• Paper 地址：https://arxiv.org/abs/1905.11946
  • PyTorch 实现：https://github.com/lukemelas/EfficientNet-PyTorch
  • TensorFlow 实现：https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

目录

一、摘要

二、介绍

三、网络放缩

3.1 问题构造

3.2 维度放缩

3.3 复合放缩

四、网络架构

五、对比实验

六、架构细节

6.1 图示

6.2 代码

七、EfficientNet V2

一、摘要

卷积神经网络 (ConvNets) 通常是在固定资源预算下开发的，若有更多资源可用，则可扩大规模以提高准确率。本文系统地研究了模型放缩，并证实了 仔细地平衡 网络深度、网络宽度和图像分辨率 可带来更好性能。基于这一观察，本文提出了一种新的放缩方法，该方法使用了一个简单但高效的 复合系数 (compound coeffificient) 统一地放缩深度/宽度/分辨率的所有维度。本文证明了这种方法在扩大 MobileNets 和 ResNet 方面的有效性。

更进一步地，本文使用 神经架构搜索 (neural architecture search) 来设计一个新的基线网络，并对其扩展以获得一系列称为 EfficientNets 的模型，它比以前的 ConvNets 的准确性和效率更佳。特别地，EfficientNet-B7 在 ImageNet 上达到了 SOTA 的 84.3% 的 top-1 准确率，同时比现有最好的 ConvNet 小 8.4 倍、推理速度快 6.1 倍。EfficientNets 在 CIFAR-100 (91.7%)、Flowers (98.8%) 和其他 3 个迁移学习数据集上也能很好地迁移，并达到 SOTA 的准确率，而参数少了一个数量级。

二、介绍

扩大 ConvNets 被广泛用于获得更佳精度。例如，ResNet 可通过使用更多层从 ResNet-18 扩大到 ResNet-200；最近，GPipe 通过扩大基线模型 4 倍，获得了 84.3% 的ImageNet top-1 准确度。然而，扩大 ConvNets 的过程从未被很好地理解过，目前有很多方法可以做到这一点。最常见的方法是通过深度或宽度扩大 ConvNets。另一种不太常见但越来越流行的方法是通过图像分辨率扩大模型。以往，通常只按深度/宽度/图像尺寸这三个维度之一来放缩。尽管可以任意放缩两个或三个维度，但需要繁琐的手工调优，且常无法达到最佳的准确度和效率。

在本文中，我们想研究和重新思考扩大 ConvNets 的过程。特别地，我们调查中心问题：是否存在一个原则性方法来扩大 ConvNets，以达到更好的准确率和效率？我们的实证研究表明，平衡网络宽度/深度/分辨率的所有维度是至关重要的，且令人惊讶的是，这种平衡可通过简单地使用固定比例缩放来实现。基于此观察，我们提出了一种简单而有效的复合放缩方法。有别于传统实践中的任意放缩这些因子，我们的方法均匀/统一地缩放网络的宽度、深度及一组固定放缩系数的分辨率。例如，如果我们想使用  倍以上的计算资源，那么我们可以简单地将网络深度增加 ，宽度增加 ，图像大小增加 ，其中 ，， 是在原始小模型上进行小网格搜索所确定的常系数。图 2 阐述了我们的放缩方法和传统方法之间的区别。

直观上，复合放缩方法是有意义的，因为若输入图像更大，那么网络需要 更多层来增加感受野 和 更多的通道来捕捉更细粒度的模式。事实上，之前的理论和实证结果均表明网络宽度和深度之间存在一定的关系，但据我们所知，我们是第一个对网络宽度、深度和分辨率三个维度之间的关系进行实证量化的人。

本文证明了上述复合缩放方法在 MobileNets 和 ResNet 上工作得很好。值得注意的是，模型缩放的有效性在很大程度上依赖于基线网络。更进一步地，我们使用 神经架构搜索 来开发一个新的基线网络，并将其扩展以获得一系列称为 EfficientNets 的模型。图 1 总结了 ImageNet 的性能，其中 EfficientNets 明显优于其他 ConvNets。特别地，EfficientNets-B7 超过了现有的最佳 GPipe 准确度，却减少了 8.4 倍参数、提高了 6.1倍推理速度。与广泛使用的 ResNet-50 相比，EffcientNet-B4 在类似的 FLOPs 下将 top-1 准确度从 76.3% 提高到 83.0%(+6.7%)。除 ImageNet，EfficientNets 也有很好的迁移能力，在 8 个广泛使用的数据集中的 5 个上实现了 SOTA 的准确度，同时减少了比现有 ConvNets 至多 21 倍的参数。

三、网络放缩

3.1 问题构造

3.2 维度放缩

问题 2 的主要难点是最优的 ，， 相互依赖，在不同的资源约束下，其值会发生变化。由于该困难，传统方法主要在以下维度之一中扩大 ConvNets：

  • 深度 

最常用的方法是放缩网络深度，这被许多 ConvNets 使用。直观地，更深的 ConvNets 可以捕获更丰富、更复杂的特征，并很好地泛化与新的任务。然而，由于梯度消失，深度网络的训练也更加困难。虽然诸如 Skip Connection 和 BN 等技术缓解了训练问题，极深网络的准确率增益仍会降低。例如，ResNet-1000 和 ResNet-101 有相似的准确度，尽管前者有更多的层。图3 (中) 显示了对不同深度系数  的基线模型的放缩实证研究，进一步表明 极深的 ConvNets 的准确率回报减少了。

