468 lines
17 KiB
Python
468 lines
17 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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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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msg = f"The output dims({output_dims}) is less than k({k}), and the argument {k} of Topk has been removed."
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logger.warning(msg)
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self.avg_meters.pop(f"top{k}")
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continue
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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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self.topk = list(filter(lambda k: k <= output_dims, self.topk))
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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 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 = 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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real_query_num = paddle.sum(equal_flag, axis=1)
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real_query_num = paddle.sum(
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paddle.greater_than(real_query_num, paddle.to_tensor(0.)).astype(
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"float32"))
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acc_sum = paddle.cumsum(equal_flag, axis=1)
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mask = paddle.greater_than(acc_sum,
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paddle.to_tensor(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)
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# TP
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result.true_pos = np.sum((gt_label == 1) * (pred_label == 1),
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axis=0).astype(float)
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# TN
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result.true_neg = np.sum((gt_label == 0) * (pred_label == 0),
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axis=0).astype(float)
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# FP
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result.false_pos = np.sum(((gt_label == 0) * (pred_label == 1)),
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axis=0).astype(float)
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# FN
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result.false_neg = np.sum(((gt_label == 1) * (pred_label == 0)),
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axis=0).astype(float)
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################
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# instance metrics
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result.gt_pos_ins = np.sum((gt_label == 1), axis=1).astype(float)
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result.true_pos_ins = np.sum((pred_label == 1), axis=1).astype(float)
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# true positive
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result.intersect_pos = np.sum((gt_label == 1) * (pred_label == 1),
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axis=1).astype(float)
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# IOU
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result.union_pos = np.sum(((gt_label == 1) + (pred_label == 1)),
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axis=1).astype(float)
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return result
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class ATTRMetric(nn.Layer):
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def __init__(self, threshold=0.5):
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super().__init__()
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self.threshold = threshold
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def reset(self):
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self.attrmeter = AttrMeter(threshold=0.5)
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def forward(self, output, target):
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metric_dict = get_attr_metrics(target[:, 0, :].numpy(),
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output.numpy(), self.threshold)
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self.attrmeter.update(metric_dict)
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return metric_dict
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