def forward(self, fms_high, fms_low):
        """
        :param fms_high:  High level Feature map. Tensor.
        :param fms_low: Low level Feature map. Tensor.
        """
        _, _, h, w = fms_low.shape

        #
        fms_low = self.conv1x1_low(fms_low)
        fms_low= self.bn_low(fms_low)
        sa_avg_out = self.sa_sigmoid(self.sa_conv(torch.mean(fms_low, dim=1, keepdim=True)))

        #
        fms_high = self.conv1x1_high(fms_high)
        fms_high = self.bn_high(fms_high)
        ca_avg_out = self.ca_sigmoid(self.relu(self.ca_conv(self.avg_pool(fms_high))))

        #
        fms_high_up = F.interpolate(fms_high, size=(h,w), mode='bilinear', align_corners=True)
        fms_sa_att = sa_avg_out * fms_high_up
        #
        fms_ca_att = ca_avg_out * fms_low

        out = fms_ca_att + fms_sa_att

        return out

        :param fms_high:  High level Feature map. Tensor.
        :param fms_low: Low level Feature map. Tensor.
        
model/FPENet.py