  • 宽度  

放缩网络宽度广泛用于小型模型。更宽的网络往往能够捕捉到更细粒度的特征，也更容易训练。然而。极宽但浅的网络在捕获更高层次的特征时往往存在困难。我们的经验结果显示于 图3 (左) ，当网络因具有更大的  而变得更宽时，准确率很快饱和。

  • 分辨率  

有了更高分辨率的输入图像，ConvNets 可以潜在地捕获更细粒度的模式。从早期 ConvNets 的 224x224 开始，现代的 ConvNets 倾向于使用 299x299 或 331x331 以提高准确率。最近，GPipe 实现了 SOTA 的 ImageNet 准确率，分辨率为 480x480。如 600x600 等更高分辨率，也被广泛应用于目标检测 ConvNets 中。图3 (右) 显示了网络分辨率放缩的结果，更高的分辨率确实提高了准确率，但是对于非常高的分辨率，准确率增益会降低 (分辨率  表示 224x224， 表示 560x560)。

以上分析使我们得出第一个结论:

  • 观察结论 1：扩大网络宽度、深度或分辨率的任意维度都可以提高准确度，但对于更大的模型，准确度增益会降低。

3.3 复合放缩

我们从经验上观察到，不同的放缩维度不是独立的。直观地，对于分辨率较高的图像，应增加网络深度，从而更大的感受野 可以在更大图像中 有助于捕获 包含更多像素的相似特征。相应地，当分辨率较高时，也应增加网络宽度，以便于在高分辨率图像中 捕获 具有更多像素的 更多细粒度模式。这些直觉表明，我们需要协调和平衡不同的放缩维度，而非传统的一维放缩。

为验证我们的直觉，我们比较了不同网络深度和分辨率下的宽度放缩，如图4所示。如果只放缩网络宽度  而不改变深度 () 和分辨率 ()，准确率会很快饱和。具有更深的 () 和更高的分辨率 ()，宽度缩放在相同的 FLOPs 代价下达到更好的准确率。

以上结果使我们得出第二个结论：

  • 观察结论2：为追求更高的准确率和效率，在放缩 ConVnet 时，关键是要平衡网络宽度、深度和分辨率的各个维度。

事实上，之前的一些工作已尝试过任意平衡网络宽度和深度，但均需繁琐的手动调整。

本文提出了一种新的复合放缩方法，利用复合系数有原则地均匀/统一放缩网络宽度、深度和分辨率：

其中，，， 均为常数，可由小网格搜索决定。直观地， 是一个用户特定的系数，用于控制多少资源可用于模型放缩，而 ，， 表明如何分配这些额外资源以分别用于网络宽度、深度和分辨率。 值得注意的是，常规卷积操作的 FLOPS 与 ，， 成正比，即令网络深度加倍将使 FLOPS 加倍，但使网络宽度或分辨率加倍则将使 FLOPS 增加 4 倍。因为卷积操作通常决定了 ConvNets 的运算成本，根据上式放缩一个 ConvNet 将近似增加  的总 FLOPS。在本文，我们约束  以便于对任意新 ，总 FLOPS 将近似增加 。

四、网络架构

由于模型缩放不会改变基线网络中的层算子 ，因此具有一个良好的基线网络也是至关重要的。我们将使用现有的 ConyNets 来评估我们的放缩方法，但为更好地证明我们的放缩方法的有效性。我们还开发了一种新的手机尺寸的基准 —— EfficientNet。

受到启发，我们通过利用 多目标 NAS 来优化基线网络，这种搜索既优化了准确度，也优化了 FLOPS。具体地，使用相同的搜索空间 并 用  作为优化目标，其中  和  表示模型  的准确率和 FLOPS， 是目标 FLOPS 且  是一个超参数，用于控制准确率和 FLOPS 之间的权衡。不同于之前的工作，这里我们优化的是 FLOPS 而非延迟，因为我们不针对任何特定的硬件设备。我们的搜索产生了一个高效的网络 —— EfficientNet - B0。由于我们使用相同的搜索空间，架构类似于 MnasNet，但 EfficientNet - B0 稍微大一些，因其 FLOPS 目标更大 (我们的 FLOPS 目标是 400M)。表 1 显示了 EfficientNet - B0 的架构。其主要构造块 (Building Block) 为 mobile inverted bottleneck MBConv，我们也对其加入了 通道注意力 进行优化。

从基线 EfficientNet - B0 开始，我们通过两个步骤应用我们的复合放缩方法来扩展它：

  • 步骤1：首先固定 ，假设有两倍以上的资源可用，则基于式 2 和 3 对 ，， 进行小网格搜索。特别地，在  的约束下，我们发现 EfficientNet - B0 的最佳值为 。
  • 步骤2：然后将 ，， 固定为常数，并根据式 3 使用不同的  扩大基线网络，从而得到 EfficientNet - B1 ~ EfficientNet - B7 (详见表2)。

值得注意的是，通过直接围绕大模型搜索 ，， 有可能获得更好的性能，但在大模型上的搜索成本会变得昂贵得令人难以接受。我们的方法解决了该问题，即 只在小的基线网络上先进行一次搜索 (步骤1)，然后对所有其他模型使用相同的放缩系数 (步骤2)。

