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  1. class Block(nn.Module):
  2.     def __init__(self, in_channels, out_channels, identity_downsample=None, stride=1):
  3.         super(Block, self).__init__()
  4.         self.expansion = 1
  5.         self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
  6.         self.bn1 = nn.BatchNorm2d(out_channels)
  7.         self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)
  8.         self.bn2 = nn.BatchNorm2d(out_channels)
  9.         self.conv3 = nn.Conv2d(out_channels, out_channels * self.expansion, kernel_size=1, stride=1, padding=0)
  10.         self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)
  11.         self.relu = nn.ReLU()
  12.         self.identity_downsample = identity_downsample
  13.  
  14.     def forward(self, x):
  15.         identity = x
  16.  
  17.         x = self.conv2(x)
  18.         x = self.bn2(x)
  19.         x = self.relu(x)
  20.         x = self.conv3(x)
  21.         x = self.bn3(x)
  22.  
  23.         if self.identity_downsample is not None:
  24.             identity = self.identity_downsample(identity)
  25.  
  26.         x += identity
  27.         x = self.relu(x)
  28.         return x
  29.  
  30.  
  31. class ResNet18(nn.Module):
  32.     def __init__(self, block):
  33.  
  34.         super(ResNet18, self).__init__()
  35.  
  36.         #input_dim = 784(??????) + 42 
  37.  
  38.         image_channels=3 
  39.         num_classes=1000
  40.  
  41.         self.label_embedding = nn.Embedding(42, 42)
  42.  
  43.         self.in_channels = 64
  44.         self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=7, stride=2, padding=3)
  45.         self.bn1 = nn.BatchNorm2d(64)
  46.         self.relu = nn.ReLU()
  47.         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
  48.  
  49.         # ResNetLayers
  50.         self.layer1 = self.make_layers(num_layers, block, 2, intermediate_channels=64, stride=1)
  51.         self.layer2 = self.make_layers(num_layers, block, 2, intermediate_channels=128, stride=2)
  52.         self.layer3 = self.make_layers(num_layers, block, 2, intermediate_channels=256, stride=2)
  53.         self.layer4 = self.make_layers(num_layers, block, 2, intermediate_channels=512, stride=2)
  54.  
  55.         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
  56.         self.fc = nn.Linear(512 * self.expansion, num_classes)
  57.  
  58.     def forward(self, x, labels):
  59.  
  60.         c = self.label_embedding(labels)
  61.         x = torch.cat([x, c], 1)
  62.  
  63.         x = self.conv1(x)
  64.         x = self.bn1(x)
  65.         x = self.relu(x)
  66.         x = self.maxpool(x)
  67.  
  68.         x = self.layer1(x)
  69.         x = self.layer2(x)
  70.         x = self.layer3(x)
  71.         x = self.layer4(x)
  72.  
  73.         x = self.avgpool(x)
  74.         x = x.reshape(x.shape[0], -1)
  75.         x = self.fc(x)
  76.         return x
  77.  
  78.     def make_layers(self, block, num_residual_blocks, intermediate_channels, stride):
  79.         layers = []
  80.  
  81.         identity_downsample = nn.Sequential(nn.Conv2d(self.in_channels, intermediate_channels*self.expansion, kernel_size=1, stride=stride),
  82.                                             nn.BatchNorm2d(intermediate_channels*self.expansion))
  83.         layers.append(block(self.in_channels, intermediate_channels, identity_downsample, stride))
  84.         self.in_channels = intermediate_channels * self.expansion # 256
  85.         for i in range(num_residual_blocks - 1):
  86.             layers.append(block(self.in_channels, intermediate_channels)) # 256 -> 64, 64*4 (256) again
  87.         return nn.Sequential(*layers)
Runtime error #stdin #stdout #stderr 0.21s 23688KB
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Standard input is empty
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Standard output is empty
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Traceback (most recent call last):
  File "./prog.py", line 1, in <module>
    class Block(nn.Module):
NameError: name 'nn' is not defined
https://ideone.com/SwhKUj
language:
Python 3 nbc (python 3.7.3)
created:
3 years ago
visibility:
public

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