def train(cfg):
# output
output_dir = cfg.OUTPUT_DIR
if os.path.exists(output_dir):
raise KeyError("Existing path: ", output_dir)
else:
os.makedirs(output_dir)
with open(os.path.join(output_dir, 'config.yaml'), 'w') as f_out:
print(cfg, file=f_out)
# logger
logger = make_logger("project", output_dir, 'log')
# device
num_gpus = 0
if cfg.DEVICE == 'cuda':
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.DEVICE_ID
num_gpus = len(cfg.DEVICE_ID.split(','))
logger.info("Using {} GPUs.\n".format(num_gpus))
cudnn.benchmark = True
device = torch.device(cfg.DEVICE)
# data
train_loader, query_loader, gallery_loader, num_classes = make_loader(cfg)
# model
model = make_model(cfg, num_classes=num_classes)
if num_gpus > 1:
model = nn.DataParallel(model)
# solver
criterion = make_loss(cfg, num_classes)
optimizer = make_optimizer(cfg, model)
scheduler = make_scheduler(cfg, optimizer)
# do_train
trainer = Trainer(model=model,
optimizer=optimizer,
criterion=criterion,
logger=logger,
scheduler=scheduler,
device=device)
trainer.run(start_epoch=0,
total_epoch=cfg.SOLVER.MAX_EPOCHS,
train_loader=train_loader,
query_loader=query_loader,
gallery_loader=gallery_loader,
print_freq=cfg.SOLVER.PRINT_FREQ,
eval_period=cfg.SOLVER.EVAL_PERIOD,
out_dir=output_dir)
print('Done.')
def main(args):
ckpt_ = checkpoint.Checkpoint(args)
# data loader
dataloader_ = data.Data(args)
# model build up
model_ = model.Model(args, ckpt_)
# loss setting
loss_ = loss.Loss(args, ckpt_)
# check module for visualization and gradient check
check_ = check.check(model_)
# class for training and testing
trainer_ = trainer.Trainer(args, model_, loss_, dataloader_, ckpt_, check_)
if args.test:
trainer_.test()
return
# train
# train with freeze first
if args.freeze > 0:
print('freeze base_params for {} epochs'.format(args.freeze))
for par in ckpt_.base_params:
par.requires_grad = False
if hasattr(model_.get_model(), 'base_params'):
for par in model_.get_model().base_params:
par.requires_grad = False
optim_tmp = optimizer.make_optimizer(args, model_)
for i in range(args.freeze):
trainer_.train(optim_tmp)
# start training
for par in model_.parameters():
par.requires_grad = True
for i in range(trainer_.epoch, args.epochs):
trainer_.train(trainer_.optimizer)
if args.test_every != 0 and (i + 1) % args.test_every == 0:
trainer_.test()
def train(config_file, **kwargs):
cfg.merge_from_file(config_file)
if kwargs:
opts = []
for k, v in kwargs.items():
opts.append(k)
opts.append(v)
cfg.merge_from_list(opts)
cfg.freeze()
#PersonReID_Dataset_Downloader('./datasets',cfg.DATASETS.NAMES)
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = make_logger("Reid_Baseline", output_dir, 'log')
logger.info("Using {} GPUS".format(1))
logger.info("Loaded configuration file {}".format(config_file))
logger.info("Running with config:\n{}".format(cfg))
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
eval_period = cfg.SOLVER.EVAL_PERIOD
output_dir = cfg.OUTPUT_DIR
device = torch.device(cfg.DEVICE)
epochs = cfg.SOLVER.MAX_EPOCHS
method = cfg.DATALOADER.SAMPLER
train_loader, val_loader, num_query, num_classes = data_loader(
cfg, cfg.DATASETS.NAMES)
model = getattr(models, cfg.MODEL.NAME)(num_classes, cfg.MODEL.LAST_STRIDE)
if 'center' in method:
loss_fn, center_criterion = make_loss(cfg)
optimizer, optimizer_center = make_optimizer_with_center(
cfg, model, center_criterion)
else:
loss_fn = make_loss(cfg)
