EfficientNet Backbone结构解析 -- 以EfficientNet-B0为例说明

一般而言，不论我们是否要将该主干结构用于何种模型，一般都是在这个基础上进行的。例如，将在Yolact中用EfficientNet替换Resnet，网上有说可以在同等效果下让模型数据量降为原来的大约1/4到1/5左右。

下面我列出一下主干结构。

self._conv_stem

Conv2dStaticSamePadding(
  (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), bias=False)
)

self._blocks

模型参数参考utils.py -- > efficientnet -- > block_args，所有的blocks都是基于这个参数自动派生出来的，

 blocks_args = [
        'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25',
        'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25',
        'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25',
        'r1_k3_s11_e6_i192_o320_se0.25',
    ]

其中的参数意义如下

  args = [
  'r%d' % block.num_repeat,
  'k%d' % block.kernel_size,
  's%d%d' % (block.strides[0], block.strides[1]),
  'e%s' % block.expand_ratio,
  'i%d' % block.input_filters,
  'o%d' % block.output_filters
  ]

在生成模型时，根据输入的模型名称（utils.py)，选择不同的（width, depth, resolution, dropout）参数，例如efficientnet-b0的参数就是(1.0, 1.0, 224, 0.2)，

def efficientnet_params(model_name):
    """ Map EfficientNet model name to parameter coefficients. """
    params_dict = {
        # Coefficients:   width,depth,res,dropout
        'efficientnet-b0': (1.0, 1.0, 224, 0.2),
        'efficientnet-b1': (1.0, 1.1, 240, 0.2),
        'efficientnet-b2': (1.1, 1.2, 260, 0.3),
        'efficientnet-b3': (1.2, 1.4, 300, 0.3),
        'efficientnet-b4': (1.4, 1.8, 380, 0.4),
        'efficientnet-b5': (1.6, 2.2, 456, 0.4),
        'efficientnet-b6': (1.8, 2.6, 528, 0.5),
        'efficientnet-b7': (2.0, 3.1, 600, 0.5),
        'efficientnet-b8': (2.2, 3.6, 672, 0.5),
        'efficientnet-l2': (4.3, 5.3, 800, 0.5),
    }
    return params_dict[model_name]

在后面生成blocks时，这个depth会和前面提到的 block_args中的num_repeat相乘，以最终决定生成多少个子block；width则会和filters(也就是channel)相乘，以决定输出的filter数目。具体可参考下面两个函数：

def round_filters(filters, global_params):
    """ Calculate and round number of filters based on depth multiplier. """
    multiplier = global_params.width_coefficient
    if not multiplier:
        return filters
    divisor = global_params.depth_divisor
    min_depth = global_params.min_depth
    filters *= multiplier
    min_depth = min_depth or divisor
    new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)
    if new_filters < 0.9 * filters:  # prevent rounding by more than 10%
        new_filters += divisor
    return int(new_filters)
​
​
def round_repeats(repeats, global_params):
    """ Round number of filters based on depth multiplier. """
    multiplier = global_params.depth_coefficient
    if not multiplier:
        return repeats
    return int(math.ceil(multiplier * repeats))

