结合源码分析EfficientNet

作者: 海盗船长_coco | 来源:发表于2020-02-03 15:25 被阅读0次

结合源码分析EfficientNet
EfficientNet
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论文地址：https://arxiv.org/abs/1905.11946
pytorch代码地址:https://github.com/lukemelas/EfficientNet-PyTorch

论文主要思想

该论文主要讲述模型缩放对模型性能的影响。通过对网络深度（层数）、宽度（通道数）以及输入图片的分辨率的调整来获取更佳的网络模型。最终获得的模型相比原来的模型不仅参数量少，而且准确率高。论文主要是依靠下图进行论述

各参数对模型准确率与运算次数的影响
传统的模型缩放方法一般只调整一个变量，例如：ResNet通过对网络层数的缩放，获得ResNet-18和ResNet-200。MobileNets通过对通道数的缩放来调整模型。也有通过增大输入图片的大小来提取模型的准确率。
本文方法为在现有baseline网络下同时调整层数、通道数和分辨率三个变量来缩放模型，以获得更好的准确率和运行速度。因此baseline网络的模型设计也非常重要，文中的EfficientNet-B0模型为下图:

EfficientNet-B0网络模型
后续工作便为调整d,w,r三个参数以达到最佳状态，对于EfficientNet-B0网络，将φ 设1，最后联合搜索出的最佳参数为a=1.2，β=1.1，γ=1.15。调整的公式为

联合调整3个参数的公式

EfficientNet-B0网络模型细节（代码）

整体模型中的特征提取过程主要由stem,blocks和head构成，最后将获得的feature map用全连接层做类型判别。

EfficientNet(
  (_conv_stem): Conv2dStaticSamePadding(
    3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False
    (static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
  )
  (_bn0): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
  (_blocks): ModuleList(
    (0): MBConvBlock(
      (_depthwise_conv): Conv2dStaticSamePadding(
        32, 32, kernel_size=(3, 3), stride=[1, 1], groups=32, bias=False
        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
      )
      (_bn1): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        32, 8, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        8, 32, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(16, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (1): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        96, 96, kernel_size=(3, 3), stride=[2, 2], groups=96, bias=False
        (static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
      )
      (_bn1): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        96, 4, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        4, 96, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (2): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        144, 144, kernel_size=(3, 3), stride=(1, 1), groups=144, bias=False
        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
      )
      (_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        144, 6, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        6, 144, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (3): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        144, 144, kernel_size=(5, 5), stride=[2, 2], groups=144, bias=False
        (static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)
      )
      (_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        144, 6, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        6, 144, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (4): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        240, 240, kernel_size=(5, 5), stride=(1, 1), groups=240, bias=False
        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
      )
      (_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        240, 10, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        10, 240, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (5): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        240, 240, kernel_size=(3, 3), stride=[2, 2], groups=240, bias=False
        (static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
      )
      (_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        240, 10, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        10, 240, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (6): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        480, 480, kernel_size=(3, 3), stride=(1, 1), groups=480, bias=False
        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
      )
      (_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        480, 20, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        20, 480, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (7): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        480, 480, kernel_size=(3, 3), stride=(1, 1), groups=480, bias=False
        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
      )
      (_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        480, 20, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        20, 480, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (8): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        480, 480, kernel_size=(5, 5), stride=[1, 1], groups=480, bias=False
        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
      )
      (_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        480, 20, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        20, 480, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (9): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        672, 672, kernel_size=(5, 5), stride=(1, 1), groups=672, bias=False
        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
      )
      (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        672, 28, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        28, 672, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (10): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        672, 672, kernel_size=(5, 5), stride=(1, 1), groups=672, bias=False
        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
      )
      (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        672, 28, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        28, 672, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (11): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        672, 672, kernel_size=(5, 5), stride=[2, 2], groups=672, bias=False
        (static_padding): ZeroPad2d(padding=(1, 2, 1, 2), value=0.0)
      )
      (_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        672, 28, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        28, 672, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (12): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False
        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
      )
      (_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        1152, 48, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        48, 1152, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (13): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False
        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
      )
      (_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        1152, 48, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        48, 1152, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (14): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False
        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2), value=0.0)
      )
      (_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        1152, 48, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        48, 1152, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
    (15): MBConvBlock(
      (_expand_conv): Conv2dStaticSamePadding(
        192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_depthwise_conv): Conv2dStaticSamePadding(
        1152, 1152, kernel_size=(3, 3), stride=[1, 1], groups=1152, bias=False
        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
      )
      (_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        1152, 48, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        48, 1152, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(320, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )
  )
  (_conv_head): Conv2dStaticSamePadding(
    320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False
    (static_padding): Identity()
  )
  (_bn1): BatchNorm2d(1280, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
  (_avg_pooling): AdaptiveAvgPool2d(output_size=1)
  (_dropout): Dropout(p=0.2, inplace=False)
  (_fc): Linear(in_features=1280, out_features=1000, bias=True)
  (_swish): MemoryEfficientSwish()
)

