blocks.py

import numpy as np
import torch
import torch.nn as nn
from torchvision import datasets
from torchvision import transforms
from torch.utils.data.sampler import SubsetRandomSampler

##################
# blocks.py
#
# Definition of residual blocks used to construct the network
# Modified from an existing
#
##################


# Code based on implementation from https://blog.paperspace.com/writing-resnet-from-scratch-in-pytorch/
class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample = None):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU())
        self.conv2 = nn.Sequential(
            nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(out_channels))
        self.downsample = downsample
        self.relu = nn.ReLU()
        self.out_channels = out_channels

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.conv2(out)
        if self.downsample:
            residual = self.downsample(x)
        out += residual
        out = self.relu(out)
        return out

# Code based on implementation at https://blog.paperspace.com/writing-resnet-from-scratch-in-pytorch/
class ResNet(nn.Module):
    def __init__(self, in_channels, block, layers, num_classes=10):
        super(ResNet, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_channels, 64, kernel_size=7, stride=1, padding=3),
            nn.BatchNorm2d(64),
            nn.ReLU())
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
        self.layer0 = self._make_layer(block, 64, layers[0], stride=1)
        self.layer1 = self._make_layer(block, 128, layers[1], stride=1)
        self.layer2 = self._make_layer(block, 256, layers[2], stride=1)
        self.layer3 = self._make_layer(block, 512, layers[3], stride=1)
        self.avgpool = nn.AvgPool2d(1, stride=1, )
        self.collapse = nn.Conv2d(512, 1, 1, 1) # Collapse down to single channel
        # We will not do FC layer since we want a 2D output
        # self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes, kernel_size=1, stride=stride),
                nn.BatchNorm2d(planes),
            )
        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        # Disable pooling
        # x = self.maxpool(x)
        x = self.layer0(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)

        # Disable 2D pooling
        # x = self.avgpool(x)

        # Final 1x1 convolution
        x = self.collapse(x).squeeze(1)

        # We will not do FC layer since we want a 2D output
        # x = self.fc(x)

        return x