fbresnet200.py 文件源码

def __init__(self, num_classes=1000, activation_fn=nn.ReLU(), drop_rate=0., global_pool='avg'):
        super(ResNet200, self).__init__()
        self.drop_rate = drop_rate
        self.global_pool = global_pool
        self.features = fbresnet200_features(activation_fn=activation_fn)
        self.fc = nn.Linear(2048 * pooling_factor(global_pool), num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()