# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. # #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 unicode_literals from __future__ import print_function from __future__ import division import logging import numpy as np import json from metrics.youtube8m import eval_util as youtube8m_metrics from metrics.kinetics import accuracy_metrics as kinetics_metrics from metrics.multicrop_test import multicrop_test_metrics as multicrop_test_metrics from metrics.detections import detection_metrics as detection_metrics from metrics.bmn_metrics import bmn_proposal_metrics as bmn_proposal_metrics from metrics.bsn_metrics import bsn_tem_metrics as bsn_tem_metrics from metrics.bsn_metrics import bsn_pem_metrics as bsn_pem_metrics from metrics.tall import accuracy_metrics as tall_metrics logger = logging.getLogger(__name__) class Metrics(object): def __init__(self, name, mode, metrics_args): """Not implemented""" pass def calculate_and_log_out(self, fetch_list, info=''): """Not implemented""" pass def accumulate(self, fetch_list, info=''): """Not implemented""" pass def finalize_and_log_out(self, info='', savedir='./'): """Not implemented""" pass def reset(self): """Not implemented""" pass class Youtube8mMetrics(Metrics): def __init__(self, name, mode, metrics_args): self.name = name self.mode = mode self.num_classes = metrics_args['MODEL']['num_classes'] self.topk = metrics_args['MODEL']['topk'] self.calculator = youtube8m_metrics.EvaluationMetrics(self.num_classes, self.topk) if self.mode == 'infer': self.infer_results = [] def calculate_and_log_out(self, fetch_list, info=''): loss = np.mean(np.array(fetch_list[0])) pred = np.array(fetch_list[1]) label = np.array(fetch_list[2]) hit_at_one = youtube8m_metrics.calculate_hit_at_one(pred, label) perr = youtube8m_metrics.calculate_precision_at_equal_recall_rate(pred, label) gap = youtube8m_metrics.calculate_gap(pred, label) logger.info(info + ' , loss = {0}, Hit@1 = {1}, PERR = {2}, GAP = {3}'.format(\ '%.6f' % loss, '%.2f' % hit_at_one, '%.2f' % perr, '%.2f' % gap)) def accumulate(self, fetch_list, info=''): if self.mode == 'infer': predictions = np.array(fetch_list[0]) video_id = fetch_list[1] for i in range(len(predictions)): topk_inds = predictions[i].argsort()[0 - self.topk:] topk_inds = topk_inds[::-1] preds = predictions[i][topk_inds] self.infer_results.append( (video_id[i], topk_inds.tolist(), preds.tolist())) else: loss = np.array(fetch_list[0]) pred = np.array(fetch_list[1]) label = np.array(fetch_list[2]) self.calculator.accumulate(loss, pred, label) def finalize_and_log_out(self, info='', savedir='./'): if self.mode == 'infer': for item in self.infer_results: logger.info('video_id {} , topk({}) preds: \n'.format(item[ 0], self.topk)) for i in range(len(item[1])): logger.info('\t class: {}, probability {} \n'.format( item[1][i], item[2][i])) # save infer result into output dir #json.dump(self.infer_results, xxxx) else: epoch_info_dict = self.calculator.get() logger.info(info + '\tavg_hit_at_one: {0},\tavg_perr: {1},\tavg_loss :{2},\taps: {3},\tgap:{4}'\ .format(epoch_info_dict['avg_hit_at_one'], epoch_info_dict['avg_perr'], \ epoch_info_dict['avg_loss'], epoch_info_dict['aps'], epoch_info_dict['gap'])) def reset(self): self.calculator.clear() if self.mode == 'infer': self.infer_results = [] class Kinetics400Metrics(Metrics): def __init__(self, name, mode, metrics_args): self.name = name self.mode = mode self.topk = metrics_args['MODEL']['topk'] self.calculator = kinetics_metrics.MetricsCalculator(name, mode.lower()) if self.mode == 'infer': self.infer_results = [] self.kinetics_labels = metrics_args['INFER']['kinetics_labels'] self.labels_list = json.load(open(self.kinetics_labels)) def calculate_and_log_out(self, fetch_list, info=''): if len(fetch_list) == 3: loss = fetch_list[0] loss = np.mean(np.array(loss)) pred = np.array(fetch_list[1]) label = np.array(fetch_list[2]) else: loss = 0. pred = np.array(fetch_list[0]) label = np.array(fetch_list[1]) acc1, acc5 = self.calculator.calculate_metrics(loss, pred, label) logger.info(info + '\tLoss: {},\ttop1_acc: {}, \ttop5_acc: {}'.format('%.6f' % loss, \ '%.2f' % acc1, '%.2f' % acc5)) return loss def accumulate(self, fetch_list, info=''): if self.mode == 'infer': predictions = np.array(fetch_list[0]) video_id = fetch_list[1] for i in range(len(predictions)): topk_inds = predictions[i].argsort()[0 - self.topk:] topk_inds = topk_inds[::-1] preds = predictions[i][topk_inds] self.infer_results.append( (video_id[i], topk_inds.tolist(), preds.tolist())) else: if len(fetch_list) == 3: loss = fetch_list[0] loss = np.mean(np.array(loss)) pred = np.array(fetch_list[1]) label = np.array(fetch_list[2]) else: loss = 0. pred = np.array(fetch_list[0]) label = np.array(fetch_list[1]) self.calculator.accumulate(loss, pred, label) def finalize_and_log_out(self, info='', savedir='./'): if self.mode == 'infer': for item in self.infer_results: logger.info('video_id {} , topk({}) preds: \n'.format(item[ 0], self.topk)) for i in range(len(item[1])): logger.info('\t class: {}, probability: {} \n'.format( self.labels_list[item[1][i]], item[2][i])) # save infer results else: self.calculator.finalize_metrics() metrics_dict = self.calculator.get_computed_metrics() loss = metrics_dict['avg_loss'] acc1 = metrics_dict['avg_acc1'] acc5 = metrics_dict['avg_acc5'] logger.info(info + '\tLoss: {},\ttop1_acc: {}, \ttop5_acc: {}'.format('%.6f' % loss, \ '%.2f' % acc1, '%.2f' % acc5)) def reset(self): self.calculator.reset() if self.mode == 'infer': self.infer_results = [] class MulticropMetrics(Metrics): def __init__(self, name, mode, metrics_args): self.name = name self.mode = mode if (mode == 'test') or (mode == 'infer'): args = {} args['num_test_clips'] = metrics_args[mode.upper()][ 'num_test_clips'] args['dataset_size'] = metrics_args.TEST.dataset_size args['filename_gt'] = metrics_args.TEST.filename_gt args['checkpoint_dir'] = metrics_args[mode.upper()][ 'checkpoint_dir'] args['num_classes'] = metrics_args.MODEL.num_classes args['labels_list'] = metrics_args.INFER.kinetics_labels self.calculator = multicrop_test_metrics.MetricsCalculator( name, mode.lower(), **args) else: self.calculator = kinetics_metrics.MetricsCalculator(name, mode.lower()) def calculate_and_log_out(self, fetch_list, info=''): if (self.mode == 'test') or (self.mode == 'infer'): pass else: if len(fetch_list) == 3: loss = fetch_list[0] loss = np.mean(np.array(loss)) pred = fetch_list[1] label = fetch_list[2] else: loss = 0. pred = fetch_list[0] label = fetch_list[1] acc1, acc5 = self.calculator.calculate_metrics(loss, pred, label) logger.info(info + '\tLoss: {},\ttop1_acc: {}, \ttop5_acc: {}'.format('%.6f' % loss, \ '%.2f' % acc1, '%.2f' % acc5)) def accumulate(self, fetch_list): if self.mode == 'test': pred = fetch_list[0] label = fetch_list[1] self.calculator.accumulate(pred, label) elif self.mode == 'infer': pred = fetch_list[0] video_id = fetch_list[1] self.calculator.accumulate_infer_results(pred, video_id) else: loss = fetch_list[0] pred = fetch_list[1] label = fetch_list[2] self.calculator.accumulate(loss, pred, label) def finalize_and_log_out(self, info='', savedir='./'): if self.mode == 'test': self.calculator.finalize_metrics() elif self.mode == 'infer': self.calculator.finalize_infer_metrics() else: self.calculator.finalize_metrics() metrics_dict = self.calculator.get_computed_metrics() loss = metrics_dict['avg_loss'] acc1 = metrics_dict['avg_acc1'] acc5 = metrics_dict['avg_acc5'] logger.info(info + '\tLoss: {},\ttop1_acc: {}, \ttop5_acc: {}'.format('%.6f' % loss, \ '%.2f' % acc1, '%.2f' % acc5)) def reset(self): self.calculator.reset() class DetectionMetrics(Metrics): def __init__(self, name, mode, cfg): self.name = name self.mode = mode args = {} args['score_thresh'] = cfg.TEST.score_thresh args['nms_thresh'] = cfg.TEST.nms_thresh args['sigma_thresh'] = cfg.TEST.sigma_thresh args['soft_thresh'] = cfg.TEST.soft_thresh args['class_label_file'] = cfg.TEST.class_label_file