release code

Dassl.ProGrad.pytorch/dassl/modeling/backbone/cnn_digit5_m3sda.py

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