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Fix stride default value None in torch.nn.functional.avg_pool #4984

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merged 5 commits into from
Mar 7, 2020
Merged

Fix stride default value None in torch.nn.functional.avg_pool #4984

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a6bfb57
fix unordered dictionary problem for python version 3.5
pyjhzwh Mar 4, 2020
66a65d4
modify style
pyjhzwh Mar 4, 2020
28a8e48
default value of stride in torch.nn.functional.avg_pool is None
pyjhzwh Mar 4, 2020
58f6e29
delete prev modifications
pyjhzwh Mar 4, 2020
2bdd1da
add testcase for nn.functional.avg_pool2d
pyjhzwh Mar 4, 2020
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11 changes: 7 additions & 4 deletions python/tvm/relay/frontend/pytorch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -458,7 +458,10 @@ def _impl(inputs, input_types):
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
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is this correct? we should use the default strides

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https://github.com/pytorch/pytorch/blob/17a5c677963dc3ecb7ff505585ed15eadaaf74ef/torch/nn/functional.py#L269-L270
According to the description of strides above, for avg_pool2d. Stride's default value kernel_size, which should have the same size as pool_size here

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ok

padding = _infer_shape(inputs[3])

ceil_mode = int(inputs[4])
Expand Down Expand Up @@ -918,7 +921,7 @@ def _get_constant(node):

def _get_operator_nodes(nodes):
""" Returns torch IR nodes that need conversion to Relay """
ops = {}
ops = []
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@masahi masahi Mar 4, 2020 •
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You should not have this change in this pr. Fetch and rebase against master.

# Traverse nodes and add to graph
for node in nodes:
if node.outputsSize() > 1:
Expand All @@ -927,7 +930,7 @@ def _get_operator_nodes(nodes):
node_name = _get_output_name(node)

if node.kind() != "prim::GetAttr":
ops[node_name] = node
ops.append((node_name, node))

return ops

Expand Down Expand Up @@ -1015,7 +1018,7 @@ def parse_params(graph, state_dict):

def parse_operators(operators, outputs, output_index_map, ret_name):
""" Convert each Torch IR operators to Relay equivalent """
for node_name, op_node in operators.items():
for node_name, op_node in operators:
operator = op_node.kind()
inputs = _get_op_inputs(op_node, outputs, output_index_map)

Expand Down