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From GitBox <...@apache.org>
Subject [GitHub] [incubator-singa] chrishkchris commented on a change in pull request #468: Distributted module
Date Tue, 27 Aug 2019 04:36:37 GMT
chrishkchris commented on a change in pull request #468: Distributted module
URL: https://github.com/apache/incubator-singa/pull/468#discussion_r317889630
 
 

 ##########
 File path: python/singa/autograd.py
 ##########
 @@ -1286,25 +1287,26 @@ def set_params(self, **parameters):
 
 
 class _BatchNorm2d(Operation):
-    def __init__(self, handle, name=None):
+    def __init__(self, handle, running_mean, running_var, name=None):
         super(_BatchNorm2d, self).__init__(name)
         self.handle = handle
+        self.running_mean = running_mean.data
+        self.running_var = running_var.data
 
-    def forward(self, x, scale, bias, running_mean, running_var):
-        self.running_mean = running_mean
-        self.running_var = running_var
+    def forward(self, x, scale, bias):
         if training:
 
             if isinstance(self.handle, singa.CudnnBatchNormHandle):
                 y, mean, var = singa.GpuBatchNormForwardTraining(
-                    self.handle, x, scale, bias, running_mean, running_var
+                    self.handle, x, scale, bias, self.running_mean, self.running_var
 
 Review comment:
   The following is the resnet18 training using CPU on CIFAR10 in the first few epochs. CPU
is slow so I trained only a few epochs
   ```
   Start intialization............
   Epoch=0: 100%|████████████████████████████████████████████████████████████████████████|
1562/1562 [2:09:57<00:00,  5.03s/it]
   Training loss = 2233.394769, training accuracy = 0.490297
   Test accuracy = 0.636218
   Epoch=1: 100%|███████████████████████████████████████████████████████████████████████████████████████████|
1562/1562 [2:10:00<00:00,  4.98s/it]
   Training loss = 1474.432049, training accuracy = 0.666633
   Test accuracy = 0.678986
   Epoch=2: 100%|███████████████████████████████████████████████████████████████████████████████████████████|
1562/1562 [2:10:11<00:00,  5.00s/it]
   Training loss = 1163.035850, training accuracy = 0.741717
   Test accuracy = 0.738181
   Epoch=3: 100%|███████████████████████████████████████████████████████████████████████████████████████████|
1562/1562 [2:10:31<00:00,  5.03s/it]
   Training loss = 979.977119, training accuracy = 0.782570
   Test accuracy = 0.800581
   Epoch=4: 100%|███████████████████████████████████████████████████████████████████████████████████████████|
1562/1562 [2:10:10<00:00,  4.98s/it]
   Training loss = 872.811802, training accuracy = 0.806098
   Test accuracy = 0.813902
   Epoch=5: 100%|███████████████████████████████████████████████████████████████████████████████████████████|
1562/1562 [2:10:05<00:00,  4.99s/it]
   Training loss = 782.525783, training accuracy = 0.826144
   Test accuracy = 0.832232
   ```
   The training loss decreases normally. Therefore seems the CPU batch norm is working.

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