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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.resnet import resnet50, resnet18
class Model(nn.Module):
def __init__(self, feature_dim=128, dataset='cifar10', arch='resnet50'):
super(Model, self).__init__()
self.f = []
if arch == 'resnet18':
temp_model = resnet18().named_children()
embedding_size = 512
elif arch == 'resnet50':
temp_model = resnet50().named_children()
embedding_size = 2048
else:
raise NotImplementedError
for name, module in temp_model:
if name == 'conv1':
module = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
if dataset == 'cifar10' or dataset == 'cifar100':
if not isinstance(module, nn.Linear) and not isinstance(module, nn.MaxPool2d):
self.f.append(module)
elif dataset == 'tiny_imagenet' or dataset == 'stl10':
if not isinstance(module, nn.Linear):
self.f.append(module)
# encoder
self.f = nn.Sequential(*self.f)
# projection head
self.g = nn.Sequential(nn.Linear(embedding_size, 512, bias=False), nn.BatchNorm1d(512),
nn.ReLU(inplace=True), nn.Linear(512, feature_dim, bias=True))
def forward(self, x):
x = self.f(x)
feature = torch.flatten(x, start_dim=1)
out = self.g(feature)
return F.normalize(feature, dim=-1), F.normalize(out, dim=-1)
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