五、对比实验

六、架构细节

6.1 图示

任何网络的 Stem 都是最关键的，确定后才会进行后续实验，上图的 头部/尾部架构 在 EfficientNet - B1 ~ EfficientNet - B7 都有出现，这些块还有不同数量的子块，子块数随版本的增加而增加。以下代码可用于查看模型架构：

import tensorflow as tf
IMG_SHAPE = (224, 224, 3)
model0 = tf.keras.applications.EfficientNetB0(input_shape=IMG_SHAPE, include_top=False, weights="imagenet")
tf.keras.utils.plot_model(model0)  # to draw and visualize
model0.summary()  # to see the list of layers and parameters

其中，EfficientNet-B0 总层数为 237、EfficientNet-B7 总层数为 813，但其实都可由以下 5 个模块和上述 2 个头部/尾部架构构成。

由上述 5 个模块 (Module) 构成以下三种块 (Block)：

其实 Sub-block1~3 分别对应以下 EfficientNet 中的 Module 1、Module 2、Module 3 不要混淆了

至此，所有用于构建 EfficientNet 的块已经确定，以下是各模型的架构 (×n means that modules inside the bracket are repeated n times)：

  • EfficientNet-B0

  • EfficientNet-B1

  • EfficientNet-B2

结构与 EfficientNet-B1 相同，唯一的区别是通道数不同，从而增加了参数量。

  • EfficientNet-B3

  • EfficientNet-B4

  • EfficientNet-B5

  • EfficientNet-B6

  • EfficientNet-B7

综上，容易看出各模型之间的差异 —— 子块数量逐渐增多。下表显示了 EfficientNet-B0 中卷积核尺寸、分辨率大小、通道数和层数：

下表展示了不同网络架构下，各阶段的通道数 (各阶段分辨率大小一一对应)：

6.2 代码

EfficientNet-PyTorch/efficientnet_pytorch/model.py 

"""model.py - Model and module class for EfficientNet.They are built to mirror those in the official TensorFlow implementation.
"""# Author: lukemelas (github username)
# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
# With adjustments and added comments by workingcoder (github username).import torch
from torch import nn
from torch.nn import functional as F
from .utils import (round_filters,round_repeats,drop_connect,get_same_padding_conv2d,get_model_params,efficientnet_params,load_pretrained_weights,Swish,MemoryEfficientSwish,calculate_output_image_size
)VALID_MODELS = ('efficientnet-b0', 'efficientnet-b1', 'efficientnet-b2', 'efficientnet-b3','efficientnet-b4', 'efficientnet-b5', 'efficientnet-b6', 'efficientnet-b7','efficientnet-b8',# Support the construction of 'efficientnet-l2' without pretrained weights'efficientnet-l2'
)class MBConvBlock(nn.Module):"""Mobile Inverted Residual Bottleneck Block.Args:block_args (namedtuple): BlockArgs, defined in utils.py.global_params (namedtuple): GlobalParam, defined in utils.py.image_size (tuple or list): [image_height, image_width].References:[1] https://arxiv.org/abs/1704.04861 (MobileNet v1)[2] https://arxiv.org/abs/1801.04381 (MobileNet v2)[3] https://arxiv.org/abs/1905.02244 (MobileNet v3)"""def __init__(self, block_args, global_params, image_size=None):super().__init__()self._block_args = block_argsself._bn_mom = 1 - global_params.batch_norm_momentum  # pytorch's difference from tensorflowself._bn_eps = global_params.batch_norm_epsilonself.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1)self.id_skip = block_args.id_skip  # whether to use skip connection and drop connect# Expansion phase (Inverted Bottleneck)inp = self._block_args.input_filters  # number of input channelsoup = self._block_args.input_filters * self._block_args.expand_ratio  # number of output channelsif self._block_args.expand_ratio != 1:Conv2d = get_same_padding_conv2d(image_size=image_size)self._expand_conv = Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False)self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)# image_size = calculate_output_image_size(image_size, 1) <-- this wouldn't modify image_size# Depthwise convolution phasek = self._block_args.kernel_sizes = self._block_args.strideConv2d = get_same_padding_conv2d(image_size=image_size)self._depthwise_conv = Conv2d(in_channels=oup, out_channels=oup, groups=oup,  # groups makes it depthwisekernel_size=k, stride=s, bias=False)self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps)image_size = calculate_output_image_size(image_size, s)# Squeeze and Excitation layer, if desiredif self.has_se:Conv2d = get_same_padding_conv2d(image_size=(1, 1))num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio))self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1)self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1)# Pointwise convolution phasefinal_oup = self._block_args.output_filtersConv2d = get_same_padding_conv2d(image_size=image_size)self._project_conv = Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False)self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps)self._swish = MemoryEfficientSwish()def forward(self, inputs, drop_connect_rate=None):"""MBConvBlock's forward function.Args:inputs (tensor): Input tensor.drop_connect_rate (bool): Drop connect rate (float, between 0 and 1).Returns:Output of this block after processing."""