333 lines
14 KiB
Python
333 lines
14 KiB
Python
from __future__ import absolute_import
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from __future__ import print_function
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from __future__ import division
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import os
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import os.path as osp
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import time
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import datetime
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.nn import functional as F
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import torchreid
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from torchreid.utils import AverageMeter, visualize_ranked_results, save_checkpoint, re_ranking
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from torchreid.losses import DeepSupervision
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from torchreid import metrics
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class Engine(object):
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r"""A generic base Engine class for both image- and video-reid.
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Args:
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datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``
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or ``torchreid.data.VideoDataManager``.
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model (nn.Module): model instance.
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optimizer (Optimizer): an Optimizer.
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scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.
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use_cpu (bool, optional): use cpu. Default is False.
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"""
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def __init__(self, datamanager, model, optimizer=None, scheduler=None, use_cpu=False):
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self.datamanager = datamanager
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self.model = model
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self.optimizer = optimizer
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self.scheduler = scheduler
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self.use_gpu = (torch.cuda.is_available() and not use_cpu)
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# check attributes
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if not isinstance(self.model, nn.Module):
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raise TypeError('model must be an instance of nn.Module')
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def run(self, save_dir='log', max_epoch=0, start_epoch=0, fixbase_epoch=0, open_layers=None,
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start_eval=0, eval_freq=-1, test_only=False, print_freq=10,
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dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=20,
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use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False):
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r"""A unified pipeline for training and evaluating a model.
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Args:
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save_dir (str): directory to save model.
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max_epoch (int): maximum epoch.
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start_epoch (int, optional): starting epoch. Default is 0.
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fixbase_epoch (int, optional): number of epochs to train ``open_layers`` (new layers)
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while keeping base layers frozen. Default is 0. ``fixbase_epoch`` is not counted
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in ``max_epoch``.
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open_layers (str or list, optional): layers (attribute names) open for training.
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start_eval (int, optional): from which epoch to start evaluation. Default is 0.
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eval_freq (int, optional): evaluation frequency. Default is -1 (meaning evaluation
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is only performed at the end of training).
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test_only (bool, optional): if True, only runs evaluation on test datasets.
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Default is False.
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print_freq (int, optional): print_frequency. Default is 10.
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dist_metric (str, optional): distance metric used to compute distance matrix
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between query and gallery. Default is "euclidean".
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normalize_feature (bool, optional): performs L2 normalization on feature vectors before
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computing feature distance. Default is False.
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visrank (bool, optional): visualizes ranked results. Default is False. Visualization
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will be performed every test time, so it is recommended to enable ``visrank`` when
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``test_only`` is True. The ranked images will be saved to
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"save_dir/ranks-epoch/dataset_name", e.g. "save_dir/ranks-60/market1501".
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visrank_topk (int, optional): top-k ranked images to be visualized. Default is 20.
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use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.
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Default is False. This should be enabled when using cuhk03 classic split.
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ranks (list, optional): cmc ranks to be computed. Default is [1, 5, 10, 20].
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rerank (bool, optional): uses person re-ranking (by Zhong et al. CVPR'17).
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Default is False. This is only enabled when test_only=True.
