ssn_models.py 文件源码

def _construct_flow_model(self, base_model):
        # modify the convolution layers
        # Torch models are usually defined in a hierarchical way.
        # nn.modules.children() return all sub modules in a DFS manner
        modules = list(self.base_model.modules())
        first_conv_idx = list(filter(lambda x: isinstance(modules[x], nn.Conv2d), list(range(len(modules)))))[0]
        conv_layer = modules[first_conv_idx]
        container = modules[first_conv_idx - 1]

        # modify parameters, assume the first blob contains the convolution kernels
        params = [x.clone() for x in conv_layer.parameters()]
        kernel_size = params[0].size()
        new_kernel_size = kernel_size[:1] + (2 * self.new_length,) + kernel_size[2:]
        new_kernels = params[0].data.mean(dim=1, keepdim=True).expand(new_kernel_size).contiguous()

        new_conv = nn.Conv2d(2 * self.new_length, conv_layer.out_channels,
                             conv_layer.kernel_size, conv_layer.stride, conv_layer.padding,
                             bias=True if len(params) == 2 else False)
        new_conv.weight.data = new_kernels
        if len(params) == 2:
            new_conv.bias.data = params[1].data  # add bias if neccessary
        layer_name = list(container.state_dict().keys())[0][:-7]  # remove .weight suffix to get the layer name

        # replace the first convlution layer
        setattr(container, layer_name, new_conv)
        return base_model