def call()

in tensorflow/sagemakercv/detection/backbones/resnet.py [0:0]

• 29 lines of code
• 12 McCabe index (conditional complexity)

    def call(self, inputs, training=False):
        """
        Args:
        inputs: `Tensor` of size `[batch, channels, height, width]`.

        Returns:
        The output `Tensor` of the block.
        """

        try:
            # Projection shortcut in first layer to match filters and strides
            shortcut = self._local_layers["projection"]["conv2d"](inputs=inputs)

            if self.norm_type == 'batchnorm':
                shortcut = self._local_layers["projection"]["batchnorm"](
                    inputs=shortcut,
                    training=training and self._trainable and self._finetune_bn
                )
            elif self.norm_type == 'groupnorm':
                shortcut = self._local_layers["projection"]["groupnorm"](
                    inputs=shortcut,
                    training=training 
                )

        except KeyError:
            shortcut = inputs

        net = inputs

        for i in range(1, 3):
            net = self._local_layers["conv2d_%d" % i](inputs=net)
            if self.norm_type == 'batchnorm':
                net = self._local_layers["batchnorm_%d" % i](
                    inputs=net,
                    training=training and self._trainable and self._finetune_bn
                )
            elif self.norm_type == 'groupnorm':
                net = self._local_layers["groupnorm_%d" % i](
                    inputs=net,
                    training=training
                )

        return self._local_layers["activation"](net + shortcut)