py-MDNet/modules/model.py函数

落日映苍穹つ 2022-11-11 05:28 102阅读 0赞

def append_params(params, module, prefix):
        for child in module.children():
            for k,p in child._parameters.items():
                if p is None: continue
    
                if isinstance(child, nn.BatchNorm2d):
                    name = prefix + '_bn_' + k
                else:
                    name = prefix + '_' + k
    
                if name not in params:
                    params[name] = p
                else:
                    raise RuntimeError('Duplicated param name: {:s}'.format(name))
    
    
    
    layers = nn.Sequential(OrderedDict([
                    ('conv1', nn.Sequential(nn.Conv2d(3, 96, kernel_size=7, stride=2),
                                            nn.ReLU(inplace=True),#激活函数，同时inplace设置为True，原值将会被覆盖。
                                            nn.LocalResponseNorm(2),#归一化
                                            nn.MaxPool2d(kernel_size=3, stride=2))),
                    ('conv2', nn.Sequential(nn.Conv2d(96, 256, kernel_size=5, stride=2),
                                            nn.ReLU(inplace=True),
                                            nn.LocalResponseNorm(2),
                                            nn.MaxPool2d(kernel_size=3, stride=2))),
                    ('conv3', nn.Sequential(nn.Conv2d(256, 512, kernel_size=3, stride=1),
                                            nn.ReLU(inplace=True))),
                    ('fc4',   nn.Sequential(nn.Linear(512 * 3 * 3, 512),
                                            nn.ReLU(inplace=True))),
                    ('fc5',   nn.Sequential(nn.Dropout(0.5),
                                            nn.Linear(512, 512),
                                            nn.ReLU(inplace=True)))]))
    # for l in layers:
    #     print(l)
    #
    params = collections.OrderedDict()
    for name, module in layers.named_children():
        append_params(params, module, name)
    #
    
    # for k,p in params.items():
    #     print(k)
    opts = ['fc']
    for h in opts:
        print(h)
    
    #此函数对应于py-MDNet/modules/model.py函数，固定卷积层，设置全连接层。
    #对应论文中，在跟踪过程中，w4：6在线更新，w1：3是固定不变的
    for k, p in params.items():
        if any([k.startswith(l) for l in opts]):
            print("求导数",any([k.startswith('fc')]),k)
    
            p.requires_grad = True
        else:
            print("不求导数",k)
            p.requires_grad = False

运行结果：

fc
    不求导数 conv1_weight
    不求导数 conv1_bias
    不求导数 conv2_weight
    不求导数 conv2_bias
    不求导数 conv3_weight
    不求导数 conv3_bias
    求导数 True fc4_weight
    求导数 True fc4_bias
    求导数 True fc5_weight
    求导数 True fc5_bias