def main():
torch.manual_seed(1)
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_devices
use_gpu = torch.cuda.is_available()
if use_cpu: use_gpu = False
if use_gpu:
print("Currently using GPU {}".format(gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(1)
else:
print("Currently using CPU (GPU is highly recommended)")
print("Initializing model: {}".format(arch))
model = init_model(name=arch, num_classes=576, loss_type=loss_type)
print("Model size: {:.3f} M".format(count_num_param(model)))
if use_gpu:
model = nn.DataParallel(model).cuda()
optimizer = init_optim(optim, model.parameters(), lr, weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=stepsize,
gamma=gamma)
model.train()
cnt = 0
for epoch in range(start_epoch, max_epoch, 2):
for step in range(2):
x = torch.randn(1, 3, 200, 200)
y = torch.randint(low=0, high=576, size=(1, ), dtype=torch.int64)
if use_gpu:
x = x.cuda()
y = y.cuda()
scheduler.step()
cnt += 1
print(cnt, scheduler.get_lr())
output = model(x)
# loss = nn.CrossEntropyLoss()(output[0], y)
loss = torch.tensor(0.0, dtype=torch.float32).cuda()
# loss = torch.tensor(0.0, dtype=torch.float32)
loss.requires_grad = True
optimizer.zero_grad()
loss.backward()
print(loss)
print(loss._grad)
optimizer.step()
print('Done.')
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_imgreid_dataset(
root=args.root, name=args.dataset, split_id=args.split_id,
cuhk03_labeled=args.cuhk03_labeled, cuhk03_classic_split=args.cuhk03_classic_split,
use_lmdb=args.use_lmdb,
)
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
trainloader = DataLoader(
ImageDataset(dataset.train, transform=transform_train,
use_lmdb=args.use_lmdb, lmdb_path=dataset.train_lmdb_path),
batch_size=args.train_batch, shuffle=True, num_workers=args.workers,
pin_memory=pin_memory, drop_last=True,
)
queryloader = DataLoader(
ImageDataset(dataset.query, transform=transform_test,
use_lmdb=args.use_lmdb, lmdb_path=dataset.query_lmdb_path),
batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
pin_memory=pin_memory, drop_last=False,
)
galleryloader = DataLoader(
ImageDataset(dataset.gallery, transform=transform_test,
use_lmdb=args.use_lmdb, lmdb_path=dataset.gallery_lmdb_path),
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'}, use_gpu=use_gpu)
print("Model size: {:.3f} M".format(count_num_param(model)))
criterion = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu)
optimizer = init_optim(args.optim, model.parameters(), args.lr, args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.stepsize, gamma=args.gamma)
start_epoch = args.start_epoch
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:
# load pretrained weights but ignore layers that don't match in size
print("Loading pretrained weights from '{}'".format(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)
if args.resume:
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
start_epoch = checkpoint['epoch']
rank1 = checkpoint['rank1']
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(start_epoch, rank1))
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, use_gpu)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
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(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, 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_imgreid_dataset(
root=args.root,
name=args.dataset,
split_id=args.split_id,
cuhk03_labeled=args.cuhk03_labeled,
cuhk03_classic_split=args.cuhk03_classic_split,
use_lmdb=args.use_lmdb,
)
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
trainloader = DataLoader(
ImageDataset(dataset.train,
transform=transform_train,
use_lmdb=args.use_lmdb,
lmdb_path=dataset.train_lmdb_path),
sampler=RandomIdentitySampler(dataset.train,
num_instances=args.num_instances),
batch_size=args.train_batch,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
queryloader = DataLoader(
ImageDataset(dataset.query,
transform=transform_test,
use_lmdb=args.use_lmdb,
lmdb_path=dataset.train_lmdb_path),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
ImageDataset(dataset.gallery,
transform=transform_test,
use_lmdb=args.use_lmdb,
lmdb_path=dataset.train_lmdb_path),
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 = CrossEntropyLabelSmooth(
num_classes=dataset.num_train_pids, use_gpu=use_gpu)
criterion_htri = TripletLoss(margin=args.margin)
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.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,
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()
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_step > 0 and (
epoch + 1) % args.eval_step == 0 or (epoch +
1) == args.max_epoch:
print("==> Test")
rank1 = test(model, queryloader, galleryloader, 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))
# Miscs
parser.add_argument('--print-freq', type=int, default=10, help="print frequency")
parser.add_argument('--seed', type=int, default=1, help="manual seed")
parser.add_argument('--resume', type=str, default='', metavar='PATH')
parser.add_argument('--evaluate', action='store_true', help="evaluation only")
parser.add_argument('--eval-step', type=int, default=-1,
help="run evaluation for every N epochs (set to -1 to test after training)")
parser.add_argument('--start-eval', type=int, default=0, help="start to evaluate after specific epoch")
parser.add_argument('--save-dir', type=str, default='log')
parser.add_argument('--use-cpu', action='store_true', help="use cpu")
parser.add_argument('--gpu-devices', default='0', type=str, help='gpu device ids for CUDA_VISIBLE_DEVICES')
args = parser.parse_args()
model = models.init_model(name=args.arch, num_classes=751, loss={'xent', 'htri'})
optimizer = init_optim(args.optim, model.parameters(), args.lr, args.weight_decay)
'''warm up与其他学习率方法的结合（基于LambdaLR）'''
config = {}
config["max_num_epochs"] = 60
warm_up_epochs = 10
lr_milestones = [20,40]
# MultiStepLR without warm up
multistep_lr = lambda epoch: 0.1**len([m for m in lr_milestones if m <= epoch])
# warm_up_with_multistep_lr
warm_up_with_multistep_lr = lambda epoch: (epoch+1) / warm_up_epochs if epoch < warm_up_epochs \
else 0.1**len([m for m in lr_milestones if m <= epoch])
# warm_up_with_step_lr
def train_each_teacher(num_epoch, train_data, train_label, test_data,
