def __init__(
self,
datamanager,
model,
optimizer,
margin=0.3,
weight_t=1,
weight_x=1,
weight_r = 0.0001,
scheduler=None,
use_gpu=True,
label_smooth=True
):
super(ImageTripletAEEngine, self
).__init__(datamanager, model, optimizer, scheduler, use_gpu)
self.weight_t = weight_t
self.weight_x = weight_x
self.weight_r = weight_r
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
self.criterion_mse = torch.nn.MSELoss()
self.random = RandomErasing(probability=0.5)
self.mgn_loss = Loss(num_classes=self.datamanager.num_train_pids,use_gpu=self.use_gpu,label_smooth=label_smooth)
self.BCE_criterion = torch.nn.BCEWithLogitsLoss()
def __init__(
self,
datamanager,
model,
optimizer,
scheduler=None,
use_gpu=False,
label_smooth=True,
mc_iter=1,
init_lmda=1.,
min_lmda=1.,
lmda_decay_step=20,
lmda_decay_rate=0.5,
fixed_lmda=False
):
super(ImageSoftmaxNASEngine, self).__init__(datamanager, use_gpu)
self.mc_iter = mc_iter
self.init_lmda = init_lmda
self.min_lmda = min_lmda
self.lmda_decay_step = lmda_decay_step
self.lmda_decay_rate = lmda_decay_rate
self.fixed_lmda = fixed_lmda
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.register_model('model', model, optimizer, scheduler)
self.criterion = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
def __init__(self,
datamanager,
model,
optimizer,
margin=0.3,
weight_o=1,
weight_x=1,
scheduler=None,
use_gpu=True,
label_smooth=True,
pooling_method='avg'):
super(VideoOIMEngine, self).__init__(datamanager, use_gpu)
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.register_model('model', model, optimizer, scheduler)
self.pooling_method = pooling_method
assert weight_o >= 0 and weight_x >= 0
assert weight_o + weight_x > 0
self.weight_o = weight_o
self.weight_x = weight_x
self.criterion_o = OIMLoss(2048, 625, scalar=30, momentum=0.5).cuda()
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
def __init__(self,
datamanager,
model,
optimizer,
margin=0.3,
weight_x=1,
weight_t=1,
weight_r=1,
weight_a=1,
scheduler=None,
use_gpu=True,
label_smooth=True,
swap_size=(8, 4)):
super(ImageSoftmaxDCLTripletEngine,
self).__init__(datamanager, use_gpu)
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.register_model('model', model, optimizer, scheduler)
self.weight_t = weight_t
self.weight_x = weight_x
self.weight_r = weight_r
self.weight_a = weight_a
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
self.criterion_trip = TripletLoss(margin=0.7)
self.criterion_rec = ReconstructionLoss()
self.criterion_adver = AdversarialLoss()
self.swap_size = swap_size
self.swap = transforms.Randomswap((2, 2))
def __init__(self,
datamanager,
model,
optimizer,
margin=0.3,
weight_t=1,
weight_x=1,
scheduler=None,
use_gpu=True,
label_smooth=True):
super(ImageTripletEngine, self).__init__(datamanager, use_gpu)
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.register_model('model', model, optimizer, scheduler)
self.weight_t = weight_t
self.weight_x = weight_x
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
def __init__(
self,
datamanager,
model,
optimizer,
margin=0.3,
weight_t=1,
weight_x=1,
weight_r=0.0001,
scheduler=None,
use_gpu=True,
label_smooth=True
):
super(ImageJointReconsVarEngine, self
).__init__(datamanager, model, optimizer, scheduler, use_gpu)
self.weight_t = weight_t
self.weight_x = weight_x
self.weight_r = weight_r
self.criterion_t = TripletLoss(margin=margin)
self.local_triplet = TripletLoss_Local(margin=margin)
self.done_once = True
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
self.criterion_mse = torch.nn.MSELoss()
self.random = RandomErasing(probability=0.5,sl=0.02)
self.criterion = torch.nn.CrossEntropyLoss()
def __init__(
self,
datamanager,
model,
optimizer,
margin=0.3,
weight_t=1,
weight_x=1,
scheduler=None,
use_gpu=True,
label_smooth=True,
mmd_only=True,
):
super(ImageMmdEngine, self).__init__(datamanager, model, optimizer, scheduler, use_gpu, mmd_only)
self.optimizer.zero_grad()
self.mmd_only = mmd_only ###
self.weight_t = weight_t
