mirror of https://github.com/sthalles/SimCLR.git
67 lines
1.6 KiB
Python
67 lines
1.6 KiB
Python
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 Encoder(nn.Module):
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def __init__(self, out_dim=64):
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super(Encoder, self).__init__()
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self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
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self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
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self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
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self.conv4 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)
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self.pool = nn.MaxPool2d(2, 2)
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# projection MLP
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self.l1 = nn.Linear(64, 64)
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self.l2 = nn.Linear(64, out_dim)
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def forward(self, x):
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x = self.conv1(x)
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x = F.relu(x)
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x = self.pool(x)
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x = self.conv2(x)
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x = F.relu(x)
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x = self.pool(x)
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x = self.conv3(x)
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x = F.relu(x)
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x = self.pool(x)
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x = self.conv4(x)
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x = F.relu(x)
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x = self.pool(x)
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h = torch.mean(x, dim=[2, 3])
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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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class ResNet18(nn.Module):
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def __init__(self, out_dim=64):
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super(ResNet18, self).__init__()
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resnet = models.resnet18(pretrained=False)
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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 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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