test_label, save_path):
torch.manual_seed(config.seed)
print('len of train_data in network', len(train_data))
os.environ['CUDA_VISIBLE_DEVICES'] = config.gpu_devices
print('it is training now')
use_gpu = torch.cuda.is_available()
if config.use_cpu: use_gpu = False
print('whether evaluate', config.evaluate)
if use_gpu:
print("Currently using GPU {}".format(config.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(config.seed)
else:
print("Currently using CPU (GPU is highly recommended)")
transform_train = T.Compose([
T.Random2DTranslation(config.height, config.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((config.height, config.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
trainloader = DataLoader(
ImageDataset(train_data, label=train_label, transform=transform_train),
batch_size=config.train_batch,
shuffle=True,
num_workers=config.workers,
pin_memory=pin_memory,
drop_last=False,
)
testloader = DataLoader(
ImageDataset(test_data, label=test_label, transform=transform_test),
batch_size=config.test_batch,
shuffle=False,
num_workers=config.workers,
pin_memory=pin_memory,
drop_last=False,
)
print("Initializing model: {}".format('resnet50m'))
model = models.init_model(name=config.arch,
num_classes=config.nb_labels,
loss={'xent'},
use_gpu=use_gpu)
if use_gpu:
model = nn.DataParallel(model).cuda()
criterion = nn.MultiLabelSoftMarginLoss()
optimizer = init_optim(config.optim, model.parameters(), config.lr,
config.weight_decay)
if config.stepsize > 0:
scheduler = lr_scheduler.StepLR(optimizer,
step_size=config.stepsize,
gamma=config.gamma)
print("==> Start training")
start_time = time.time()
for epoch in range(num_epoch):
train(epoch, model, criterion, optimizer, trainloader, use_gpu)
if config.stepsize > 0: scheduler.step()
rank1 = test(model, testloader, use_gpu)
rank1 = test(model, testloader, use_gpu)
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
print('save model', save_path)
torch.save(state_dict, save_path)
#print("==> Hamming Score {:.3%}".format(rank1))
elapsed = round(time.time() - start_time)
print("Finished. Training time (h:m:s): {}.".format(elapsed))
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_imgreid_dataset(
root=args.root,
name=args.dataset,
split_id=args.split_id,
cuhk03_labeled=args.cuhk03_labeled,
cuhk03_classic_split=args.cuhk03_classic_split,
use_lmdb=args.use_lmdb,
)
transform_train = T.Compose([
T.Random2DTranslation(args.height, args.width),
# T.Resize((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
trainset = ImageDataset(dataset.train,
transform=transform_train,
use_lmdb=args.use_lmdb,
lmdb_path=dataset.train_lmdb_path)
trainloader = DataLoader(
trainset,
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
queryset = ImageDataset(dataset.query,
transform=transform_test,
use_lmdb=args.use_lmdb,
lmdb_path=dataset.query_lmdb_path)
queryloader = DataLoader(
queryset,
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryset = ImageDataset(dataset.gallery,
transform=transform_test,
use_lmdb=args.use_lmdb,
lmdb_path=dataset.gallery_lmdb_path)
galleryloader = DataLoader(
galleryset,
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'},
use_gpu=use_gpu)
print("Model size: {:.3f} M".format(count_num_param(model)))
# summary(model, (3, 160, 64))
criterion = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids,
use_gpu=use_gpu)
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
start_epoch = args.start_epoch
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:
# load pretrained weights but ignore layers that don't match in size
print("Loading pretrained weights from '{}'".format(args.load_weights))
if torch.cuda.is_available():
checkpoint = torch.load(args.load_weights)
else:
checkpoint = torch.load(args.load_weights, map_location='cpu')
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)
if args.resume:
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
start_epoch = checkpoint['epoch']
rank1 = checkpoint['rank1']
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(start_epoch, rank1))
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, use_gpu)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
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(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, 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'))
'''
if use_gpu:
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
save_checkpoint({
'state_dict': state_dict,
'epoch': epoch,
}, True, 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 not args.evaluate:
sys.stdout = Logger(osp.join(args.save_dir, 'log_train.txt'), mode='a')
else:
sys.stdout = Logger(osp.join(args.save_dir, 'log_test.txt'), mode='a')
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_imgreid_dataset(name=args.dataset,
dataset_dir=args.root,
fore_dir=args.fore_dir)
transform_train = ST.Compose([
ST.Scale((args.height, args.width), interpolation=3),
ST.RandomHorizontalFlip(),
ST.ToTensor(),
ST.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
ST.RandomErasing(0.5)
])
transform_test = ST.Compose([
ST.Scale((args.height, args.width), interpolation=3),
ST.ToTensor(),
ST.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
pin_memory = True if use_gpu else False
trainloader = DataLoader(
ImageDataset_hardSplit_seg(dataset.train, transform=transform_train),
sampler=RandomIdentitySampler(dataset.train,
num_instances=args.num_instances),
batch_size=args.train_batch,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
queryloader = DataLoader(
ImageDataset(dataset.query, transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
ImageDataset(dataset.gallery, 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)
print(model)
criterion_xent = CrossEntropyLabelSmooth(use_gpu=use_gpu)
criterion_htri = TripletLoss()
criterion_mask = MaskLoss()
criterion_split = HardSplitLoss()
criterion_cluster = ClusterLoss()
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.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")
test(model, queryloader, galleryloader, use_gpu)
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()
train(epoch, model, criterion_xent, criterion_htri, criterion_mask,
