_acc = 0.
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def forward(self, x, label):
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if isinstance(x, dict):
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x = x["logits"]
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x = F.sigmoid(x)
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label = label[:, 0, :]
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for i in range(len(x)):
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self.gt_all_score_list.append(x[i].numpy())
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self.gt_label_score_list.append(label[i].numpy())
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return {}
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def reset(self):
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self.gt_all_score_list = []
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self.gt_label_score_list = []
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self.max_acc = 0.
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@property
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def avg(self):
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return self.max_acc
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@property
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def avg_info(self):
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max_acc = 0.
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result = ""
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gt_all_score_list = np.array(self.gt_all_score_list)
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gt_label_score_list = np.array(self.gt_label_score_list)
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for i in range(self.num_iterations):
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threshold = self.start_threshold + i * (self.end_threshold -
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self.start_threshold
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) / self.num_iterations
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pred_label = (gt_all_score_list > threshold).astype(int)
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TP = np.sum(
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(gt_label_score_list == 1) * (pred_label == 1)).astype(float)
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TN = np.sum(
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(gt_label_score_list == 0) * (pred_label == 0)).astype(float)
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acc = (TP + TN) / len(gt_all_score_list)
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if max_acc <= acc:
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max_acc = acc
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result = "threshold: {}, mean_acc: {}".format(
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threshold, max_acc / len(gt_label_score_list[0]))
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self.max_acc = max_acc / len(gt_label_score_list[0])
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return result
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class Recallk(nn.Layer):
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def __init__(self, topk=(1, 5), descending=True):
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super().__init__()
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assert isinstance(topk, (int, list, tuple))
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if isinstance(topk, int):
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topk = [topk]
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self.topk = topk
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self.descending = descending
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def forward(self, similarities_matrix, query_img_id, gallery_img_id,
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keep_mask):
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metric_dict = dict()
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# get cmc
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choosen_indices = paddle.argsort(
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similarities_matrix, axis=1, descending=self.descending)
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gallery_labels_transpose = gallery_img_id.t()
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gallery_labels_transpose = paddle.broadcast_to(
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gallery_labels_transpose,
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shape=[
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choosen_indices.shape[0], gallery_labels_transpose.shape[1]
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])
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choosen_label = paddle.index_sample(gallery_labels_transpose,
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choosen_indices)
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equal_flag = paddle.equal(choosen_label, query_img_id)
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if keep_mask is not None:
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keep_mask = paddle.index_sample(
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keep_mask.astype("float32"), choosen_indices)
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equal_flag = equal_flag & keep_mask.astype("bool")
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equal_flag = paddle.cast(equal_flag, "float32")
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real_query_num = paddle.sum(equal_flag, axis=1)
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real_query_num = paddle.sum((real_query_num > 0.0).astype("float32"))
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acc_sum = paddle.cumsum(equal_flag, axis=1)
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mask = (acc_sum > 0.0).astype("float32")
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all_cmc = (paddle.sum(mask, axis=0) / real_query_num).numpy()
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for k in self.topk:
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metric_dict["recall{}".format(k)] = all_cmc[k - 1]
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return metric_dict
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class Precisionk(nn.Layer):
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def __init__(self, topk=(1, 5), descending=True):
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super().__init__()
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assert isinstance(topk, (int, list, tuple))
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if isinstance(topk, int):
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topk = [topk]
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self.topk = topk
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self.descending = descending
