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import torch
import torch.nn as nn
import torchvision.models as models
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_channels, out_channels, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(out_channels)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3,
stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion,
kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet50(nn.Module):
def __init__(self, num_classes=1000):
super(ResNet50, self).__init__()
self.in_channels = 64
# Initial layers
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# Residual layers
self.layer1 = self._make_layer(64, 3)
self.layer2 = self._make_layer(128, 4, stride=2)
self.layer3 = self._make_layer(256, 6, stride=2)
self.layer4 = self._make_layer(512, 3, stride=2)
# Classification head
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * Bottleneck.expansion, num_classes)
# Weight initialization
self._initialize_weights()
def _make_layer(self, out_channels, blocks, stride=1):
downsample = None
if stride != 1 or self.in_channels != out_channels * Bottleneck.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.in_channels, out_channels * Bottleneck.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels * Bottleneck.expansion),
)
layers = []
layers.append(Bottleneck(self.in_channels, out_channels, stride, downsample))
self.in_channels = out_channels * Bottleneck.expansion
for _ in range(1, blocks):
layers.append(Bottleneck(self.in_channels, out_channels))
return nn.Sequential(*layers)
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
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 = torch.flatten(x, 1)
x = self.fc(x)
return x
def create_model(num_classes, pretrained=False):
"""
Create a ResNet-50 model
Args:
num_classes: Number of output classes
pretrained: Whether to use pretrained weights from ImageNet
"""
# Load model with or without pretrained weights
model = models.resnet50(pretrained=pretrained)
# Modify the final layer for our number of classes
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, num_classes)
return model |