from __future__ import absolute_import from __future__ import print_function from __future__ import division import os.path as osp import time import datetime import numpy as np import torch import torch.nn as nn from torch.nn import functional as F from torch.utils.tensorboard import SummaryWriter from torchreid.utils import AverageMeter, visualize_ranked_results, save_checkpoint, re_ranking from torchreid.losses import DeepSupervision from torchreid import metrics class Engine(object): r"""A generic base Engine class for both image- and video-reid. Args: datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager`` or ``torchreid.data.VideoDataManager``. model (nn.Module): model instance. optimizer (Optimizer): an Optimizer. scheduler (LRScheduler, optional): if None, no learning rate decay will be performed. use_gpu (bool, optional): use gpu. Default is True. """ def __init__(self, datamanager, model, optimizer=None, scheduler=None, use_gpu=True): self.datamanager = datamanager self.model = model self.optimizer = optimizer self.scheduler = scheduler self.use_gpu = (torch.cuda.is_available() and use_gpu) self.writer = None self.train_loader = self.datamanager.train_loader self.test_loader = self.datamanager.test_loader def run( self, save_dir='log', max_epoch=0, start_epoch=0, print_freq=10, fixbase_epoch=0, open_layers=None, start_eval=0, eval_freq=-1, test_only=False, dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10, use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False ): r"""A unified pipeline for training and evaluating a model. Args: save_dir (str): directory to save model. max_epoch (int): maximum epoch. start_epoch (int, optional): starting epoch. Default is 0. print_freq (int, optional): print_frequency. Default is 10. fixbase_epoch (int, optional): number of epochs to train ``open_layers`` (new layers) while keeping base layers frozen. Default is 0. ``fixbase_epoch`` is counted in ``max_epoch``. open_layers (str or list, optional): layers (attribute names) open for training. start_eval (int, optional): from which epoch to start evaluation. Default is 0. eval_freq (int, optional): evaluation frequency. Default is -1 (meaning evaluation is only performed at the end of training). test_only (bool, optional): if True, only runs evaluation on test datasets. Default is False. dist_metric (str, optional): distance metric used to compute distance matrix between query and gallery. Default is "euclidean". normalize_feature (bool, optional): performs L2 normalization on feature vectors before computing feature distance. Default is False. visrank (bool, optional): visualizes ranked results. Default is False. It is recommended to enable ``visrank`` when ``test_only`` is True. The ranked images will be saved to "save_dir/visrank_dataset", e.g. "save_dir/visrank_market1501". visrank_topk (int, optional): top-k ranked images to be visualized. Default is 10. use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03. Default is False. This should be enabled when using cuhk03 classic split. ranks (list, optional): cmc ranks to be computed. Default is [1, 5, 10, 20]. rerank (bool, optional): uses person re-ranking (by Zhong et al. CVPR'17). Default is False. This is only enabled when test_only=True. """ if visrank and not test_only: raise ValueError('visrank can be set to True only if test_only=True') if test_only: self.test( 0, dist_metric=dist_metric, normalize_feature=normalize_feature, visrank=visrank, visrank_topk=visrank_topk, save_dir=save_dir, use_metric_cuhk03=use_metric_cuhk03, ranks=ranks, rerank=rerank ) return if self.writer is None: self.writer = SummaryWriter(log_dir=save_dir) time_start = time.time() print('=> Start training') for epoch in range(start_epoch, max_epoch): self.train( epoch, max_epoch, self.writer, print_freq=print_freq, fixbase_epoch=fixbase_epoch, open_layers=open_layers ) if (epoch+1)>=start_eval and eval_freq>0 and (epoch+1)%eval_freq==0 and (epoch+1)!