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import torch
import torch.nn as nn
class ResBlock(nn.Module):
def __init__(self, channels):
super(ResBlock, self).__init__()
self.resblock = nn.Sequential(
nn.Conv2d(
in_channels=channels,
out_channels=channels,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
nn.BatchNorm2d(channels),
nn.ReLU(),
nn.Conv2d(
in_channels=channels,
out_channels=channels,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
nn.BatchNorm2d(channels),
nn.ReLU(),
)
def forward(self, x):
return x + self.resblock(x)
class CustomResnet(nn.Module):
def __init__(self):
super(CustomResnet, self).__init__()
self.prep = nn.Sequential(
nn.Conv2d(
in_channels=3,
out_channels=64,
kernel_size=3,
stride=1,
padding=1,
bias=False,
),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.layer1 = nn.Sequential(
nn.Conv2d(
in_channels=64,
out_channels=128,
kernel_size=3,
padding=1,
stride=1,
bias=False,
),
nn.MaxPool2d(kernel_size=2),
nn.BatchNorm2d(128),
nn.ReLU(),
ResBlock(channels=128),
)
self.layer2 = nn.Sequential(
nn.Conv2d(
in_channels=128,
out_channels=256,
kernel_size=3,
padding=1,
stride=1,
bias=False,
),
nn.MaxPool2d(kernel_size=2),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.layer3 = nn.Sequential(
nn.Conv2d(
in_channels=256,
out_channels=512,
kernel_size=3,
padding=1,
stride=1,
bias=False,
),
nn.MaxPool2d(kernel_size=2),
nn.BatchNorm2d(512),
nn.ReLU(),
ResBlock(channels=512),
)
self.pool = nn.MaxPool2d(kernel_size=4)
self.fc = nn.Linear(in_features=512, out_features=10, bias=False)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x):
x = self.prep(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.pool(x)
x = x.view(-1, 512)
x = self.fc(x)
# x = self.softmax(x)
return x
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