optimizer = make_optimizer(cfg, model)
scheduler = make_scheduler(cfg, optimizer)
logger.info("Start training")
since = time.time()
for epoch in range(epochs):
count = 0
running_loss = 0.0
running_acc = 0
for data in tqdm(train_loader, desc='Iteration', leave=False):
model.train()
images, labels = data
if device:
model.to(device)
images, labels = images.to(device), labels.to(device)
optimizer.zero_grad()
if 'center' in method:
optimizer_center.zero_grad()
scores, feats = model(images)
loss = loss_fn(scores, feats, labels)
loss.backward()
optimizer.step()
if 'center' in method:
for param in center_criterion.parameters():
param.grad.data *= (1. / cfg.SOLVER.CENTER_LOSS_WEIGHT)
optimizer_center.step()
count = count + 1
running_loss += loss.item()
running_acc += (scores.max(1)[1] == labels).float().mean().item()
logger.info(
"Epoch[{}] Iteration[{}/{}] Loss: {:.3f}, Acc: {:.3f}, Base Lr: {:.2e}"
.format(epoch + 1, count, len(train_loader), running_loss / count,
running_acc / count,
scheduler.get_lr()[0]))
scheduler.step()
if (epoch + 1) % checkpoint_period == 0:
model.cpu()
model.save(output_dir, epoch + 1)
# Validation
if (epoch + 1) % eval_period == 0:
all_feats = []
all_pids = []
all_camids = []
for data in tqdm(val_loader,
desc='Feature Extraction',
leave=False):
model.eval()
with torch.no_grad():
images, pids, camids = data
if device:
model.to(device)
images = images.to(device)
feats = model(images)
all_feats.append(feats)
all_pids.extend(np.asarray(pids))
all_camids.extend(np.asarray(camids))
logger.info("start evaluation")
cmc, mAP = evaluation(all_feats, all_pids, all_camids, num_query)
logger.info("Validation Results - Epoch: {}".format(epoch + 1))
logger.info("mAP: {:.1%}".format(mAP))
for r in [1, 5, 10]:
logger.info("CMC curve, Rank-{:<3}:{:.1%}".format(
r, cmc[r - 1]))
time_elapsed = time.time() - since
logger.info('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
logger.info('-' * 10)
def train(config_file, **kwargs):
# 1. config
cfg.merge_from_file(config_file)
if kwargs:
opts = []
for k, v in kwargs.items():
opts.append(k)
opts.append(v)
cfg.merge_from_list(opts)
cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = make_logger("Reid_Baseline", output_dir, 'log')
logger.info("Using {} GPUS".format(1))
logger.info("Loaded configuration file {}".format(config_file))
logger.info("Running with config:\n{}".format(cfg))
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
eval_period = cfg.SOLVER.EVAL_PERIOD
device = torch.device(cfg.DEVICE)
epochs = cfg.SOLVER.MAX_EPOCHS
# 2. datasets
# Load the original dataset
dataset_reference = init_dataset(cfg, cfg.DATASETS.NAMES +
'_origin') #'Market1501_origin'
train_set_reference = ImageDataset(dataset_reference.train,
train_transforms)
train_loader_reference = DataLoader(train_set_reference,
batch_size=128,
shuffle=False,
num_workers=cfg.DATALOADER.NUM_WORKERS,
collate_fn=train_collate_fn)
# Load the one-shot dataset
train_loader, val_loader, num_query, num_classes = data_loader(
cfg, cfg.DATASETS.NAMES)
# 3. load the model and optimizer
model = getattr(models, cfg.MODEL.NAME)(num_classes)
optimizer = make_optimizer(cfg, model)
scheduler = make_scheduler(cfg, optimizer)
loss_fn = make_loss(cfg)
logger.info("Start training")
since = time.time()
top = 0 # the choose of the nearest sample
top_update = 0 # the first iteration train 80 steps and the following train 40
