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nnUnet/initialization.py

+#    Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
+#
+#    Licensed under the Apache License, Version 2.0 (the "License");
+#    you may not use this file except in compliance with the License.
+#    You may obtain a copy of the License at
+#
+#        http://www.apache.org/licenses/LICENSE-2.0
+#
+#    Unless required by applicable law or agreed to in writing, software
+#    distributed under the License is distributed on an "AS IS" BASIS,
+#    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#    See the License for the specific language governing permissions and
+#    limitations under the License.
+
+
+from torch import nn
+
+
+class InitWeights_He(object):
+    def __init__(self, neg_slope=1e-2):
+        self.neg_slope = neg_slope
+
+    def __call__(self, module):
+        if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d):
+            module.weight = nn.init.kaiming_normal_(module.weight, a=self.neg_slope)
+            if module.bias is not None:
+                module.bias = nn.init.constant_(module.bias, 0)
+
+
+class InitWeights_XavierUniform(object):
+    def __init__(self, gain=1):
+        self.gain = gain
+
+    def __call__(self, module):
+        if isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d):
+            module.weight = nn.init.xavier_uniform_(module.weight, self.gain)
+            if module.bias is not None:
+                module.bias = nn.init.constant_(module.bias, 0)