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# model.py file
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU()
def forward(self, x):
x = self.relu(self.bn1(self.conv1(x)))
x = self.relu(self.bn2(self.conv2(x)))
return x
class ResNet(nn.Module):
def __init__(self, block, num_classes=10):
super(ResNet, self).__init__()
self.preparation = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU()
)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(128),
nn.ReLU()
)
self.residual1 = block(128, 128, 1)
self.layer2 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(256),
nn.ReLU()
)
self.layer3 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1, bias=False),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(512),
nn.ReLU()
)
self.residual3 = block(512, 512, 1)
self.maxpool2d = nn.MaxPool2d(4, 4)
self.fc = nn.Linear(512, num_classes)
def forward(self, x):
x = self.preparation(x)
x = self.layer1(x)
res1 = self.residual1(x)
x = x + res1
x = self.layer2(x)
x = self.layer3(x)
res3 = self.residual3(x)
x = x + res3
x = self.maxpool2d(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def Custom_ResNet():
return ResNet(BasicBlock, num_classes=10) |