# 4. Train and test
for epoch in range(epochs):
running_loss = 0.0
running_acc = 0
count = 1
# get nearest samples and reset the model
if top_update < 80:
train_step = 80
else:
train_step = 40
if top_update % train_step == 0:
print("top: ", top)
A, path_labeled = PSP(model, train_loader_reference, train_loader,
top, cfg)
top += cfg.DATALOADER.NUM_JUMP
model = getattr(models, cfg.MODEL.NAME)(num_classes)
optimizer = make_optimizer(cfg, model)
scheduler = make_scheduler(cfg, optimizer)
A_store = A.clone()
top_update += 1
for data in tqdm(train_loader, desc='Iteration', leave=False):
model.train()
images, labels_batch, img_path = data
index, index_labeled = find_index_by_path(img_path,
dataset_reference.train,
path_labeled)
images_relevant, GCN_index, choose_from_nodes, labels = load_relevant(
cfg, dataset_reference.train, index, A_store, labels_batch,
index_labeled)
# if device:
model.to(device)
images = images_relevant.to(device)
scores, feat = model(images)
del images
loss = loss_fn(scores, feat, labels.to(device), choose_from_nodes)
optimizer.zero_grad()
loss.backward()
optimizer.step()
count = count + 1
running_loss += loss.item()
running_acc += (scores[choose_from_nodes].max(1)[1].cpu() ==
labels_batch).float().mean().item()
scheduler.step()
# for model save if you need
# if (epoch+1) % checkpoint_period == 0:
# model.cpu()
# model.save(output_dir,epoch+1)
# Validation
if (epoch + 1) % eval_period == 0:
all_feats = []
all_pids = []
all_camids = []
for data in tqdm(val_loader,
desc='Feature Extraction',
leave=False):
model.eval()
with torch.no_grad():
images, pids, camids = data
model.to(device)
images = images.to(device)
feats = model(images)
del images
all_feats.append(feats.cpu())
all_pids.extend(np.asarray(pids))
all_camids.extend(np.asarray(camids))
cmc, mAP = evaluation(all_feats, all_pids, all_camids, num_query)
logger.info("Validation Results - Epoch: {}".format(epoch + 1))
logger.info("mAP: {:.1%}".format(mAP))
for r in [1, 5, 10, 20]:
logger.info("CMC curve, Rank-{:<3}:{:.1%}".format(
r, cmc[r - 1]))
time_elapsed = time.time() - since
logger.info('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
logger.info('-' * 10)
def main(self):
parser = create_parser()
args = parser.parse_args()
verify_arguments(args)
# Load model
models = [np.load(model)[()] for model in args.model]
# Load input image
input_images = []
for i in args.input:
input_images.append(itk_image.read(i))
# Load camera parameters
geometries = []
for c, image in zip(args.camera, input_images):
geometry = render.GeometryContext.load_from_file(c)
if not args.ignore_image_information:
geometry.pixel_spacing = image[1]
geometry.image_size = image[0].shape[::-1]
geometries.append(geometry)
# Setup registration framework
self.renderer = render.SurfaceRenderer()
self.deformator = transform.SsmDeformator()
self.calculators = [metric.make_metric(m) for m in args.metric]
self.optimizer = optimizer.make_optimizer(args.optimizer)
self.optimizer.set_hyper_parameters(
**parse_parameter(args.optimizer_params))
# Set initial guess
total_dimension = sum(
[6 + deformator.get_using_dimension(model) for in self.models]
)
initial_guess = [0.0 for _ in range(total_dimension)]
self.optimizer.set_initial_guess(initial_guess)
# Setup extensional framework
for f, f_conf in zip_longest(args.framework, args.framework_config):
assert f is None, \
'A number of frameworks and configuration file is a mismatch.'
