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from torch import nn
import torch.nn.functional as F
class BadNet(nn.Module):
""" Badnet model class based on the description of table1 of the paper with two convolution
and two fully connected layers """
def __init__(self, input_size=3, output=10):
super().__init__()
self.input_size = input_size
self.output = output
self.conv1 = nn.Conv2d(in_channels=input_size, out_channels=16, kernel_size=(5, 5))
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(5, 5))
self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
if input_size == 3:
self.fc_features = 800
else:
self.fc_features = 512
self.fc1 = nn.Linear(self.fc_features, 512)
self.fc2 = nn.Linear(512, output)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.pool(x)
x = self.conv2(x)
x = F.relu(x)
x = self.pool(x)
x = x.contiguous().view(-1, self.fc_features)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
x = F.softmax(x)
return x
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