# Expansion and Depthwise Convolutionx = inputsif self._block_args.expand_ratio != 1:x = self._expand_conv(inputs)x = self._bn0(x)x = self._swish(x)x = self._depthwise_conv(x)x = self._bn1(x)x = self._swish(x)# Squeeze and Excitationif self.has_se:x_squeezed = F.adaptive_avg_pool2d(x, 1)x_squeezed = self._se_reduce(x_squeezed)x_squeezed = self._swish(x_squeezed)x_squeezed = self._se_expand(x_squeezed)x = torch.sigmoid(x_squeezed) * x# Pointwise Convolutionx = self._project_conv(x)x = self._bn2(x)# Skip connection and drop connectinput_filters, output_filters = self._block_args.input_filters, self._block_args.output_filtersif self.id_skip and self._block_args.stride == 1 and input_filters == output_filters:# The combination of skip connection and drop connect brings about stochastic depth.if drop_connect_rate:x = drop_connect(x, p=drop_connect_rate, training=self.training)x = x + inputs  # skip connectionreturn xdef set_swish(self, memory_efficient=True):"""Sets swish function as memory efficient (for training) or standard (for export).Args:memory_efficient (bool): Whether to use memory-efficient version of swish."""self._swish = MemoryEfficientSwish() if memory_efficient else Swish()class EfficientNet(nn.Module):"""EfficientNet model.Most easily loaded with the .from_name or .from_pretrained methods.Args:blocks_args (list[namedtuple]): A list of BlockArgs to construct blocks.global_params (namedtuple): A set of GlobalParams shared between blocks.References:[1] https://arxiv.org/abs/1905.11946 (EfficientNet)Example:>>> import torch>>> from efficientnet.model import EfficientNet>>> inputs = torch.rand(1, 3, 224, 224)>>> model = EfficientNet.from_pretrained('efficientnet-b0')>>> model.eval()>>> outputs = model(inputs)"""def __init__(self, blocks_args=None, global_params=None):super().__init__()assert isinstance(blocks_args, list), 'blocks_args should be a list'assert len(blocks_args) > 0, 'block args must be greater than 0'self._global_params = global_paramsself._blocks_args = blocks_args# Batch norm parametersbn_mom = 1 - self._global_params.batch_norm_momentumbn_eps = self._global_params.batch_norm_epsilon# Get stem static or dynamic convolution depending on image sizeimage_size = global_params.image_sizeConv2d = get_same_padding_conv2d(image_size=image_size)# Stemin_channels = 3  # rgbout_channels = round_filters(32, self._global_params)  # number of output channelsself._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)self._bn0 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)image_size = calculate_output_image_size(image_size, 2)# Build blocksself._blocks = nn.ModuleList([])for block_args in self._blocks_args:# Update block input and output filters based on depth multiplier.block_args = block_args._replace(input_filters=round_filters(block_args.input_filters, self._global_params),output_filters=round_filters(block_args.output_filters, self._global_params),num_repeat=round_repeats(block_args.num_repeat, self._global_params))# The first block needs to take care of stride and filter size increase.self._blocks.append(MBConvBlock(block_args, self._global_params, image_size=image_size))image_size = calculate_output_image_size(image_size, block_args.stride)if block_args.num_repeat > 1:  # modify block_args to keep same output sizeblock_args = block_args._replace(input_filters=block_args.output_filters, stride=1)for _ in range(block_args.num_repeat - 1):self._blocks.append(MBConvBlock(block_args, self._global_params, image_size=image_size))# image_size = calculate_output_image_size(image_size, block_args.stride)  # stride = 1# Headin_channels = block_args.output_filters  # output of final blockout_channels = round_filters(1280, self._global_params)Conv2d = get_same_padding_conv2d(image_size=image_size)self._conv_head = Conv2d(in_channels, out_channels, kernel_size=1, bias=False)self._bn1 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps)# Final linear layerself._avg_pooling = nn.AdaptiveAvgPool2d(1)if self._global_params.include_top:self._dropout = nn.Dropout(self._global_params.dropout_rate)self._fc = nn.Linear(out_channels, self._global_params.num_classes)# set activation to memory efficient swish by defaultself._swish = MemoryEfficientSwish()def set_swish(self, memory_efficient=True):"""Sets swish function as memory efficient (for training) or standard (for export).Args:memory_efficient (bool): Whether to use memory-efficient version of swish."""self._swish = MemoryEfficientSwish() if memory_efficient else Swish()for block in self._blocks:block.set_swish(memory_efficient)def extract_endpoints(self, inputs):"""Use convolution layer to extract featuresfrom reduction levels i in [1, 2, 3, 4, 5].Args:inputs (tensor): Input tensor.Returns:Dictionary of last intermediate featureswith reduction levels i in [1, 2, 3, 4, 5].Example:>>> import torch>>> from efficientnet.model import EfficientNet>>> inputs = torch.rand(1, 3, 224, 224)>>> model = EfficientNet.from_pretrained('efficientnet-b0')>>> endpoints = model.extract_endpoints(inputs)>>> print(endpoints['reduction_1'].shape)  # torch.Size([1, 16, 112, 112])>>> print(endpoints['reduction_2'].shape)  # torch.Size([1, 