mirror of https://github.com/sthalles/SimCLR.git
37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.models as models
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class ResNetSimCLR(nn.Module):
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def __init__(self, base_model="resnet18", out_dim=64):
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super(ResNetSimCLR, self).__init__()
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self.resnet_dict = {"resnet18": models.resnet18(pretrained=False),
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"resnet50": models.resnet50(pretrained=False)}
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resnet = self._get_basemodel(base_model)
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num_ftrs = resnet.fc.in_features
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self.features = nn.Sequential(*list(resnet.children())[:-1])
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# projection MLP
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self.l1 = nn.Linear(num_ftrs, num_ftrs)
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self.l2 = nn.Linear(num_ftrs, out_dim)
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def _get_basemodel(self, model_name):
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try:
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return self.resnet_dict[model_name]
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except:
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raise ("Invalid model name. Check the config file and pass one of: resnet18 or resnet50")
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def forward(self, x):
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h = self.features(x)
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h = h.squeeze()
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x = self.l1(h)
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x = F.relu(x)
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x = self.l2(x)
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return h, x
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