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import torch
import torch.nn as nn
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(
planes, planes, kernel_size=3, stride=1, padding=1, bias=False
)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(
in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False,
),
nn.BatchNorm2d(self.expansion * planes),
)
def forward(self, x):
out = torch.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = torch.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(
planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(
planes, self.expansion * planes, kernel_size=1, bias=False
)
self.bn3 = nn.BatchNorm2d(self.expansion * planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(
in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False,
),
nn.BatchNorm2d(self.expansion * planes),
)
def forward(self, x):
out = torch.relu(self.bn1(self.conv1(x)))
out = torch.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = torch.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=1000, K=10, T=0.5):
super(ResNet, self).__init__()
self.in_planes = 64
self.K = K
self.T = T
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.fc = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def t_max_avg_pooling(self, x):
B, C, H, W = x.shape
x_flat = x.view(B, C, -1)
top_k_values, _ = torch.topk(x_flat, self.K, dim=2)
max_values = top_k_values.max(dim=2)[0]
avg_values = top_k_values.mean(dim=2)
output = torch.where(max_values >= self.T, max_values, avg_values)
return output
def forward(self, x):
out = torch.relu(self.bn1(self.conv1(x)))
out = self.maxpool(out)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = self.t_max_avg_pooling(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
def ResNet18(num_classes=1000, K=10, T=0.5):
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes, K, T)
def ResNet34(num_classes=1000, K=10, T=0.5):
return ResNet(BasicBlock, [3, 4, 6, 3], num_classes, K, T)
def ResNet50(num_classes=1000, K=10, T=0.5):
return ResNet(Bottleneck, [3, 4, 6, 3], num_classes, K, T)
def ResNet101(num_classes=1000, K=10, T=0.5):
return ResNet(Bottleneck, [3, 4, 23, 3], num_classes, K, T)
def ResNet152(num_classes=1000, K=10, T=0.5):
return ResNet(Bottleneck, [3, 8, 36, 3], num_classes, K, T)
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