reimplemented the Cross_Pseudo_Supervision for medical image application

szy-9916 · Jul 16, 2021 · b2edda0 · b2edda0
1 parent ba4c557
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diff --git a/.idea/.gitignore b/.idea/.gitignore
diff --git a/.idea/SSL4MIS.iml b/.idea/SSL4MIS.iml
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
diff --git a/.idea/misc.xml b/.idea/misc.xml
diff --git a/.idea/modules.xml b/.idea/modules.xml
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
diff --git a/code/networks/unet.py b/code/networks/unet.py
@@ -9,7 +9,25 @@
 import torch.nn as nn
 from torch.distributions.uniform import Uniform
 
-
+def kaiming_normal_init_weight(model):
+    for m in model.modules():
+        if isinstance(m, nn.Conv3d):
+            torch.nn.init.kaiming_normal_(m.weight)
+        elif isinstance(m, nn.BatchNorm3d):
+            m.weight.data.fill_(1)
+            m.bias.data.zero_()
+    return model
+
+def sparse_init_weight(model):
+    for m in model.modules():
+        if isinstance(m, nn.Conv3d):
+            torch.nn.init.sparse_(m.weight, sparsity=0.1)
+        elif isinstance(m, nn.BatchNorm3d):
+            m.weight.data.fill_(1)
+            m.bias.data.zero_()
+    return model
+
+
 class ConvBlock(nn.Module):
     """two convolution layers with batch norm and leaky relu"""