self.weight_x = weight_x
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
self.criterion_mmd = MaximumMeanDiscrepancy(
instances=self.datamanager.train_loader.sampler.num_instances,
batch_size=self.datamanager.train_loader.batch_size,
global_only=False,
distance_only=True,
all=False
)
def __init__(self,
datamanager,
model,
optimizer,
margin=0.3,
weight_t=1,
weight_x=1,
scheduler=None,
use_gpu=True,
label_smooth=True,
conf_penalty=0.0):
super(ImageTripletEngine, self).__init__(datamanager, model, optimizer,
scheduler, use_gpu)
self.weight_t = weight_t
self.weight_x = weight_x
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(use_gpu=self.use_gpu,
label_smooth=label_smooth,
conf_penalty=conf_penalty)
assert len(self.models) == 1
self.model = self.models['model']
self.optimizer = self.optims['model']
def main():
global args
set_random_seed(1)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
log_name = 'test.log' if args.evaluate else 'train.log'
log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print('** Arguments **')
arg_keys = list(args.__dict__.keys())
arg_keys.sort()
for key in arg_keys:
print('{}: {}'.format(key, args.__dict__[key]))
torch.backends.cudnn.benchmark = True
datamanager = ImageDataManager(batch_size=args.batch_size)
trainloader, queryloader, galleryloader = datamanager.return_dataloaders()
print('Building model: {}'.format(args.arch))
model = build_model(args.arch,
4000,
args.bias,
args.bnneck,
pretrained=(not args.no_pretrained))
if args.load_weights and check_isfile(args.load_weights):
load_pretrained_weights(model, args.load_weights)
model.cuda()
if args.evaluate:
evaluate(model, queryloader, galleryloader, args.dist_metric,
args.normalize_feature)
return
criterion = CrossEntropyLoss(4000)
optimizer = torch.optim.Adam(model.parameters(),
lr=0.0003,
weight_decay=5e-04,
betas=(0.9, 0.999))
scheduler = build_lr_scheduler(optimizer, args.lr_scheduler, args.stepsize)
time_start = time.time()
print('=> Start training')
for epoch in range(args.start_epoch, args.max_epoch):
train(epoch, model, criterion, optimizer, trainloader)
scheduler.step()
if (epoch + 1) % 20 == 0:
save_checkpoint(
{
'state_dict': model.state_dict(),
'epoch': epoch + 1,
'optimizer': optimizer.state_dict(),
}, args.save_dir)
evaluate(model, queryloader, galleryloader, args.dist_metric,
args.normalize_feature)
elapsed = round(time.time() - time_start)
elapsed = str(datetime.timedelta(seconds=elapsed))
print('Elapsed {}'.format(elapsed))
def __init__(self, datamanager, model, optimizer, scheduler=None, use_gpu=True,
label_smooth=True):
super(ImageSoftmaxEngine, self).__init__(datamanager, model, optimizer, scheduler, use_gpu)
self.criterion = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
def __init__(self,
datamanager,
model,
optimizer,
margin=0.3,
weight_t=1,
weight_x=1,
weight_db_t=1,
weight_db_x=1,
weight_b_db_t=1,
weight_b_db_x=1,
scheduler=None,
use_gpu=True,
label_smooth=True,
top_drop_epoch=-1):
super(ImageTripletDropBatchDropBotFeaturesEngine,
self).__init__(datamanager, model, optimizer, scheduler, use_gpu)
self.weight_t = weight_t
self.weight_x = weight_x
self.weight_db_t = weight_db_t
self.weight_db_x = weight_db_x
self.weight_b_db_t = weight_b_db_t
self.weight_b_db_x = weight_b_db_x
self.top_drop_epoch = top_drop_epoch
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
self.criterion_db_t = TripletLoss(margin=margin)
self.criterion_db_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
self.criterion_b_db_t = TripletLoss(margin=margin)
self.criterion_b_db_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
def __init__(self,
datamanager,
model,
optimizer,
reg_cfg,
metric_cfg,
scheduler=None,
use_gpu=False,
softmax_type='stock',
label_smooth=True,
conf_penalty=False,
m=0.35,
s=10,
feature_dim=256):
super(ImageSoftmaxEngine, self).__init__(datamanager, model, reg_cfg,
optimizer, scheduler, use_gpu)
if softmax_type == 'stock':
