def create_model(args, ncs, wopre=False):
model_1 = models.create(args.arch,
num_features=args.features,
dropout=args.dropout,
num_classes=ncs)
model_1_ema = models.create(args.arch,
num_features=args.features,
dropout=args.dropout,
num_classes=ncs)
if not wopre:
initial_weights = load_checkpoint(args.init_1)
copy_state_dict(initial_weights['state_dict'], model_1)
copy_state_dict(initial_weights['state_dict'], model_1_ema)
print('load pretrain model:{}'.format(args.init_1))
# adopt domain-specific BN
convert_dsbn(model_1)
convert_dsbn(model_1_ema)
model_1.cuda()
model_1_ema.cuda()
model_1 = nn.DataParallel(model_1)
model_1_ema = nn.DataParallel(model_1_ema)
for i, cl in enumerate(ncs):
exec(
'model_1_ema.module.classifier{}_{}.weight.data.copy_(model_1.module.classifier{}_{}.weight.data)'
.format(i, cl, i, cl))
return model_1, None, model_1_ema, None
def main_worker(args):
global start_epoch, best_mAP
cudnn.benchmark = True
if not args.evaluate:
sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt'))
else:
log_dir = osp.dirname(args.resume)
sys.stdout = Logger(osp.join(log_dir, 'log_test.txt'))
print("==========\nArgs:{}\n==========".format(args))
# Create data loaders
iters = args.iters if (args.iters > 0) else None
dataset_source, num_classes, train_loader_source, test_loader_source = \
get_data(args.dataset_source, args.data_dir, args.height,
args.width, args.batch_size, args.workers, args.num_instances, iters)
dataset_target, _, train_loader_target, test_loader_target = \
get_data(args.dataset_target, args.data_dir, args.height,
args.width, args.batch_size, args.workers, 0, iters)
# Create model
model = models.create(args.arch,
num_features=args.features,
dropout=args.dropout,
num_classes=[num_classes])
model.cuda()
model = nn.DataParallel(model)
print(model)
# Load from checkpoint
if args.resume:
checkpoint = load_checkpoint(args.resume)
copy_state_dict(checkpoint['state_dict'], model)
start_epoch = checkpoint['epoch']
best_mAP = checkpoint['best_mAP']
print("=> Start epoch {} best mAP {:.1%}".format(
start_epoch, best_mAP))
# Evaluator
evaluator = Evaluator(model)
# args.evaluate=True
if args.evaluate:
print("Test on source domain:")
evaluator.evaluate(test_loader_source,
dataset_source.query,
dataset_source.gallery,
cmc_flag=True,
rerank=args.rerank)
print("Test on target domain:")
evaluator.evaluate(test_loader_target,
dataset_target.query,
dataset_target.gallery,
cmc_flag=True,
rerank=args.rerank)
return
params = []
for key, value in model.named_parameters():
if not value.requires_grad:
continue
params += [{
"params": [value],
"lr": args.lr,
"weight_decay": args.weight_decay
}]
optimizer = torch.optim.Adam(params)
lr_scheduler = WarmupMultiStepLR(optimizer,
args.milestones,
gamma=0.1,
warmup_factor=0.01,
warmup_iters=args.warmup_step)
# Trainer
trainer = PreTrainer(model, num_classes, margin=args.margin)
# Start training
for epoch in range(start_epoch, args.epochs):
lr_scheduler.step()
train_loader_source.new_epoch()
train_loader_target.new_epoch()
trainer.train(epoch,
train_loader_source,
train_loader_target,
optimizer,
train_iters=len(train_loader_source),
print_freq=args.print_freq)
if ((epoch + 1) % args.eval_step == 0 or (epoch == args.epochs - 1)):
_, mAP = evaluator.evaluate(test_loader_source,
dataset_source.query,
dataset_source.gallery,
cmc_flag=True)
is_best = mAP > best_mAP
best_mAP = max(mAP, best_mAP)
save_checkpoint(
{
'state_dict': model.state_dict(),
'epoch': epoch + 1,
'best_mAP': best_mAP,
},
is_best,
fpath=osp.join(args.logs_dir, 'checkpoint.pth.tar'))
print(
'\n * Finished epoch {:3d} source mAP: {:5.1%} best: {:5.1%}{}\n'
.format(epoch, mAP, best_mAP, ' *' if is_best else ''))
print("Test on target domain:")
evaluator.evaluate(test_loader_target,
dataset_target.query,
dataset_target.gallery,
cmc_flag=True,
rerank=args.rerank)