|
- import torch.nn as nn
-
-
- class BasicBlock(nn.Module):
- expansion = 1
-
- def __init__(self, in_channels, out_channels, stride=1):
- super().__init__()
-
- self.residual_function = nn.Sequential(
- nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
- nn.BatchNorm2d(out_channels),
- nn.ReLU(inplace=True),
- nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
- nn.BatchNorm2d(out_channels * BasicBlock.expansion))
-
- self.shortcut = nn.Sequential()
-
- if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
- self.shortcut = nn.Sequential(
- nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(out_channels * BasicBlock.expansion))
-
- def forward(self, x):
- return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
-
-
- class BottleNeck(nn.Module):
- expansion = 4
-
- def __init__(self, in_channels, out_channels, stride=1):
- super().__init__()
- self.residual_function = nn.Sequential(
- nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
- nn.BatchNorm2d(out_channels),
- nn.ReLU(inplace=True),
- nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
- nn.BatchNorm2d(out_channels),
- nn.ReLU(inplace=True),
- nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
- nn.BatchNorm2d(out_channels * BottleNeck.expansion),
- )
-
- self.shortcut = nn.Sequential()
-
- if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
- self.shortcut = nn.Sequential(
- nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
- nn.BatchNorm2d(out_channels * BottleNeck.expansion)
- )
-
- def forward(self, x):
- return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))
-
-
- class ResNet(nn.Module):
-
- def __init__(self, block, num_block, num_classes=100):
- super().__init__()
-
- self.in_channels = 64
-
- self.conv1 = nn.Sequential(
- nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
- nn.BatchNorm2d(64),
- nn.ReLU(inplace=True))
-
- self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
- self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
- self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
- self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
- self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
- self.fc = nn.Linear(512 * block.expansion, num_classes)
-
- def _make_layer(self, block, out_channels, num_blocks, stride):
- strides = [stride] + [1] * (num_blocks - 1)
- layers = []
- for stride in strides:
- layers.append(block(self.in_channels, out_channels, stride))
- self.in_channels = out_channels * block.expansion
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- output = self.conv1(x)
- output = self.conv2_x(output)
- output = self.conv3_x(output)
- output = self.conv4_x(output)
- output = self.conv5_x(output)
- output = self.avg_pool(output)
- output = output.view(output.size(0), -1)
- output = self.fc(output)
-
- return output
-
-
- def generate_resnet(model_depth=18, num_classes=10):
- if model_depth == 18:
- model = ResNet(BasicBlock, [2, 2, 2, 2], num_classes)
- elif model_depth == 34:
- model = ResNet(BasicBlock, [3, 4, 6, 3], num_classes)
- elif model_depth == 50:
- model = ResNet(BottleNeck, [3, 4, 6, 3], num_classes)
- elif model_depth == 101:
- model = ResNet(BottleNeck, [3, 4, 23, 3], num_classes)
- elif model_depth == 152:
- model = ResNet(BottleNeck, [3, 8, 36, 3], num_classes)
- else:
- model = ResNet(BottleNeck, [3, 24, 36, 3], num_classes)
- return model
|