=max_epoch: rank1 = self.test( epoch, dist_metric=dist_metric, normalize_feature=normalize_feature, visrank=visrank, visrank_topk=visrank_topk, save_dir=save_dir, use_metric_cuhk03=use_metric_cuhk03, ranks=ranks ) self._save_checkpoint(epoch, rank1, save_dir) if max_epoch > 0: print('=> Final test') rank1 = self.test( epoch, dist_metric=dist_metric, normalize_feature=normalize_feature, visrank=visrank, visrank_topk=visrank_topk, save_dir=save_dir, use_metric_cuhk03=use_metric_cuhk03, ranks=ranks ) self._save_checkpoint(epoch, rank1, save_dir) elapsed = round(time.time() - time_start) elapsed = str(datetime.timedelta(seconds=elapsed)) print('Elapsed {}'.format(elapsed)) if self.writer is not None: self.writer.close() def train(self): r"""Performs training on source datasets for one epoch. This will be called every epoch in ``run()``, e.g. .. code-block:: python for epoch in range(start_epoch, max_epoch): self.train(some_arguments) .. note:: This must be implemented in subclasses. """ raise NotImplementedError def test( self, epoch, dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False ): r"""Tests model on target datasets. .. note:: This function has been called in ``run()``. .. note:: The test pipeline implemented in this function suits both image- and video-reid. In general, a subclass of Engine only needs to re-implement ``_extract_features()`` and ``_parse_data_for_eval()`` (most of the time), but not a must. Please refer to the source code for more details. """ targets = list(self.test_loader.keys()) for name in targets: domain = 'source' if name in self.datamanager.sources else 'target' print('##### Evaluating {} ({}) #####'.format(name, domain)) query_loader = self.test_loader[name]['query'] gallery_loader = self.test_loader[name]['gallery'] rank1 = self._evaluate( epoch, dataset_name=name, query_loader=query_loader, gallery_loader=gallery_loader, dist_metric=dist_metric, normalize_feature=normalize_feature, visrank=visrank, visrank_topk=visrank_topk, save_dir=save_dir, use_metric_cuhk03=use_metric_cuhk03, ranks=ranks, rerank=rerank ) return rank1 @torch.no_grad() def _evaluate( self, epoch, dataset_name='', query_loader=None, gallery_loader=None, dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False ): batch_time = AverageMeter() def _feature_extraction(data_loader): f_, pids_, camids_ = [], [], [] for batch_idx, data in enumerate(data_loader): imgs, pids, camids = self._parse_data_for_eval(data) if self.use_gpu: imgs = imgs.cuda() end = time.time() features = self._extract_features(imgs) batch_time.update(time.time() - end) features = features.data.cpu() f_.append(features) pids_.extend(pids) camids_.extend(camids) f_ = torch.cat(f_, 0) pids_ = np.asarray(pids_) camids_ = np.asarray(camids_) return f_, pids_, camids_ print('Extracting features from query set ...') qf, q_pids, q_camids = _feature_extraction(query_loader) print('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1))) print('Extracting features from gallery set ...') gf, g_pids, g_camids = _feature_extraction(gallery_loader) print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1))) print('Speed: {:.4f} sec/batch'.format(batch_time.avg)) if normalize_feature: print('Normalzing features with L2 norm ...') qf = F.normalize(qf, p=2, dim=1) gf = F.normalize(gf, p=2, dim=1) print('Computing distance matrix with metric={} ...'.format(dist_metric)) distmat = metrics.compute_distance_matrix(qf, gf, dist_metric) distmat = distmat.numpy() if rerank: print('Applying person re-ranking ...') distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric) distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric) distmat = re_ranking(distmat, distmat_qq, distmat_gg) print('Computing CMC and mAP ...') cmc, mAP = metrics.evaluate_rank( distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=use_metric_cuhk03 ) print('** Results **') print('mAP: {:.1%}'.format(mAP)) print('CMC curve') for r in ranks: print('Rank-{:<3}: {:.1%}'.format(r, cmc[r-1])) if visrank: visualize_ranked_results( distmat, self.datamanager.return_query_and_gallery_by_name(dataset_name), self.datamanager.data_type, width=self.datamanager.width, height=self.datamanager.height, save_dir=osp.join(save_dir, 'visrank_'+dataset_name), topk=visrank_topk ) return cmc[0] def _compute_loss(self, criterion, outputs, targets): if isinstance(outputs, (tuple, list)): loss = DeepSupervision(criterion, outputs, targets) else: loss = criterion(outputs, targets) return loss def _extract_features(self, input): self.model.eval() return self.model(input) def _parse_data_for_train(self, data): imgs = data[0] pids = data[1] return imgs, pids def _parse_data_for_eval(self, data): imgs = data[0] pids = data[1] camids = data[2] return imgs, pids, camids def _save_checkpoint(self, epoch, rank1, save_dir, is_best=False): save_checkpoint({ 'state_dict': self.model.state_dict(), 'epoch': epoch + 1, 'rank1': rank1, 'optimizer': self.optimizer.state_dict(), }, save_dir, is_best=is_best)