其ModuleList共计有(r1+r2+r2+r3+r3+r4+r1) = 16层，如下，​

ModuleList(
(0): MBConvBlock(
(_depthwise_conv): Conv2dStaticSamePadding( (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1,), groups=32, bias=False) )
(_bn1): BatchNorm2d(32, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(8, 32, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(16, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(1): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(     (conv): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(  (conv): Conv2d(96, 96, kernel_size=(3, 3), stride=(2,), groups=96, bias=False) )
(_bn1): BatchNorm2d(96, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(       (conv): Conv2d(96, 4, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(       (conv): Conv2d(4, 96, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(    (conv): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(2): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(     (conv): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(  (conv): Conv2d(144,144, kernel_size=(3,3), stride=(1,1), groups=144,bias=False))
(_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(       (conv): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(       (conv): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(    (conv): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(3): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(     (conv): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(  (conv): Conv2d(144,144,kernel_size=(5,5), stride=(2,), groups=144, bias=False) )
(_bn1): BatchNorm2d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(       (conv): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(       (conv): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(    (conv): Conv2d(144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(4): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(    (conv): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding( (conv): Conv2d(240,240, kernel_size=(5,5), stride=(1,1), groups=240,bias=False))
(_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(5): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(    (conv): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding( (conv): Conv2d(240, 240, kernel_size=(3, 3), stride=(2,), groups=240, bias=False)  )
(_bn1): BatchNorm2d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))   )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False) )
(_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(6): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(   (conv): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)   )
(_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding((conv): Conv2d(480,480, kernel_size=(3,3), stride=(1,1), groups=480,bias=False))
(_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(     (conv): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(     (conv): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(  (conv): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(7): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(    (conv): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding((conv):  Conv2d(480,480, kernel_size=(3,3), stride=(1,1), groups=480, bias=False))
(_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(80, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(8): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(     (conv): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding(
(conv): Conv2d(480, 480, kernel_size=(5, 5), stride=(1,), groups=480, bias=False)
)
(_bn1): BatchNorm2d(480, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(9): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(    (conv): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding((conv):  Conv2d(672,672, kernel_size=(5,5), stride=(1,1), groups=672, bias=False))
(_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(10): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(    (conv): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding((conv):  Conv2d(672,672, kernel_size=(5,5), stride=(1,1), groups=672, bias=False))
(_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(112, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(11): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(    (conv): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding( (conv): Conv2d(672, 672, kernel_size=(5,5), stride=(2,), groups=672, bias=False))
(_bn1): BatchNorm2d(672, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(12): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(    (conv): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding( (conv):Conv2d(1152, 1152, kernel_size=(5,5), stride=(1,1), groups=1152,bias=False)
)
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(13): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(    (conv): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding((conv):  Conv2d(1152,1152, kernel_size=(5,5), stride=(1,1), groups=1152,bias=False))
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(14): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(    (conv): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding((conv):  Conv2d(1152,1152, kernel_size=(5,5), stride=(1,1), groups=1152,bias=False))
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(192, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
(15): MBConvBlock(
(_expand_conv): Conv2dStaticSamePadding(    (conv): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn0): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_depthwise_conv): Conv2dStaticSamePadding( (conv): Conv2d(1152,1152, kernel_size=(3,3), stride=(1,),groups=1152,bias=False))
(_bn1): BatchNorm2d(1152, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_se_reduce): Conv2dStaticSamePadding(      (conv): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))    )
(_se_expand): Conv2dStaticSamePadding(      (conv): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))    )
(_project_conv): Conv2dStaticSamePadding(   (conv): Conv2d(1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)    )
(_bn2): BatchNorm2d(320, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)
(_swish): MemoryEfficientSwish()
)
)

这里，注意到1, 3, 5, 11 层的结果其stride都是[2, 2]，一般在这里输出 feature map，加上最后一层（第15层），共计5层，也就是FPN和各层。EfficientDet就是利用这5层作为Feature Map的输入，事实上EfficientDet只用到了最后3层（p3, p4, p5)，p5上采样生成p6，p7，这5层通过BiFPN操作后，最终生成class和box prediction layer，这些就不在这里具体展开了，可参考backbone.py：GitHub - zylo117/Yet-Another-EfficientDet-Pytorch: The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.。 

后面的_conv_head，_fc，还有一些没列出来的averagepool等在EfficientDet应用中没有用到，直接删除掉了。

self._conv_head

Conv2dStaticSamePadding(
  (conv): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
)

self._fc

Linear(in_features=1280, out_features=1000, bias=True)

主要参考模型：

GitHub - zylo117/Yet-Another-EfficientDet-Pytorch: The pytorch re-implement of the official efficientdet with SOTA performance in real time and pretrained weights.

GitHub - lukemelas/EfficientNet-PyTorch: A PyTorch implementation of EfficientNet and EfficientNetV2 (coming soon!)