retinanet.py 文件源码

def forward(self, x):

        # don't need resnet_feature_2 as it is too large
        _, resnet_feature_3, resnet_feature_4, resnet_feature_5 = self.resnet(x)

        pyramid_feature_6 = self.pyramid_transformation_6(resnet_feature_5)
        pyramid_feature_7 = self.pyramid_transformation_7(F.relu(pyramid_feature_6))

        pyramid_feature_5 = self.pyramid_transformation_5(resnet_feature_5)
        pyramid_feature_4 = self.pyramid_transformation_4(resnet_feature_4)
        upsampled_feature_5 = self._upsample(pyramid_feature_5, pyramid_feature_4)

        pyramid_feature_4 = self.upsample_transform_1(
            torch.add(upsampled_feature_5, pyramid_feature_4)
        )

        pyramid_feature_3 = self.pyramid_transformation_3(resnet_feature_3)
        upsampled_feature_4 = self._upsample(pyramid_feature_4, pyramid_feature_3)

        pyramid_feature_3 = self.upsample_transform_2(
            torch.add(upsampled_feature_4, pyramid_feature_3)
        )

        return (pyramid_feature_3,
                pyramid_feature_4,
                pyramid_feature_5,
                pyramid_feature_6,
                pyramid_feature_7)