def main():
global best_rank1, best_mAP
random.seed(args.seed)
np.random.seed(args.seed)
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
if not args.evaluate:
sys.stdout = Logger(
osp.join(
args.save_dir,
'log_train{}.txt'.format(time.strftime('-%Y-%m-%d-%H-%M-%S'))))
else:
sys.stdout = Logger(
osp.join(
args.save_dir,
'log_test{}.txt'.format(time.strftime('-%Y-%m-%d-%H-%M-%S'))))
writer = SummaryWriter(log_dir=args.save_dir, comment=args.arch)
print("==========\nArgs:{}\n==========".format(args))
if use_gpu:
print("Currently using GPU {}".format(args.gpu_devices))
cudnn.benchmark = False if 'resnet3dt' in args.arch else True
torch.cuda.manual_seed_all(args.seed)
else:
print("Currently using CPU (GPU is highly recommended)")
print("Initializing dataset {}".format(args.dataset))
dataset = data_manager.init_vidreid_dataset(root=args.root,
name=args.dataset,
split_id=args.split_id,
use_pose=args.use_pose)
transform_train = list()
print('Transform:')
if args.misalign_aug:
print('+ Misalign Augmentation')
transform_train.append(T.GroupMisAlignAugment())
if args.rand_crop:
print('+ Random Crop')
transform_train.append(T.GroupRandomCrop(size=(240, 120)))
print('+ Resize to ({} x {})'.format(args.height, args.width))
transform_train.append(T.GroupResize((args.height, args.width)))
if args.flip_aug:
print('+ Random HorizontalFlip')
transform_train.append(T.GroupRandomHorizontalFlip())
print('+ ToTensor')
transform_train.append(T.GroupToTensor())
print(
'+ Normalize with mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]'
)
transform_train.append(
T.GroupNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]))
if args.rand_erase:
print('+ Random Erasing')
transform_train.append(T.GroupRandomErasing())
transform_train = T.Compose(transform_train)
transform_test = T.Compose([
T.GroupResize((args.height, args.width)),
T.GroupToTensor(),
T.GroupNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]),
])
pin_memory = True if use_gpu else False
trainloader = DataLoader(
VideoDataset(dataset.train,
seq_len=args.seq_len,
sample=args.train_sample,
transform=transform_train,
training=True,
pose_info=dataset.process_poses,
num_split=args.num_split,
num_parts=args.num_parts,
num_scale=args.num_scale,
pyramid_part=args.pyramid_part,
enable_pose=args.use_pose),
sampler=eval(args.train_sampler)(dataset.train,
batch_size=args.train_batch,
num_instances=args.num_instances),
batch_size=args.train_batch,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
queryloader = DataLoader(
VideoDataset(dataset.query,
seq_len=args.seq_len,
sample=args.test_sample,
transform=transform_test,
pose_info=dataset.process_poses,
num_split=args.num_split,
num_parts=args.num_parts,
num_scale=args.num_scale,
pyramid_part=args.pyramid_part,
enable_pose=args.use_pose),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
VideoDataset(dataset.gallery,
seq_len=args.seq_len,
sample=args.test_sample,
transform=transform_test,
pose_info=dataset.process_poses,
num_split=args.num_split,
num_parts=args.num_parts,
num_scale=args.num_scale,
pyramid_part=args.pyramid_part,
enable_pose=args.use_pose),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
print("Initializing model: {}".format(args.arch))
model = models.init_model(name=args.arch,
num_classes=dataset.num_train_pids,
loss={'xent', 'htri'},
last_stride=args.last_stride,
num_parts=args.num_parts,
num_scale=args.num_scale,
num_split=args.num_split,
pyramid_part=args.pyramid_part,
num_gb=args.num_gb,
use_pose=args.use_pose,
learn_graph=args.learn_graph,
consistent_loss=args.consistent_loss,
bnneck=args.bnneck,
save_dir=args.save_dir)
input_size = sum(calc_splits(
args.num_split)) if args.pyramid_part else args.num_split
input_size *= args.num_scale * args.seq_len
num_params, flops = compute_model_complexity(
model,
input=[
torch.randn(1, args.seq_len, 3, args.height, args.width),
torch.ones(1, input_size, input_size)
],
verbose=True,
only_conv_linear=False)
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if args.label_smooth:
criterion_xent = CrossEntropyLabelSmooth(
num_classes=dataset.num_train_pids, use_gpu=use_gpu)
else:
criterion_xent = nn.CrossEntropyLoss()
criterion_htri = TripletLoss(margin=args.margin, soft=args.soft_margin)
param_groups = model.parameters()
optimizer = init_optim(args.optim, param_groups, args.lr,
args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
if args.warmup:
scheduler = lr_scheduler.WarmupMultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma,
warmup_iters=10,
warmup_factor=0.01)
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):
print("Loaded checkpoint from '{}'".format(args.resume))
from functools import partial
import pickle
pickle.load = partial(pickle.load, encoding="latin1")
pickle.Unpickler = partial(pickle.Unpickler, encoding="latin1")
checkpoint = torch.load(args.resume,
map_location=lambda storage, loc: storage,