args['video_duration_file'] = cfg.TEST.video_duration_file args['gt_label_file'] = cfg.TEST.filelist args['mode'] = mode args['name'] = name self.calculator = detection_metrics.MetricsCalculator(**args) def calculate_and_log_out(self, fetch_list, info=''): total_loss = np.array(fetch_list[0]) loc_loss = np.array(fetch_list[1]) cls_loss = np.array(fetch_list[2]) logger.info( info + '\tLoss = {}, \tloc_loss = {}, \tcls_loss = {}'.format( np.mean(total_loss), np.mean(loc_loss), np.mean(cls_loss))) def accumulate(self, fetch_list): if self.mode == 'infer': self.calculator.accumulate_infer_results(fetch_list) else: self.calculator.accumulate(fetch_list) def finalize_and_log_out(self, info='', savedir='./'): if self.mode == 'infer': self.calculator.finalize_infer_metrics(savedir) #pass else: self.calculator.finalize_metrics(savedir) metrics_dict = self.calculator.get_computed_metrics() loss = metrics_dict['avg_loss'] loc_loss = metrics_dict['avg_loc_loss'] cls_loss = metrics_dict['avg_cls_loss'] logger.info(info + '\tLoss: {},\tloc_loss: {}, \tcls_loss: {}'.format('%.6f' % loss, \ '%.6f' % loc_loss, '%.6f' % cls_loss)) def reset(self): self.calculator.reset() class BmnMetrics(Metrics): def __init__(self, name, mode, cfg): self.name = name self.mode = mode self.calculator = bmn_proposal_metrics.MetricsCalculator( cfg=cfg, name=self.name, mode=self.mode) def calculate_and_log_out(self, fetch_list, info=''): total_loss = np.array(fetch_list[0]) tem_loss = np.array(fetch_list[1]) pem_reg_loss = np.array(fetch_list[2]) pem_cls_loss = np.array(fetch_list[3]) logger.info( info + '\tLoss = {}, \ttem_loss = {}, \tpem_reg_loss = {}, \tpem_cls_loss = {}'.format( '%.04f' % np.mean(total_loss), '%.04f' % np.mean(tem_loss), \ '%.04f' % np.mean(pem_reg_loss), '%.04f' % np.mean(pem_cls_loss))) def accumulate(self, fetch_list): if self.mode == 'infer': self.calculator.accumulate_infer_results(fetch_list) else: self.calculator.accumulate(fetch_list) def finalize_and_log_out(self, info='', savedir='./'): if self.mode == 'infer': self.calculator.finalize_infer_metrics() else: self.calculator.finalize_metrics() metrics_dict = self.calculator.get_computed_metrics() loss = metrics_dict['avg_loss'] tem_loss = metrics_dict['avg_tem_loss'] pem_reg_loss = metrics_dict['avg_pem_reg_loss'] pem_cls_loss = metrics_dict['avg_pem_cls_loss'] logger.info( info + '\tLoss = {}, \ttem_loss = {}, \tpem_reg_loss = {}, \tpem_cls_loss = {}'. format('%.04f' % loss, '%.04f' % tem_loss, '%.04f' % pem_reg_loss, '%.04f' % pem_cls_loss)) def reset(self): self.calculator.reset() class BsnTemMetrics(Metrics): def __init__(self, name, mode, cfg): self.name = name self.mode = mode self.calculator = bsn_tem_metrics.MetricsCalculator( cfg=cfg, name=self.name, mode=self.mode) def calculate_and_log_out(self, fetch_list, info=''): total_loss = np.array(fetch_list[0]) start_loss = np.array(fetch_list[1]) end_loss = np.array(fetch_list[2]) action_loss = np.array(fetch_list[3]) logger.info( info + '\tLoss = {}, \tstart_loss = {}, \tend_loss = {}, \taction_loss = {}'. format('%.04f' % np.mean(total_loss), '%.04f' % np.mean(start_loss), '%.04f' % np.mean(end_loss), '%.04f' % np.mean(action_loss))) def accumulate(self, fetch_list): if self.mode == 'infer': self.calculator.accumulate_infer_results(fetch_list) else: self.calculator.accumulate(fetch_list) def finalize_and_log_out(self, info='', savedir='./'): if self.mode == 'infer': self.calculator.finalize_infer_metrics() else: self.calculator.finalize_metrics() metrics_dict = self.calculator.get_computed_metrics() loss = metrics_dict['avg_loss'] start_loss = metrics_dict['avg_start_loss'] end_loss = metrics_dict['avg_end_loss'] action_loss = metrics_dict['avg_action_loss'] logger.info( info + '\tLoss = {}, \tstart_loss = {}, \tend_loss = {}, \taction_loss = {}'. format('%.04f' % loss, '%.04f' % start_loss, '%.04f' % end_loss, '%.04f' % action_loss)) def reset(self): self.calculator.reset() class