fix batchnorm

IntelLabs · Apr 19, 2023 · 86adb6d · 86adb6d
1 parent c3e47ed
commit 86adb6d
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Showing 2 changed files with 16 additions and 9 deletions.
diff --git a/bayesian_torch/layers/batchnorm.py b/bayesian_torch/layers/batchnorm.py
@@ -54,7 +54,6 @@ def _check_input_dim(self, input):
                 input.dim()))
 
     def forward(self, input):
-        self._check_input_dim(input[0])
         exponential_average_factor = 0.0
         if self.training and self.track_running_stats:
             self.num_batches_tracked += 1
@@ -63,13 +62,21 @@ def forward(self, input):
         else:  # use exponential moving average
             exponential_average_factor = self.momentum
 
-        out = F.batch_norm(input[0], self.running_mean, self.running_var,
-                           self.weight, self.bias, self.training
-                           or not self.track_running_stats,
-                           exponential_average_factor, self.eps)
-        kl = 0
-        return out, kl
-
+        if len(input) == 2:
+            self._check_input_dim(input[0])
+            out = F.batch_norm(input[0], self.running_mean, self.running_var,
+                            self.weight, self.bias, self.training
+                            or not self.track_running_stats,
+                            exponential_average_factor, self.eps)
+            kl = 0
+            return out, kl
+        else:
+            out = F.batch_norm(input, self.running_mean, self.running_var,
+                            self.weight, self.bias, self.training
+                            or not self.track_running_stats,
+                            exponential_average_factor, self.eps)
+            return out
+
 
 class BatchNorm1dLayer(nn.Module):
     def __init__(self,

diff --git a/bayesian_torch/models/bayesian/resnet_variational_large.py b/bayesian_torch/models/bayesian/resnet_variational_large.py
@@ -200,7 +200,7 @@ def _make_layer(self, block, planes, blocks, stride=1):
                                          posterior_mu_init=posterior_mu_init,
                                          posterior_rho_init=posterior_rho_init,
                                          bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
+                BatchNorm2dLayer(planes * block.expansion),
             )
 
         layers = []