if f_conf is None or f_conf.lower() == 'none':
continue
with open(f_conf) as fp:
conf = yaml.load(fp)
self.optimizer.add_framework(f, **conf)
self.optimizer.add_framework(framework.TqdmReport())
self.optimizer.add_framework(framework.MatplotReport(self.draw_status))
self.optimizer.add_framework(HistoryCapture(self.history))
proj_images = None
faces = np.array([model['faces'] for model in models])
with self.optimizer.setup():
for population in self.optimizer.generate():
if proj_images is None:
proj_images = np.tile(np.expand_dims(
proj_image, axis=2), (1, 1, len(population))
)
images, transformed_polys = render_population(
population, models, props
)
metrics = sum([c.calculate(proj_images, images)
for c in self.calculators])
self.optimizer.update(metrics.tolist())
args=args)
# ensure that the model lives on the cpu (for multi-gpu training)
if args.multi_gpu:
with tf.device('/cpu:0'):
model = make_model_impl()
else:
model = make_model_impl()
print(model.summary())
if args.lsuv_init:
model = LSUVinit(model, x_train[:args.batch_size, :, :, :])
optimizer = make_optimizer(args.optimizer,
lr=args.lr_values[0],
momentum=args.momentum)
orig_model = model
if args.multi_gpu:
model = multi_gpu_model(model)
model.compile(loss=args.loss, optimizer=optimizer, metrics=['accuracy'])
def check_params():
for layer in model.layers:
if layer.__class__.__name__ in [
'BLU', 'PReLU', 'SoftExp', 'ScaledReLU'
]:
for weight in layer.weights:
def train(config_file, resume=False, **kwargs):
cfg.merge_from_file(config_file)
if kwargs:
opts = []
for k, v in kwargs.items():
opts.append(k)
opts.append(v)
cfg.merge_from_list(opts)
cfg.freeze()
# [PersonReID_Dataset_Downloader(cfg.DATASETS.STORE_DIR,dataset) for dataset in cfg.DATASETS.SOURCE]
# [PersonReID_Dataset_Downloader(cfg.DATASETS.STORE_DIR,dataset) for dataset in cfg.DATASETS.TARGET]
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = make_logger("Reid_Baseline", output_dir, 'log', resume)
if not resume:
logger.info("Using {} GPUS".format(1))
logger.info("Loaded configuration file {}".format(config_file))
logger.info("Running with config:\n{}".format(cfg))
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
eval_period = cfg.SOLVER.EVAL_PERIOD
output_dir = cfg.OUTPUT_DIR
device = torch.device(cfg.DEVICE)
epochs = cfg.SOLVER.MAX_EPOCHS
train_loader, _, _, num_classes = data_loader(cfg,
cfg.DATASETS.SOURCE,
merge=cfg.DATASETS.MERGE)
model = getattr(models, cfg.MODEL.NAME)(num_classes, cfg.MODEL.LAST_STRIDE,
cfg.MODEL.POOL)
if resume:
checkpoints = get_last_stats(output_dir)
try:
model_dict = torch.load(checkpoints[cfg.MODEL.NAME])
except KeyError:
model_dict = torch.load(checkpoints[str(type(model))])
model.load_state_dict(model_dict)
if device:
model.to(device) # must be done before the optimizer generation
optimizer = make_optimizer(cfg, model)
scheduler = make_scheduler(cfg, optimizer)
base_epo = 0
if resume:
optimizer.load_state_dict(torch.load(checkpoints['opt']))
sch_dict = torch.load(checkpoints['sch'])
scheduler.load_state_dict(sch_dict)
base_epo = checkpoints['epo']
loss_fn = make_loss(cfg)
if not resume:
logger.info("Start training")
since = time.time()
for epoch in range(epochs):
count = 0
running_loss = 0.0
running_acc = 0
for data in tqdm(train_loader, desc='Iteration', leave=False):
model.train()
images, labels, domains = data
if device:
model.to(device)
images, labels, domains = images.to(device), labels.to(
device), domains.to(device)
optimizer.zero_grad()
scores, feats = model(images)
loss = loss_fn(scores, feats, labels)
loss.backward()
optimizer.step()
count = count + 1
running_loss += loss.item()
running_acc += (
scores[0].max(1)[1] == labels).float().mean().item()
logger.info(
"Epoch[{}] Iteration[{}/{}] Loss: {:.3f}, Acc: {:.3f}, Base Lr: {:.2e}"