self.criterion = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth,
conf_penalty=conf_penalty)
elif softmax_type == 'am':
self.criterion = AMSoftmaxLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
conf_penalty=conf_penalty,
m=m,
s=s)
elif softmax_type == 'ada':
self.criterion = AdaCosLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
conf_penalty=conf_penalty)
elif softmax_type == 'd_sm':
self.criterion = DSoftmaxLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
conf_penalty=conf_penalty)
if metric_cfg.enabled:
self.metric_losses = MetricLosses(self.datamanager.num_train_pids,
feature_dim, self.writer,
metric_cfg.soft_margin,
metric_cfg.balance_losses)
self.metric_losses.center_coeff = metric_cfg.center_coeff
self.metric_losses.glob_push_plus_loss_coeff = metric_cfg.glob_push_plus_loss_coeff
self.metric_losses.push_loss_coeff = metric_cfg.push_loss_coeff
self.metric_losses.push_plus_loss_coeff = metric_cfg.push_plus_loss_coeff
self.metric_losses.min_margin_loss_coeff = metric_cfg.min_margin_loss_coeff
else:
self.metric_losses = None
def __init__(self, datamanager, model, optimizer, margin=0.3,
weight_t=1, weight_x=1, scheduler=None, use_cpu=False,
label_smooth=True, experiment=None, combine_method="mean", save_embed=None):
super(ImageTripletEngine, self).__init__(datamanager, model, optimizer, scheduler, use_cpu, experiment, combine_method, save_embed)
self.weight_t = weight_t
self.weight_x = weight_x
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
def __init__(self, datamanager, model, optimizer, margin=0.3,
weight_t=1, weight_x=1, scheduler=None, use_gpu=True,
label_smooth=True):
super(ImageTripletEngine, self).__init__(datamanager, model, optimizer, scheduler, use_gpu)
self.weight_t = weight_t
self.weight_x = weight_x
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
self.criterion_c = CenterLoss(num_classes=751, feat_dim=2048)
def __init__(self,
datamanager,
model,
optimizer,
scheduler=None,
use_cpu=False,
label_smooth=True):
super(PoseSoftmaxEngine, self).__init__(datamanager, model, optimizer,
scheduler, use_cpu)
# TODO modify the criterion for pairwise comparison
self.criterion = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
self.att_criterion = Isolate_loss()
def __init__(self,
datamanager,
model,
optimizer,
scheduler=None,
use_cpu=False,
label_smooth=True,
experiment=None,
combine_method="mean",
save_embed=None):
super(ImageSoftmaxEngine,
self).__init__(datamanager, model, optimizer, scheduler, use_cpu,
experiment, combine_method, save_embed)
self.criterion = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
def __init__(self,
datamanager,
model,
optimizer,
scheduler=None,
use_gpu=True,
label_smooth=True,
conf_penalty=0.0):
super(ImageSoftmaxEngine, self).__init__(datamanager, use_gpu)
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.register_model('model', model, optimizer, scheduler)
self.main_real_loss = CrossEntropyLoss(use_gpu=self.use_gpu,
label_smooth=label_smooth,
conf_penalty=conf_penalty)
def __init__(
self,
datamanager,
model,
optimizer,
margin=0.27,
weight_t=1,
weight_x=1,
weight_r = 0.0000000001, #lambda
scheduler=None,
use_gpu=True,
label_smooth=True,
mmd_only=True,
datamanager2=None,
):
super(ImageMmdAEEngine, self).__init__(datamanager, model, optimizer, scheduler, use_gpu, mmd_only,datamanager2)
self.optimizer.zero_grad()
self.mmd_only = mmd_only ###
self.weight_t = weight_t
self.weight_x = weight_x
self.weight_r = weight_r
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
self.criterion_mmd = MaximumMeanDiscrepancy(
instances=self.datamanager.train_loader.sampler.num_instances,
batch_size=self.datamanager.train_loader.batch_size,
global_only=False,
distance_only=True,
all=False,
)
self.criterion_mse = torch.nn.MSELoss()
self.random = RandomErasing(probability=0.5,sl=0.07)
self.randomt = RandomErasing(probability=0.5,sl=0.01)
self.mgn_loss = Loss(num_classes=self.datamanager.num_train_pids,use_gpu=self.use_gpu,label_smooth=label_smooth)
