gcn.py 文件源码

def __init__(self, num_classes, input_size, pretrained=True):
        super(GCN, self).__init__()
        self.input_size = input_size
        resnet = models.resnet152()
        if pretrained:
            resnet.load_state_dict(torch.load(res152_path))
        self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu)
        self.layer1 = nn.Sequential(resnet.maxpool, resnet.layer1)
        self.layer2 = resnet.layer2
        self.layer3 = resnet.layer3
        self.layer4 = resnet.layer4

        self.gcm1 = _GlobalConvModule(2048, num_classes, (7, 7))
        self.gcm2 = _GlobalConvModule(1024, num_classes, (7, 7))
        self.gcm3 = _GlobalConvModule(512, num_classes, (7, 7))
        self.gcm4 = _GlobalConvModule(256, num_classes, (7, 7))

        self.brm1 = _BoundaryRefineModule(num_classes)
        self.brm2 = _BoundaryRefineModule(num_classes)
        self.brm3 = _BoundaryRefineModule(num_classes)
        self.brm4 = _BoundaryRefineModule(num_classes)
        self.brm5 = _BoundaryRefineModule(num_classes)
        self.brm6 = _BoundaryRefineModule(num_classes)
        self.brm7 = _BoundaryRefineModule(num_classes)
        self.brm8 = _BoundaryRefineModule(num_classes)
        self.brm9 = _BoundaryRefineModule(num_classes)

        initialize_weights(self.gcm1, self.gcm2, self.gcm3, self.gcm4, self.brm1, self.brm2, self.brm3,
                           self.brm4, self.brm5, self.brm6, self.brm7, self.brm8, self.brm9)