在pytorch中自定义网络，集成nn.Module类并重载__init__(self)和forward，分别定义网络组成和前向传播，这里有一个简单的例子。
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 5)
self.conv2 = nn.Conv2d(20, 20, 5)
def forward(self, x):
x = F.relu(self.conv1(x))
return F.relu(self.conv2(x))
下面先看一下PSPNet的论文介绍，网络结构非常简单，在ResNet之后接一个PPM模块。
此外PSPNet还采用了辅助损失分支。
import torch.nn as nn
from torch.nn import functional as F
import math
import torch.utils.model_zoo as model_zoo
import torch
import numpy as np
from torch.autograd import Variable
affine_par = True
import functools
import sys, os
from libs import InPlaceABN, InPlaceABNSync
BatchNorm2d = functools.partial(InPlaceABNSync, activation='none')
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
#ResNet的Bottleneck
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, fist_dilation=1, multi_grid=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=dilation*multi_grid, dilation=dilation*multi_grid, bias=False)
self.bn2 = BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=False)
self.relu_inplace = nn.ReLU(inplace=True)
self.downsample = downsample
self.dilation = dilation
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out = out + residual
out = self.relu_inplace(out)
return out
#PPM模块
class PSPModule(nn.Module):
"""
Reference:
Zhao, Hengshuang, et al. *"Pyramid scene parsing network."*
"""
def __init__(self, features, out_features=512, sizes=(1, 2, 3, 6)):
super(PSPModule, self).__init__()
self.stages = []
self.stages = nn.ModuleList([self._make_stage(features, out_features, size) for size in sizes])
self.bottleneck = nn.Sequential(
nn.Conv2d(features+len(sizes)*out_features, out_features, kernel_size=3, padding=1, dilation=1, bias=False),
InPlaceABNSync(out_features),
nn.Dropout2d(0.1)
)
def _make_stage(self, features, out_features, size):
prior = nn.AdaptiveAvgPool2d(output_size=(size, size))
conv = nn.Conv2d(features, out_features, kernel_size=1, bias=False)
bn = InPlaceABNSync(out_features)
return nn.Sequential(prior, conv, bn)
def forward(self, feats):
h, w = feats.size(2), feats.size(3)
priors = [F.upsample(input=stage(feats), size=(h, w), mode='bilinear', align_corners=True) for stage in self.stages] + [feats]
bottle = self.bottleneck(torch.cat(priors, 1))
return bottle
#PSPNet网络整体
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes):
self.inplanes = 128
super(ResNet, self).__init__()
self.conv1 = conv3x3(3, 64, stride=2)
self.bn1 = BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=False)
self.conv2 = conv3x3(64, 64)
self.bn2 = BatchNorm2d(64)
self.relu2 = nn.ReLU(inplace=False)
self.conv3 = conv3x3(64, 128)
self.bn3 = BatchNorm2d(128)
self.relu3 = nn.ReLU(inplace=False)
#
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.relu = nn.ReLU(inplace=False)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, multi_grid=(1,1,1))
self.head = nn.Sequential(PSPModule(2048, 512),
nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True))
#辅助损失
self.dsn = nn.Sequential(
nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
InPlaceABNSync(512),
nn.Dropout2d(0.1),
nn.Conv2d(512, num_classes, kernel_size=1, stride=1, padding=0, bias=True)
)
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, multi_grid=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
BatchNorm2d(planes * block.expansion,affine = affine_par))
layers = []
generate_multi_grid = lambda index, grids: grids[index%len(grids)] if isinstance(grids, tuple) else 1
layers.append(block(self.inplanes, planes, stride,dilation=dilation, downsample=downsample, multi_grid=generate_multi_grid(0, multi_grid)))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=dilation, multi_grid=generate_multi_grid(i, multi_grid)))
return nn.Sequential(*layers)
def forward(self, x): #(1,3,769,769)
x = self.relu1(self.bn1(self.conv1(x))) #(1,64,385,385)
x = self.relu2(self.bn2(self.conv2(x))) #(1,64,385,385)
x = self.relu3(self.bn3(self.conv3(x))) #(1,128,385,385)
x = self.maxpool(x) #(1,128,193,193)
x = self.layer1(x) #(1,256,97,97)
x = self.layer2(x) #(1,512,97,97)
x = self.layer3(x) #(1,1024,97,97)
x_dsn = self.dsn(x) #(1,19,97,97)
x = self.layer4(x) #(1,2048,97,97)
x = self.head(x) #(1,19,769,769)
return [x, x_dsn]
def Res_Deeplab(num_classes=21):
model = ResNet(Bottleneck,[3, 4, 23, 3], num_classes)
return model
