resnet26.py

import os

import math
from functools import partial

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


def conv3x3x3(in_planes, out_planes, stride=1):
    return nn.Conv3d(in_planes,
                     out_planes,
                     kernel_size=3,
                     stride=stride,
                     padding=1,
                     bias=False)


def conv1x1x1(in_planes, out_planes, stride=1):
    return nn.Conv3d(in_planes,
                     out_planes,
                     kernel_size=1,
                     stride=stride,
                     bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, downsample=None):
        super().__init__()

        self.conv1 = conv3x3x3(in_planes, planes, stride)
        self.bn1 = nn.BatchNorm3d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3x3(planes, planes)
        self.bn2 = nn.BatchNorm3d(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 ResNet(nn.Module):
    
    def __init__(self, block, shortcut_type='B', n_classes=1):
        super(ResNet, self).__init__()
        self.in_planes = 16
        self.conv1 = nn.Conv3d(1, 16, kernel_size=(7, 7, 7), stride=(2, 2, 2), bias=False)
        self.bn1 = nn.BatchNorm3d(self.in_planes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, 1, shortcut_type, stride=2)
        self.layer2 = self._make_layer(block, 64, 2, shortcut_type, stride=1)
        self.layer3 = self._make_layer(block, 128, 1, shortcut_type, stride=2)
        self.layer4 = self._make_layer(block, 128, 2, shortcut_type, stride=1)
        self.layer5 = self._make_layer(block, 256, 1, shortcut_type, stride=2)
        self.layer6 = self._make_layer(block, 256, 2, shortcut_type, stride=1)
        self.layer7 = self._make_layer(block, 512, 1, shortcut_type, stride=2)
        self.layer8 = self._make_layer(block, 512, 2, shortcut_type, stride=1)
        self.gap = nn.AvgPool3d(kernel_size=(2, 3, 2), stride=1, padding=0)
        
        self.fc = nn.Linear(512, n_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv3d):
                nn.init.kaiming_normal_(m.weight,
                                        mode='fan_out',
                                        nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm3d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _downsample_basic_block(self, x, planes, stride):
        out = F.avg_pool3d(x, kernel_size=1, stride=stride)
        zero_pads = torch.zeros(out.size(0), planes - out.size(1), out.size(2),
                                out.size(3), out.size(4))
        if isinstance(out.data, torch.cuda.FloatTensor):
            zero_pads = zero_pads.cuda()

        out = torch.cat([out.data, zero_pads], dim=1)

        return out

    def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):
        downsample = None
        if stride != 1 or self.in_planes != planes * block.expansion:
            if shortcut_type == 'A':
                downsample = partial(self._downsample_basic_block,
                                     planes=planes * block.expansion,
                                     stride=stride)
            else:
                downsample = nn.Sequential(
                    conv1x1x1(self.in_planes, planes * block.expansion, stride),
                    nn.BatchNorm3d(planes * block.expansion))

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

        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 = self.layer3(x)
        x = self.layer4(x)
        x = self.layer5(x)
        x = self.layer6(x)
        x = self.layer7(x)
        x = self.layer8(x)
  
        x = self.gap(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
  
        return x 

# The number of output channels of the convolutional layers is not specified in the paper. 
# So I used a random value.
def generate_model(**kwargs):
    model = ResNet(BasicBlock, **kwargs)
  

    return model