CoinCheung commented on a change in pull request #10000: fix average pooling 
kernel size assignment error
URL: https://github.com/apache/incubator-mxnet/pull/10000#discussion_r173058901
 
 

 ##########
 File path: tests/python/gpu/test_operator_gpu.py
 ##########
 @@ -904,86 +904,87 @@ def test_1d_pooling(pool_type):
         kernel = (4,)
         pad = (2,)
         stride = (2,)
-    
+
         ctx_list = []
         sym_list = []
-    
+
         pooling_convention = 'valid'
-    
+
         ctx_list.append({'ctx': mx.cpu(0), 'pool_data': data, 'type_dict': 
{'pool_data': np.float32}})
-        sym_list.append(mx.sym.Pooling(kernel=kernel, pad=pad, stride=stride, 
pool_type=pool_type,
+        sym_list.append(mx.sym.Pooling(pad=pad, stride=stride, 
pool_type=pool_type,
                                        pooling_convention=pooling_convention, 
global_pool=True, name='pool'))
-    
+
         ctx_list.append({'ctx': mx.cpu(0), 'pool_data': data, 'type_dict': 
{'pool_data': np.float32}})
-        sym_list.append(mx.sym.Pooling(kernel=kernel, pool_type=pool_type,
+        sym_list.append(mx.sym.Pooling(pool_type=pool_type,
                                        pooling_convention=pooling_convention, 
global_pool=True, name='pool'))
-    
+
         ctx_list.append({'ctx': mx.gpu(0), 'pool_data': data, 'type_dict': 
{'pool_data': np.float32}})
-        sym_list.append(mx.sym.Pooling(kernel=kernel, pad=pad, stride=stride, 
pool_type=pool_type,
+        sym_list.append(mx.sym.Pooling(pad=pad, stride=stride, 
pool_type=pool_type,
                                        pooling_convention=pooling_convention, 
global_pool=True, cudnn_off=False, name='pool'))
-    
+
         ctx_list.append({'ctx': mx.gpu(0), 'pool_data': data, 'type_dict': 
{'pool_data': np.float32}})
-        sym_list.append(mx.sym.Pooling(kernel=kernel, pool_type=pool_type,
+        sym_list.append(mx.sym.Pooling(pool_type=pool_type,
                                        pooling_convention=pooling_convention, 
global_pool=True, cudnn_off=False, name='pool'))
-    
+
         ctx_list.append({'ctx': mx.gpu(0), 'pool_data': data, 'type_dict': 
{'pool_data': np.float32}})
-        sym_list.append(mx.sym.Pooling(kernel=kernel, pad=pad, stride=stride, 
pool_type=pool_type,
+        sym_list.append(mx.sym.Pooling(pad=pad, stride=stride, 
pool_type=pool_type,
                                        pooling_convention=pooling_convention, 
global_pool=True, cudnn_off=True, name='pool'))
-    
+
         ctx_list.append({'ctx': mx.gpu(0), 'pool_data': data, 'type_dict': 
{'pool_data': np.float32}})
-        sym_list.append(mx.sym.Pooling(kernel=kernel, pool_type=pool_type,
+        sym_list.append(mx.sym.Pooling(pool_type=pool_type,
                                        pooling_convention=pooling_convention, 
global_pool=True, cudnn_off=True, name='pool'))
-    
+
         check_consistency(sym_list, ctx_list)
-    
+
     def test_2d_pooling(pool_type):
         data = (2, 3, 20, 20)
         kernel = (4, 4)
         pad = (2, 2)
         stride = (2, 2)
-    
+
         ctx_list = []
         sym_list = []
-    
+
         pooling_convention = 'valid'
-    
+
         ctx_list.append({'ctx': mx.cpu(0), 'pool_data': data, 'type_dict': 
{'pool_data': np.float32}})
         sym_list.append(mx.sym.Pooling_v1(kernel=kernel, pad=pad, 
stride=stride, pool_type=pool_type,
                                        pooling_convention=pooling_convention, 
global_pool=True, name='pool'))
-    
+
         ctx_list.append({'ctx': mx.cpu(0), 'pool_data': data, 'type_dict': 
{'pool_data': np.float32}})
         sym_list.append(mx.sym.Pooling_v1(kernel=kernel, pool_type=pool_type,
                                        pooling_convention=pooling_convention, 
global_pool=True, name='pool'))
 
 Review comment:
   Do I need to remove the blank lines? I only removed the kernel parameter 
assignment did not touch these white lines.

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