model.py

import torch
import torch.nn as nn
import torch.nn.init as init
from torchvision import models
import torchvision
import torch.nn.functional as F
import numpy as np
from torchstat import stat  # 查看网络参数
from torchsummary import summary  # 查看网络结构
import math


def conv3x3(in_planes, out_planes, stride=1, dilation=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, dilation=dilation, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(
            inplanes, planes, stride=stride, dilation=dilation)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        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)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, dilation=dilation,
                               padding=dilation, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        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 += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(self, block, layers=(3, 4, 23, 3)):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        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)

        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_layer(self, block, planes, blocks, stride=1, dilation=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),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = [block(self.inplanes, planes, stride, downsample)]
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, dilation=dilation))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x_3 = self.layer3(x)
        x = self.layer4(x_3)

        return x, x_3


class PSPModule(nn.Module):
    def __init__(self, features, out_features=1024, sizes=(1, 2, 3, 6)):
        super().__init__()
        self.stages = []
        self.stages = nn.ModuleList(
            [self._make_stage(features, size) for size in sizes])
        self.bottleneck = nn.Conv2d(
            features * (len(sizes) + 1), out_features, kernel_size=1)
        self.relu = nn.ReLU()

    def _make_stage(self, features, size):
        prior = nn.AdaptiveAvgPool2d(output_size=(size, size))
        conv = nn.Conv2d(features, features, kernel_size=1, bias=False)
        return nn.Sequential(prior, conv)

    def forward(self, feats):
        h, w = feats.size(2), feats.size(3)
        priors = [F.interpolate(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 self.relu(bottle)


class PSPUpsample(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.PReLU()
        )

    def forward(self, x):
        h, w = 2 * x.size(2), 2 * x.size(3)
        p = F.interpolate(input=x, size=(
            h, w), mode='bilinear', align_corners=True)
        return self.conv(p)


class PSPNet(nn.Module):
    def __init__(self, n_classes=3, sizes=(1, 2, 3, 6), psp_size=2048):
        super().__init__()
        self.feats = ResNet(Bottleneck, [3, 4, 6, 3])
        self.psp = PSPModule(psp_size, 1024, sizes)
        self.drop_1 = nn.Dropout2d(p=0.3)

        self.up_1 = PSPUpsample(1024, 256)
        self.up_2 = PSPUpsample(256, 64)
        self.up_3 = PSPUpsample(64, 64)

        self.drop_2 = nn.Dropout2d(p=0.15)
        self.final = nn.Sequential(
            nn.Conv2d(64, n_classes, kernel_size=1)
        )

        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 forward(self, x):
        f, class_f = self.feats(x)
        p = self.psp(f)
        p = self.drop_1(p)

        p = self.up_1(p)
        p = self.drop_2(p)

        p = self.up_2(p)
        p = self.drop_2(p)

        p = self.up_3(p)
        p = self.drop_2(p)

        return self.final(p)


if __name__ == '__main__':
    m = PSPNet(3)
    # print(m.resnet)
    print("")
    stat(m, input_size=(1, 224, 224))
    print("")
    testdata = torch.zeros(1, 1, 224, 224)
    print(testdata)
    print(testdata.size())
    out = m(testdata)
    print(out.size())