def __init__(self):
self.model = 'models/r2d2_WASF_N16.pt'
self.tag = 'r2d2' # output file tag
self.top_k = 2000 # number of keypoints
self.scale_f = 2**0.25
self.min_size = 256
self.max_size = 1024
self.min_scale = 0
self.max_scale = 1
self.reliability_thr = 0.7
self.repeatability_thr = 0.7
self.gpu = [0]
self.iscuda = common.torch_set_gpu(self.gpu)
self.net = None
# load_network
checkpoint = torch.load(self.model)
print("\n>> Creating net = " + checkpoint['net'])
net = eval(checkpoint['net'])
nb_of_weights = common.model_size(net)
#print(f"( Model size: {nb_of_weights/1000:.0f}K parameters )")
# initialization
weights = checkpoint['state_dict']
net.load_state_dict(
{k.replace('module.', ''): v
for k, v in weights.items()})
self.net = net.eval()
def load_network(model_fn):
checkpoint = torch.load(model_fn)
print("\n>> Creating net = " + checkpoint['net'])
net = eval(checkpoint['net'])
nb_of_weights = common.model_size(net)
print(f" ( Model size: {nb_of_weights/1000:.0f}K parameters )")
# initialization
weights = checkpoint['state_dict']
net.load_state_dict({k.replace('module.',''):v for k,v in weights.items()})
return net.eval()
def load_network(model_fn):
checkpoint = torch.load(model_fn)
print("\n>> Creating net = " + checkpoint['net'])
# checkpoint['net'] = "Quad_L2Net_ConfCFS()" 字符串
# checkpoint['state_dict'] = Dict 存储网络的参数值
net = eval(checkpoint['net'])
# 这里就是执行函数 Quad_L2Net_ConfCFS() 返回一个网络
nb_of_weights = common.model_size(net)
print(f" ( Model size: {nb_of_weights / 1000:.0f}K parameters )")
# 初始化
weights = checkpoint['state_dict']
# 这里将k(key，即层的名称)修改个名字后和k对应的一个数组重新组成一个新的dict
net.load_state_dict(
{k.replace('module.', ''): v
for k, v in weights.items()})
return net.eval()