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def forward(self, similarities_matrix, query_img_id, gallery_img_id,
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keep_mask):
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metric_dict = dict()
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#get cmc
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choosen_indices = paddle.argsort(
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similarities_matrix, axis=1, descending=self.descending)
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gallery_labels_transpose = paddle.transpose(gallery_img_id, [1, 0])
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gallery_labels_transpose = paddle.broadcast_to(
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gallery_labels_transpose,
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shape=[
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choosen_indices.shape[0], gallery_labels_transpose.shape[1]
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])
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choosen_label = paddle.index_sample(gallery_labels_transpose,
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choosen_indices)
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equal_flag = paddle.equal(choosen_label, query_img_id)
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if keep_mask is not None:
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keep_mask = paddle.index_sample(
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keep_mask.astype('float32'), choosen_indices)
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equal_flag = paddle.logical_and(equal_flag,
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keep_mask.astype('bool'))
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equal_flag = paddle.cast(equal_flag, 'float32')
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Ns = paddle.arange(gallery_img_id.shape[0]) + 1
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equal_flag_cumsum = paddle.cumsum(equal_flag, axis=1)
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Precision_at_k = (paddle.mean(equal_flag_cumsum, axis=0) / Ns).numpy()
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for k in self.topk:
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metric_dict["precision@{}".format(k)] = Precision_at_k[k - 1]
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return metric_dict
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class DistillationTopkAcc(TopkAcc):
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def __init__(self, model_key, feature_key=None, topk=(1, 5)):
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super().__init__(topk=topk)
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self.model_key = model_key
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self.feature_key = feature_key
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def forward(self, x, label):
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if isinstance(x, dict):
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x = x[self.model_key]
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if self.feature_key is not None:
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x = x[self.feature_key]
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return super().forward(x, label)
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class GoogLeNetTopkAcc(TopkAcc):
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def __init__(self, topk=(1, 5)):
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super().__init__()
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assert isinstance(topk, (int, list, tuple))
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if isinstance(topk, int):
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topk = [topk]
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self.topk = topk
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def forward(self, x, label):
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return super().forward(x[0], label)
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class MultiLabelMetric(AvgMetrics):
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def __init__(self, bi_threshold=0.5):
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super().__init__()
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self.bi_threshold = bi_threshold
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def _multi_hot_encode(self, output):
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logits = F.sigmoid(output).numpy()
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return binarize(logits, threshold=self.bi_threshold)
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class HammingDistance(MultiLabelMetric):
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"""
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Soft metric based label for multilabel classification
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Returns:
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The smaller the return value is, the better model is.
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"""
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def __init__(self):
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super().__init__()
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self.reset()
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def reset(self):
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self.avg_meters = {"HammingDistance": AverageMeter("HammingDistance")}
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def forward(self, output, target):
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preds = super()._multi_hot_encode(output)
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metric_dict = dict()
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metric_dict["HammingDistance"] = paddle.to_tensor(
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hamming_loss(target, preds))
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self.avg_meters["HammingDistance"].update(
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float(metric_dict["HammingDistance"]), output.shape[0])
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return metric_dict
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class AccuracyScore(MultiLabelMetric):
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"""
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Hard metric for multilabel classification
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Args:
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base: ["sample", "label"], default="sample"
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if "sample", return metric score based sample,
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if "label", return metric score based label.