24, 56, 56])>>> print(endpoints['reduction_3'].shape)  # torch.Size([1, 40, 28, 28])>>> print(endpoints['reduction_4'].shape)  # torch.Size([1, 112, 14, 14])>>> print(endpoints['reduction_5'].shape)  # torch.Size([1, 320, 7, 7])>>> print(endpoints['reduction_6'].shape)  # torch.Size([1, 1280, 7, 7])"""endpoints = dict()# Stemx = self._swish(self._bn0(self._conv_stem(inputs)))prev_x = x# Blocksfor idx, block in enumerate(self._blocks):drop_connect_rate = self._global_params.drop_connect_rateif drop_connect_rate:drop_connect_rate *= float(idx) / len(self._blocks)  # scale drop connect_ratex = block(x, drop_connect_rate=drop_connect_rate)if prev_x.size(2) > x.size(2):endpoints['reduction_{}'.format(len(endpoints) + 1)] = prev_xelif idx == len(self._blocks) - 1:endpoints['reduction_{}'.format(len(endpoints) + 1)] = xprev_x = x# Headx = self._swish(self._bn1(self._conv_head(x)))endpoints['reduction_{}'.format(len(endpoints) + 1)] = xreturn endpointsdef extract_features(self, inputs):"""use convolution layer to extract feature .Args:inputs (tensor): Input tensor.Returns:Output of the final convolutionlayer in the efficientnet model."""# Stemx = self._swish(self._bn0(self._conv_stem(inputs)))# Blocksfor idx, block in enumerate(self._blocks):drop_connect_rate = self._global_params.drop_connect_rateif drop_connect_rate:drop_connect_rate *= float(idx) / len(self._blocks)  # scale drop connect_ratex = block(x, drop_connect_rate=drop_connect_rate)# Headx = self._swish(self._bn1(self._conv_head(x)))return xdef forward(self, inputs):"""EfficientNet's forward function.Calls extract_features to extract features, applies final linear layer, and returns logits.Args:inputs (tensor): Input tensor.Returns:Output of this model after processing."""# Convolution layersx = self.extract_features(inputs)# Pooling and final linear layerx = self._avg_pooling(x)if self._global_params.include_top:x = x.flatten(start_dim=1)x = self._dropout(x)x = self._fc(x)return x@classmethoddef from_name(cls, model_name, in_channels=3, **override_params):"""Create an efficientnet model according to name.Args:model_name (str): Name for efficientnet.in_channels (int): Input data's channel number.override_params (other key word params):Params to override model's global_params.Optional key:'width_coefficient', 'depth_coefficient','image_size', 'dropout_rate','num_classes', 'batch_norm_momentum','batch_norm_epsilon', 'drop_connect_rate','depth_divisor', 'min_depth'Returns:An efficientnet model."""cls._check_model_name_is_valid(model_name)blocks_args, global_params = get_model_params(model_name, override_params)model = cls(blocks_args, global_params)model._change_in_channels(in_channels)return model@classmethoddef from_pretrained(cls, model_name, weights_path=None, advprop=False,in_channels=3, num_classes=1000, **override_params):"""Create an efficientnet model according to name.Args:model_name (str): Name for efficientnet.weights_path (None or str):str: path to pretrained weights file on the local disk.None: use pretrained weights downloaded from the Internet.advprop (bool):Whether to load pretrained weightstrained with advprop (valid when weights_path is None).in_channels (int): Input data's channel number.num_classes (int):Number of categories for classification.It controls the output size for final linear layer.override_params (other key word params):Params to override model's global_params.Optional key:'width_coefficient', 'depth_coefficient','image_size', 'dropout_rate','batch_norm_momentum','batch_norm_epsilon', 'drop_connect_rate','depth_divisor', 'min_depth'Returns:A pretrained efficientnet model."""model = cls.from_name(model_name, num_classes=num_classes, **override_params)load_pretrained_weights(model, model_name, weights_path=weights_path,load_fc=(num_classes == 1000), advprop=advprop)model._change_in_channels(in_channels)return model@classmethoddef get_image_size(cls, model_name):"""Get the input image size for a given efficientnet model.Args:model_name (str): Name for efficientnet.Returns:Input image size (resolution)."""cls._check_model_name_is_valid(model_name)_, _, res, _ = efficientnet_params(model_name)return res@classmethoddef _check_model_name_is_valid(cls, model_name):"""Validates model name.Args:model_name (str): Name for efficientnet.Returns:bool: Is a valid name or not."""if model_name not in VALID_MODELS:raise ValueError('model_name should be one of: ' + ', '.join(VALID_MODELS))def _change_in_channels(self, in_channels):"""Adjust model's first convolution layer to in_channels, if in_channels not equals 3.Args:in_channels (int): Input data's channel number."""if in_channels != 3:Conv2d = get_same_padding_conv2d(image_size=self._global_params.image_size)out_channels = round_filters(32, self._global_params)self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False)