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"""
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trainloader, testloader = self.datamanager.return_dataloaders()
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if test_only:
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self.test(
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0,
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testloader,
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dist_metric=dist_metric,
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normalize_feature=normalize_feature,
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visrank=visrank,
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visrank_topk=visrank_topk,
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save_dir=save_dir,
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use_metric_cuhk03=use_metric_cuhk03,
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ranks=ranks,
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rerank=rerank
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)
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return
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time_start = time.time()
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print('=> Start training')
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if fixbase_epoch>0 and (open_layers is not None):
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print('Pretrain open layers ({}) for {} epochs'.format(open_layers, fixbase_epoch))
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for epoch in range(fixbase_epoch):
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self.train(epoch, trainloader, fixbase=True, open_layers=open_layers,
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print_freq=print_freq)
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print('Done. From now on all layers are open to train for {} epochs'.format(max_epoch))
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for epoch in range(start_epoch, max_epoch):
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self.train(epoch, trainloader, print_freq=print_freq)
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if (epoch+1)>start_eval and eval_freq>0 and (epoch+1)%eval_freq==0 and (epoch+1)!=max_epoch:
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rank1 = self.test(
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epoch,
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testloader,
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dist_metric=dist_metric,
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normalize_feature=normalize_feature,
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visrank=visrank,
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visrank_topk=visrank_topk,
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save_dir=save_dir,
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use_metric_cuhk03=use_metric_cuhk03,
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ranks=ranks
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)
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self._save_checkpoint(epoch, rank1, save_dir)
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if max_epoch > 0:
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print('=> Final test')
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rank1 = self.test(
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epoch,
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testloader,
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dist_metric=dist_metric,
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normalize_feature=normalize_feature,
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visrank=visrank,
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visrank_topk=visrank_topk,
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save_dir=save_dir,
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use_metric_cuhk03=use_metric_cuhk03,
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ranks=ranks
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)
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self._save_checkpoint(epoch, rank1, save_dir)
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elapsed = round(time.time() - time_start)
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elapsed = str(datetime.timedelta(seconds=elapsed))
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print('Elapsed {}'.format(elapsed))
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def train(self):
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r"""Performs training on source datasets for one epoch.
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This will be called every epoch in ``run()``, e.g.
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.. code-block:: python
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for epoch in range(start_epoch, max_epoch):
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self.train(some_arguments)
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.. note::
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This needs to be implemented in subclasses.
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"""
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raise NotImplementedError
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def test(self, epoch, testloader, dist_metric='euclidean', normalize_feature=False,
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visrank=False, visrank_topk=20, save_dir='', use_metric_cuhk03=False,
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ranks=[1, 5, 10, 20], rerank=False):
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r"""Tests model on target datasets.
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.. note::
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This function has been called in ``run()`` when necessary.
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.. note::
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The test pipeline implemented in this function suits both image- and
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video-reid. In general, a subclass of Engine only needs to re-implement
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``_extract_features()`` and ``_parse_data_for_eval()`` when necessary,
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but not a must. Please refer to the source code for more details.
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Args:
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epoch (int): current epoch.
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testloader (dict): dictionary containing
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{dataset_name: 'query': queryloader, 'gallery': galleryloader}.
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dist_metric (str, optional): distance metric used to compute distance matrix
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between query and gallery. Default is "euclidean".
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normalize_feature (bool, optional): performs L2 normalization on feature vectors before
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computing feature distance. Default is False.
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visrank (bool, optional): visualizes ranked results. Default is False. Visualization
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will be performed every test time, so it is recommended to enable ``visrank`` when
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``test_only`` is True. The ranked images will be saved to
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"save_dir/ranks-epoch/dataset_name", e.g. "save_dir/ranks-60/market1501".
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visrank_topk (int, optional): top-k ranked images to be visualized. Default is 20.
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save_dir (str): directory to save visualized results if ``visrank`` is True.
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use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.
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Default is False. This should be enabled when using cuhk03 classic split.
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ranks (list, optional): cmc ranks to be computed. Default is [1, 5, 10, 20].
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rerank (bool, optional): uses person re-ranking (by Zhong et al. CVPR'17).
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Default is False.
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"""
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targets = list(testloader.keys())
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for name in targets:
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domain = 'source' if name in self.datamanager.sources else 'target'
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print('\n##### Evaluating {} ({}) #####'.format(name, domain))
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queryloader = testloader[name]['query']
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galleryloader = testloader[name]['gallery']
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rank1 = self._evaluate(
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epoch,
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dataset_name=name,
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queryloader=queryloader,
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galleryloader=galleryloader,
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dist_metric=dist_metric,
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normalize_feature=normalize_feature,
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visrank=visrank,
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visrank_topk=visrank_topk,
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save_dir=save_dir,
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use_metric_cuhk03=use_metric_cuhk03,
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ranks=ranks,
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rerank=rerank
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)
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return rank1
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@torch.no_grad()
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def _evaluate(self, epoch, dataset_name='', queryloader=None, galleryloader=None,
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dist_metric='euclidean', normalize_feature=False, visrank=False,
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visrank_topk=20, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20],
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rerank=False):
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batch_time = AverageMeter()
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self.model.eval()
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print('Extracting features from query set ...')
