import torch.nn as nn
import torch.nn.functional as F
[docs]class CifarNet(nn.Module):
def __init__(self):
super(CifarNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, 3)
self.conv2 = nn.Conv2d(64, 128, 3)
self.conv3 = nn.Conv2d(128, 256, 3)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(64 * 4 * 4, 128)
self.fc2 = nn.Linear(128, 256)
self.fc3 = nn.Linear(256, 10)
[docs] def forward(self, x, last=False):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = self.pool(F.relu(self.conv3(x)))
x = x.view(-1, 64 * 4 * 4)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
output = self.fc3(x)
if last:
return output, x
else:
return output
[docs] def get_embedding_dim(self):
return 256