criterion_split, criterion_cluster, optimizer, trainloader,
use_gpu)
train_time += round(time.time() - start_train_time)
scheduler.step()
if (epoch + 1) > args.start_eval and (
epoch + 1) % args.eval_step == 0 or epoch == 0:
print("==> Test")
rank1 = test(model, queryloader, galleryloader, 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))
print("==========\nArgs:{}\n==========".format(args))
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, 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='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 = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu)
criterion_htri = TripletLoss(margin=args.margin)
optimizer = init_optim(args.optim, model.parameters(), args.lr, args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=args.stepsize, gamma=args.gamma)
start_epoch = args.start_epoch
if args.load_weights:
# load pretrained weights but ignore layers that don't match in size
print("Loading pretrained weights from '{}'".format(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)
if args.resume:
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
start_epoch = checkpoint['epoch']
rank1 = checkpoint['rank1']
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}\n- rank1: {}".format(start_epoch, rank1))
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, args.pool, use_gpu)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
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)
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
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)))
criterion = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids,
use_gpu=use_gpu)
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
start_epoch = args.start_epoch
if args.fixbase_epoch > 0:
optimizer_tmp = init_optim(args.optim, model.classifier.parameters(),
args.fixbase_lr, args.weight_decay)
if args.resume:
print("Loading checkpoint from '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
start_epoch = checkpoint['epoch']
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, args.pool, use_gpu)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
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(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))
ckpt_path2 = os.path.join(
args.snapshot_root,
'six_snapshot_iter_' + str(parameters['snap_num']) + '.pth')
ckpt_path3 = os.path.join(
args.snapshot_root,
'att_snapshot_iter_' + str(parameters['snap_num']) + '.pth')
model_rgb.load_state_dict(torch.load(ckpt_path1, map_location='cpu'))
model_six.load_state_dict(torch.load(ckpt_path2, map_location='cpu'))
model_att.load_state_dict(torch.load(ckpt_path3, map_location='cpu'))
else:
if args.arch == 'vgg16':
load_pretrain_vgg16(model_rgb, pretrain=False)
"""""" """"" ~~~train or test~~~ """ """"""
#Trainer: class, defined in trainer.py
optimizer_rgb = init_optim(config.OPTIMIZERS, model_rgb.parameters(),
config.LR, config.WEIGHT_DECAY)
optimizer_six = init_optim(config.OPTIMIZERS, model_six.parameters(),
config.LR, config.WEIGHT_DECAY)
optimizer_att = init_optim(config.OPTIMIZERS, model_att.parameters(),
config.LR, config.WEIGHT_DECAY)
training = Trainer(
cuda=cuda,
model_rgb=model_rgb,
model_six=model_six,
model_att=model_att,
optimizer_rgb=optimizer_rgb,
optimizer_six=optimizer_six,
optimizer_att=optimizer_att,
train_sub=train_sub,
val_sub=val_sub,
train_loader=train_loader,
def main():
if args.dataset == 'ChestXray-NIHCC':
if args.no_fiding:
classes = [
'Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration',
'Mass', 'Nodule', 'Pneumonia', 'Pneumothorax', 'Consolidation',
'Edema', 'Emphysema', 'Fibrosis', 'Pleural_Thickening',
'Hernia', 'No Fiding'
]
else:
classes = [
'Atelectasis', 'Cardiomegaly', 'Effusion', 'Infiltration',
'Mass', 'Nodule', 'Pneumonia', 'Pneumothorax', 'Consolidation',
'Edema', 'Emphysema', 'Fibrosis', 'Pleural_Thickening',
'Hernia'
]
elif args.dataset == 'CheXpert-v1.0-small':
classes = [
'No Finding', 'Enlarged Cardiomediastinum', 'Cardiomegaly',
'Lung Opacity', 'Lung Lesion', 'Edema', 'Consolidation',
'Pneumonia', 'Atelectasis', 'Pneumothorax', 'Pleural Effusion',
'Pleural Other', 'Fracture', 'Support Devices'
]
else:
print('--dataset incorrect')
return
torch.manual_seed(args.seed)
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)")
pin_memory = True if use_gpu else False
print("Initializing dataset: {}".format(args.dataset))
data_transforms = {
'train':
transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.Resize(556),
transforms.CenterCrop(512),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
'valid':
transforms.Compose([
transforms.Resize(556),
transforms.CenterCrop(512),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
}
datasetTrain = DatasetGenerator(path_base=args.base_dir,
dataset_file='train',
transform=data_transforms['train'],
dataset_=args.dataset,
no_fiding=args.no_fiding)
datasetVal = DatasetGenerator(path_base=args.base_dir,
dataset_file='valid',
transform=data_transforms['valid'],
dataset_=args.dataset,
no_fiding=args.no_fiding)
train_loader = DataLoader(dataset=datasetTrain,
batch_size=args.train_batch,
shuffle=args.train_shuffle,
num_workers=args.workers,
pin_memory=pin_memory)
valid_loader = DataLoader(dataset=datasetVal,
batch_size=args.valid_batch,
shuffle=args.valid_shuffle,
num_workers=args.workers,
pin_memory=pin_memory)
with open(args.infos_densenet) as f:
cfg = edict(json.load(f))
print('Initializing densenet branch')
model_dense = Classifier(cfg)
print("Model size: {:.5f}M".format(
sum(p.numel() for p in model_dense.parameters()) / 1000000.0))
with open(args.infos_resnet) as f:
cfg = edict(json.load(f))
print('Initializing resnet branch')
model_res = Classifier(cfg)
print("Model size: {:.5f}M".format(
sum(p.numel() for p in model_res.parameters()) / 1000000.0))
print('Initializing fusion branch')
model_fusion = Fusion(input_size=7424, output_size=len(classes))
print("Model size: {:.5f}M".format(
sum(p.numel() for p in model_fusion.parameters()) / 1000000.0))
print("Initializing optimizers")
optimizer_dense = init_optim(args.optim, model_dense.parameters(),
args.learning_rate, args.weight_decay,
args.momentum)
optimizer_res = init_optim(args.optim, model_res.parameters(),