self.mgn_targetPredict =FC_Model().cuda()
self.BCE_criterion = torch.nn.BCEWithLogitsLoss()
def __init__(self, datamanager, model, optimizer, weight_t=1, weight_x=1, scheduler=None, use_cpu=False, label_smooth=True): super(ImageCenterEngine, self).__init__(datamanager, model, optimizer, scheduler, use_cpu) self.weight_t = weight_t self.weight_x = weight_x # self.optimizer_cri = optimizer self.criterion_t = CenterLoss( num_classes=self.datamanager.num_train_pids, feat_dim=2048, use_gpu= self.use_gpu ) self.criterion_x = CrossEntropyLoss( num_classes=self.datamanager.num_train_pids, use_gpu=self.use_gpu, label_smooth=label_smooth )
def __init__(self,
datamanager,
model,
optimizer,
scheduler=None,
use_gpu=True,
label_smooth=True):
super(ImageSoftmaxEngine, self).__init__(datamanager, use_gpu)
self.model = model
self.optimizer = optimizer
self.scheduler = scheduler
self.register_model('model', model, optimizer, scheduler)
self.scaler = torch.cuda.amp.GradScaler()
self.criterion = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
def __init__(
self,
datamanager,
model1,
optimizer1,
scheduler1,
model2,
optimizer2,
scheduler2,
margin=0.3,
weight_t=0.5,
weight_x=1.,
weight_ml=1.,
use_gpu=True,
label_smooth=True,
deploy='model1'
):
super(ImageDMLEngine, self).__init__(datamanager, use_gpu)
self.model1 = model1
self.optimizer1 = optimizer1
self.scheduler1 = scheduler1
self.register_model('model1', model1, optimizer1, scheduler1)
self.model2 = model2
self.optimizer2 = optimizer2
self.scheduler2 = scheduler2
self.register_model('model2', model2, optimizer2, scheduler2)
self.weight_t = weight_t
self.weight_x = weight_x
self.weight_ml = weight_ml
assert deploy in ['model1', 'model2', 'both']
self.deploy = deploy
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
def __init__(
self,
datamanager,
model,
optimizer,
scheduler=None,
use_gpu=True,
label_smooth=True,
visdom=False
):
super(ImageSoftmaxEngine, self).__init__(datamanager, model, optimizer, scheduler, use_gpu)
#if self.visdom:
self.vis = Visualizations(env_name="turing.livia.etsmtl.ca", port=4242)
self.criterion = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth
)
def __init__(self,
datamanager,
model,
optimizer,
margin=0.3,
weight_t=1,
weight_x=1,
scheduler=None,
use_cpu=False,
label_smooth=True):
super(PoseTripleEngine, self).__init__(datamanager, model, optimizer,
scheduler, use_cpu)
self.weight_t = weight_t
self.weight_x = weight_x
self.criterion_t = TripletLoss(margin=margin)
# TODO modify the criterion for pairwise comparison
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
self.att_criterion = Isolate_loss()
def __init__(
self,
datamanager,
model,
optimizer,
margin=0.3,
weight_t=1,
weight_x=1,
weight_r=0.0001,
scheduler=None,
use_gpu=True,
label_smooth=True,
only_recons=True,
):
super(ImageReconsVarEngine,
self).__init__(datamanager, model, optimizer, scheduler, use_gpu)
self.optimizer.zero_grad()
self.weight_t = weight_t
self.weight_x = weight_x
self.weight_r = weight_r
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
self.criterion_mse = torch.nn.MSELoss()
self.criterion_mmd = MaximumMeanDiscrepancy(
instances=self.datamanager.train_loader.sampler.num_instances,
batch_size=self.datamanager.train_loader.batch_size,
global_only=False,
distance_only=True,
all=False)
self.only_recons = only_recons
self.random = RandomErasing(probability=0.5)
self.random2 = RandomErasing(probability=0.65, sl=0.15)
def __init__(self,
datamanager,
model,
optimizer,
scheduler=None,
use_cpu=False,
label_smooth=True,
use_att_loss=True,
reg_matching_score_epoch=0,
num_att=6):
super(PoseSoftmaxEngine_wscorereg,
self).__init__(datamanager, model, optimizer, scheduler, use_cpu)
self.criterion = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
self.part_c_criterion = Part_similarity_constrain(
part_num=num_att).cuda()
self.use_att_loss = use_att_loss
if self.use_att_loss:
self.att_criterion = Isolate_loss()
self.reg_matching_score_epoch = reg_matching_score_epoch
