Xception Pytorch代码复现

文章目录

• 深度可分离卷积
• Inception发展
• • GoogleNet
  • Inception Network
  • • inception V1
    • inception V2
    • inception V3
    • inception V4
• Xception

参考文章 经典卷积架构的PyTorch实现：Xception
参考文章 卷积神经网络结构简述（二）Inception系列网络
github 项目 Xception backbone

深度可分离卷积

在轻量级的网络中，会有深度可分离卷积 depthwise separable convolution，由depthwise 和pointwise组合，用于提取feature map。
depthwise convolution的一个卷积核只负责一个通道，输入与输出的通道数相同，无法扩展featuremap，没有有效利用不同通道在相同空间位置上的feature信息，因此需要pointwise covolution将feature map进行有效组合。

depthwise convolution， $N=3\ast 3\ast 3$

pointwise convolution, $N=1\ast 1\ast 3\ast 4$

如果是常规卷积操作的话，需要用到$3\ast 3\ast 4$的参数，因此使用深度可分离卷积能有效减少参数的个数。

class SeparableConv2d(nn.Module):
    def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,dilation=1,bias=False):
        super(SeparableConv2d,self).__init__()

        self.conv1 = nn.Conv2d(in_channels,in_channels,kernel_size,stride,padding,dilation,groups=in_channels,bias=bias)
        self.pointwise = nn.Conv2d(in_channels,out_channels,1,1,0,1,1,bias=bias)

    def forward(self,x):
        x = self.conv1(x)
        x = self.pointwise(x)
        return x

Inception发展

GoogleNet

与VGG等网络以为堆叠深度不同，为了解决随着网络加深面临的参数数量过多，过拟合的问题，GoogleNet率先提出了卷积核的并行合并。与VGG相比有以下改进：

• 使用不同大小的kernel，网络在每一层中都可以学习到稀疏特征（$3\ast 3$, $5\ast 5$ kernel）与非稀疏特征($1\ast 1$ kernel)。
• 增加网络宽度，增加网络对尺度的适应性。
• 通过不同block的concat，获得非线性属性。
  但同样面临着随着网络深度加深计算量大的问题。因此借鉴Network in network思想，使用$1\ast 1$的卷积核进行降维操作，减小网络的参数量，即inception V1。

Inception Network

inception V1

based on inception V1的结构，inception V2和V3的改进遵循以下原则：

• 特征图从输入到输出应该缓慢减小
• 在卷积网络中增加非线性可以使网络训练更快
• 在大尺度卷积之前先对输入进行降维不会影响特征的表达能力。即在空间聚合可以以低维度嵌入进行。
• 平衡网络的深度和宽度。

inception V2

inception V2 使用了：

• BN层
• 使用两个$3\ast 3$的卷积核代替了$5\ast 5$的卷积核，具有更少的参数，间接增加了网络的深度。

由于inception V2采用的是先pooling再inception的结构，会造成表达的瓶颈问题，即特征图的大小不能急剧缩减，只经过一层就骤降，会丢失大量信息，对模型训练造成困难。

inception V3

inception V3 采用并联降维的结构，即经过conv和pool然后concat出结果。


35-17

17-8

inception V4

在inception V3的基础上，将网络前几层的卷积、池化操作替换为stem模块，加深网络结构。

Xception

Xception 将 inception中的inception模块替换成深度可分离卷积。

网络引入ResNet思想，引入skip connection，每个卷积后都做了BN操作。

"""
Xception is adapted from https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/xception.py

Ported to pytorch thanks to [tstandley](https://github.com/tstandley/Xception-PyTorch)
@author: tstandley
Adapted by cadene
Creates an Xception Model as defined in:
Francois Chollet
Xception: Deep Learning with Depthwise Separable Convolutions
https://arxiv.org/pdf/1610.02357.pdf
This weights ported from the Keras implementation. Achieves the following performance on the validation set:
Loss:0.9173 Prec@1:78.892 Prec@5:94.292
REMEMBER to set your image size to 3x299x299 for both test and validation
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5],
                                  std=[0.5, 0.5, 0.5])
The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
"""
from __future__ import print_function, division, absolute_import
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch.nn import init

__all__ = ['xception']

pretrained_settings = {
    'xception': {
        'imagenet': {
            'url': 'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth',
            'input_space': 'RGB',
            'input_size': [3, 299, 299],
            'input_range': [0, 1],
            'mean': [0.5, 0.5, 0.5],
            'std': [0.5, 0.5, 0.5],
            'num_classes': 1000,
            'scale': 0.8975 # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
        }
    }
}


class SeparableConv2d(nn.Module):
    def __init__(self,in_channels,out_channels,kernel_size=1,stride=1,padding=0,dilation=1,bias=False):
        super(SeparableConv2d,self).__init__()

        self.conv1 = nn.Conv2d(in_channels,in_channels,kernel_size,stride,padding,dilation,groups=in_channels,bias=bias)
        self.pointwise = nn.Conv2d(in_channels,out_channels,1,1,0,1,1,bias=bias)

    def forward(self,x):
        x = self.conv1(x)
        x = self.pointwise(x)
        return x


class Block(nn.Module):
    def __init__(self,in_filters,out_filters,reps,strides=1,start_with_relu=True,grow_first=True, dilation=1):
        super(Block, self).__init__()
		