EfficientNet-PyTorch/efficientnet_pytorch/utils.py

"""utils.py - Helper functions for building the model and for loading model parameters.These helper functions are built to mirror those in the official TensorFlow implementation.
"""# Author: lukemelas (github username)
# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
# With adjustments and added comments by workingcoder (github username).import re
import math
import collections
from functools import partial
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import model_zoo################################################################################
# Help functions for model architecture
################################################################################# GlobalParams and BlockArgs: Two namedtuples
# Swish and MemoryEfficientSwish: Two implementations of the method
# round_filters and round_repeats:
#     Functions to calculate params for scaling model width and depth ! ! !
# get_width_and_height_from_size and calculate_output_image_size
# drop_connect: A structural design
# get_same_padding_conv2d:
#     Conv2dDynamicSamePadding
#     Conv2dStaticSamePadding
# get_same_padding_maxPool2d:
#     MaxPool2dDynamicSamePadding
#     MaxPool2dStaticSamePadding
#     It's an additional function, not used in EfficientNet,
#     but can be used in other model (such as EfficientDet).# Parameters for the entire model (stem, all blocks, and head)
GlobalParams = collections.namedtuple('GlobalParams', ['width_coefficient', 'depth_coefficient', 'image_size', 'dropout_rate','num_classes', 'batch_norm_momentum', 'batch_norm_epsilon','drop_connect_rate', 'depth_divisor', 'min_depth', 'include_top'])# Parameters for an individual model block
BlockArgs = collections.namedtuple('BlockArgs', ['num_repeat', 'kernel_size', 'stride', 'expand_ratio','input_filters', 'output_filters', 'se_ratio', 'id_skip'])# Set GlobalParams and BlockArgs's defaults
GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields)
BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields)# Swish activation function
if hasattr(nn, 'SiLU'):Swish = nn.SiLU
else:# For compatibility with old PyTorch versionsclass Swish(nn.Module):def forward(self, x):return x * torch.sigmoid(x)# A memory-efficient implementation of Swish function
class SwishImplementation(torch.autograd.Function):@staticmethoddef forward(ctx, i):result = i * torch.sigmoid(i)ctx.save_for_backward(i)return result@staticmethoddef backward(ctx, grad_output):i = ctx.saved_tensors[0]sigmoid_i = torch.sigmoid(i)return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i)))class MemoryEfficientSwish(nn.Module):def forward(self, x):return SwishImplementation.apply(x)def round_filters(filters, global_params):"""Calculate and round number of filters based on width multiplier.Use width_coefficient, depth_divisor and min_depth of global_params.Args:filters (int): Filters number to be calculated.global_params (namedtuple): Global params of the model.Returns:new_filters: New filters number after calculating."""multiplier = global_params.width_coefficientif not multiplier:return filters# TODO: modify the params names.#       maybe the names (width_divisor,min_width)#       are more suitable than (depth_divisor,min_depth).divisor = global_params.depth_divisormin_depth = global_params.min_depthfilters *= multipliermin_depth = min_depth or divisor  # pay attention to this line when using min_depth# follow the formula transferred from official TensorFlow implementationnew_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)if new_filters < 0.9 * filters:  # prevent rounding by more than 10%new_filters += divisorreturn int(new_filters)def round_repeats(repeats, global_params):"""Calculate module's repeat number of a block based on depth multiplier.Use depth_coefficient of global_params.Args:repeats (int): num_repeat to be calculated.global_params (namedtuple): Global params of the model.Returns:new repeat: New repeat number after calculating."""multiplier = global_params.depth_coefficientif not multiplier:return repeats# follow the formula transferred from official TensorFlow implementationreturn int(math.ceil(multiplier * repeats))def drop_connect(inputs, p, training):"""Drop connect.Args:input (tensor: BCWH): Input of this structure.p (float: 0.0~1.0): Probability of drop connection.training (bool): The running mode.Returns:output: Output after drop connection."""assert 0 <= p <= 1, 'p must be in range of [0,1]'if not training:return inputsbatch_size = inputs.shape[0]keep_prob = 1 - p# generate binary_tensor mask according to probability (p for 0, 1-p for 1)random_tensor = keep_probrandom_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device)binary_tensor = torch.floor(random_tensor)output = inputs / keep_prob * binary_tensorreturn outputdef get_width_and_height_from_size(x):"""Obtain height and width from x.Args:x (int, tuple or list): Data size.Returns:size: A tuple or list (H,W)."""if isinstance(x, int):return x, xif isinstance(x, list) or isinstance(x, tuple):return xelse:raise TypeError()def calculate_output_image_size(input_image_size, stride):"""Calculates the output image size when using Conv2dSamePadding with a stride.Necessary for static padding. Thanks to mannatsingh for pointing this out.Args:input_image_size (int, tuple or list): Size of input image.stride (int, tuple or list): Conv2d operation's stride.Returns:output_image_size: A list [H,W]."""if input_image_size is None:return Noneimage_height, image_width = get_width_and_height_from_size(input_image_size)stride = stride if isinstance(stride, int) else stride[0]image_height = int(math.ceil(image_height / stride))image_width = int(math.ceil(image_width / stride))return [image_height, image_width]# Note:
