forked from PulseFocusPlatform/PulseFocusPlatform
184 lines
6.2 KiB
Python
184 lines
6.2 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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 __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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import paddle
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import numpy as np
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from scipy import interpolate
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import paddle.nn.functional as F
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from .map_utils import ap_per_class
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from ppdet.modeling.bbox_utils import bbox_iou_np_expand
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from .mot_eval_utils import MOTEvaluator
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from .metrics import Metric
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from ppdet.utils.logger import setup_logger
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logger = setup_logger(__name__)
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__all__ = ['JDEDetMetric', 'JDEReIDMetric', 'MOTMetric']
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class JDEDetMetric(Metric):
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def __init__(self, overlap_thresh=0.5):
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self.overlap_thresh = overlap_thresh
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self.reset()
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def reset(self):
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self.AP_accum = np.zeros(1)
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self.AP_accum_count = np.zeros(1)
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def update(self, inputs, outputs):
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bboxes = outputs['bbox'][:, 2:].numpy()
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scores = outputs['bbox'][:, 1].numpy()
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labels = outputs['bbox'][:, 0].numpy()
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bbox_lengths = outputs['bbox_num'].numpy()
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if bboxes.shape[0] == 1 and bboxes.sum() == 0.0:
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return
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gt_boxes = inputs['gt_bbox'].numpy()[0]
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gt_labels = inputs['gt_class'].numpy()[0]
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if gt_labels.shape[0] == 0:
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return
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correct = []
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detected = []
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for i in range(bboxes.shape[0]):
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obj_pred = 0
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pred_bbox = bboxes[i].reshape(1, 4)
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# Compute iou with target boxes
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iou = bbox_iou_np_expand(pred_bbox, gt_boxes, x1y1x2y2=True)[0]
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# Extract index of largest overlap
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best_i = np.argmax(iou)
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# If overlap exceeds threshold and classification is correct mark as correct
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if iou[best_i] > self.overlap_thresh and obj_pred == gt_labels[
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best_i] and best_i not in detected:
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correct.append(1)
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detected.append(best_i)
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else:
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correct.append(0)
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# Compute Average Precision (AP) per class
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target_cls = list(gt_labels.T[0])
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AP, AP_class, R, P = ap_per_class(
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tp=correct,
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conf=scores,
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pred_cls=np.zeros_like(scores),
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target_cls=target_cls)
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self.AP_accum_count += np.bincount(AP_class, minlength=1)
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self.AP_accum += np.bincount(AP_class, minlength=1, weights=AP)
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def accumulate(self):
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logger.info("Accumulating evaluatation results...")
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self.map_stat = self.AP_accum[0] / (self.AP_accum_count[0] + 1E-16)
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def log(self):
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map_stat = 100. * self.map_stat
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logger.info("mAP({:.2f}) = {:.2f}%".format(self.overlap_thresh,
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map_stat))
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def get_results(self):
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return self.map_stat
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class JDEReIDMetric(Metric):
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def __init__(self, far_levels=[1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]):
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self.far_levels = far_levels
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self.reset()
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def reset(self):
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self.embedding = []
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self.id_labels = []
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self.eval_results = {}
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def update(self, inputs, outputs):
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for out in outputs:
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feat, label = out[:-1].clone().detach(), int(out[-1])
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if label != -1:
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self.embedding.append(feat)
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self.id_labels.append(label)
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def accumulate(self):
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logger.info("Computing pairwise similairity...")
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assert len(self.embedding) == len(self.id_labels)
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if len(self.embedding) < 1:
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return None
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embedding = paddle.stack(self.embedding, axis=0)
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emb = F.normalize(embedding, axis=1).numpy()
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pdist = np.matmul(emb, emb.T)
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id_labels = np.array(self.id_labels, dtype='int32').reshape(-1, 1)
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n = len(id_labels)
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id_lbl = np.tile(id_labels, n).T
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gt = id_lbl == id_lbl.T
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up_triangle = np.where(np.triu(pdist) - np.eye(n) * pdist != 0)
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pdist = pdist[up_triangle]
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gt = gt[up_triangle]
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# lazy import metrics here
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from sklearn import metrics
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far, tar, threshold = metrics.roc_curve(gt, pdist)
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interp = interpolate.interp1d(far, tar)
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tar_at_far = [interp(x) for x in self.far_levels]
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for f, fa in enumerate(self.far_levels):
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self.eval_results['TPR@FAR={:.7f}'.format(fa)] = ' {:.4f}'.format(
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tar_at_far[f])
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def log(self):
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for k, v in self.eval_results.items():
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logger.info('{}: {}'.format(k, v))
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def get_results(self):
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return self.eval_results
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class MOTMetric(Metric):
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def __init__(self, save_summary=False):
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self.save_summary = save_summary
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self.MOTEvaluator = MOTEvaluator
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self.result_root = None
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self.reset()
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def reset(self):
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self.accs = []
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self.seqs = []
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def update(self, data_root, seq, data_type, result_root, result_filename):
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evaluator = self.MOTEvaluator(data_root, seq, data_type)
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self.accs.append(evaluator.eval_file(result_filename))
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self.seqs.append(seq)
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self.result_root = result_root
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def accumulate(self):
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import motmetrics as mm
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metrics = mm.metrics.motchallenge_metrics
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mh = mm.metrics.create()
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summary = self.MOTEvaluator.get_summary(self.accs, self.seqs, metrics)
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self.strsummary = mm.io.render_summary(
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summary,
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formatters=mh.formatters,
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namemap=mm.io.motchallenge_metric_names)
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if self.save_summary:
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self.MOTEvaluator.save_summary(
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summary, os.path.join(self.result_root, 'summary.xlsx'))
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def log(self):
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print(self.strsummary)
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def get_results(self):
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return self.strsummary
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