sxjscience 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_r173059994
##########
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:
You can do it if you have time. It should be due to the difference between
windows and unix.
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