def __init__(self,
datamanager,
model,
optimizer,
margin=0.3,
weight_t=1,
weight_x=1,
weight_r=1,
scheduler=None,
use_gpu=True,
label_smooth=True):
super(ImageJointReconsEngine,
self).__init__(datamanager, model, optimizer, scheduler, use_gpu)
self.weight_t = weight_t
self.weight_x = weight_x
self.weight_r = weight_r
self.criterion_t = TripletLoss(margin=margin)
self.criterion_x = CrossEntropyLoss(
num_classes=self.datamanager.num_train_pids,
use_gpu=self.use_gpu,
label_smooth=label_smooth)
self.criterion_mse = torch.nn.MSELoss()
def main():
global args
set_random_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
if args.use_cpu: use_gpu = False
log_name = 'log_test.txt' if args.evaluate else 'log_train.txt'
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print('==========\nArgs:{}\n=========='.format(args))
if use_gpu:
print('Currently using GPU {}'.format(args.gpu_devices))
cudnn.benchmark = True
else:
print('Currently using CPU, however, GPU is highly recommended')
print('Initializing video data manager')
dm = VideoDataManager(use_gpu, **video_dataset_kwargs(args))
trainloader, testloader_dict = dm.return_dataloaders()
print('Initializing model: {}'.format(args.arch))
model = models.init_model(name=args.arch,
num_classes=dm.num_train_pids,
loss={'xent', 'htri'},
pretrained=not args.no_pretrained,
use_gpu=use_gpu)
print('Model size: {:.3f} M'.format(count_num_param(model)))
if args.load_weights and check_isfile(args.load_weights):
load_pretrained_weights(model, args.load_weights)
if args.resume and check_isfile(args.resume):
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch'] + 1
best_rank1 = checkpoint['rank1']
print('Loaded checkpoint from "{}"'.format(args.resume))
print('- start_epoch: {}\n- rank1: {}'.format(args.start_epoch,
best_rank1))
model = nn.DataParallel(model).cuda() if use_gpu else model
criterion = CrossEntropyLoss(num_classes=dm.num_train_pids,
use_gpu=use_gpu,
label_smooth=args.label_smooth)
criterion_htri = TripletLoss(margin=args.margin)
optimizer = init_optimizer(model, **optimizer_kwargs(args))
scheduler = init_lr_scheduler(optimizer, **lr_scheduler_kwargs(args))
if args.evaluate:
print('Evaluate only')
for name in args.target_names:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
distmat = test(model,
queryloader,
galleryloader,
args.pool_tracklet_features,
use_gpu,
return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(
args.save_dir, 'ranked_results',
name),
topk=20)
return
start_time = time.time()
ranklogger = RankLogger(args.source_names, args.target_names)
train_time = 0
print('=> Start training')
if args.fixbase_epoch > 0:
print(
'Train {} for {} epochs while keeping other layers frozen'.format(
args.open_layers, args.fixbase_epoch))
initial_optim_state = optimizer.state_dict()
for epoch in range(args.fixbase_epoch):
start_train_time = time.time()
train(epoch,
model,
criterion_xent,
criterion_htri,
optimizer,
trainloader,
use_gpu,
fixbase=True)
train_time += round(time.time() - start_train_time)
print('Done. All layers are open to train for {} epochs'.format(
args.max_epoch))
optimizer.load_state_dict(initial_optim_state)
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
train(epoch, model, criterion_xent, criterion_htri, optimizer,
trainloader, use_gpu)
train_time += round(time.time() - start_train_time)
scheduler.step()
if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (
epoch + 1) % args.eval_freq == 0 or (epoch +
1) == args.max_epoch:
print('=> Test')
for name in args.target_names:
print('Evaluating {} ...'.format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1 = test(model, queryloader, galleryloader,
args.pool_tracklet_features, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
save_checkpoint(
{
'state_dict': model.state_dict(),
'rank1': rank1,
'epoch': epoch,
}, False,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
'Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.'.
