# Finetuning the convnet
# ----------------------
#
# Load a pretrainied model and reset final fully connected layer.
#
if opt.views == 2:
model = two_view_net(len(class_names),
droprate=opt.droprate,
stride=opt.stride,
pool=opt.pool,
share_weight=opt.share)
elif opt.views == 3:
model = three_view_net(len(class_names),
droprate=opt.droprate,
stride=opt.stride,
pool=opt.pool,
share_weight=opt.share)
opt.nclasses = len(class_names)
print(model)
# For resume:
if start_epoch >= 40:
opt.lr = opt.lr * 0.1
ignored_params = list(map(id, model.classifier.parameters()))
base_params = filter(lambda p: id(p) not in ignored_params, model.parameters())
optimizer_ft = optim.SGD([{
'params': base_params,
'lr': 0.1 * opt.lr
def load_network(name, opt):
# Load config
dirname = os.path.join('./model',name)
last_model_name = os.path.basename(get_model_list(dirname, 'net'))
epoch = last_model_name.split('_')[1]
epoch = epoch.split('.')[0]
if not epoch=='last':
epoch = int(epoch)
config_path = os.path.join(dirname,'opts.yaml')
with open(config_path, 'r') as stream:
config = yaml.load(stream)
opt.name = config['name']
opt.data_dir = config['data_dir']
opt.train_all = config['train_all']
opt.droprate = config['droprate']
opt.color_jitter = config['color_jitter']
opt.batchsize = config['batchsize']
opt.h = config['h']
opt.w = config['w']
opt.share = config['share']
opt.stride = config['stride']
if 'pool' in config:
opt.pool = config['pool']
if 'h' in config:
opt.h = config['h']
opt.w = config['w']
if 'gpu_ids' in config:
opt.gpu_ids = config['gpu_ids']
opt.erasing_p = config['erasing_p']
opt.lr = config['lr']
opt.nclasses = config['nclasses']
opt.erasing_p = config['erasing_p']
opt.use_dense = config['use_dense']
opt.fp16 = config['fp16']
opt.views = config['views']
if opt.use_dense:
model = ft_net_dense(opt.nclasses, opt.droprate, opt.stride, None, opt.pool)
if opt.PCB:
model = PCB(opt.nclasses)
if opt.views == 2:
model = two_view_net(opt.nclasses, opt.droprate, stride = opt.stride, pool = opt.pool, share_weight = opt.share)
elif opt.views == 3:
model = three_view_net(opt.nclasses, opt.droprate, stride = opt.stride, pool = opt.pool, share_weight = opt.share)
if 'use_vgg16' in config:
opt.use_vgg16 = config['use_vgg16']
if opt.views == 2:
model = two_view_net(opt.nclasses, opt.droprate, stride = opt.stride, pool = opt.pool, share_weight = opt.share, VGG16 = opt.use_vgg16)
elif opt.views == 3:
model = three_view_net(opt.nclasses, opt.droprate, stride = opt.stride, pool = opt.pool, share_weight = opt.share, VGG16 = opt.use_vgg16)
# load model
if isinstance(epoch, int):
save_filename = 'net_%03d.pth'% epoch
else:
save_filename = 'net_%s.pth'% epoch
save_path = os.path.join('./model',name,save_filename)
print('Load the model from %s'%save_path)
network = model
network.load_state_dict(torch.load(save_path))
return network, opt, epoch
