duc_hdc.py 文件源码

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

        for n, m in self.layer3.named_modules():
            if 'conv2' in n or 'downsample.0' in n:
                m.stride = (1, 1)
        for n, m in self.layer4.named_modules():
            if 'conv2' in n or 'downsample.0' in n:
                m.stride = (1, 1)
        layer3_group_config = [1, 2, 5, 9]
        for idx in range(len(self.layer3)):
            self.layer3[idx].conv2.dilation = (layer3_group_config[idx % 4], layer3_group_config[idx % 4])
            self.layer3[idx].conv2.padding = (layer3_group_config[idx % 4], layer3_group_config[idx % 4])
        layer4_group_config = [5, 9, 17]
        for idx in range(len(self.layer4)):
            self.layer4[idx].conv2.dilation = (layer4_group_config[idx], layer4_group_config[idx])
            self.layer4[idx].conv2.padding = (layer4_group_config[idx], layer4_group_config[idx])

        self.duc = _DenseUpsamplingConvModule(8, 2048, num_classes)