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qf, q_pids, q_camids = [], [], [] # query features, query person IDs and query camera IDs
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for batch_idx, data in enumerate(queryloader):
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imgs, pids, camids = self._parse_data_for_eval(data)
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if self.use_gpu:
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imgs = imgs.cuda()
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end = time.time()
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features = self._extract_features(imgs)
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batch_time.update(time.time() - end)
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features = features.data.cpu()
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qf.append(features)
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q_pids.extend(pids)
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q_camids.extend(camids)
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qf = torch.cat(qf, 0)
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q_pids = np.asarray(q_pids)
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q_camids = np.asarray(q_camids)
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print('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
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print('Extracting features from gallery set ...')
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gf, g_pids, g_camids = [], [], [] # gallery features, gallery person IDs and gallery camera IDs
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end = time.time()
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for batch_idx, data in enumerate(galleryloader):
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imgs, pids, camids = self._parse_data_for_eval(data)
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if self.use_gpu:
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imgs = imgs.cuda()
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end = time.time()
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features = self._extract_features(imgs)
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batch_time.update(time.time() - end)
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features = features.data.cpu()
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gf.append(features)
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g_pids.extend(pids)
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g_camids.extend(camids)
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gf = torch.cat(gf, 0)
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g_pids = np.asarray(g_pids)
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g_camids = np.asarray(g_camids)
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print('Done, obtained {}-by-{} matrix'.format(gf.size(0), gf.size(1)))
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print('Speed: {:.4f} sec/batch'.format(batch_time.avg))
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if normalize_feature:
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qf = F.normalize(qf, p=2, dim=1)
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gf = F.normalize(gf, p=2, dim=1)
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distmat = metrics.compute_distance_matrix(qf, gf, dist_metric)
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distmat = distmat.numpy()
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if rerank:
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print('Applying person re-ranking ...')
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distmat_qq = metrics.compute_distance_matrix(qf, qf, dist_metric)
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distmat_gg = metrics.compute_distance_matrix(gf, gf, dist_metric)
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distmat = re_ranking(distmat, distmat_qq, distmat_gg)
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print('Computing CMC and mAP ...')
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cmc, mAP = metrics.evaluate_rank(
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distmat,
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q_pids,
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g_pids,
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q_camids,
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g_camids,
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use_metric_cuhk03=use_metric_cuhk03
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)
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print('** Results **')
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print('mAP: {:.1%}'.format(mAP))
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print('CMC curve')
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for r in ranks:
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print('Rank-{:<3}: {:.1%}'.format(r, cmc[r-1]))
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if visrank:
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visualize_ranked_results(
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distmat,
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self.datamanager.return_testdataset_by_name(dataset_name),
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save_dir=osp.join(save_dir, 'visrank-'+str(epoch+1), dataset_name),
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topk=visrank_topk
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)
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return cmc[0]
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def _compute_loss(self, criterion, outputs, targets):
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if isinstance(outputs, (tuple, list)):
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loss = DeepSupervision(criterion, outputs, targets)
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else:
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loss = criterion(outputs, targets)
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return loss
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def _extract_features(self, input):
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self.model.eval()
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return self.model(input)
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def _parse_data_for_train(self, data):
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imgs = data[0]
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pids = data[1]
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return imgs, pids
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def _parse_data_for_eval(self, data):
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imgs = data[0]
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pids = data[1]
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camids = data[2]
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return imgs, pids, camids
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def _save_checkpoint(self, epoch, rank1, save_dir, is_best=False):
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save_checkpoint({
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'state_dict': self.model.state_dict(),
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'epoch': epoch + 1,
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'rank1': rank1,
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'optimizer': self.optimizer.state_dict(),
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}, save_dir, is_best=is_best) |