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Returns:
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accuracy:
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"""
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def __init__(self, base="label"):
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super().__init__()
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assert base in ["sample", "label"
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], 'must be one of ["sample", "label"]'
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self.base = base
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self.reset()
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def reset(self):
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self.avg_meters = {"AccuracyScore": AverageMeter("AccuracyScore")}
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def forward(self, output, target):
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preds = super()._multi_hot_encode(output)
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metric_dict = dict()
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if self.base == "sample":
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accuracy = accuracy_metric(target, preds)
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elif self.base == "label":
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mcm = multilabel_confusion_matrix(target, preds)
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tns = mcm[:, 0, 0]
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fns = mcm[:, 1, 0]
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tps = mcm[:, 1, 1]
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fps = mcm[:, 0, 1]
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accuracy = (sum(tps) + sum(tns)) / (
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sum(tps) + sum(tns) + sum(fns) + sum(fps))
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metric_dict["AccuracyScore"] = paddle.to_tensor(accuracy)
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self.avg_meters["AccuracyScore"].update(
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float(metric_dict["AccuracyScore"]), output.shape[0])
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return metric_dict
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def get_attr_metrics(gt_label, preds_probs, threshold):
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"""
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index: evaluated label index
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adapted from "https://github.com/valencebond/Rethinking_of_PAR/blob/master/metrics/pedestrian_metrics.py"
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"""
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pred_label = (preds_probs > threshold).astype(int)
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eps = 1e-20
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result = EasyDict()
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has_fuyi = gt_label == -1
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pred_label[has_fuyi] = -1
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###############################
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# label metrics
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# TP + FN
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result.gt_pos = np.sum((gt_label == 1), axis=0).astype(float)
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# TN + FP
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result.gt_neg = np.sum((gt_label == 0), axis=0).astype(float)
|
|
# TP
|
|
result.true_pos = np.sum((gt_label == 1) * (pred_label == 1),
|
|
axis=0).astype(float)
|
|
# TN
|
|
result.true_neg = np.sum((gt_label == 0) * (pred_label == 0),
|
|
axis=0).astype(float)
|
|
# FP
|
|
result.false_pos = np.sum(((gt_label == 0) * (pred_label == 1)),
|
|
axis=0).astype(float)
|
|
# FN
|
|
result.false_neg = np.sum(((gt_label == 1) * (pred_label == 0)),
|
|
axis=0).astype(float)
|
|
|
|
################
|
|
# instance metrics
|
|
result.gt_pos_ins = np.sum((gt_label == 1), axis=1).astype(float)
|
|
result.true_pos_ins = np.sum((pred_label == 1), axis=1).astype(float)
|
|
# true positive
|
|
result.intersect_pos = np.sum((gt_label == 1) * (pred_label == 1),
|
|
axis=1).astype(float)
|
|
# IOU
|
|
result.union_pos = np.sum(((gt_label == 1) + (pred_label == 1)),
|
|
axis=1).astype(float)
|
|
|
|
return result
|
|
|
|
|
|
class ATTRMetric(nn.Layer):
|
|
def __init__(self, threshold=0.5):
|
|
super().__init__()
|
|
self.threshold = threshold
|
|
|
|
def reset(self):
|
|
self.attrmeter = AttrMeter(threshold=0.5)
|
|
|
|
def forward(self, output, target):
|
|
metric_dict = get_attr_metrics(target[:, 0, :].numpy(),
|
|
output.numpy(), self.threshold)
|
|
self.attrmeter.update(metric_dict)
|
|
return metric_dict
|
|
|
|
|
|
class MultiLabelMAP(nn.Layer):
|
|
"""
|
|
Calculate multi-label classification mean average precision.
|
|
Currently, support two types: 11point and integral
|
|
|
|
The code base on:
|
|
https://github.com/PaddlePaddle/PaddleDetection/blob/develop/ppdet/metrics/map_utils.py
|
|
|
|
Args:
|
|
map_type (str): Calculation method of mean average.
|
|
"""
|
|
|
|
def __init__(self, map_type='integral'):
|
|
super().__init__()
|
|
assert map_type in ['11point', 'integral'], \
|
|
"map_type currently only support '11point' and 'integral'"
|
|
self.map_type = map_type
|
|
|
|
self.reset()
|
|
|
|
def reset(self):
|
|
self.is_latest = True
|
|
self.class_score_poss = None
|
|
self.class_gt_counts = None
|
|
self.mAP = 0.0
|
|
|
|
def one_class_update(self, score, gt_label, class_idx):
|
|
topk_idx = np.argsort(score)[::-1]
|
|
topk_score = score[topk_idx]
|
|
topk_gt_label = gt_label[topk_idx]
|
|
for s, l in zip(topk_score, topk_gt_label):
|
|
if int(l) == 1:
|
|
self.class_score_poss[class_idx].append([s, 1.])
|
|
self.class_gt_counts[class_idx] += 1
|
|
else:
|
|
self.class_score_poss[class_idx].append([s, 0.])