# The following 'SamePadding' functions make output size equal ceil(input size/stride).
# Only when stride equals 1, can the output size be the same as input size.
# Don't be confused by their function names ! ! !def get_same_padding_conv2d(image_size=None):"""Chooses static padding if you have specified an image size, and dynamic padding otherwise.Static padding is necessary for ONNX exporting of models.Args:image_size (int or tuple): Size of the image.Returns:Conv2dDynamicSamePadding or Conv2dStaticSamePadding."""if image_size is None:return Conv2dDynamicSamePaddingelse:return partial(Conv2dStaticSamePadding, image_size=image_size)class Conv2dDynamicSamePadding(nn.Conv2d):"""2D Convolutions like TensorFlow, for a dynamic image size.The padding is operated in forward function by calculating dynamically."""# Tips for 'SAME' mode padding.#     Given the following:#         i: width or height#         s: stride#         k: kernel size#         d: dilation#         p: padding#     Output after Conv2d:#         o = floor((i+p-((k-1)*d+1))/s+1)# If o equals i, i = floor((i+p-((k-1)*d+1))/s+1),# => p = (i-1)*s+((k-1)*d+1)-idef __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True):super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias)self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2def forward(self, x):ih, iw = x.size()[-2:]kh, kw = self.weight.size()[-2:]sh, sw = self.strideoh, ow = math.ceil(ih / sh), math.ceil(iw / sw)  # change the output size according to stride ! ! !pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)if pad_h > 0 or pad_w > 0:x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)class Conv2dStaticSamePadding(nn.Conv2d):"""2D Convolutions like TensorFlow's 'SAME' mode, with the given input image size.The padding mudule is calculated in construction function, then used in forward."""# With the same calculation as Conv2dDynamicSamePaddingdef __init__(self, in_channels, out_channels, kernel_size, stride=1, image_size=None, **kwargs):super().__init__(in_channels, out_channels, kernel_size, stride, **kwargs)self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2# Calculate padding based on image size and save itassert image_size is not Noneih, iw = (image_size, image_size) if isinstance(image_size, int) else image_sizekh, kw = self.weight.size()[-2:]sh, sw = self.strideoh, ow = math.ceil(ih / sh), math.ceil(iw / sw)pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)if pad_h > 0 or pad_w > 0:self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2,pad_h // 2, pad_h - pad_h // 2))else:self.static_padding = nn.Identity()def forward(self, x):x = self.static_padding(x)x = F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)return xdef get_same_padding_maxPool2d(image_size=None):"""Chooses static padding if you have specified an image size, and dynamic padding otherwise.Static padding is necessary for ONNX exporting of models.Args:image_size (int or tuple): Size of the image.Returns:MaxPool2dDynamicSamePadding or MaxPool2dStaticSamePadding."""if image_size is None:return MaxPool2dDynamicSamePaddingelse:return partial(MaxPool2dStaticSamePadding, image_size=image_size)class MaxPool2dDynamicSamePadding(nn.MaxPool2d):"""2D MaxPooling like TensorFlow's 'SAME' mode, with a dynamic image size.The padding is operated in forward function by calculating dynamically."""def __init__(self, kernel_size, stride, padding=0, dilation=1, return_indices=False, ceil_mode=False):super().__init__(kernel_size, stride, padding, dilation, return_indices, ceil_mode)self.stride = [self.stride] * 2 if isinstance(self.stride, int) else self.strideself.kernel_size = [self.kernel_size] * 2 if isinstance(self.kernel_size, int) else self.kernel_sizeself.dilation = [self.dilation] * 2 if isinstance(self.dilation, int) else self.dilationdef forward(self, x):ih, iw = x.size()[-2:]kh, kw = self.kernel_sizesh, sw = self.strideoh, ow = math.ceil(ih / sh), math.ceil(iw / sw)pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)if pad_h > 0 or pad_w > 0:x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2])return F.max_pool2d(x, self.kernel_size, self.stride, self.padding,self.dilation, self.ceil_mode, self.return_indices)class MaxPool2dStaticSamePadding(nn.MaxPool2d):"""2D MaxPooling like TensorFlow's 'SAME' mode, with the given input image size.The padding mudule is calculated in construction function, then used in forward."""def __init__(self, kernel_size, stride, image_size=None, **kwargs):super().__init__(kernel_size, stride, **kwargs)self.stride = [self.stride] * 2 if isinstance(self.stride, int) else self.strideself.kernel_size = [self.kernel_size] * 2 if isinstance(self.kernel_size, int) else self.kernel_sizeself.dilation = [self.dilation] * 2 if isinstance(self.dilation, int) else self.dilation# Calculate padding based on image size and save itassert image_size is not Noneih, iw = (image_size, image_size) if isinstance(image_size, int) else image_sizekh, kw = self.kernel_sizesh, sw = self.strideoh, ow = math.ceil(ih / sh), math.ceil(iw / sw)pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0)pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0)if pad_h > 0 or pad_w > 0:self.static_padding = nn.ZeroPad2d((pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2))else:self.static_padding = nn.Identity()def forward(self, x):x = self.static_padding(x)x = F.max_pool2d(x, self.kernel_size, self.stride, self.padding,self.dilation, self.ceil_mode, self.return_indices)return x################################################################################
# Helper functions for loading model params
################################################################################# BlockDecoder: A Class for encoding and decoding BlockArgs
# efficientnet_params: A function to query compound coefficient
# get_model_params and efficientnet:
#     Functions to get BlockArgs and GlobalParams for efficientnet
# url_map and url_map_advprop: Dicts of url_map for pretrained weights
# load_pretrained_weights: A function to load pretrained weightsclass BlockDecoder(object):"""Block Decoder for readability,straight from the official TensorFlow repository."""@staticmethoddef _decode_block_string(block_string):"""Get a block through a string notation of arguments.Args:block_string (str): A string notation of arguments.Examples: 'r1_k3_s11_e1_i32_o16_se0.25_noskip'.Returns:BlockArgs: The namedtuple defined at the top of this file."""assert isinstance(block_string, str)ops = block_string.split('_')options = {}for op in ops:splits = re.split(r'(\d.*)', op)if len(splits) >= 2:key, value = splits[:2]options[key] = value# Check strideassert (('s' in options and len(options['s']) == 1) or(len(options['s']) == 2 and options['s'][0] == options['s'][1]))return BlockArgs(num_repeat=int(options['r']),kernel_size=int(options['k']),stride=[int(options['s'][0])],expand_ratio=int(options['e']),input_filters=int(options['i']),output_filters=int(options['o']),se_ratio=float(options['se']) if 'se' in options else None,id_skip=('noskip' not in block_string))@staticmethoddef _encode_block_string(block):"""Encode a block to a string.Args:block (namedtuple): A BlockArgs type argument.Returns:block_string: A String form of BlockArgs."""