args.learning_rate, args.weight_decay,
args.momentum)
optimizer_fusion = init_optim(args.optim, model_fusion.parameters(),
args.learning_rate, args.weight_decay,
args.momentum)
criterion = nn.BCELoss()
print("Initializing scheduler: {}".format(args.scheduler))
if args.stepsize > 0:
scheduler_dense = init_scheduler(args.scheduler, optimizer_dense,
args.stepsize, args.gamma)
scheduler_res = init_scheduler(args.scheduler, optimizer_res,
args.stepsize, args.gamma)
scheduler_fusion = init_scheduler(args.scheduler, optimizer_fusion,
args.stepsize, args.gamma)
start_epoch = args.start_epoch
best_loss = np.inf
if args.resume_densenet:
checkpoint_dense = torch.load(args.resume_densenet)
model_dense.load_state_dict(checkpoint_dense['state_dict'])
epoch_dense = checkpoint_dense['epoch']
print("Resuming densenet from epoch {}".format(epoch_dense + 1))
if args.resume_resnet:
checkpoint_res = torch.load(args.resume_resnet)
model_res.load_state_dict(checkpoint_res['state_dict'])
epoch_res = checkpoint_res['epoch']
print("Resuming resnet from epoch {}".format(epoch_res + 1))
if args.resume_fusion:
checkpoint_fusion = torch.load(args.resume_fusion)
model_fusion.load_state_dict(checkpoint_fusion['state_dict'])
epoch_fusion = checkpoint_fusion['epoch']
print("Resuming fusion from epoch {}".format(epoch_fusion + 1))
if use_gpu:
model_dense = nn.DataParallel(model_dense).cuda()
model_res = nn.DataParallel(model_res).cuda()
model_fusion = nn.DataParallel(model_fusion).cuda()
if args.evaluate:
print("Evaluate only")
if args.step == 1:
valid('step1', model_dense, model_res, model_fusion, valid_loader,
criterion, args.print_freq, classes, cfg,
data_transforms['valid'])
elif args.step == 2:
valid('step2', model_dense, model_res, model_fusion, valid_loader,
criterion, args.print_freq, classes, cfg,
data_transforms['valid'])
elif args.step == 3:
valid('step3', model_dense, model_res, model_fusion, valid_loader,
criterion, args.print_freq, classes, cfg,
data_transforms['valid'])
else:
print('args.step not found')
return
if args.step == 1:
#################################### DENSENET BRANCH INIT ##########################################
start_time = time.time()
train_time = 0
best_epoch = 0
print("==> Start training of densenet branch")
for p in model_dense.parameters():
p.requires_grad = True
for p in model_res.parameters():
p.requires_grad = False
for p in model_fusion.parameters():
p.requires_grad = True
for epoch in range(start_epoch, args.max_epoch):
start_train_time = time.time()
train('step1', model_dense, model_res, model_fusion, train_loader,
optimizer_dense, optimizer_res, optimizer_fusion, criterion,
args.print_freq, epoch, args.max_epoch, cfg,
data_transforms['train'])
train_time += round(time.time() - start_train_time)
if args.eval_step > 0 and (epoch + 1) % args.eval_step == 0 or (
epoch + 1) == args.max_epoch:
print("==> Validation")
loss_val = valid('step1', model_dense, model_res, model_fusion,
valid_loader, criterion, args.print_freq,
classes, cfg, data_transforms['valid'])
if args.stepsize > 0:
if args.scheduler == 'ReduceLROnPlateau':
scheduler_dense.step(loss_val)
scheduler_fusion.step(loss_val)
else:
scheduler_dense.step()
scheduler_fusion.step()
is_best = loss_val < best_loss
if is_best:
best_loss = loss_val
best_epoch = epoch + 1
if use_gpu:
state_dict_dense = model_dense.module.state_dict()
state_dict_fusion = model_fusion.module.state_dict()
else:
state_dict_dense = model_dense.state_dict()
state_dict_fusion = model_fusion.state_dict()
save_checkpoint(
{
'state_dict': state_dict_dense,
'loss': best_loss,
'epoch': epoch,
}, is_best, args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar', 'dense')
save_checkpoint(
{
'state_dict': state_dict_fusion,
'loss': best_loss,
'epoch': epoch,
}, is_best, args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar', 'fusion')
print("==> Best Validation Loss {:.4%}, achieved at epoch {}".format(
best_loss, best_epoch))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
"Dense branch finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}."
.format(elapsed, train_time))
#################################### DENSENET BRANCH END ##########################################
elif args.step == 2:
#################################### RESNET BRANCH INIT ##########################################
start_time = time.time()
train_time = 0
best_epoch = 0
print("==> Start training of local branch")
for p in model_dense.parameters():
p.requires_grad = False
for p in model_res.parameters():
p.requires_grad = True
for p in model_fusion.parameters():
p.requires_grad = True
for epoch in range(start_epoch, args.max_epoch):
start_train_time = time.time()
train('step2', model_dense, model_res, model_fusion, train_loader,
optimizer_dense, optimizer_res, optimizer_fusion, criterion,
args.print_freq, epoch, args.max_epoch, cfg,
data_transforms['train'])
train_time += round(time.time() - start_train_time)
if args.eval_step > 0 and (epoch + 1) % args.eval_step == 0 or (
epoch + 1) == args.max_epoch:
print("==> Validation")
loss_val = valid('step2', model_dense, model_res, model_fusion,
valid_loader, criterion, args.print_freq,
classes, cfg, data_transforms['valid'])
if args.stepsize > 0:
if args.scheduler == 'ReduceLROnPlateau':
scheduler_res.step(loss_val)
scheduler_fusion.step(loss_val)
else:
scheduler_res.step()
scheduler_fusion.step()
is_best = loss_val < best_loss
if is_best:
best_loss = loss_val
best_epoch = epoch + 1
if use_gpu:
state_dict_res = model_res.module.state_dict()
state_dict_fusion = model_fusion.module.state_dict()
else:
state_dict_res = model_res.state_dict()
state_dict_fusion = model_fusion.state_dict()
save_checkpoint(
{
'state_dict': state_dict_res,
'loss': best_loss,
'epoch': epoch,
}, is_best, args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar', 'res')
save_checkpoint(
{
'state_dict': state_dict_fusion,
'loss': best_loss,
'epoch': epoch,
}, is_best, args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar', 'fusion')
print("==> Best Validation Loss {:.4%}, achieved at epoch {}".format(
best_loss, best_epoch))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
"Resnet branch finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}."