pickle_module=pickle)
print('Loaded model weights')
model.load_state_dict(checkpoint['state_dict'])
if optimizer is not None and 'optimizer' in checkpoint:
print('Loaded optimizer')
optimizer.load_state_dict(checkpoint['optimizer'])
if use_gpu:
for state in optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
start_epoch = checkpoint['epoch'] + 1
print('- start_epoch: {}'.format(start_epoch))
best_rank1 = checkpoint['rank1']
print("- rank1: {}".format(best_rank1))
if 'mAP' in checkpoint:
best_mAP = checkpoint['mAP']
print("- mAP: {}".format(best_mAP))
else:
start_epoch = 0
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
distmat = test(model,
queryloader,
galleryloader,
args.pool,
use_gpu,
return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(
distmat,
dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
start_time = time.time()
train_time = 0
best_epoch = start_epoch
print("==> Start training")
for epoch in range(start_epoch, args.max_epoch):
start_train_time = time.time()
train(epoch,
model,
criterion_xent,
criterion_htri,
optimizer,
trainloader,
use_gpu,
writer=writer)
train_time += round(time.time() - start_train_time)
if epoch >= args.zero_wd > 0:
set_wd(optimizer, 0)
for group in optimizer.param_groups:
assert group['weight_decay'] == 0, '{} is not zero'.format(
group['weight_decay'])
scheduler.step(epoch)
if (epoch + 1) > args.start_eval and args.eval_step > 0 and (
epoch + 1) % args.eval_step == 0 or (epoch +
1) == args.max_epoch:
print("==> Test")
rank1, mAP = test(model, queryloader, galleryloader, args.pool,
use_gpu)
is_best = rank1 > best_rank1
if is_best:
best_rank1 = rank1
best_mAP = mAP
best_epoch = epoch + 1
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint(
{
'state_dict': state_dict,
'optimizer': optimizer.state_dict(),
'rank1': rank1,
'mAP': mAP,
'epoch': epoch,
}, False,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
writer.add_scalar(tag='acc/rank1',
scalar_value=rank1,
global_step=epoch + 1)
writer.add_scalar(tag='acc/mAP',
scalar_value=mAP,
global_step=epoch + 1)
print("==> Best Rank-1 {:.2%}, mAP: {:.2%}, achieved at epoch {}".format(
best_rank1, best_mAP, best_epoch))
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))
def main():
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
if not args.evaluate:
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
else:
sys.stdout = Logger(osp.join(args.save_dir, 'log_test.txt'))
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 (GPU is highly recommended)")
print("Initializing dataset {}".format(args.dataset))
dataset = data_manager.init_vidreid_dataset(root=args.root,
name=args.dataset)
transform_train = T.Compose([
T.Random2DTranslation(args.height, args.width),
T.RandomHorizontalFlip(),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
transform_test = T.Compose([
T.Resize((args.height, args.width)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
pin_memory = True if use_gpu else False
# decompose tracklets into images
new_train = []
for img_paths, pid, camid in dataset.train:
for img_path in img_paths:
new_train.append((img_path, pid, camid))
trainloader = DataLoader(
ImageDataset(new_train, transform=transform_train),
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
queryloader = DataLoader(
VideoDataset(dataset.query,
seq_len=args.seq_len,
sample='evenly',
transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
VideoDataset(dataset.gallery,
seq_len=args.seq_len,
sample='evenly',
transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
print("Initializing model: {}".format(args.arch))
model = models.init_model(name=args.arch,
num_classes=dataset.num_train_pids,
loss={'xent'})
print("Model size: {:.3f} M".format(count_num_param(model)))
if args.label_smooth:
criterion = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids,
use_gpu=use_gpu)
else:
criterion = nn.CrossEntropyLoss()
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
if args.fixbase_epoch > 0:
if hasattr(model, 'classifier') and isinstance(model.classifier,
nn.Module):
optimizer_tmp = init_optim(args.optim,
model.classifier.parameters(),
args.fixbase_lr, args.weight_decay)
else:
print(
"Warn: model has no attribute 'classifier' and fixbase_epoch is reset to 0"
)
args.fixbase_epoch = 0
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
best_rank1 = checkpoint['rank1']
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(args.start_epoch,
best_rank1))
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
distmat = test(model,
queryloader,
galleryloader,
args.pool,
use_gpu,
return_distmat=True)
if args.visualize_ranks:
visualize_ranked_results(
distmat,
dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
start_time = time.time()
train_time = 0
best_epoch = args.start_epoch
print("==> Start training")
if args.fixbase_epoch > 0:
print(
"Train classifier for {} epochs while keeping base network frozen".