BsnPemMetrics(Metrics): def __init__(self, name, mode, cfg): self.name = name self.mode = mode self.calculator = bsn_pem_metrics.MetricsCalculator( cfg=cfg, name=self.name, mode=self.mode) def calculate_and_log_out(self, fetch_list, info=''): total_loss = np.array(fetch_list[0]) logger.info(info + '\tLoss = {}'.format('%.04f' % np.mean(total_loss))) def accumulate(self, fetch_list): if self.mode == 'infer': self.calculator.accumulate_infer_results(fetch_list) else: self.calculator.accumulate(fetch_list) def finalize_and_log_out(self, info='', savedir='./'): if self.mode == 'infer': self.calculator.finalize_infer_metrics() else: self.calculator.finalize_metrics() metrics_dict = self.calculator.get_computed_metrics() loss = metrics_dict['avg_loss'] logger.info(info + '\tLoss = {}'.format('%.04f' % loss)) def reset(self): self.calculator.reset() ##shipping class TallMetrics(Metrics): def __init__(self, name, model, cfg): self.name = name self.mode =mode self.calculator = tall_metrics.MetricsCalculator(cfg=cfg, name=self.name, mode=self.mode) def calculator_and_log_out(self, fetch_list, info=""): if self.mode == "train": loss = np.array(fetch_list[0]) logger.info(info +'\tLoss = {}'.format('%.6f' % np.mean(loss))) elif self.mode == "valid": outs = fetch_list[0] outputs = np.squeeze(outs) start = fetch_list[1] end = fetch_list[2] k = fetch_list[3] t = fetch_list[4] movie_clip_sentences = fetch_list[5] movie_clip_featmaps = fetch_lkist[6] sentence_image_mat = np.zeros([len(movie_clip_sentences), len(movie_clip_featmaps)]) sentence_image_reg_mat = np.zeros([len(movie_clip_sentences), len(movie_clip_featmaps ), 2]) sentence_image_mat[k, t] = outputs[0] # sentence_image_mat[k, t] = expit(outputs[0]) * conf_score reg_end = end + outputs[2] reg_start = start + outputs[1] sentence_image_reg_mat[k, t, 0] = reg_start sentence_image_reg_mat[k, t, 1] = reg_end clips = [b[0] for b in movie_clip_featmaps] sclips = [b[0] for b in movie_clip_sentences] for i in range(len(IoU_thresh)): IoU = IoU_thresh[i] correct_num_10 = compute_IoU_recall_top_n_forreg(10, IoU, sentence_image_mat, sentence_image_reg_mat, sclips, iclips) correct_num_5 = compute_IoU_recall_top_n_forreg(5, IoU, sentence_image_mat, sentence_image_reg_mat, sclips, iclips) correct_num_1 = compute_IoU_recall_top_n_forreg(1, IoU, sentence_image_mat, sentence_image_reg_mat, sclips, iclips) logger.info(info + " IoU=" + str(IoU) + ", R@10: " + str(correct_num_10 / len(sclips)) + "; IoU=" + str(IoU) + ", R@5: " + str(correct_num_5 / len(sclips)) + "; IoU=" + str(IoU) + ", R@1: " + str(correct_num_1 / len(sclips))) all_correct_num_10[i] += correct_num_10 all_correct_num_5[i] += correct_num_5 all_correct_num_1[i] += correct_num_1 all_retrievd += len(sclips) else: pass def accumalate(): def finalize_and_log_out(self, info="", savedir="/"): def reset(self): self.calculator.clear() class MetricsZoo(object): def __init__(self): self.metrics_zoo = {} def regist(self, name, metrics): assert metrics.__base__ == Metrics, "Unknow model type {}".format( type(metrics)) self.metrics_zoo[name] = metrics def get(self, name, mode, cfg): for k, v in self.metrics_zoo.items(): if k == name: return v(name, mode, cfg) raise MetricsNotFoundError(name, self.metrics_zoo.keys()) # singleton metrics_zoo metrics_zoo = MetricsZoo() def regist_metrics(name, metrics): metrics_zoo.regist(name, metrics) def get_metrics(name, mode, cfg): return metrics_zoo.get(name, mode, cfg) # sort by alphabet regist_metrics("ATTENTIONCLUSTER", Youtube8mMetrics) regist_metrics("ATTENTIONLSTM", Youtube8mMetrics) regist_metrics("NEXTVLAD", Youtube8mMetrics) regist_metrics("NONLOCAL", MulticropMetrics) regist_metrics("TSM", Kinetics400Metrics) regist_metrics("TSN", Kinetics400Metrics) regist_metrics("STNET", Kinetics400Metrics) regist_metrics("CTCN", DetectionMetrics) regist_metrics("BMN", BmnMetrics) regist_metrics("BSNTEM", BsnTemMetrics) regist_metrics("BSNPEM", BsnPemMetrics) redist_metrics("TALL", TallMetrics)