Mobile 具有qnnpack精度的奇怪行为

我正在使用

timm

s

mobilenetw2\u 100

import timm
model = timm.create_model('mobilenetv2_100')

和

关于量子化模型的精确性，我得到了一个非常奇怪的行为。如果这个问题引起人们的兴趣，我很乐意提供更多细节，但现在这里有一些线索：

• 使用

  get\u default\u qconfig（“fgemm”）

  我获得了100%的准确性（我只测试了10个样本，所以这是公平的）
• 使用

  get\u default\u qconfig（“qnnpack”）

  我获得了0%的准确率，但请继续阅读以获得有趣的线索
• 如果我只量化一些主干块而不是整个模型，我可以使用

  get\u default\u qconfig（“qnnpack”）

  • 仅量化块[0]->100%
  • [0,2,3,4,5]>100%
  • [0,1,2,3,4,5]>0%
  • [1] ->100%
  • [0,2,3,4,5,6]>0%
  • [6] ->0%
• 所有上述结果都是通过使用常规火炬后端运行模型收集的。当我设置'torch.backends.quantized.engine='qnnpack'时，即使我以前恢复100%的情况也会变为0%

我从这里到哪里去了解发生了什么？我不熟悉量化，所以我不一定知道这些选项

是否存在一些不足/溢出问题，根据量化的块的组合，这些问题只是随机发生的

作为参考，模型块如下所示：

(blocks): Sequential(
    (0): Sequential(
      (0): DepthwiseSeparableConv(
        (conv_dw): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
        (bn1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (se): Identity()
        (conv_pw): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): Identity()
      )
    )
    (1): Sequential(
      (0): InvertedResidual(
        (conv_pw): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)
        (bn2): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): InvertedResidual(
        (conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
        (bn2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (2): Sequential(
      (0): InvertedResidual(
        (conv_pw): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(144, 144, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=144, bias=False)
        (bn2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(144, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): InvertedResidual(
        (conv_pw): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
        (bn2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (2): InvertedResidual(
        (conv_pw): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=192, bias=False)
        (bn2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(192, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (3): Sequential(
      (0): InvertedResidual(
        (conv_pw): Conv2d(32, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(192, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=192, bias=False)
        (bn2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): InvertedResidual(
        (conv_pw): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (bn2): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (2): InvertedResidual(
        (conv_pw): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (bn2): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (3): InvertedResidual(
        (conv_pw): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (bn2): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (4): Sequential(
      (0): InvertedResidual(
        (conv_pw): Conv2d(64, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=384, bias=False)
        (bn2): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(384, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): InvertedResidual(
        (conv_pw): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (bn2): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (2): InvertedResidual(
        (conv_pw): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (bn2): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(576, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (5): Sequential(
      (0): InvertedResidual(
        (conv_pw): Conv2d(96, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(576, 576, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=576, bias=False)
        (bn2): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(576, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (1): InvertedResidual(
        (conv_pw): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (bn2): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (2): InvertedResidual(
        (conv_pw): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (bn2): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (6): Sequential(
      (0): InvertedResidual(
        (conv_pw): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act1): ReLU6(inplace=True)
        (conv_dw): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
        (bn2): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        (act2): ReLU6(inplace=True)
        (se): Identity()
        (conv_pwl): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (bn3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )