请问如果使用自己的feature-imitation方法应该在哪里添加，您的feature-imitation代码写在哪里了，我没有找到

[HikariTJU]

计算imitation区域的在 https://github.com/HikariTJU/LD/blob/bc60bbcd48e9305e61e32a0d2c981621e2ae0d05/mmdet/models/dense_heads/ld_head.py#L580

计算loss的在 https://github.com/HikariTJU/LD/blob/bc60bbcd48e9305e61e32a0d2c981621e2ae0d05/mmdet/models/dense_heads/ld_head.py#L170

[yg333]

请问我的环境都是对的 但是您的源码训练就报错，弄了好几天，实在没辙了

[HikariTJU]

请使用标准模板报告bug， 我推荐你可以先试一下单卡能不能跑

python tools/train.py ${config}

以下是标准模板

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[yg333]

谢谢您，单卡训练就可以了。还有个问题我没搞清楚。比如我想用Masked Generative Distillation论文的特征图蒸馏方法。我只需要改这段代码就可以了吗？谢谢您的及时回复

  if self.imitation_method == 'gibox':
            gi_idx = self.get_gi_region(soft_label, cls_score, anchors,
                                        bbox_pred, soft_targets, stride)
            gi_teacher = teacher_x[gi_idx]
            gi_student = x[gi_idx]

            loss_im = self.loss_im(gi_student, gi_teacher)

[HikariTJU]

1. 单卡能跑多卡不能跑说明，你可能多卡的launch命令错了
2. 你需要写一个函数计算蒸馏区域，然后把计算loss改成对应蒸馏区域的教师和学生特征图。很难用文字描述，你需要自己意会一下，参考get_im_region函数

[yg333]

可是特征图蒸馏区域不是整个feature map吗，经过一系列网络模块得到最终的学生特征图后，直接与教师特征图做L2损失。我不太理解您说的这个蒸馏区域是啥？