new_state_dict = vgg.state_dict()
dd = net.state_dict()
for k in new_state_dict.keys():
print(k)
if k in dd.keys() and k.startswith('features'):
print('yes')
dd[k] = new_state_dict[k]
net.load_state_dict(dd)
if use_gpu:
print('this computer has gpu %d and current is %s' %
(torch.cuda.device_count(), torch.cuda.current_device()))
net.cuda()
# ---------------------损失函数---------------------
criterion = yoloLoss(7, 2, 5, 0.5)
# ---------------------优化器----------------------
# different learning rate
params = []
params_dict = dict(net.named_parameters())
for key, value in params_dict.items():
if key.startswith('features'):
params += [{'params': [value], 'lr': learning_rate * 1}]
else:
params += [{'params': [value], 'lr': learning_rate}]
optimizer = torch.optim.SGD(params,
lr=learning_rate,
momentum=0.9,
weight_decay=5e-4)
else:
vgg = models.vgg16_bn(pretrained=True)
new_state_dict = vgg.state_dict()
dd = net.state_dict()
for k in new_state_dict.keys():
if k in dd.keys() and k.startswith('features'):
dd[k] = new_state_dict[k]
net.load_state_dict(dd)
if use_gpu:
print('this computer has gpu %d and current is %s' %
(torch.cuda.device_count(), torch.cuda.current_device()))
net.cuda()
# ---------------------损失函数---------------------
criterion = yoloLoss(7, 2, 5, 0.5) # S=7,B=2,coord=5,noobj=0.5
# ---------------------优化器----------------------
# different learning rate
params = []
params_dict = dict(net.named_parameters())
for key, value in params_dict.items():
if key.startswith('features'):
params += [{'params': [value], 'lr': learning_rate * 1}]
else:
params += [{'params': [value], 'lr': learning_rate}]
optimizer = torch.optim.SGD(params,
lr=learning_rate,
momentum=0.9,
weight_decay=5e-4)