.format(elapsed, train_time))
#################################### RESNET BRANCH END ##########################################
elif args.step == 3:
#################################### FUSION BRANCH INIT ##########################################
start_time = time.time()
train_time = 0
best_epoch = 0
print("==> Start training of fusion branch")
for p in model_dense.parameters():
p.requires_grad = True
for p in model_res.parameters():
p.requires_grad = True
for p in model_fusion.parameters():
p.requires_grad = True
for epoch in range(start_epoch, args.max_epoch):
start_train_time = time.time()
train('step3', model_dense, model_res, model_fusion, train_loader,
optimizer_dense, optimizer_res, optimizer_fusion, criterion,
args.print_freq, epoch, args.max_epoch, cfg,
data_transforms['train'])
train_time += round(time.time() - start_train_time)
if args.eval_step > 0 and (epoch + 1) % args.eval_step == 0 or (
epoch + 1) == args.max_epoch:
print("==> Validation")
loss_val = valid('step3', model_dense, model_res, model_fusion,
valid_loader, criterion, args.print_freq,
classes, cfg, data_transforms['valid'])
if args.stepsize > 0:
if args.scheduler == 'ReduceLROnPlateau':
scheduler_dense.step(loss_val)
scheduler_res.step(loss_val)
scheduler_fusion.step(loss_val)
else:
scheduler_dense.step()
scheduler_res.step()
scheduler_fusion.step()
is_best = loss_val < best_loss
if is_best:
best_loss = loss_val
best_epoch = epoch + 1
if use_gpu:
state_dict_dense = model_dense.module.state_dict()
state_dict_res = model_res.module.state_dict()
state_dict_fusion = model_fusion.module.state_dict()
else:
state_dict_dense = model_dense.state_dict()
state_dict_res = model_res.state_dict()
state_dict_fusion = model_fusion.state_dict()
save_checkpoint(
{
'state_dict': state_dict_dense,
'loss': best_loss,
'epoch': epoch,
}, is_best, args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar', 'dense')
save_checkpoint(
{
'state_dict': state_dict_res,
'loss': best_loss,
'epoch': epoch,
}, is_best, args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar', 'res')
save_checkpoint(
{
'state_dict': state_dict_fusion,
'loss': best_loss,
'epoch': epoch,
}, is_best, args.save_dir,
'checkpoint_ep' + str(epoch + 1) + '.pth.tar', 'fusion')
print("==> Best Validation Loss {:.4%}, achieved at epoch {}".format(
best_loss, best_epoch))
elapsed = round(time.time() - start_time)
elapsed = str(datetime.timedelta(seconds=elapsed))
train_time = str(datetime.timedelta(seconds=train_time))
print(
"Fusion branch finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}."
.format(elapsed, train_time))
#################################### FUSION BRANCH END ##########################################
else:
print('args.step not found')
def main():
torch.manual_seed(1)
os.environ['CUDA_VISIBLE_DEVICES'] = config.gpu_devices
use_gpu = torch.cuda.is_available()
sys.stdout = Logger(config.save_dir, config.checkpoint_suffix,
config.evaluate)
print("\n==========\nArgs:")
config.print_parameter()
print("==========\n")
if use_gpu:
print("Currently using GPU {}".format(config.gpu_devices))
cudnn.benchmark = True
torch.cuda.manual_seed_all(1)
else:
print("Currently using CPU (GPU is highly recommended)")
print("Initializing dataset {}".format(config.dataset))
dataset = data_manager.init_imgreid_dataset(name=config.dataset,
root=config.data_root)
transform_train = T.Compose([
T.Random2DTranslation(config.height, config.width),
T.RandomHorizontalFlip(),
T.ToTensor(),
T.Normalize(mean=data_mean, std=data_std),
])
transform_test = T.Compose([
T.Resize((config.height, config.width)),
T.ToTensor(),
T.Normalize(mean=data_mean, std=data_std),
])
pin_memory = True if use_gpu else False
# train_batch_sampler = BalancedBatchSampler(dataset.train, n_classes=8, n_samples=8)
# train_batch_sampler = CCLBatchSampler(dataset.train, n_classes=n_classes, n_samples=n_samples)
# train_batch_sampler = CCLBatchSamplerV2(dataset.train, n_classes=n_classes, pos_samp_cnt=pos_samp_cnt,
# neg_samp_cnt=neg_samp_cnt, each_cls_max_cnt=each_cls_max_cnt)
train_batch_sampler = ClassSampler(dataset.train,
sample_cls_cnt=config.sample_cls_cnt,
each_cls_cnt=config.each_cls_cnt)
# trainloader = DataLoader(
# ImageDataset(dataset.train, transform=transform_train),
# batch_sampler=train_batch_sampler, batch_size=args.train_batch,
# shuffle=True, num_workers=args.workers, pin_memory=pin_memory, drop_last=True
# )
trainloader = DataLoader(ImageDatasetWCL(dataset,
data_type='train',
merge_h=256,
merge_w=256,
mean_std=[data_mean, data_std]),
batch_sampler=train_batch_sampler,
num_workers=config.workers,
pin_memory=pin_memory)
queryloader = DataLoader(
ImageDatasetWCL(dataset.query,
data_type='query',
merge_h=256,
merge_w=256,
mean_std=[data_mean, data_std]),
batch_size=config.test_batch,
shuffle=False,
num_workers=config.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
ImageDatasetWCL(dataset.gallery,
data_type='gallery',
merge_h=256,
merge_w=256,
mean_std=[data_mean, data_std]),
batch_size=config.test_batch,
shuffle=False,
num_workers=config.workers,
pin_memory=pin_memory,
drop_last=False,
)
if config.dataset == 'vehicleid':
train_query_loader = None
train_gallery_loader = None
else:
train_query_loader = DataLoader(
ImageDatasetWCL(dataset.train_query,
data_type='train_query',
merge_h=256,
merge_w=256,
mean_std=[data_mean, data_std]),
batch_size=config.test_batch,
shuffle=False,
num_workers=config.workers,
pin_memory=pin_memory,
drop_last=False,
)
train_gallery_loader = DataLoader(
ImageDatasetWCL(dataset.train_gallery,
data_type='train_gallery',
merge_h=256,
merge_w=256,
mean_std=[data_mean, data_std]),
batch_size=config.test_batch,