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]if 0 < block.se_ratio <= 1:args.append('se%s' % block.se_ratio)if block.id_skip is False:args.append('noskip')return '_'.join(args)@staticmethoddef decode(string_list):"""Decode a list of string notations to specify blocks inside the network.Args:string_list (list[str]): A list of strings, each string is a notation of block.Returns:blocks_args: A list of BlockArgs namedtuples of block args."""assert isinstance(string_list, list)blocks_args = []for block_string in string_list:blocks_args.append(BlockDecoder._decode_block_string(block_string))return blocks_args@staticmethoddef encode(blocks_args):"""Encode a list of BlockArgs to a list of strings.Args:blocks_args (list[namedtuples]): A list of BlockArgs namedtuples of block args.Returns:block_strings: A list of strings, each string is a notation of block."""block_strings = []for block in blocks_args:block_strings.append(BlockDecoder._encode_block_string(block))return block_stringsdef efficientnet_params(model_name):"""Map EfficientNet model name to parameter coefficients.Args:model_name (str): Model name to be queried.Returns:params_dict[model_name]: A (width,depth,res,dropout) tuple."""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]def efficientnet(width_coefficient=None, depth_coefficient=None, image_size=None,dropout_rate=0.2, drop_connect_rate=0.2, num_classes=1000, include_top=True):"""Create BlockArgs and GlobalParams for efficientnet model.Args:width_coefficient (float)depth_coefficient (float)image_size (int)dropout_rate (float)drop_connect_rate (float)num_classes (int)Meaning as the name suggests.Returns:blocks_args, global_params."""# Blocks args for the whole model(efficientnet-b0 by default)# It will be modified in the construction of EfficientNet Class according to modelblocks_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',]blocks_args = BlockDecoder.decode(blocks_args)global_params = GlobalParams(width_coefficient=width_coefficient,depth_coefficient=depth_coefficient,image_size=image_size,dropout_rate=dropout_rate,num_classes=num_classes,batch_norm_momentum=0.99,batch_norm_epsilon=1e-3,drop_connect_rate=drop_connect_rate,depth_divisor=8,min_depth=None,include_top=include_top,)return blocks_args, global_paramsdef get_model_params(model_name, override_params):"""Get the block args and global params for a given model name.Args:model_name (str): Model's name.override_params (dict): A dict to modify global_params.Returns:blocks_args, global_params"""if model_name.startswith('efficientnet'):w, d, s, p = efficientnet_params(model_name)# note: all models have drop connect rate = 0.2blocks_args, global_params = efficientnet(width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s)else:raise NotImplementedError('model name is not pre-defined: {}'.format(model_name))if override_params:# ValueError will be raised here if override_params has fields not included in global_params.global_params = global_params._replace(**override_params)return blocks_args, global_params# train with Standard methods
# check more details in paper(EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks)
url_map = {'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth','efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth','efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth','efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth','efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth','efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth','efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth','efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth',
}# train with Adversarial Examples(AdvProp)
# check more details in paper(Adversarial Examples Improve Image Recognition)
url_map_advprop = {'efficientnet-b0': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b0-b64d5a18.pth','efficientnet-b1': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b1-0f3ce85a.pth','efficientnet-b2': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b2-6e9d97e5.pth','efficientnet-b3': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b3-cdd7c0f4.pth','efficientnet-b4': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b4-44fb3a87.pth','efficientnet-b5': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b5-86493f6b.pth','efficientnet-b6': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b6-ac80338e.pth','efficientnet-b7': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b7-4652b6dd.pth','efficientnet-b8': 'https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/adv-efficientnet-b8-22a8fe65.pth',
}# TODO: add the petrained weights url map of 'efficientnet-l2'def load_pretrained_weights(model, model_name, weights_path=None, load_fc=True, advprop=False, verbose=True):"""Loads pretrained weights from weights path or download using url.Args:model (Module): The whole model of efficientnet.model_name (str): Model name of efficientnet.weights_path (None or str):str: path to pretrained weights file on the local disk.None: use pretrained weights downloaded from the Internet.load_fc (bool): Whether to load pretrained weights for fc layer at the end of the model.advprop (bool): Whether to load pretrained weightstrained with advprop (valid when weights_path is None)."""if isinstance(weights_path, str):state_dict = torch.load(weights_path)else:# AutoAugment or Advprop (different preprocessing)url_map_ = url_map_advprop if advprop else url_mapstate_dict = model_zoo.load_url(url_map_[model_name])if load_fc:ret = model.load_state_dict(state_dict, strict=False)assert not ret.missing_keys, 'Missing keys when loading pretrained weights: {}'.format(ret.missing_keys)else:state_dict.pop('_fc.weight')state_dict.pop('_fc.bias')ret = model.load_state_dict(state_dict, strict=False)assert set(ret.missing_keys) == set(['_fc.weight', '_fc.bias']), 'Missing keys when loading pretrained weights: {}'.format(ret.missing_keys)assert not ret.unexpected_keys, 'Missing keys when loading pretrained weights: {}'.format(ret.unexpected_keys)if verbose:print('Loaded pretrained weights for {}'.format(model_name))

七、EfficientNet V2

参考资料：

【论文解读】一文看懂EfficientnetB0~B7模型所有细节 - 知乎

后ResNet时代的顶流EfficientNet

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