[incubator-tvm] branch master updated: Fix stride default value None in torch.nn.functional.avg_pool (#4984)

[masahi]

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The following commit(s) were added to refs/heads/master by this push:
     new de0869d  Fix stride default value None in torch.nn.functional.avg_pool 
(#4984)
de0869d is described below

commit de0869de10bd353fc0daf6872a68677c6154483e
Author: pyjhzwh <pyjh...@gmail.com>
AuthorDate: Fri Mar 6 19:39:33 2020 -0500

    Fix stride default value None in torch.nn.functional.avg_pool (#4984)
    
    * fix unordered dictionary problem for python version 3.5
    
    * modify style
    
    * default value of stride in torch.nn.functional.avg_pool is None
    
    * delete prev modifications
    
    * add testcase for nn.functional.avg_pool2d
---
 python/tvm/relay/frontend/pytorch.py          | 5 ++++-
 tests/python/frontend/pytorch/test_forward.py | 5 +++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/python/tvm/relay/frontend/pytorch.py 
b/python/tvm/relay/frontend/pytorch.py
index 1bdcf0a..5716837 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -470,7 +470,10 @@ def _avg_pool2d():
         data = inputs[0]
 
         pool_size = _infer_shape(inputs[1])
-        strides = _infer_shape(inputs[2])
+        if inputs[2]:
+            strides = _infer_shape(inputs[2])
+        else:
+            strides = pool_size
         padding = _infer_shape(inputs[3])
 
         ceil_mode = int(inputs[4])
diff --git a/tests/python/frontend/pytorch/test_forward.py 
b/tests/python/frontend/pytorch/test_forward.py
index 641f5c9..eed47ea 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -375,8 +375,13 @@ def test_forward_avgpool():
         def forward(self, *args):
             return torch.nn.AvgPool2d(kernel_size=[10, 10])(args[0])
 
+    class AvgPool2D2(Module):
+        def forward(self, *args):
+            return torch.nn.functional.avg_pool2d(args[0], kernel_size=[10, 
10])
+
     input_data = torch.rand(input_shape).float()
     verify_model(AvgPool2D1().float().eval(), input_data=input_data)
+    verify_model(AvgPool2D2().float().eval(), input_data=input_data)
 
 def test_forward_hardtanh():
     torch.set_grad_enabled(False)