def main():
global args
set_random_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available() and not args.use_cpu
log_name = 'test.log' if args.evaluate else 'train.log'
log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print('** Arguments **')
arg_keys = list(args.__dict__.keys())
arg_keys.sort()
for key in arg_keys:
print('{}: {}'.format(key, args.__dict__[key]))
print('\n')
print('Collecting env info ...')
print('** System info **\n{}\n'.format(collect_env_info()))
if use_gpu:
torch.backends.cudnn.benchmark = True
else:
warnings.warn(
'Currently using CPU, however, GPU is highly recommended')
datamanager = build_datamanager(args)
print('Building model: {}'.format(args.arch))
model = torchreid.models.build_model(
name=args.arch,
num_classes=datamanager.num_train_pids,
loss=args.loss.lower(),
pretrained=(not args.no_pretrained),
use_gpu=use_gpu)
num_params, flops = compute_model_complexity(
model, (1, 3, args.height, args.width))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if args.load_weights and check_isfile(args.load_weights):
load_pretrained_weights(model, args.load_weights)
if use_gpu:
model = nn.DataParallel(model).cuda()
optimizer = torchreid.optim.build_optimizer(model,
**optimizer_kwargs(args))
scheduler = torchreid.optim.build_lr_scheduler(optimizer,
**lr_scheduler_kwargs(args))
if args.resume and check_isfile(args.resume):
args.start_epoch = resume_from_checkpoint(args.resume,
model,
optimizer=optimizer)
print('Building {}-engine for {}-reid'.format(args.loss, args.app))
engine = build_engine(args, datamanager, model, optimizer, scheduler)
engine.run(**engine_run_kwargs(args))
def main():
global args
set_random_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = (torch.cuda.is_available() and not args.use_cpu)
log_name = 'test.log' if args.evaluate else 'train.log'
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print('==========\nArgs:{}\n=========='.format(args))
if use_gpu:
print('Currently using GPU {}'.format(args.gpu_devices))
torch.backends.cudnn.benchmark = True
else:
warnings.warn(
'Currently using CPU, however, GPU is highly recommended')
datamanager = build_datamanager(args)
print('Building model: {}'.format(args.arch))
model = torchreid.models.build_model(
name=args.arch,
num_classes=datamanager.num_train_pids,
loss=args.loss.lower(),
pretrained=(not args.no_pretrained),
use_gpu=use_gpu)
num_params, flops = compute_model_complexity(
model, (1, 3, args.height, args.width))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if args.load_weights and check_isfile(args.load_weights):
load_pretrained_weights(model, args.load_weights)
if use_gpu:
model = nn.DataParallel(model).cuda()
optimizer = torchreid.optim.build_optimizer(model,
**optimizer_kwargs(args))
scheduler = torchreid.optim.build_lr_scheduler(optimizer,
**lr_scheduler_kwargs(args))
if args.resume and check_isfile(args.resume):
args.start_epoch = resume_from_checkpoint(args.resume,
model,
optimizer=optimizer)
print('Building {}-engine for {}-reid'.format(args.loss, args.app))
engine = build_engine(args, datamanager, model, optimizer, scheduler)
engine.run(**engine_run_kwargs(args))
def main():
global args
set_random_seed(args.seed)
use_gpu = torch.cuda.is_available() and not args.use_cpu
log_name = 'test.log' if args.evaluate else 'train.log'
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print('** Arguments **')
arg_keys = list(args.__dict__.keys())
arg_keys.sort()
for key in arg_keys:
print('{}: {}'.format(key, args.__dict__[key]))
print('\n')
print('Collecting env info ...')
print('** System info **\n{}\n'.format(collect_env_info()))
if use_gpu:
torch.backends.cudnn.benchmark = True
else:
warnings.warn(
'Currently using CPU, however, GPU is highly recommended')
dataset_vars = init_dataset(use_gpu)
trainloader, valloader, testloader, num_attrs, attr_dict = dataset_vars
if args.weighted_bce:
print('Use weighted binary cross entropy')
print('Computing the weights ...')
bce_weights = torch.zeros(num_attrs, dtype=torch.float)
for _, attrs, _ in trainloader:
bce_weights += attrs.sum(0) # sum along the batch dim
bce_weights /= len(trainloader) * args.batch_size
print('Sample ratio for each attribute: {}'.format(bce_weights))
bce_weights = torch.exp(-1 * bce_weights)
print('BCE weights: {}'.format(bce_weights))
bce_weights = bce_weights.expand(args.batch_size, num_attrs)
criterion = nn.BCEWithLogitsLoss(weight=bce_weights)
else:
print('Use plain binary cross entropy')
criterion = nn.BCEWithLogitsLoss()
print('Building model: {}'.format(args.arch))
model = models.build_model(args.arch,
num_attrs,
pretrained=not args.no_pretrained,
use_gpu=use_gpu)
num_params, flops = compute_model_complexity(
model, (1, 3, args.height, args.width))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if args.load_weights and check_isfile(args.load_weights):
load_pretrained_weights(model, args.load_weights)
if use_gpu:
model = nn.DataParallel(model).cuda()
criterion = criterion.cuda()
if args.evaluate:
test(model, testloader, attr_dict, use_gpu)
return
optimizer = torchreid.optim.build_optimizer(model,
**optimizer_kwargs(args))
scheduler = torchreid.optim.build_lr_scheduler(optimizer,
**lr_scheduler_kwargs(args))
start_epoch = args.start_epoch
best_result = -np.inf
if args.resume and check_isfile(args.resume):
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = checkpoint['epoch']
best_result = checkpoint['label_mA']
print('Loaded checkpoint from "{}"'.format(args.resume))
print('- start epoch: {}'.format(start_epoch))
print('- label_mA: {}'.format(best_result))
time_start = time.time()
for epoch in range(start_epoch, args.max_epoch):
train(epoch, model, criterion, optimizer, scheduler, trainloader,
use_gpu)
test_outputs = test(model, testloader, attr_dict, use_gpu)
label_mA = test_outputs[0]
is_best = label_mA > best_result
if is_best:
best_result = label_mA
save_checkpoint(
{
'state_dict': model.state_dict(),
'epoch': epoch + 1,
'label_mA': label_mA,
'optimizer': optimizer.state_dict(),
},
args.save_dir,
is_best=is_best)
elapsed = round(time.time() - time_start)
elapsed = str(datetime.timedelta(seconds=elapsed))
print('Elapsed {}'.format(elapsed))
