duc_hdc.py

import torch
from torch import nn
from torchvision import models

class _DenseUpsamplingConvModule(nn.Module):
    def __init__(self, down_factor, in_dim, num_classes):
        super(_DenseUpsamplingConvModule, self).__init__()
        upsample_dim = (down_factor ** 2) * num_classes
        self.conv = nn.Conv2d(in_dim, upsample_dim, kernel_size=3, padding=1)
        self.bn = nn.BatchNorm2d(upsample_dim)
        self.relu = nn.ReLU(inplace=True)
        self.pixel_shuffle = nn.PixelShuffle(down_factor)

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        x = self.pixel_shuffle(x)
        return x


class ResNetDUC(nn.Module):
    # the size of image should be multiple of 8
    def __init__(self, num_classes, pretrained=True):
        super(ResNetDUC, self).__init__()
        resnet = models.resnet152(pretrained=pretrained)
        self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, 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 = (2, 2)
                m.padding = (2, 2)
                m.stride = (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 = (4, 4)
                m.padding = (4, 4)
                m.stride = (1, 1)
            elif 'downsample.0' in n:
                m.stride = (1, 1)

        self.duc = _DenseUpsamplingConvModule(8, 2048, num_classes)

    def forward(self, x):
        x = self.layer0(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.duc(x)
        return x


class ResNetDUCHDC(nn.Module):
    # the size of image should be multiple of 8
    def __init__(self, num_classes, pretrained=True):
        super(ResNetDUCHDC, self).__init__()
        resnet = models.resnet152()
        if pretrained:
            resnet.load_state_dict(torch.load(res152_path))
        self.layer0 = nn.Sequential(resnet.conv1, resnet.bn1, 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 or 'downsample.0' in n:
                m.stride = (1, 1)
        for n, m in self.layer4.named_modules():
            if 'conv2' in n or 'downsample.0' in n:
                m.stride = (1, 1)
        layer3_group_config = [1, 2, 5, 9]
        for idx in range(len(self.layer3)):
            self.layer3[idx].conv2.dilation = (layer3_group_config[idx % 4], layer3_group_config[idx % 4])
            self.layer3[idx].conv2.padding = (layer3_group_config[idx % 4], layer3_group_config[idx % 4])
        layer4_group_config = [5, 9, 17]
        for idx in range(len(self.layer4)):
            self.layer4[idx].conv2.dilation = (layer4_group_config[idx], layer4_group_config[idx])
            self.layer4[idx].conv2.padding = (layer4_group_config[idx], layer4_group_config[idx])

        self.duc = _DenseUpsamplingConvModule(8, 2048, num_classes)

    def forward(self, x):
        x = self.layer0(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.duc(x)
        return x