shuffle=False,
num_workers=config.workers,
pin_memory=pin_memory,
drop_last=False,
)
print("Initializing model: {}".format(config.arch))
model = init_model(name=config.arch,
num_classes=dataset.num_train_pids,
loss_type=config.loss_type)
print("Model size: {:.3f} M".format(count_num_param(model)))
if config.loss_type == 'xent':
criterion = [nn.CrossEntropyLoss(), nn.CrossEntropyLoss()]
elif config.loss_type == 'xent_triplet':
criterion = XentTripletLoss(
margin=config.margin,
triplet_selector=RandomNegativeTripletSelector(
margin=config.margin),
each_cls_cnt=config.each_cls_cnt,
n_class=config.sample_cls_cnt)
elif config.loss_type == 'xent_tripletv2':
criterion = XentTripletLossV2(
margin=config.margin,
triplet_selector=RandomNegativeTripletSelectorV2(
margin=config.margin),
each_cls_cnt=config.each_cls_cnt,
n_class=config.sample_cls_cnt)
# criterion = XentTripletLossV2(margin=0.04, triplet_selector=RandomNegativeTripletSelectorV2(margin=0.04),
# each_cls_cnt=config.each_cls_cnt, n_class=config.sample_cls_cnt)
# criterion = XentGroupTripletLossV2(margin=0.8, triplet_selector=AllTripletSelector(margin=0.8),
# each_cls_cnt=config.each_cls_cnt, n_class=config.sample_cls_cnt)
else:
raise KeyError("Unsupported loss: {}".format(config.loss_type))
optimizer = init_optim(config.optim, model.parameters(), config.lr,
config.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=config.stepsize,
gamma=config.gamma)
if config.resume is not None:
if check_isfile(config.resume):
checkpoint = torch.load(config.resume)
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)
config.start_epoch = checkpoint['epoch']
rank1 = checkpoint['rank1']
if 'mAP' in checkpoint:
mAP = checkpoint['mAP']
else:
mAP = 0
print("Loaded checkpoint from '{}'".format(config.resume))
print("- start_epoch: {}\n- rank1: {}\n- mAP: {}".format(
config.start_epoch, rank1, mAP))
if use_gpu:
model = nn.DataParallel(model).cuda()
if config.evaluate:
print("Evaluate only")
test_model(model, queryloader, galleryloader, train_query_loader,
train_gallery_loader, use_gpu, config.test_batch,
config.loss_type, config.euclidean_distance_loss)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_map = 0
best_epoch = 0
for epoch in range(config.start_epoch, config.max_epoch):
print("==> Start training")
start_train_time = time.time()
scheduler.step()
print('epoch:', epoch, 'lr:', scheduler.get_lr())
train(epoch, model, criterion, optimizer, trainloader,
config.loss_type, config.print_freq)
train_time += round(time.time() - start_train_time)
if epoch >= config.start_eval and config.eval_step > 0 and epoch % config.eval_step == 0 \
or epoch == config.max_epoch:
print("==> Test")
rank1, mAP = test_model(model, queryloader, galleryloader,
train_query_loader, train_gallery_loader,
use_gpu, config.test_batch,
config.loss_type,
config.euclidean_distance_loss)
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,
'rank1': rank1,
'mAP': mAP,
'epoch': epoch + 1,
},
is_best,
use_gpu_suo=False,
fpath=osp.join(
config.save_dir, 'checkpoint_ep' + str(epoch + 1) +
config.checkpoint_suffix + '.pth.tar'))
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)
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_img_dataset(
root=args.root,
name=args.dataset,
split_id=args.split_id,
cuhk03_labeled=args.cuhk03_labeled,
cuhk03_classic_split=args.cuhk03_classic_split,
)
transform_train = T.Compose([
T.Resize((args.height, args.width)),
T.RandomHorizontalFlip(p=0.5),
T.Pad(10),
T.RandomCrop([args.height, args.width]),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
torchvision.transforms.RandomErasing(p=0.5,
scale=(0.02, 0.4),
ratio=(0.3, 3.33),
value=(0.4914, 0.4822, 0.4465))
# T.RandomErasing(probability=0.5, sh=0.4, mean=(0.4914, 0.4822, 0.4465)),
])
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
trainloader = DataLoader(
ImageDataset(dataset.train, transform=transform_train),
sampler=RandomIdentitySampler2(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(
ImageDataset(dataset.query, transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
ImageDataset(dataset.gallery, 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: {:.5f}M".format(
sum(p.numel() for p in model.parameters()) / 1000000.0))
#embed()
criterion_xent = CrossEntropyLabelSmooth(
num_classes=dataset.num_train_pids, use_gpu=use_gpu)
criterion_htri = TripletLoss(margin=args.margin)
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
# if args.stepsize > 0:
# scheduler = lr_scheduler.StepLR(optimizer, step_size=args.stepsize, gamma=args.gamma)
'''------Modify lr_schedule here------'''
current_schedule = init_lr_schedule(schedule=args.schedule,
warm_up_epoch=args.warm_up_epoch,
half_cos_period=args.half_cos_period,
lr_milestone=args.lr_milestone,
gamma=args.gamma,
stepsize=args.stepsize)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,
lr_lambda=current_schedule)
'''------Please refer to the args.xxx for details of hyperparams------'''
#embed()
start_epoch = args.start_epoch
if args.resume:
print("Loading checkpoint from '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
start_epoch = checkpoint['epoch']
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, use_gpu)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
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)
train_time += round(time.time() - start_train_time)
if args.schedule: 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, 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))
batch_size=valid_batch,
shuffle=valid_shuffle,
num_workers=workers,
pin_memory=pin_memory)
print("Initializing model: {}".format(arch))