|
|
|
|
@staticmethod
|
|
def get_tp_fp_accum(score_pos_list):
|
|
"""
|
|
Calculate accumulating true/false positive results from
|
|
[score, pos] records
|
|
"""
|
|
sorted_list = sorted(score_pos_list, key=lambda s: s[0], reverse=True)
|
|
|
|
accum_tp = 0
|
|
accum_fp = 0
|
|
accum_tp_list = []
|
|
accum_fp_list = []
|
|
for (score, pos) in sorted_list:
|
|
accum_tp += int(pos)
|
|
accum_tp_list.append(accum_tp)
|
|
accum_fp += 1 - int(pos)
|
|
accum_fp_list.append(accum_fp)
|
|
|
|
return accum_tp_list, accum_fp_list
|
|
|
|
def compute_mAP(self):
|
|
if not self.is_latest:
|
|
mAP = 0.
|
|
valid_cnt = 0
|
|
for score_pos, count in zip(self.class_score_poss,
|
|
self.class_gt_counts):
|
|
if count == 0:
|
|
continue
|
|
|
|
if len(score_pos) == 0:
|
|
valid_cnt += 1
|
|
continue
|
|
|
|
accum_tp_list, accum_fp_list = \
|
|
self.get_tp_fp_accum(score_pos)
|
|
precision = []
|
|
recall = []
|
|
for ac_tp, ac_fp in zip(accum_tp_list, accum_fp_list):
|
|
precision.append(float(ac_tp) / (ac_tp + ac_fp))
|
|
recall.append(float(ac_tp) / count)
|
|
|
|
one_class_ap = 0.0
|
|
if self.map_type == '11point':
|
|
max_precisions = [0.] * 11
|
|
start_idx = len(precision) - 1
|
|
for j in range(10, -1, -1):
|
|
for i in range(start_idx, -1, -1):
|
|
if recall[i] < float(j) / 10.:
|
|
start_idx = i
|
|
if j > 0:
|
|
max_precisions[j - 1] = max_precisions[j]
|
|
break
|
|
else:
|
|
if max_precisions[j] < precision[i]:
|
|
max_precisions[j] = precision[i]
|
|
one_class_ap = sum(max_precisions) / 11.
|
|
mAP += one_class_ap
|
|
valid_cnt += 1
|
|
elif self.map_type == 'integral':
|
|
import math
|
|
prev_recall = 0.
|
|
for i in range(len(precision)):
|
|
recall_gap = math.fabs(recall[i] - prev_recall)
|
|
if recall_gap > 1e-6:
|
|
one_class_ap += precision[i] * recall_gap
|
|
prev_recall = recall[i]
|
|
mAP += one_class_ap
|
|
valid_cnt += 1
|
|
else:
|
|
raise NotImplementedError(
|
|
f"Unsupported mAP type {self.map_type}")
|
|
|
|
self.mAP = mAP / float(valid_cnt) if valid_cnt > 0 else mAP
|
|
|
|
self.is_latest = True
|
|
|
|
def forward(self, output, target):
|
|
scores = F.sigmoid(output).numpy()
|
|
gt_labels = target.numpy()
|
|
|
|
if self.class_score_poss is None:
|
|
self.class_score_poss = [[] for _ in range(scores.shape[-1])]
|
|
if self.class_gt_counts is None:
|
|
self.class_gt_counts = [0] * scores.shape[-1]
|
|
|
|
for class_idx in range(scores.shape[-1]):
|
|
score = scores[:, class_idx]
|
|
gt_label = gt_labels[:, class_idx]
|
|
self.one_class_update(score, gt_label, class_idx)
|
|
|
|
self.is_latest = False
|
|
|
|
return {}
|
|
|
|
@property
|
|
def avg_info(self):
|
|
self.compute_mAP()
|
|
return f"MultiLabelMAP({self.map_type}): {self.mAP:.3f}"
|
|
|
|
@property
|
|
def avg(self):
|
|
self.compute_mAP()
|
|
return self.mAP
|