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import torch
from torch import nn, Tensor
from torchvision.models import ResNet, VisionTransformer
from torchvision.models.resnet import BasicBlock
class CIFARResNet50(ResNet):
def __init__(self, num_classes):
super(CIFARResNet50, self).__init__(
block=BasicBlock, layers=[3, 4, 6, 3], num_classes=num_classes
)
self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
stride=1, padding=1, bias=False)
def forward(self, x: Tensor) -> Tensor:
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
class CIFARViTTiny(VisionTransformer):
# Hyperparams copied from https://github.com/omihub777/ViT-CIFAR/blob/f5c8f122b4a825bf284bc9b471ec895cc9f847ae/README.md#3-hyperparams
def __init__(self, num_classes):
super().__init__(
image_size=32,
patch_size=4,
num_layers=7,
num_heads=12,
hidden_dim=384,
mlp_dim=384,
num_classes=num_classes,
)
class ImageNetResNet50(ResNet):
def __init__(self):
super(ImageNetResNet50, self).__init__(
block=BasicBlock, layers=[3, 4, 6, 3], num_classes=1000
)
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