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这是我的模型（float32）的一部分，我将融合它进行量化。我的方法是使用named_modules遍历每个子模块并检查它们是conv2d batchnormlization还是relu。

(scratch): Module(
    (layer1_rn): Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (layer2_rn): Conv2d(40, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (layer3_rn): Conv2d(112, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (layer4_rn): Conv2d(320, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (activation): ReLU()
    (refinenet4): FeatureFusionBlock_custom(
      (out_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (resConfUnit1): ResidualConvUnit_custom(
        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (activation): ReLU()
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (resConfUnit2): ResidualConvUnit_custom(
        (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (activation): ReLU()
        (skip_add): FloatFunctional(
          (activation_post_process): Identity()
        )
      )
      (skip_add): FloatFunctional(
        (activation_post_process): Identity()
      )
    )

for name, module in m.named_modules():
    print(name, module)

我发现如果它错过了scratch.refinenet4.resConfUnit1.activation 同样的事情发生在resConfUnit2中的激活。

这是一个错误吗？

    scratch.layer1_rn Conv2d(24, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
scratch.layer2_rn Conv2d(40, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
scratch.layer3_rn Conv2d(112, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
scratch.layer4_rn Conv2d(320, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
scratch.activation ReLU()
here
scratch.refinenet4 FeatureFusionBlock_custom(
  (out_conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
  (resConfUnit1): ResidualConvUnit_custom(
    (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (activation): ReLU()
    (skip_add): FloatFunctional(
      (activation_post_process): Identity()
    )
  )
  (resConfUnit2): ResidualConvUnit_custom(
    (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (activation): ReLU()
    (skip_add): FloatFunctional(
      (activation_post_process): Identity()
    )
  )
  (skip_add): FloatFunctional(
    (activation_post_process): Identity()
  )
)
scratch.refinenet4.out_conv Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
scratch.refinenet4.resConfUnit1 ResidualConvUnit_custom(
  (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (activation): ReLU()
  (skip_add): FloatFunctional(
    (activation_post_process): Identity()
  )
)
> scratch.refinenet4.resConfUnit1.conv1 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
> scratch.refinenet4.resConfUnit1.conv2 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
> scratch.refinenet4.resConfUnit1.skip_add FloatFunctional(
>   (activation_post_process): Identity()
> )
scratch.refinenet4.resConfUnit1.skip_add.activation_post_process Identity()
scratch.refinenet4.resConfUnit2 ResidualConvUnit_custom(
  (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (activation): ReLU()
  (skip_add): FloatFunctional(
    (activation_post_process): Identity()
  )
)
scratch.refinenet4.resConfUnit2.conv1 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
scratch.refinenet4.resConfUnit2.conv2 Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
scratch.refinenet4.resConfUnit2.skip_add FloatFunctional(
  (activation_post_process): Identity()
)
scratch.refinenet4.resConfUnit2.skip_add.activation_post_process Identity()
scratch.refinenet4.skip_add FloatFunctional(
  (activation_post_process): Identity()
)

...

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