以224x224的一张彩色图片为例，其维度为[1,3,224,224]。
stem的前向传播，其中swish在之后的运算中大量使用，命名字面意思说能够有效利用内存，但还不能理解其中原理。

(_conv_stem): Conv2dStaticSamePadding(
    3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False
    (static_padding): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)
)
(_bn0): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()

维度变化：[1,3,224,224]--pad-->[1,3,225,225]--conv-->[1,32,112,112]--bn0-->[1,32,112,112]--swish-->[1,32,112,112]
blocks的前向传播，以其中第一个MBConvBlock为例：

  (0): MBConvBlock(
      (_depthwise_conv): Conv2dStaticSamePadding(
        32, 32, kernel_size=(3, 3), stride=[1, 1], groups=32, bias=False
        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1), value=0.0)
      )
      (_bn1): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_se_reduce): Conv2dStaticSamePadding(
        32, 8, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_se_expand): Conv2dStaticSamePadding(
        8, 32, kernel_size=(1, 1), stride=(1, 1)
        (static_padding): Identity()
      )
      (_project_conv): Conv2dStaticSamePadding(
        32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
        (static_padding): Identity()
      )
      (_bn2): BatchNorm2d(16, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
      (_swish): MemoryEfficientSwish()
    )

前向传播中的维度变化
其中depthwise深度卷积的讲解可以参考该篇博客https://www.jianshu.com/p/38dc74d12fcf?utm_source=oschina-app
经过blocks层的特征提取，可以得到[1,320,7,7]的feature map。
head层设计

  (_conv_head): Conv2dStaticSamePadding(
    320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False
    (static_padding): Identity()
  )
  (_bn1): BatchNorm2d(1280, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
  (_swish): MemoryEfficientSwish()

前向传播维度变化：[1,320,7,7]--head-->[1,1280,7,7]--bn1-->[1,1280,7,7]--swish-->[1,1280,7,7]
池化层及最后的全连接层

  (_avg_pooling): AdaptiveAvgPool2d(output_size=1)
[1,1280]--dropout-->[1,1280]--fc-->[1,1000]
  (_dropout): Dropout(p=0.2, inplace=False)
  (_fc): Linear(in_features=1280, out_features=1000, bias=True)

经过conv_head后的维度[1,1280,7,7]--avg_pool-->[1,1280,1,1]--view-->[1,1280]--dropout-->[1,1280]--fc-->[1,1000]，一张图片的最终输出为[1,1000]，因为预测类别数目为1000。至此网络的前向传播过程已经讲完。
网络中存在dropout和drop connect两种对于过拟合的解决方法，两者的区别可以参考该博客https://www.cnblogs.com/tornadomeet/p/3430312.html
最后通过修改a，β，γ及不同的φ 参数来缩放baseline网络，以获得EfficientNet-B1到B7。最后对于如何获取最佳的参数，只有不停的去试，但我们是否有大量的硬件设备和时间呢?这也是十分尴尬的情况。

参考博客：

depthwise的讲解：https://www.jianshu.com/p/38dc74d12fcf?utm_source=oschina-app
dropout和drop connect的区别：https://www.cnblogs.com/tornadomeet/p/3430312.html
对EfficieentNet的评价：https://www.zhihu.com/question/326833457/answer/700322601

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