test_dset = VOCDetection(root=data_root, split='test')
test_dloader = DataLoader(test_dset,
batch_size=batch_size,
shuffle=False,
drop_last=False,
num_workers=8)
model = Yolo(grid_size, num_boxes, num_classes)
model = model.to(device)
pretrained_weights = torch.load(pretrained_backbone_path)
model.load_state_dict(pretrained_weights)
print('loaded pretrained weight')
# Freeze the backbone network.
model.features.requires_grad_(False)
model_params = [v for v in model.parameters() if v.requires_grad is True]
optimizer = optim.SGD(model_params, lr=lr, momentum=0.9, weight_decay=5e-4)
compute_loss = Loss(grid_size, num_boxes, num_classes)
# Load the last checkpoint if exits.
ckpt_path = os.path.join(ckpt_dir, 'last.pth')
if os.path.exists(ckpt_path):
ckpt = torch.load(ckpt_path)
model.load_state_dict(ckpt['model'])
optimizer.load_state_dict(ckpt['optimizer'])
last_epoch = ckpt['epoch'] + 1
print('Last checkpoint is loaded. start_epoch:', last_epoch)
else:
print('No checkpoint is found.')
# Get dataloader
train_dataset = VOCDetection(args.train_path, args.img_size)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=train_dataset.collate_fn)
val_dataset = VOCDetection(args.val_path, args.img_size)
val_loader = DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=val_dataset.collate_fn)
optimizer = optim.SGD(model.parameters(),
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay)
scheduler = optim.lr_scheduler.LambdaLR(
optimizer, lr_lambda=lambda epoch: 0.98**epoch)
for epoch in range(args.epochs):
# model.train()
# start_time = time.time()
# for ind, (imgs, targets) in enumerate(train_loader):
# print(ind, imgs.shape)
# batches_done = len(train_loader) * epoch + ind
