59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
"""
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Reference
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https://github.com/VisionLearningGroup/VisionLearningGroup.github.io/tree/master/M3SDA
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"""
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import torch.nn as nn
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from torch.nn import functional as F
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from .build import BACKBONE_REGISTRY
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from .backbone import Backbone
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class FeatureExtractor(Backbone):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=5, stride=1, padding=2)
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self.bn1 = nn.BatchNorm2d(64)
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self.conv2 = nn.Conv2d(64, 64, kernel_size=5, stride=1, padding=2)
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self.bn2 = nn.BatchNorm2d(64)
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self.conv3 = nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=2)
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self.bn3 = nn.BatchNorm2d(128)
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self.fc1 = nn.Linear(8192, 3072)
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self.bn1_fc = nn.BatchNorm1d(3072)
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self.fc2 = nn.Linear(3072, 2048)
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self.bn2_fc = nn.BatchNorm1d(2048)
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self._out_features = 2048
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def _check_input(self, x):
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H, W = x.shape[2:]
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assert (
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H == 32 and W == 32
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), "Input to network must be 32x32, " "but got {}x{}".format(H, W)
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def forward(self, x):
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self._check_input(x)
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x = F.relu(self.bn1(self.conv1(x)))
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x = F.max_pool2d(x, stride=2, kernel_size=3, padding=1)
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x = F.relu(self.bn2(self.conv2(x)))
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x = F.max_pool2d(x, stride=2, kernel_size=3, padding=1)
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x = F.relu(self.bn3(self.conv3(x)))
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x = x.view(x.size(0), 8192)
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x = F.relu(self.bn1_fc(self.fc1(x)))
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x = F.dropout(x, training=self.training)
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x = F.relu(self.bn2_fc(self.fc2(x)))
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return x
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@BACKBONE_REGISTRY.register()
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def cnn_digit5_m3sda(**kwargs):
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"""
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This architecture was used for the Digit-5 dataset in:
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- Peng et al. Moment Matching for Multi-Source
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Domain Adaptation. ICCV 2019.
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"""
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return FeatureExtractor()
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