File size: 1,703 Bytes
b3c95c7 d9382d1 b3c95c7 d9382d1 b3c95c7 d9382d1 b3c95c7 d9382d1 b3c95c7 d9382d1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
from torch import nn
import torchvision
class BadNet(nn.Module):
# def __init__(self, input_channel, output_label) -> None:
# 目前只假设cifar10
def __init__(self, output_label) -> None:
super(BadNet, self).__init__()
self.model = torchvision.models.resnet18(pretrained=True)
num_features = self.model.fc.out_features
self.fc = nn.Linear(in_features=num_features, out_features=output_label)
def forward(self, xs):
out = self.model(xs)
return self.fc(out)
# class BadNet(nn.Module):
# def __init__(self, input_channels, output_num):
# super().__init__()
# self.conv1 = nn.Sequential(
# nn.Conv2d(in_channels=input_channels, out_channels=16, kernel_size=5, stride=1),
# nn.ReLU(),
# nn.AvgPool2d(kernel_size=2, stride=2)
# )
# self.conv2 = nn.Sequential(
# nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1),
# nn.ReLU(),
# nn.AvgPool2d(kernel_size=2, stride=2)
# )
# fc1_input_features = 800 if input_channels == 3 else 512
# self.fc1 = nn.Sequential(
# nn.Linear(in_features=fc1_input_features, out_features=512),
# nn.ReLU()
# )
# self.fc2 = nn.Sequential(
# nn.Linear(in_features=512, out_features=output_num),
# nn.Softmax(dim=-1)
# )
# self.dropout = nn.Dropout(p=.5)
# def forward(self, x):
# x = self.conv1(x)
# x = self.conv2(x)
# print(x.shape)
# x = x.view(x.size(0), -1)
# x = self.fc1(x)
# x = self.fc2(x)
# return x |