		# skip connections
        if out_filters != in_filters or strides!=1:
            self.skip = nn.Conv2d(in_filters,out_filters,1,stride=strides, bias=False)
            self.skipbn = nn.BatchNorm2d(out_filters)
        else:
            self.skip=None

        rep=[]

        filters=in_filters
        if grow_first:
            rep.append(nn.ReLU(inplace=True))
            rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=dilation, dilation=dilation, bias=False))
            rep.append(nn.BatchNorm2d(out_filters))
            filters = out_filters

        for i in range(reps-1):
            rep.append(nn.ReLU(inplace=True))
            rep.append(SeparableConv2d(filters,filters,3,stride=1,padding=dilation,dilation=dilation,bias=False))
            rep.append(nn.BatchNorm2d(filters))

        if not grow_first:
            rep.append(nn.ReLU(inplace=True))
            rep.append(SeparableConv2d(in_filters,out_filters,3,stride=1,padding=dilation,dilation=dilation,bias=False))
            rep.append(nn.BatchNorm2d(out_filters))

        if not start_with_relu:
            rep = rep[1:]
        else:
            rep[0] = nn.ReLU(inplace=False)

        if strides != 1:
            rep.append(nn.MaxPool2d(3,strides,1))
        self.rep = nn.Sequential(*rep)

    def forward(self,inp):
        x = self.rep(inp)

        if self.skip is not None:
            skip = self.skip(inp)
            skip = self.skipbn(skip)
        else: # middle flow
            skip = inp
        x+=skip
        return x


class Xception(nn.Module):
    """
    Xception optimized for the ImageNet dataset, as specified in
    https://arxiv.org/pdf/1610.02357.pdf
    """
    def __init__(self, num_classes=1000, replace_stride_with_dilation=None):
        """ Constructor
        Args:
            num_classes: number of classes
        """
        super(Xception, self).__init__()

        self.num_classes = num_classes
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False, False]
        if len(replace_stride_with_dilation) != 4:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 4-element tuple, got {}".format(replace_stride_with_dilation))

        self.conv1 = nn.Conv2d(3, 32, 3,2, 0, bias=False) # 1 / 2
        self.bn1 = nn.BatchNorm2d(32)
        self.relu1 = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(32,64,3,bias=False)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=True)
        #do relu here

        self.block1=self._make_block(64,128,2,2,start_with_relu=False,grow_first=True, dilate=replace_stride_with_dilation[0]) # 1 / 4
        self.block2=self._make_block(128,256,2,2,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[1]) # 1 / 8
        self.block3=self._make_block(256,728,2,2,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2]) # 1 / 16

        self.block4=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2])
        self.block5=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2])
        self.block6=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2])
        self.block7=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2])

        self.block8=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2])
        self.block9=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2])
        self.block10=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2])
        self.block11=self._make_block(728,728,3,1,start_with_relu=True,grow_first=True, dilate=replace_stride_with_dilation[2])

        self.block12=self._make_block(728,1024,2,2,start_with_relu=True,grow_first=False, dilate=replace_stride_with_dilation[3]) # 1 / 32

        self.conv3 = SeparableConv2d(1024,1536,3,1,1, dilation=self.dilation)
        self.bn3 = nn.BatchNorm2d(1536)
        self.relu3 = nn.ReLU(inplace=True)

        #do relu here
        self.conv4 = SeparableConv2d(1536,2048,3,1,1, dilation=self.dilation)
        self.bn4 = nn.BatchNorm2d(2048)

        self.fc = nn.Linear(2048, num_classes)

        # #------- init weights --------
        # for m in self.modules():
        #     if isinstance(m, nn.Conv2d):
        #         n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
        #         m.weight.data.normal_(0, math.sqrt(2. / n))
        #     elif isinstance(m, nn.BatchNorm2d):
        #         m.weight.data.fill_(1)
        #         m.bias.data.zero_()
        # #-----------------------------

    def _make_block(self, in_filters,out_filters,reps,strides=1,start_with_relu=True,grow_first=True, dilate=False):
        if dilate:
            self.dilation *= strides
            strides = 1
        return Block(in_filters,out_filters,reps,strides,start_with_relu=start_with_relu,grow_first=grow_first, dilation=self.dilation)

    def features(self, input):
        x = self.conv1(input)
        x = self.bn1(x)
        x = self.relu1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)

        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.block4(x)
        x = self.block5(x)
        x = self.block6(x)
        x = self.block7(x)
        x = self.block8(x)
        x = self.block9(x)
        x = self.block10(x)
        x = self.block11(x)
        x = self.block12(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)

        x = self.conv4(x)
        x = self.bn4(x)
        return x

    def logits(self, features):
        x = nn.ReLU(inplace=True)(features)

        x = F.adaptive_avg_pool2d(x, (1, 1))
        x = x.view(x.size(0), -1)
        x = self.last_linear(x)
        return x

    def forward(self, input):
        x = self.features(input)
        x = self.logits(x)
        return x


def xception(num_classes=1000, pretrained='imagenet', replace_stride_with_dilation=None):
    model = Xception(num_classes=num_classes, replace_stride_with_dilation=replace_stride_with_dilation)
    if pretrained:
        settings = pretrained_settings['xception'][pretrained]
        assert num_classes == settings['num_classes'], \
            "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)

        model = Xception(num_classes=num_classes, replace_stride_with_dilation=replace_stride_with_dilation)
        model.load_state_dict(model_zoo.load_url(settings['url']))

    # TODO: ugly
    model.last_linear = model.fc
    del model.fc
    return model