format(elapsed, train_time))
ranklogger.show_summary()
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config-file',
type=str,
default='',
help='path to config file')
parser.add_argument(
'--gpu-devices',
type=str,
default='',
)
parser.add_argument('opts',
default=None,
nargs=argparse.REMAINDER,
help='Modify config options using the command-line')
args = parser.parse_args()
cfg = get_default_config()
if args.config_file:
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
set_random_seed(cfg.train.seed)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
log_name = 'test.log' if cfg.test.evaluate else 'train.log'
log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
print('Show configuration\n{}\n'.format(cfg))
torch.backends.cudnn.benchmark = True
datamanager = ImageDataManager(**imagedata_kwargs(cfg))
trainloader, queryloader, galleryloader = datamanager.return_dataloaders()
print('Building model: {}'.format(cfg.model.name))
model = build_model(cfg.model.name,
datamanager.num_train_pids,
'softmax',
pretrained=cfg.model.pretrained)
if cfg.model.load_weights and check_isfile(cfg.model.load_weights):
load_pretrained_weights(model, cfg.model.load_weights)
model = nn.DataParallel(model).cuda()
criterion = CrossEntropyLoss(datamanager.num_train_pids,
label_smooth=cfg.loss.softmax.label_smooth)
optimizer = build_optimizer(model, **optimizer_kwargs(cfg))
scheduler = build_lr_scheduler(optimizer, **lr_scheduler_kwargs(cfg))
if cfg.model.resume and check_isfile(cfg.model.resume):
cfg.train.start_epoch = resume_from_checkpoint(cfg.model.resume,
model,
optimizer=optimizer)
if cfg.test.evaluate:
distmat = evaluate(model,
queryloader,
galleryloader,
dist_metric=cfg.test.dist_metric,
normalize_feature=cfg.test.normalize_feature,
rerank=cfg.test.rerank,
return_distmat=True)
if cfg.test.visrank:
visualize_ranked_results(distmat,
datamanager.return_testdataset(),
'image',
width=cfg.data.width,
height=cfg.data.height,
save_dir=osp.join(cfg.data.save_dir,
'visrank'))
return
time_start = time.time()
print('=> Start training')
for epoch in range(cfg.train.start_epoch, cfg.train.max_epoch):
train(epoch,
cfg.train.max_epoch,
model,
criterion,
optimizer,
trainloader,
fixbase_epoch=cfg.train.fixbase_epoch,
open_layers=cfg.train.open_layers)
scheduler.step()
if (epoch + 1) % cfg.test.eval_freq == 0 or (epoch +
1) == cfg.train.max_epoch:
rank1 = evaluate(model,
queryloader,
galleryloader,
dist_metric=cfg.test.dist_metric,
normalize_feature=cfg.test.normalize_feature,
rerank=cfg.test.rerank)
save_checkpoint(
{
'state_dict': model.state_dict(),
'epoch': epoch + 1,
'rank1': rank1,
'optimizer': optimizer.state_dict(),
}, cfg.data.save_dir)
elapsed = round(time.time() - time_start)
elapsed = str(datetime.timedelta(seconds=elapsed))
print('Elapsed {}'.format(elapsed))
def main():
global args
torch.manual_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
if args.use_cpu: use_gpu = False
log_name = 'log_test.txt' if args.evaluate else 'log_train.txt'
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU, however, GPU is highly recommended")
print("Initializing image data manager")
dm = ImageDataManager(use_gpu, **image_dataset_kwargs(args))
trainloader, testloader_dict = dm.return_dataloaders()
# ReID-Stream:
print("Initializing ReID-Stream: {}".format(args.arch))
model = models.init_model(name=args.arch,
num_classes=dm.num_train_pids,
reid_dim=args.reid_dim,
loss={'xent', 'htri'})
print("ReID Model size: {:.3f} M".format(count_num_param(model)))
criterion_xent = CrossEntropyLoss(num_classes=dm.num_train_pids,
use_gpu=use_gpu,
label_smooth=args.label_smooth)
criterion_htri = TripletLoss(margin=args.margin)
# 2. Optimizer
# Main ReID-Stream:
optimizer = init_optimizer(model.parameters(), **optimizer_kwargs(args))
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
for name in args.target_names:
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
distmat = test(model,
queryloader,
galleryloader,
use_gpu,
return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(distmat,
dm.return_testdataset_by_name(name),
save_dir=osp.join(
args.save_dir, 'ranked_results',
name),
topk=20)
return
start_time = time.time()
ranklogger = RankLogger(args.source_names, args.target_names)
train_time = 0
print("==> Start training")
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
train(epoch, model, criterion_xent, criterion_htri, \
optimizer, trainloader, use_gpu)
train_time += round(time.time() - start_train_time)
scheduler.step()
if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (
epoch + 1) % args.eval_freq == 0 or (epoch +
1) == args.max_epoch:
print("==> Test")
for name in args.target_names:
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1 = test(model, queryloader, galleryloader, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint(
{
'state_dict': state_dict,
'rank1': rank1,
'epoch': epoch,
}, False,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
"Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".