.format(epoch + 1 + base_epo, count, len(train_loader),
running_loss / count, running_acc / count,
scheduler.get_lr()[0]))
scheduler.step()
if (epoch + 1 + base_epo) % checkpoint_period == 0:
model.cpu()
model.save(output_dir, epoch + 1 + base_epo)
torch.save(
optimizer.state_dict(),
os.path.join(output_dir,
'opt_epo' + str(epoch + 1 + base_epo) + '.pth'))
torch.save(
scheduler.state_dict(),
os.path.join(output_dir,
'sch_epo' + str(epoch + 1 + base_epo) + '.pth'))
# Validation
if (epoch + base_epo + 1) % eval_period == 0:
# Validation on Target Dataset
for target in cfg.DATASETS.TARGET:
mAPs = []
cmcs = []
for i in range(iteration):
set_seeds(i)
_, val_loader, num_query, _ = data_loader(cfg, (target, ),
merge=False)
all_feats = []
all_pids = []
all_camids = []
since = time.time()
for data in tqdm(val_loader,
desc='Feature Extraction',
leave=False):
model.eval()
with torch.no_grad():
images, pids, camids = data
if device:
model.to(device)
images = images.to(device)
feats = model(images)
feats /= feats.norm(dim=-1, keepdim=True)
all_feats.append(feats)
all_pids.extend(np.asarray(pids))
all_camids.extend(np.asarray(camids))
cmc, mAP = evaluation(all_feats, all_pids, all_camids,
num_query)
mAPs.append(mAP)
cmcs.append(cmc)
mAP = np.mean(np.array(mAPs))
cmc = np.mean(np.array(cmcs), axis=0)
mAP_std = np.std(np.array(mAPs))
cmc_std = np.std(np.array(cmcs), axis=0)
logger.info("Validation Results: {} - Epoch: {}".format(
target, epoch + 1 + base_epo))
logger.info("mAP: {:.1%} (std: {:.3%})".format(mAP, mAP_std))
for r in [1, 5, 10]:
logger.info(
"CMC curve, Rank-{:<3}:{:.1%} (std: {:.3%})".format(
r, cmc[r - 1], cmc_std[r - 1]))
reset()
time_elapsed = time.time() - since
logger.info('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
logger.info('-' * 10)
def train(config_file1, config_file2, **kwargs):
# 1. config
cfg.merge_from_file(config_file1)
if kwargs:
opts = []
for k, v in kwargs.items():
opts.append(k)
opts.append(v)
cfg.merge_from_list(opts)
#cfg.freeze()
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = make_logger("Reid_Baseline", output_dir, 'log')
#logger.info("Using {} GPUS".format(1))
logger.info("Loaded configuration file {}".format(config_file1))
logger.info("Running with config:\n{}".format(cfg))
checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
eval_period = cfg.SOLVER.EVAL_PERIOD
#device = torch.device(cfg.DEVICE)
epochs = cfg.SOLVER.MAX_EPOCHS
# 2. datasets
# Load the original dataset
#dataset_reference = init_dataset(cfg, cfg.DATASETS.NAMES )
dataset_reference = init_dataset(cfg, cfg.DATASETS.NAMES +
'_origin') #'Market1501_origin'
train_set_reference = ImageDataset(dataset_reference.train,
train_transforms)
train_loader_reference = DataLoader(train_set_reference,
batch_size=128,
shuffle=False,
num_workers=cfg.DATALOADER.NUM_WORKERS,
collate_fn=train_collate_fn)
#不用放到网络里，所以不用transform
# Load the one-shot dataset
train_loader, val_loader, num_query, num_classes = data_loader(
cfg, cfg.DATASETS.NAMES)
# 3. load the model and optimizer
model = getattr(models, cfg.MODEL.NAME)(num_classes)
optimizer = make_optimizer(cfg, model)
scheduler = make_scheduler(cfg, optimizer)
loss_fn = make_loss(cfg)
logger.info("Start training")
since = time.time()
if torch.cuda.device_count() > 1:
print("Use", torch.cuda.device_count(), 'gpus')
elif torch.cuda.device_count() == 1:
print("Use", torch.cuda.device_count(), 'gpu')
model = nn.DataParallel(model)
top = 0 # the choose of the nearest sample
top_update = 0 # the first iteration train 80 steps and the following train 40
train_time = 0 #1表示训练几次gan
bound = 1 #究竟训练几次，改成多次以后再说
lock = False
train_compen = 0
# 4. Train and test
for epoch in range(epochs):
running_loss = 0.0
running_acc = 0
count = 1
