# 是否加载之前保存的模型
if args.resume:
print(' # Resuming from checkpoint # ')
checkpoint = torch.load(opt.ckpt_path)
net.load_state_dict(checkpoint['net'])
best_loss = checkpoint['loss']
start_epoch = checkpoint['epoch']
# 加载预训练的模型
else:
print(' # Loading pretrained model # ')
net.load_state_dict(torch.load(opt.pretrained_model))
criterion = MultiBoxLoss()
if use_cuda:
net.cuda()
criterion.cuda()
cudnn.benchmark = True
optimizer = optim.SGD(net.parameters(),
lr=args.lr,
momentum=0.9,
weight_decay=1e-4)
print('## SSD Build success ##')
#for param in net.parameters():
# if param.requires_grad==True:
# print('param autograd')
# break
def train(epoch):
#random_seed
random.seed(opt.seed)
np.random.seed(opt.seed)
torch.manual_seed(opt.seed)
torch.cuda.manual_seed_all(opt.seed)
PRNG = RandomState(opt.seed)
# model
model = SSD(opt.n_classes)
cfg = model.config
model.init_parameters(opt.pretrainedvgg)
criterion = MultiBoxLoss()
model.cuda()
criterion.cuda()
cudnn.benchmark = True
#print(cfg)
#print('')
#dataload
dataset = data.loader(cfg, opt.augmentation, opt.data_path ,PRNG)
print('size of dataset:', len(dataset))
# optimizer
optimizer = optim.SGD(model.parameters(), lr=opt.lr, momentum=opt.momentum, weight_decay=opt.weight_decay)
def train():