models.py

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


########################### SIMPLE FC NETWORK for SYNTHETIC DATASET ###########################

class SimpleNetwork(nn.Module): 
    def __init__(self): 
        super(SimpleNetwork, self).__init__() 
        self.fc1 = nn.Linear(5, 32) 
        self.fc2 = nn.Linear(32, 64) 
        self.fc3 = nn.Linear(64, 128) 
        self.fc4 = nn.Linear(128, 32) 
        self.fc5 = nn.Linear(32, 4) 
        self.relu = nn.ReLU() 
    
    def forward(self, x):
        x = self.relu(self.fc1(x)) 
        x = self.relu(self.fc2(x)) 
        x = self.relu(self.fc3(x)) 
        x = self.relu(self.fc4(x)) 
        x = self.fc5(x) 
        return x
    
    
############################## RESNET ARCH for CIFAR-10 DATASET ##############################

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(
            in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out = out + self.shortcut(x)
        out = F.relu(out)
        return out 

class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear = nn.Linear(512*block.expansion, num_classes) # for cifar10

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out
    
    def randomize_weights(self):
        for m in self.modules():
            if isinstance(m, (nn.Conv2d, nn.Linear)): 
                m.weight.data.normal_()
                if m.bias is not None:
                    m.bias.data.normal_()
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.uniform_()
                m.bias.data.zero_()


def resnet34_model():
    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=10) # resnet34