resnet.py

import pandas as pd
import os
import torch
import torch.nn as nn
from PIL import Image, ImageFile
import torchvision.transforms.functional_tensor
from torch.utils.data import Dataset
from torchvision import transforms

class Fakeddit(Dataset):
    def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):
        self.img_labels = pd.read_csv(annotations_file, usecols=['2_way_label'])
        self.img_ids = pd.read_csv(annotations_file, usecols=['id'])
        self.img_dir = img_dir
        self.transform = transform
        self.target_transform = target_transform

    def __len__(self):
        return len(self.img_ids)

    def __getitem__(self, idx):
        img_path = os.path.join(self.img_dir, self.img_ids.iloc[idx, 0]) + ".jpg"
        image = Image.open(img_path)
        toTensor = transforms.ToTensor()
        image = toTensor(image)
        image = torchvision.transforms.functional_tensor.convert_image_dtype(image, torch.float32)
        label = self.img_labels.iloc[idx, 0]
        if self.transform:
            image = self.transform(image)
        return image, label


class block(nn.Module):
    def __init__(
        self, in_channels, intermediate_channels, identity_downsample=None, stride=1
    ):
        super(block, self).__init__()
        self.expansion = 4
        self.conv1 = nn.Conv2d(
            in_channels, intermediate_channels, kernel_size=1, stride=1, padding=0, bias=False
        )
        self.bn1 = nn.BatchNorm2d(intermediate_channels)
        self.conv2 = nn.Conv2d(
            intermediate_channels,
            intermediate_channels,
            kernel_size=3,
            stride=stride,
            padding=1,
            bias=False
        )
        self.bn2 = nn.BatchNorm2d(intermediate_channels)
        self.conv3 = nn.Conv2d(
            intermediate_channels,
            intermediate_channels * self.expansion,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=False
        )
        self.bn3 = nn.BatchNorm2d(intermediate_channels * self.expansion)
        self.relu = nn.ReLU()
        self.identity_downsample = identity_downsample
        self.stride = stride

    def forward(self, x):
        identity = x.clone()

        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.conv3(x)
        x = self.bn3(x)

        if self.identity_downsample is not None:
            identity = self.identity_downsample(identity)

        x += identity
        x = self.relu(x)
        return x


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

        # Essentially the entire ResNet architecture are in these 4 lines below
        self.layer1 = self._make_layer(
            block, layers[0], intermediate_channels=64, stride=1
        )
        self.layer2 = self._make_layer(
            block, layers[1], intermediate_channels=128, stride=2
        )
        self.layer3 = self._make_layer(
            block, layers[2], intermediate_channels=256, stride=2
        )
        self.layer4 = self._make_layer(
            block, layers[3], intermediate_channels=512, stride=2
        )

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * 4, num_classes)
        self.out = nn.Softmax(1)

    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.avgpool(x)
        x = x.reshape(x.shape[0], -1)
        x = self.fc(x)
        x = self.out(x)

        return x

    def _make_layer(self, block, num_residual_blocks, intermediate_channels, stride):
        identity_downsample = None
        layers = []

        # Either if we half the input space for ex, 56x56 -> 28x28 (stride=2), or channels changes
        # we need to adapt the Identity (skip connection) so it will be able to be added
        # to the layer that's ahead
        if stride != 1 or self.in_channels != intermediate_channels * 4:
            identity_downsample = nn.Sequential(
                nn.Conv2d(
                    self.in_channels,
                    intermediate_channels * 4,
                    kernel_size=1,
                    stride=stride,
                    bias=False
                ),
                nn.BatchNorm2d(intermediate_channels * 4),
            )

        layers.append(
            block(self.in_channels, intermediate_channels, identity_downsample, stride)
        )

        # The expansion size is always 4 for ResNet 50,101,152
        self.in_channels = intermediate_channels * 4

        # For example for first resnet layer: 256 will be mapped to 64 as intermediate layer,
        # then finally back to 256. Hence no identity downsample is needed, since stride = 1,
        # and also same amount of channels.
        for i in range(num_residual_blocks - 1):
            layers.append(block(self.in_channels, intermediate_channels))

        return nn.Sequential(*layers)

def ResNet18(img_channel=3, num_classes=2):
    return ResNet(block, [2, 2, 2, 2], img_channel, num_classes)


def ResNet50(img_channel=3, num_classes=2):
    return ResNet(block, [3, 4, 6, 3], img_channel, num_classes)


def ResNet101(img_channel=3, num_classes=2):
    return ResNet(block, [3, 4, 23, 3], img_channel, num_classes)


def ResNet152(img_channel=3, num_classes=2):
    return ResNet(block, [3, 8, 36, 3], img_channel, num_classes)