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import torch
import torch.nn as nn
class ZFNet(nn.Module):
def __init__(self, num_classes=10):
super(ZFNet, self).__init__()
self.features = nn.Sequential(
# conv1
nn.Conv2d(3, 96, kernel_size=7, stride=2, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2),
# conv2
nn.Conv2d(96, 256, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
nn.LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=2),
# conv3
nn.Conv2d(256, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
# conv4
nn.Conv2d(384, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
# conv5
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
)
self.classifier = nn.Sequential(
nn.Linear(256 * 2 * 2, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
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