def get_body()

in sagemaker/src/handwriting_line_recognition.py [0:0]

• 26 lines of code
• 1 McCabe index (conditional complexity)

    def get_body(self, resnet_layer_id):
        '''
        Create the feature extraction network based on resnet34.
        The first layer of the res-net is converted into grayscale by averaging the weights of the 3 channels
        of the original resnet.
        
        Parameters
        ----------
        resnet_layer_id: int
            The resnet_layer_id specifies which layer to take from 
            the bottom of the network.
        Returns
        -------
        network: gluon.nn.HybridSequential
            The body network for feature extraction based on resnet
        '''
        
        pretrained = resnet34_v1(pretrained=True, ctx=self.ctx)
        pretrained_2 = resnet34_v1(pretrained=True, ctx=mx.cpu(0))
        first_weights = pretrained_2.features[0].weight.data().mean(axis=1).expand_dims(axis=1)
        # First weights could be replaced with individual channels.
        
        body = gluon.nn.HybridSequential()
        with body.name_scope():
            first_layer = gluon.nn.Conv2D(channels=64, kernel_size=(7, 7), padding=(3, 3), strides=(2, 2), in_channels=1, use_bias=False)
            first_layer.initialize(mx.init.Xavier(), ctx=self.ctx)
            first_layer.weight.set_data(first_weights)
            body.add(first_layer)
            body.add(*pretrained.features[1:-resnet_layer_id])
        
        return body