# get nearest samples and reset the model
if top_update < 80:
train_step = 80
#重新gan生成的图像第一次是否需要训练80次，看看是否下一次输入的图片变少了吧
else:
train_step = 40
#if top_update % train_step == 0:
if top_update % train_step == 0 and train_compen == 0:
print("top: ", top)
#作者原来的实验top取到41，这里折中(是否要折中也是个实验测试的点)
#if 1==1:
if top >= 8 and train_time < bound:
train_compen = (top - 1) * 40 + 80
#build_image(A,train_loader_reference,train_loader)
train_time += 1
#gan的训练模式
mode = 'train'
retrain(mode)
#gan生成图像到原来数据集
produce()
cfg.merge_from_file(config_file2)
output_dir = cfg.OUTPUT_DIR
if output_dir and not os.path.exists(output_dir):
os.makedirs(output_dir)
logger = make_logger("Reid_Baseline", output_dir, 'log')
logger.info(
"Loaded configuration file {}".format(config_file2))
logger.info("Running with config:\n{}".format(cfg))
dataset_reference = init_dataset(
cfg, cfg.DATASETS.NAMES + '_origin') #'Market1501_origin'
train_set_reference = ImageDataset(dataset_reference.train,
train_transforms)
train_loader_reference = DataLoader(
train_set_reference,
batch_size=128,
shuffle=False,
num_workers=cfg.DATALOADER.NUM_WORKERS,
collate_fn=train_collate_fn)
dataset_ref = init_dataset(cfg, cfg.DATASETS.NAMES +
'_ref') #'Market1501_origin'
train_set_ref = ImageDataset(dataset_ref.train,
train_transforms)
train_loader_ref = DataLoader(
train_set_ref,
batch_size=128,
shuffle=False,
num_workers=cfg.DATALOADER.NUM_WORKERS,
collate_fn=train_collate_fn)
lock = True
if lock == True:
A, path_labeled = PSP2(model, train_loader_reference,
train_loader, train_loader_ref, top,
logger, cfg)
lock = False
else:
A, path_labeled = PSP(model, train_loader_reference,
train_loader, top, logger, cfg)
#vis = len(train_loader_reference.dataset)
#A= torch.ones(vis, len(train_loader_reference.dataset))
#build_image(A,train_loader_reference,train_loader)
top += cfg.DATALOADER.NUM_JUMP
model = getattr(models, cfg.MODEL.NAME)(num_classes)
model = nn.DataParallel(model)
optimizer = make_optimizer(cfg, model)
scheduler = make_scheduler(cfg, optimizer)
A_store = A.clone()
top_update += 1
for data in tqdm(train_loader, desc='Iteration', leave=False):
model.train()
images, labels_batch, img_path = data
index, index_labeled = find_index_by_path(img_path,
dataset_reference.train,
path_labeled)
images_relevant, GCN_index, choose_from_nodes, labels = load_relevant(
cfg, dataset_reference.train, index, A_store, labels_batch,
index_labeled)
# if device:
model.to(device)
images = images_relevant.to(device)
scores, feat = model(images)
del images
loss = loss_fn(scores, feat, labels.to(device), choose_from_nodes)
optimizer.zero_grad()
loss.backward()
optimizer.step()
count = count + 1
running_loss += loss.item()
running_acc += (scores[choose_from_nodes].max(1)[1].cpu() ==
labels_batch).float().mean().item()
scheduler.step()
# for model save if you need
# if (epoch+1) % checkpoint_period == 0:
# model.cpu()
# model.save(output_dir,epoch+1)
# Validation
if (epoch + 1) % eval_period == 0:
all_feats = []
all_pids = []
all_camids = []
for data in tqdm(val_loader,
desc='Feature Extraction',
leave=False):
model.eval()
with torch.no_grad():
images, pids, camids = data
model.to(device)
images = images.to(device)
feats = model(images)
del images
all_feats.append(feats.cpu())
all_pids.extend(np.asarray(pids))
all_camids.extend(np.asarray(camids))
cmc, mAP = evaluation(all_feats, all_pids, all_camids, num_query)
logger.info("Validation Results - Epoch: {}".format(epoch + 1))
logger.info("mAP: {:.1%}".format(mAP))
for r in [1, 5, 10, 20]:
logger.info("CMC curve, Rank-{:<3}:{:.1%}".format(
r, cmc[r - 1]))
if train_compen > 0:
train_compen -= 1
time_elapsed = time.time() - since
logger.info('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
logger.info('-' * 10)