format(elapsed, train_time))
ranklogger.show_summary()
def main():
global args
torch.manual_seed(args.seed)
if not args.use_avai_gpus: os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
if args.use_cpu: use_gpu = False
log_name = 'log_test.txt' if args.evaluate else 'log_train.txt'
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU, however, GPU is highly recommended")
print("Initializing image data manager")
dm = ImageDataManager(use_gpu, **image_dataset_kwargs(args))
trainloader, testloader_dict = dm.return_dataloaders()
print("Initializing model: {}".format(args.arch))
model = models.init_model(name=args.arch, num_classes=dm.num_train_pids, loss={'xent', 'htri'})
print("Model size: {:.3f} M".format(count_num_param(model)))
criterion_xent = CrossEntropyLoss(num_classes=dm.num_train_pids, use_gpu=use_gpu, label_smooth=args.label_smooth)
criterion_htri = TripletLoss(margin=args.margin)
optimizer = init_optimizer(model.parameters(), **optimizer_kwargs(args))
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.stepsize, gamma=args.gamma)
if args.load_weights and check_isfile(args.load_weights):
# load pretrained weights but ignore layers that don't match in size
checkpoint = torch.load(args.load_weights)
pretrain_dict = checkpoint['state_dict']
model_dict = model.state_dict()
pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
model_dict.update(pretrain_dict)
model.load_state_dict(model_dict)
print("Loaded pretrained weights from '{}'".format(args.load_weights))
if args.resume and check_isfile(args.resume):
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch'] + 1
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(args.start_epoch, checkpoint['rank1']))
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
for name in args.target_names:
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
distmat = test(model, queryloader, galleryloader, use_gpu, return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(
distmat, dm.return_testdataset_by_name(name),
save_dir=osp.join(args.save_dir, 'ranked_results', name),
topk=20
)
return
start_time = time.time()
ranklogger = RankLogger(args.source_names, args.target_names)
train_time = 0
print("=> Start training")
if args.fixbase_epoch > 0:
print("Train {} for {} epochs while keeping other layers frozen".format(args.open_layers, args.fixbase_epoch))
initial_optim_state = optimizer.state_dict()
for epoch in range(args.fixbase_epoch):
start_train_time = time.time()
train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu, fixbase=True)
train_time += round(time.time() - start_train_time)
print("Done. All layers are open to train for {} epochs".format(args.max_epoch))
optimizer.load_state_dict(initial_optim_state)
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu)
train_time += round(time.time() - start_train_time)
scheduler.step()
if (epoch + 1) > args.start_eval and args.eval_freq > 0 and (epoch + 1) % args.eval_freq == 0 or (epoch + 1) == args.max_epoch:
print("=> Test")
for name in args.target_names:
print("Evaluating {} ...".format(name))
queryloader = testloader_dict[name]['query']
galleryloader = testloader_dict[name]['gallery']
rank1 = test(model, queryloader, galleryloader, use_gpu)
ranklogger.write(name, epoch + 1, rank1)
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint({
'state_dict': state_dict,
'rank1': rank1,
'epoch': epoch,
}, False, osp.join(args.save_dir, 'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print("Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.".format(elapsed, train_time))
ranklogger.show_summary()
