| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184 |
- # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- import os
- import paddle
- import numpy as np
- from scipy import interpolate
- import paddle.nn.functional as F
- from .map_utils import ap_per_class
- from paddlex.ppdet.modeling.bbox_utils import bbox_iou_np_expand
- from .mot_eval_utils import MOTEvaluator
- from .metrics import Metric
- from paddlex.ppdet.utils.logger import setup_logger
- logger = setup_logger(__name__)
- __all__ = ['JDEDetMetric', 'JDEReIDMetric', 'MOTMetric']
- class JDEDetMetric(Metric):
- def __init__(self, overlap_thresh=0.5):
- self.overlap_thresh = overlap_thresh
- self.reset()
- def reset(self):
- self.AP_accum = np.zeros(1)
- self.AP_accum_count = np.zeros(1)
- def update(self, inputs, outputs):
- bboxes = outputs['bbox'][:, 2:].numpy()
- scores = outputs['bbox'][:, 1].numpy()
- labels = outputs['bbox'][:, 0].numpy()
- bbox_lengths = outputs['bbox_num'].numpy()
- if bboxes.shape[0] == 1 and bboxes.sum() == 0.0:
- return
- gt_boxes = inputs['gt_bbox'].numpy()[0]
- gt_labels = inputs['gt_class'].numpy()[0]
- if gt_labels.shape[0] == 0:
- return
- correct = []
- detected = []
- for i in range(bboxes.shape[0]):
- obj_pred = 0
- pred_bbox = bboxes[i].reshape(1, 4)
- # Compute iou with target boxes
- iou = bbox_iou_np_expand(pred_bbox, gt_boxes, x1y1x2y2=True)[0]
- # Extract index of largest overlap
- best_i = np.argmax(iou)
- # If overlap exceeds threshold and classification is correct mark as correct
- if iou[best_i] > self.overlap_thresh and obj_pred == gt_labels[
- best_i] and best_i not in detected:
- correct.append(1)
- detected.append(best_i)
- else:
- correct.append(0)
- # Compute Average Precision (AP) per class
- target_cls = list(gt_labels.T[0])
- AP, AP_class, R, P = ap_per_class(
- tp=correct,
- conf=scores,
- pred_cls=np.zeros_like(scores),
- target_cls=target_cls)
- self.AP_accum_count += np.bincount(AP_class, minlength=1)
- self.AP_accum += np.bincount(AP_class, minlength=1, weights=AP)
- def accumulate(self):
- logger.info("Accumulating evaluatation results...")
- self.map_stat = self.AP_accum[0] / (self.AP_accum_count[0] + 1E-16)
- def log(self):
- map_stat = 100. * self.map_stat
- logger.info("mAP({:.2f}) = {:.2f}%".format(self.overlap_thresh,
- map_stat))
- def get_results(self):
- return self.map_stat
- class JDEReIDMetric(Metric):
- def __init__(self, far_levels=[1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1]):
- self.far_levels = far_levels
- self.reset()
- def reset(self):
- self.embedding = []
- self.id_labels = []
- self.eval_results = {}
- def update(self, inputs, outputs):
- for out in outputs:
- feat, label = out[:-1].clone().detach(), int(out[-1])
- if label != -1:
- self.embedding.append(feat)
- self.id_labels.append(label)
- def accumulate(self):
- logger.info("Computing pairwise similairity...")
- assert len(self.embedding) == len(self.id_labels)
- if len(self.embedding) < 1:
- return None
- embedding = paddle.stack(self.embedding, axis=0)
- emb = F.normalize(embedding, axis=1).numpy()
- pdist = np.matmul(emb, emb.T)
- id_labels = np.array(self.id_labels, dtype='int32').reshape(-1, 1)
- n = len(id_labels)
- id_lbl = np.tile(id_labels, n).T
- gt = id_lbl == id_lbl.T
- up_triangle = np.where(np.triu(pdist) - np.eye(n) * pdist != 0)
- pdist = pdist[up_triangle]
- gt = gt[up_triangle]
- # lazy import metrics here
- from sklearn import metrics
- far, tar, threshold = metrics.roc_curve(gt, pdist)
- interp = interpolate.interp1d(far, tar)
- tar_at_far = [interp(x) for x in self.far_levels]
- for f, fa in enumerate(self.far_levels):
- self.eval_results['TPR@FAR={:.7f}'.format(fa)] = ' {:.4f}'.format(
- tar_at_far[f])
- def log(self):
- for k, v in self.eval_results.items():
- logger.info('{}: {}'.format(k, v))
- def get_results(self):
- return self.eval_results
- class MOTMetric(Metric):
- def __init__(self, save_summary=False):
- self.save_summary = save_summary
- self.MOTEvaluator = MOTEvaluator
- self.result_root = None
- self.reset()
- def reset(self):
- self.accs = []
- self.seqs = []
- def update(self, data_root, seq, data_type, result_root, result_filename):
- evaluator = self.MOTEvaluator(data_root, seq, data_type)
- self.accs.append(evaluator.eval_file(result_filename))
- self.seqs.append(seq)
- self.result_root = result_root
- def accumulate(self):
- import motmetrics as mm
- import openpyxl
- metrics = mm.metrics.motchallenge_metrics
- mh = mm.metrics.create()
- summary = self.MOTEvaluator.get_summary(self.accs, self.seqs, metrics)
- self.strsummary = mm.io.render_summary(
- summary,
- formatters=mh.formatters,
- namemap=mm.io.motchallenge_metric_names)
- if self.save_summary:
- self.MOTEvaluator.save_summary(
- summary, os.path.join(self.result_root, 'summary.xlsx'))
- def log(self):
- print(self.strsummary)
- def get_results(self):
- return self.strsummary
|