model = models.init_model(name=arch,
num_classes=len(classes),
is_trained=trained)
print("Model size: {:.5f}M".format(
sum(p.numel() for p in model.parameters()) / 1000000.0))
# print(model)
print("Initializing optimizer: {}".format(optim))
optimizer = init_optim(optim, model.parameters(), learning_rate, weight_decay,
momentum)
if use_gpu:
model = nn.DataParallel(model).cuda()
model.train()
losses = AverageMeter()
for batch_idx, tuple_i in enumerate(valid_loader):
data, target = tuple_i
data = Variable(torch.FloatTensor(data).cuda(), requires_grad=True)
target = Variable(torch.FloatTensor(target).cuda())
output = model(data)
print(output.shape)
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_img_dataset(
root=args.root,
name=args.dataset,
split_id=args.split_id,
cuhk03_labeled=args.cuhk03_labeled,
cuhk03_classic_split=args.cuhk03_classic_split,
)
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
trainloader = DataLoader(
ImageDataset(dataset.train, transform=transform_train),
batch_size=args.train_batch,
shuffle=True,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=True,
)
queryloader = DataLoader(
ImageDataset(dataset.query, transform=transform_test),
batch_size=args.test_batch,
shuffle=False,
num_workers=args.workers,
pin_memory=pin_memory,
drop_last=False,
)
galleryloader = DataLoader(
ImageDataset(dataset.gallery, 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'},
use_gpu=use_gpu)
print("Model size: {:.5f}M".format(
sum(p.numel() for p in model.parameters()) / 1000000.0))
criterion = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids,
use_gpu=use_gpu)
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
if args.stepsize > 0:
scheduler = lr_scheduler.StepLR(optimizer,
step_size=args.stepsize,
gamma=args.gamma)
start_epoch = args.start_epoch
if args.resume:
print("Loading checkpoint from '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
start_epoch = checkpoint['epoch']
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, use_gpu)
return
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
print("==> Start training")
for epoch in range(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)
if args.stepsize > 0: scheduler.step()
if args.eval_step > 0 and (epoch + 1) % args.eval_step == 0 or (
epoch + 1) == args.max_epoch:
print("==> Test")
rank1 = test(model, queryloader, galleryloader, 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))
train_dataset = data_manager.init_dataset(root=args.root,
name='json',
phase='train')
valid_dataset = data_manager.init_dataset(root=args.root,
name='json',
phase='valid')
test_dataset = data_manager.init_dataset(root=args.root,
name='json',
phase='test')
test_mask = test_dataset.mask
pin_memory = True if use_gpu else False
trainloader = DataLoader(JsonDataset(train_dataset),
num_workers=4,
batch_size=args.train_batch,
pin_memory=pin_memory,
drop_last=True)
validloader = DataLoader(JsonDataset(valid_dataset),
num_workers=4,
batch_size=args.test_batch,
pin_memory=pin_memory,
drop_last=True)
testloader = DataLoader(JsonDataset(test_dataset),
num_workers=4,
batch_size=args.test_batch,
pin_memory=pin_memory,
drop_last=True)
print("Initializing model: {}".format(args.arch))
model = models.init_model(name=args.arch, use_gpu=use_gpu)
print("Model size: {:.3f} M".format(count_num_param(model)))
model.init_weights()
criterion_label = LabelLoss()
optimizer = init_optim(args.optim, model.parameters(), args.lr,
args.weight_decay)
scheduler = lr_scheduler.MultiStepLR(optimizer,
milestones=args.stepsize,
gamma=args.gamma)
start_epoch = args.start_epoch
if args.load_weights:
# load pretrained weights but ignore layers that don't match in size
print("Loading pretrained weights from '{}'".format(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)
if args.resume:
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
start_epoch = checkpoint['epoch']
print("Loaded checkpoint from '{}'".format(args.resume))
print("- start_epoch: {}".format(start_epoch))
# if use_gpu:
# str_ids = args.gpu_devices.split(',')
# gpu_ids = []
# for str_id in str_ids:
# id = int(str_id)
# if id >= 0:
# gpu_ids.append(id)
# model = nn.DataParallel(model, gpu_ids)
device = torch.device(
'cuda' if use_gpu and torch.cuda.is_available() else 'cpu')
model.to(device)
if args.evaluate:
print("Evaluate only")
start_evaluate_time = time.time()
test_thetas = evaluate(model, testloader, use_gpu, args.test_batch,
test_mask)
# test_thetas = evaluate(model, validloader, use_gpu, args.test_batch, test_mask)
evaluate_time = time.time() - start_evaluate_time
print('Evaluate: {} secs'.format(evaluate_time))
with open("auto_sample.csv", "r") as csvfiler:
with open("test_thetas.csv", "w") as csvfilew:
reader = csv.reader(csvfiler)
for item in reader:
if reader.line_num == 1:
writer = csv.writer(csvfilew)
writer.writerow(['test_id', 'result'])
continue
writer = csv.writer(csvfilew)
writer.writerow(
[item[0],
str(test_thetas[reader.line_num - 2])])
# writer.writerows(map(lambda x: [x], test_thetas))
return
start_time = time.time()
train_time = 0
best_label_loss = np.inf
best_epoch = 0
#
# print("==> Test")
# label_loss = test(model, validloader, criterion_label, use_gpu, args.test_batch)
# print("test label loss RMES() is {}".format(label_loss))
#
# print("==> Start training")
for epoch in range(start_epoch, args.max_epoch):
start_train_time = time.time()
train(epoch, model, criterion_label, optimizer, trainloader, use_gpu)
train_time += round(time.time() - start_train_time)
scheduler.step()
# save model every epoch
if (epoch + 1) % args.save_step == 0:
print("==> Now save epoch {} \'s model".format(epoch + 1))
