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from pathlib import Path
import sys
from torch import nn
from torchvision.models.resnet import resnet50
path = str(Path(Path(__file__).parent.absolute()).parent.absolute())
sys.path.insert(0, path)
from supervised.models import CIFARViTTiny, CIFARResNet50
class SimCLRBase(nn.Module):
def __init__(
self,
backbone: nn.Module,
hid_dim: int = 2048,
out_dim: int = 128,
pretrain: bool = True
):
super().__init__()
self.backbone = backbone
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):
h = self.backbone(x)
if self.pretrain:
z = self.projector(h)
return z
else:
return h
def cifar_simclr_resnet50(hid_dim, *args, **kwargs):
backbone = CIFARResNet50(num_classes=hid_dim)
return SimCLRBase(backbone, hid_dim, *args, **kwargs)
def cifar_simclr_vit_tiny(hid_dim, *args, **kwargs):
backbone = CIFARViTTiny(num_classes=hid_dim)
return SimCLRBase(backbone, hid_dim, *args, **kwargs)
def imagenet_simclr_resnet50(hid_dim, *args, **kwargs):
backbone = resnet50(num_classes=hid_dim)
return SimCLRBase(backbone, hid_dim, *args, **kwargs)
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