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import torch
import torch.nn as nn
import torch.nn.functional as F
# from common import BaseNet
class ResBlock(nn.Module):
def __init__(self, in_planes: int, out_planes: int, stride: int = 1, drop: float = 0) -> None:
super().__init__()
self.dropout = nn.Dropout2d(drop)
self.conv1 = nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False,
)
self.bn1 = nn.BatchNorm2d(out_planes)
self.conv2 = nn.Conv2d(
out_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False,
)
self.bn2 = nn.BatchNorm2d(out_planes)
def forward(self, x: torch.Tensor) -> torch.Tensor:
out = F.relu(self.bn1(self.conv1(x)))
out = self.dropout(out)
out = self.bn2(self.conv2(out))
out += x
out = F.relu(out)
out = self.dropout(out)
return out
class CustomResNet(nn.Module):
def __init__(self, drop: float = 0, num_classes: int = 10) -> None:
super().__init__()
# perp layer
self.perlayer = nn.Sequential(
nn.Conv2d(3, 64, 3, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(),
nn.Dropout2d(drop),
)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 128, 3, padding=1, bias=False),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(128),
nn.ReLU(),
nn.Dropout2d(drop),
ResBlock(128, 128, drop=drop),
)
self.layer2 = nn.Sequential(
nn.Conv2d(128, 256, 3, padding=1, bias=False),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(256),
nn.ReLU(),
nn.Dropout2d(drop),
)
self.layer3 = nn.Sequential(
nn.Conv2d(256, 512, 3, padding=1, bias=False),
nn.MaxPool2d(2, 2),
nn.BatchNorm2d(512),
nn.ReLU(),
nn.Dropout2d(drop),
ResBlock(512, 512, drop=drop),
)
self.pool = nn.MaxPool2d(4)
self.out = nn.Conv2d(512, num_classes, 1, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.perlayer(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.pool(x)
x = self.out(x)
return x.view(-1, 10) |