# if use_gpu:
# state_dict = model.state_dict() # module.
# else:
state_dict = model.state_dict()
save_checkpoint({
'state_dict': state_dict,
'epoch': epoch
}, False, osp.join(args.save_dir, 'checkpoint_latest.pth'))
# test model every eval_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")
label_loss = test(model, validloader, criterion_label, use_gpu,
args.test_batch)
is_best = label_loss < best_label_loss
if is_best:
best_label_loss = label_loss
best_epoch = epoch + 1
# if use_gpu:
# state_dict = model.state_dict()
# else:
state_dict = model.state_dict()
save_checkpoint(
{
'state_dict': state_dict,
'label_loss': label_loss,
'epoch': epoch,
}, is_best,
osp.join(args.save_dir, 'checkpoint_ep' + str(epoch + 1) +
'.pth')) # .pth.tar
print("==> Best Label Loss {:.3}, achieved at epoch {}".format(
best_label_loss, 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'))
# tensorboardX
# writer = SummaryWriter(log_dir=osp.join(args.save_dir,'summary'))
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_img_dataset(
root=args.root, name=args.dataset, split_id=args.split_id,
cuhk03_labeled=args.cuhk03_labeled, cuhk03_classic_split=args.cuhk03_classic_split,
)
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]),
])
if args.random_erasing:
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]),
RandomErasing(probability=args.probability, mean=[0.0, 0.0, 0.0]),
])
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
if args.loss == 'xent,htri':
trainloader = DataLoader(
ImageDataset(dataset.train, transform=transform_train),
sampler=RandomIdentitySampler(dataset.train, num_instances=args.num_instances),
batch_size=args.train_batch, num_workers=args.workers,
pin_memory=pin_memory, drop_last=True,
)
elif args.loss == 'xent':
trainloader = DataLoader(
ImageDataset(dataset.train, transform=transform_train),
batch_size=args.train_batch, shuffle=True, num_workers=args.workers,
pin_memory=pin_memory, drop_last=True,
)
queryloader = DataLoader(
ImageDataset(dataset.query, transform=transform_test),
batch_size=args.test_batch, shuffle=False, num_workers=args.workers,
pin_memory=pin_memory, drop_last=False,
)
galleryloader = DataLoader(
ImageDataset(dataset.gallery, 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=args.loss)
print("Model size: {:.5f}M".format(sum(p.numel() for p in model.parameters())/1000000.0))
criterion_xent = CrossEntropyLabelSmooth(num_classes=dataset.num_train_pids, use_gpu=use_gpu)
criterion_htri = TripletLoss(margin=args.margin)
optimizer = init_optim(args.optim, model.parameters(), args.lr, args.weight_decay)
if args.stepsize > 0:
if not args.warmup:
scheduler = lr_scheduler.StepLR(optimizer, step_size=args.stepsize, gamma=args.gamma)
start_epoch = args.start_epoch
if args.resume:
print("Loading checkpoint from '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
start_epoch = checkpoint['epoch']
if use_gpu:
model = nn.DataParallel(model).cuda()
if args.evaluate:
print("Evaluate only")
test(model, queryloader, galleryloader, use_gpu)
return
def adjust_lr(optimizer, ep):
if ep < 20:
lr = 1e-4 * (ep + 1) / 2
elif ep < 80:
#lr = 1e-3 * len(args.gpu_devices)
lr = 1e-3
elif ep < 180:
#lr = 1e-4 * len(args.gpu_devices)
lr = 1e-4
elif ep < 300:
#lr = 1e-5 * len(args.gpu_devices)
lr = 1e-5
elif ep < 320:
#lr = 1e-5 * 0.1 ** ((ep - 320) / 80) * len(args.gpu_devices)
lr = 1e-5 * 0.1 ** ((ep - 320) / 80)
elif ep < 400:
lr = 1e-6
elif ep < 480:
#lr = 1e-4 * len(args.gpu_devices)
lr = 1e-4
else:
#lr = 1e-5 * len(args.gpu_devices)
lr = 1e-5
for p in optimizer.param_groups:
p['lr'] = lr
length = len(trainloader)
start_time = time.time()
train_time = 0
best_rank1 = -np.inf
best_epoch = 0
#best_rerank1 = -np.inf
#best_rerankepoch = 0
print("==> Start training")
for epoch in range(start_epoch, args.max_epoch):
start_train_time = time.time()
if args.stepsize > 0:
if args.warmup:
adjust_lr(optimizer, epoch + 1)
else:
scheduler.step()
train(epoch, model, criterion_xent, criterion_htri, optimizer, trainloader, use_gpu=use_gpu, summary=None, length=length)
train_time += round(time.time() - start_train_time)
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(epoch, model, queryloader, galleryloader, use_gpu=True, summary=None)
is_best = rank1 > best_rank1
if is_best:
best_rank1 = rank1
best_epoch = epoch + 1
####### Best Rerank
#is_rerankbest = rerank1 > best_rerank1
#if is_rerankbest:
# best_rerank1 = rerank1
# best_rerankepoch = 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'))
writer.close()
print("==> Best Rank-1 {:.1%}, achieved at epoch {}".format(best_rank1, best_epoch))
#print("==> Best Rerank-1 {:.1%}, achieved at epoch {}".format(best_rerank1, best_rerankepoch))
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))
