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Some questions about quantization

See original GitHub issue

您好,我在量化RepVGG到INT8时遇到一些问题: 完全按照您在README中提到的量化流程,先插入BN后QAT量化,掉点非常严重。以大模型(b3g4)为例,在ImageNet分类任务上量化后仅有20左右的accuracy。 但假如将代码修改为:` torch.quantization.fuse_modules(m, [‘conv’], inplace=True)` 可以直接运行QAT量化,则掉点只有2.7%,调参后掉点大约1.7%。(参数为epochs=20 batch_size:与训练时bs保持一致 lr=1e-3 weight_decay=55e-6) 但对于分类任务而言量化后掉点也有点多,不知道这样做是否正确?请问您有其他量化方法的见解吗?

Issue Analytics

  • State:open
  • Created 2 years ago
  • Comments:9

github_iconTop GitHub Comments

Desmond-97commented, Apr 18, 2022

@twmht 我后来尝试直接用PTQ量化效果会好很多,TensorRT实现PTQ量化,用交叉熵做校准loss,RepVGG_A0模型在ImageNet分类任务上掉点仅0.16%。 我后来思考QAT量化掉点多的原因可能是:在QAT阶段用的模型是融合后的单分支推理模型,用这个模型进行QAT的finetune会使得模型退化,类似VGG的精度。但我并没有尝试用训练阶段模型进行QAT量化。

Desmond-97commented, May 2, 2022

@hobbitlzy 没有用insert_bn。

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