def forward()

in models/modules.py [0:0]

• 35 lines of code
• 13 McCabe index (conditional complexity)

    def forward(self, input):
        # call get_weight, which samples from the subspace, then use the corresponding weight.
        w, b = self.get_weight()

        # The rest is code in the PyTorch source forward pass for batchnorm.
        if self.momentum is None:
            exponential_average_factor = 0.0
        else:
            exponential_average_factor = self.momentum

        if self.training and self.track_running_stats:
            if self.num_batches_tracked is not None:
                self.num_batches_tracked = self.num_batches_tracked + 1
                if self.momentum is None:  # use cumulative moving average
                    exponential_average_factor = 1.0 / float(
                        self.num_batches_tracked
                    )
                else:  # use exponential moving average
                    exponential_average_factor = self.momentum
        if self.training:
            bn_training = True
        else:
            bn_training = (self.running_mean is None) and (
                self.running_var is None
            )
        return F.batch_norm(
            input,
            # If buffers are not to be tracked, ensure that they won't be updated
            self.running_mean
            if not self.training or self.track_running_stats
            else None,
            self.running_var
            if not self.training or self.track_running_stats
            else None,
            w,
            b,
            bn_training,
            exponential_average_factor,
            self.eps,
        )