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import torch
from torch import nn, Tensor
from torchvision.models import ResNet
from torchvision.models.resnet import BasicBlock
# TODO Make a SimCLR base class
class CIFARSimCLRResNet50(ResNet):
def __init__(self, hid_dim, out_dim=128, pretrain=True):
super(CIFARSimCLRResNet50, self).__init__(
block=BasicBlock, layers=[3, 4, 6, 3], num_classes=hid_dim
)
self.pretrain = pretrain
self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
stride=1, padding=1, bias=False)
if pretrain:
self.projector = nn.Sequential(
nn.Linear(hid_dim, hid_dim),
nn.ReLU(inplace=True),
nn.Linear(hid_dim, out_dim),
)
def backbone(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
def forward(self, x: Tensor) -> Tensor:
h = self.backbone(x)
if self.pretrain:
z = self.projector(h)
return z
else:
return h
class ImageNetSimCLRResNet50(ResNet):
def __init__(self, hid_dim, out_dim=128, pretrain=True):
super(ImageNetSimCLRResNet50, self).__init__(
block=BasicBlock, layers=[3, 4, 6, 3], num_classes=hid_dim
)
self.pretrain = pretrain
if pretrain:
self.projector = nn.Sequential(
nn.Linear(hid_dim, hid_dim),
nn.ReLU(inplace=True),
nn.Linear(hid_dim, out_dim),
)
def forward(self, x: Tensor) -> Tensor:
h = super(ImageNetSimCLRResNet50, self).forward(x)
if self.pretrain:
z = self.projector(h)
return z
else:
return h
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