Pyramid Scene Parsing Network (PSPNet) Code

#!/usr/bin/env python
# coding: utf-8

import torch
import torch.nn as nn
import torch.nn.functional as F

import math
import torch.utils.model_zoo as model_zoo


__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


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)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        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):
        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,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        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, num_classes=1000, deep_base=True):
        super(ResNet, self).__init__()
        self.deep_base = deep_base
        if not self.deep_base:
            self.inplanes = 64
            self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
            self.bn1 = nn.BatchNorm2d(64)
        else:
            self.inplanes = 128
            self.conv1 = conv3x3(3, 64, stride=2)
            self.bn1 = nn.BatchNorm2d(64)
            self.conv2 = conv3x3(64, 64)
            self.bn2 = nn.BatchNorm2d(64)
            self.conv3 = conv3x3(64, 128)
            self.bn3 = nn.BatchNorm2d(128)
        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=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

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

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.relu(self.bn1(self.conv1(x)))
        if self.deep_base:
            x = self.relu(self.bn2(self.conv2(x)))
            x = self.relu(self.bn3(self.conv3(x)))
        x = self.maxpool(x)

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

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
        model_path = './initmodel/resnet50_v2.pth'
        model.load_state_dict(torch.load(model_path), strict=False)
    return model


def resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        # model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
        model_path = './initmodel/resnet101_v2.pth'
        model.load_state_dict(torch.load(model_path), strict=False)
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        # model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
        model_path = './initmodel/resnet152_v2.pth'
        model.load_state_dict(torch.load(model_path), strict=False)
    return model


class PPM(nn.Module):
    def __init__(self, in_dim, reduction_dim, bins):
        super(PPM, self).__init__()

        self.features = []

        # bins = (1, 2, 3, 6) : 1x1, 2x2, 3x3, 6x6
        for bin in bins:
            self.features.append(nn.Sequential(
                # Pyramid scale에 따라 각각의 pooling을 생성
                nn.AdaptiveAvgPool2d(bin),

                # 1/N으로 dimension reduction (reduction_dim = 4, pyramid level의 수)
                nn.Conv2d(in_dim, reduction_dim, kernel_size=1, bias=False),
                nn.BatchNorm2d(reduction_dim),
                nn.ReLU(inplace=True)
            ))
        self.features = nn.ModuleList(self.features)

    def forward(self, x):
        x_size = x.size()
        out = [x]
        for f in self.features:
            out.append(F.interpolate(f(x), x_size[2:], mode='bilinear', align_corners=True))

        # 각각의 pyramid scale에 따른 pooling 결과들을 concatenate
        return torch.cat(out, 1)

class PSPNet(nn.Module):
    def __init__(self, layers=50, bins=(1, 2, 3, 6), dropout=0.1, classes=2, zoom_factor=8, pretrained=True):
        super(PSPNet, self).__init__()

        # output의 크기를 원본 이미지와 동일하게 복원하기 위한 값
        # Feature map의 크기는 원본 이미지의 1/8
        self.zoom_factor = zoom_factor

        self.criterion = nn.CrossEntropyLoss()

        # ResNet
        if layers == 50:
            resnet = resnet50(pretrained=pretrained)
        elif layers == 101:
            resnet = resnet101(pretrained=pretrained)
        else:
            resnet = resnet152(pretrained=pretrained)

        # ResNet with dilated network
        self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu,
                                    resnet.conv2, resnet.bn2, resnet.relu,
                                    resnet.conv3, resnet.bn3, resnet.relu, resnet.maxpool)
        self.layer1 = resnet.layer1
        self.layer2 = resnet.layer2
        self.layer3 = resnet.layer3
        self.layer4 = resnet.layer4

        for n, m in self.layer3.named_modules():
            if 'conv2' in n:
                m.dilation, m.padding, m.stride = (2, 2), (2, 2), (1, 1)
            elif 'downsample.0' in n:
                m.stride = (1, 1)
        for n, m in self.layer4.named_modules():
            if 'conv2' in n:
                m.dilation, m.padding, m.stride = (4, 4), (4, 4), (1, 1)
            elif 'downsample.0' in n:
                m.stride = (1, 1)

        # Dilated ResNet output size : torch.Size([4, 2048, 60, 60])
        fea_dim = 2048
        self.ppm = PPM(in_dim = fea_dim, reduction_dim = int(fea_dim / len(bins)), bins=bins)

        # Pyramid Pooling Module output size : torch.Size([4, 4096, 60, 60])
        fea_dim *= 2 # 4096

        self.cls = nn.Sequential(
            nn.Conv2d(fea_dim, 512, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Dropout2d(p=dropout),
            nn.Conv2d(512, classes, kernel_size=1)
        )
        if self.training:
            self.aux = nn.Sequential(
                nn.Conv2d(1024, 256, kernel_size=3, padding=1, bias=False),
                nn.BatchNorm2d(256),
                nn.ReLU(inplace=True),
                nn.Dropout2d(p=dropout),
                nn.Conv2d(256, classes, kernel_size=1)
            )

    def forward(self, x, y=None):
        x_size = x.size()

        # Input image's height, width
        h = int((x_size[2] - 1) / 8 * self.zoom_factor + 1)
        w = int((x_size[3] - 1) / 8 * self.zoom_factor + 1)

        # Resnet with dilated network
        x = self.layer0(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x_tmp = self.layer3(x)
        x = self.layer4(x_tmp)

        # Pyramid Pooling Module
        x = self.ppm(x)

        # Master branch
        x = self.cls(x)

        # 원본 이미지 크기로 upsampling
        if self.zoom_factor != 1:
            x = F.interpolate(x, size=(h, w), mode='bilinear', align_corners=True)

        if self.training:
            # Auxiliary Loss는 training에서만 사용
            aux = self.aux(x_tmp)

            # 원본 이미지 크기로 upsampling
            if self.zoom_factor != 1:
                aux = F.interpolate(aux, size=(h, w), mode='bilinear', align_corners=True)

            main_loss = self.criterion(x, y)
            aux_loss = self.criterion(aux, y)
            return x.max(1)[1], main_loss, aux_loss
        else:
            return x


# pspnet = PSPNet(layers=50, bins=(1, 2, 3, 6), dropout=0.1, classes=2, zoom_factor=8, pretrained=False)