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 cv2
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, mkdir_if_missing
from torchreid.losses import DeepSupervision
from torchreid import metrics
GRID_SPACING = 10
[docs]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
# check attributes
if not isinstance(self.model, nn.Module):
raise TypeError('model must be an instance of nn.Module')
[docs] def run(self, save_dir='log', max_epoch=0, start_epoch=0, fixbase_epoch=0, open_layers=None,
start_eval=0, eval_freq=-1, test_only=False, print_freq=10,
dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10,
use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False, visactmap=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.
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.
print_freq (int, optional): print_frequency. Default is 10.
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.
visactmap (bool, optional): visualizes activation maps. Default is False.
"""
trainloader, testloader = self.datamanager.return_dataloaders()
if visrank and not test_only:
raise ValueError('visrank=True is valid only if test_only=True')
if test_only:
self.test(
0,
testloader,
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)
if visactmap:
self.visactmap(testloader, save_dir, self.datamanager.width, self.datamanager.height, print_freq)
return
time_start = time.time()
print('=> Start training')
for epoch in range(start_epoch, max_epoch):
self.train(epoch, max_epoch, trainloader, fixbase_epoch, open_layers, print_freq)
if (epoch+1)>=start_eval and eval_freq>0 and (epoch+1)%eval_freq==0 and (epoch+1)!=max_epoch:
rank1 = self.test(
epoch,
testloader,
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,
testloader,
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 None:
self.writer.close()
[docs] 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
[docs] def test(self, epoch, testloader, 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(testloader.keys())
for name in targets:
domain = 'source' if name in self.datamanager.sources else 'target'
print('##### Evaluating {} ({}) #####'.format(name, domain))
queryloader = testloader[name]['query']
galleryloader = testloader[name]['gallery']
rank1 = self._evaluate(
epoch,
dataset_name=name,
queryloader=queryloader,
galleryloader=galleryloader,
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='', queryloader=None, galleryloader=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()
print('Extracting features from query set ...')
qf, q_pids, q_camids = [], [], [] # query features, query person IDs and query camera IDs
for batch_idx, data in enumerate(queryloader):
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()
qf.append(features)
q_pids.extend(pids)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print('Done, obtained {}-by-{} matrix'.format(qf.size(0), qf.size(1)))
print('Extracting features from gallery set ...')
gf, g_pids, g_camids = [], [], [] # gallery features, gallery person IDs and gallery camera IDs
for batch_idx, data in enumerate(galleryloader):
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()
gf.append(features)
g_pids.extend(pids)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
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_testdataset_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]
[docs] @torch.no_grad()
def visactmap(self, testloader, save_dir, width, height, print_freq):
"""Visualizes CNN activation maps to see where the CNN focuses on to extract features.
This function takes as input the query images of target datasets
Reference:
- Zagoruyko and Komodakis. Paying more attention to attention: Improving the
performance of convolutional neural networks via attention transfer. ICLR, 2017
- Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
"""
self.model.eval()
imagenet_mean = [0.485, 0.456, 0.406]
imagenet_std = [0.229, 0.224, 0.225]
for target in list(testloader.keys()):
queryloader = testloader[target]['query']
# original images and activation maps are saved individually
actmap_dir = osp.join(save_dir, 'actmap_'+target)
mkdir_if_missing(actmap_dir)
print('Visualizing activation maps for {} ...'.format(target))
for batch_idx, data in enumerate(queryloader):
imgs, paths = data[0], data[3]
if self.use_gpu:
imgs = imgs.cuda()
# forward to get convolutional feature maps
try:
outputs = self.model(imgs, return_featuremaps=True)
except TypeError:
raise TypeError('forward() got unexpected keyword argument "return_featuremaps". ' \
'Please add return_featuremaps as an input argument to forward(). When ' \
'return_featuremaps=True, return feature maps only.')
if outputs.dim() != 4:
raise ValueError('The model output is supposed to have ' \
'shape of (b, c, h, w), i.e. 4 dimensions, but got {} dimensions. '
'Please make sure you set the model output at eval mode '
'to be the last convolutional feature maps'.format(outputs.dim()))
# compute activation maps
outputs = (outputs**2).sum(1)
b, h, w = outputs.size()
outputs = outputs.view(b, h*w)
outputs = F.normalize(outputs, p=2, dim=1)
outputs = outputs.view(b, h, w)
if self.use_gpu:
imgs, outputs = imgs.cpu(), outputs.cpu()
for j in range(outputs.size(0)):
# get image name
path = paths[j]
imname = osp.basename(osp.splitext(path)[0])
# RGB image
img = imgs[j, ...]
for t, m, s in zip(img, imagenet_mean, imagenet_std):
t.mul_(s).add_(m).clamp_(0, 1)
img_np = np.uint8(np.floor(img.numpy() * 255))
img_np = img_np.transpose((1, 2, 0)) # (c, h, w) -> (h, w, c)
# activation map
am = outputs[j, ...].numpy()
am = cv2.resize(am, (width, height))
am = 255 * (am - np.max(am)) / (np.max(am) - np.min(am) + 1e-12)
am = np.uint8(np.floor(am))
am = cv2.applyColorMap(am, cv2.COLORMAP_JET)
# overlapped
overlapped = img_np * 0.3 + am * 0.7
overlapped[overlapped>255] = 255
overlapped = overlapped.astype(np.uint8)
# save images in a single figure (add white spacing between images)
# from left to right: original image, activation map, overlapped image
grid_img = 255 * np.ones((height, 3*width+2*GRID_SPACING, 3), dtype=np.uint8)
grid_img[:, :width, :] = img_np[:, :, ::-1]
grid_img[:, width+GRID_SPACING: 2*width+GRID_SPACING, :] = am
grid_img[:, 2*width+2*GRID_SPACING:, :] = overlapped
cv2.imwrite(osp.join(actmap_dir, imname+'.jpg'), grid_img)
if (batch_idx+1) % print_freq == 0:
print('- done batch {}/{}'.format(batch_idx+1, len(queryloader)))
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)