Create model.py

model.py

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+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader
+
+
+# Bottleneck block (same as before)
+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
+
+
+# ResNet tailored for CIFAR (no initial 7x7 stride-2 conv + no maxpool)
+class ResNetCIFAR(nn.Module):
+ def __init__(self, block, layers, num_classes=100):
+  super(ResNetCIFAR, self).__init__()
+  self.in_channels = 64
+
+  # Adjusted first conv for CIFAR (3x3, stride=1)
+  self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
+  self.bn1 = nn.BatchNorm2d(64)
+  self.relu = nn.ReLU(inplace=True)
+
+  # NOTE: we do NOT use the 7x7 stride-2 conv or the 3x3 maxpool used for ImageNet
+  # Stage layers
+  self.layer1 = self._make_layer(block, 64, layers[0], stride=1)
+  self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+  self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+  self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+
+  self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+  self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+  # Weight initialization
+  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 _make_layer(self, block, out_channels, blocks, stride=1):
+  downsample = None
+  if stride != 1 or self.in_channels != out_channels * block.expansion:
+   downsample = nn.Sequential(
+    nn.Conv2d(
+     self.in_channels,
+     out_channels * block.expansion,
+     kernel_size=1,
+     stride=stride,
+     bias=False,
+    ),
+    nn.BatchNorm2d(out_channels * block.expansion),
+   )
+
+  layers = []
+  layers.append(block(self.in_channels, out_channels, stride, downsample))
+  self.in_channels = out_channels * block.expansion
+  for _ in range(1, blocks):
+   layers.append(block(self.in_channels, out_channels))
+
+  return nn.Sequential(*layers)
+
+ def forward(self, x):
+  x = self.conv1(x)
+  x = self.bn1(x)
+  x = self.relu(x)
+  # no maxpool
+
+  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 get_model(num_classes=100):
+ return ResNetCIFAR(Bottleneck, [3, 4, 6, 3], num_classes=num_classes)