format(args.fixbase_epoch))
for epoch in range(args.fixbase_epoch):
start_train_time = time.time()
train(epoch,
model,
criterion,
optimizer_tmp,
trainloader,
use_gpu,
freeze_bn=True)
train_time += round(time.time() - start_train_time)
del optimizer_tmp
print("Now open all layers for training")
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
train(epoch, model, criterion, optimizer, trainloader, use_gpu)
train_time += round(time.time() - start_train_time)
scheduler.step()
if (epoch + 1) > args.start_eval and args.eval_step > 0 and (
epoch + 1) % args.eval_step == 0 or (epoch +
1) == args.max_epoch:
print("==> Test")
rank1 = test(model, queryloader, galleryloader, args.pool, use_gpu)
is_best = rank1 > best_rank1
if is_best:
best_rank1 = rank1
best_epoch = epoch + 1
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,
}, is_best,
osp.join(args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
print("==> Best Rank-1 {:.1%}, achieved at epoch {}".format(
best_rank1, best_epoch))
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))
def main():
torch.manual_seed(args.seed)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available()
if args.use_cpu: use_gpu = False
if not args.evaluate:
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'))
else:
sys.stdout = Logger(osp.join(args.save_dir, 'log_test.txt'))
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 (GPU is highly recommended)")
print("Initializing dataset {}".format(args.dataset))
dataset = data_manager.init_vidreid_dataset(root=args.root, name=args.dataset)
transform_train = T.Compose([
T.Random2DTranslation(args.height, args.width),
T.RandomHorizontalFlip(),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
transform_test = T.Compose([
T.Resize((args.height, args.width)),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
pin_memory = True if use_gpu else False
# decompose tracklets into images for image-based training
new_train = []
for img_paths, pid, camid in dataset.train:
for img_path in img_paths:
new_train.append((img_path, pid, camid))
trainloader = DataLoader(
ImageDataset(new_train, transform=transform_train),
sampler=RandomIdentitySampler(new_train, args.train_batch, args.num_instances),
batch_size=args.train_batch, num_workers=args.workers,
pin_memory=pin_memory, drop_last=True,
)
queryloader = DataLoader(
VideoDataset(dataset.query, seq_len=args.seq_len, sample='evenly', transform=transform_test),
batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
pin_memory=pin_memory, drop_last=False,
)
galleryloader = DataLoader(
VideoDataset(dataset.gallery, seq_len=args.seq_len, sample='evenly', transform=transform_test),
batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
pin_memory=pin_memory, drop_last=False,
)
print("Initializing model: {}".format(args.arch))
model = models.init_model(name=args.arch, num_classes=dataset.num_train_pids, loss={'xent', 'htri'})
print("Model size: {:.3f} M".format(count_num_param(model)))
criterion_xent = nn.CrossEntropyLoss()
criterion_htri = TripletLoss(margin=args.margin)
criterion_KA = KALoss(margin=args.margin, same_margin = args.same_margin, use_auto_samemargin = args.use_auto_samemargin)
cirterion_lifted = LiftedLoss(margin=args.margin)
cirterion_batri = BA_TripletLoss(margin=args.margin)
if args.use_auto_samemargin == True:
G_params = [{'params': model.parameters(), 'lr': args.lr }, {'params': criterion_KA.auto_samemargin, 'lr': args.lr}]
else :
G_params = [para for _, para in model.named_parameters()]
optimizer = init_optim(args.optim, G_params, args.lr, args.weight_decay)
if args.load_weights:
# load pretrained weights but ignore layers that don't match in size
if check_isfile(args.load_weights):
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:
if check_isfile(args.resume):
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch']
rank1 = checkpoint['rank1']
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(args.start_epoch, rank1))
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
distmat = test(model, queryloader, galleryloader, args.pool, use_gpu, return_distmat=True)
if args.vis_ranked_res:
visualize_ranked_results(
distmat, dataset,
save_dir=osp.join(args.save_dir, 'ranked_results'),
topk=20,
)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
print("==> Start training")
for epoch in range(args.start_epoch, args.max_epoch):
start_train_time = time.time()
adjust_learning_rate(optimizer, epoch)
train(epoch, model, cirterion_batri, cirterion_lifted, criterion_xent, criterion_htri, criterion_KA, optimizer, trainloader, use_gpu)
train_time += round(time.time() - start_train_time)
scheduler.step()
if (epoch + 1) > args.start_eval and args.eval_step > 0 and (epoch + 1) % args.eval_step == 0 or (epoch + 1) == args.max_epoch:
print("==> Test")
rank1 = test(model, queryloader, galleryloader, args.pool, use_gpu)
is_best = rank1 > best_rank1
if is_best:
best_rank1 = rank1
best_epoch = epoch + 1
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,
}, is_best, osp.join(args.save_dir, 'checkpoint_ep' + str(epoch + 1) + '.pth.tar'))
print("==> Best Rank-1 {:.1%}, achieved at epoch {}".format(best_rank1, best_epoch))
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))