Shashankwer edited a comment on issue #700:
URL: https://github.com/apache/singa/issues/700#issuecomment-636353079
Hi,
Shufflenetv2 for average pooling layer the conversion from onnx fails on
below error:
ValueError: Not implemented yet for count_include_pad or ceil_mode
with below stack trace
ValueError Traceback (most recent call last)
<ipython-input-22-f6c52060eea0> in <module>()
----> 1 sg_ir = sonnx.prepare(onnx_model, device=dev)
3 frames
/usr/local/lib/python3.7/site-packages/singa/sonnx.py in prepare(cls, model,
device, **kwargs)
2033 opset_version = 1
2034 weights, singa_ops = cls._onnx_model_to_singa_net(
-> 2035 model, init_inputs, device, opset_version)
2036 return SingaRep(model, weights, singa_ops,
2037 cls.keep_initializers_as_inputs)
/usr/local/lib/python3.7/site-packages/singa/sonnx.py in
_onnx_model_to_singa_net(cls, model, init_inputs, device, opset_version)
1971 ]
1972 handle, forward = cls._onnx_node_to_singa_op(
-> 1973 node, inputs, opset_version)
1974 # if it is Constant, we hanlde it as a weight
1975 # otherwise, we run it and add its output into map for
being used by later operators
/usr/local/lib/python3.7/site-packages/singa/sonnx.py in
_onnx_node_to_singa_op(cls, onnx_node, inputs, opset_version)
1816 else:
1817 translator = cls._common_onnx_node_to_singa_op
-> 1818 return translator(onnx_node, inputs, opset_version)
1819
1820 classmethod
/usr/local/lib/python3.7/site-packages/singa/sonnx.py in
_create_max_avg_pool(cls, onnx_node, inputs, opset_version)
1635 if "count_include_pad" in onnx_node.attrs or "ceil_mode" in
onnx_node.attrs:
1636 raise ValueError(
-> 1637 "Not implemented yet for count_include_pad or
ceil_mode")
1638
1639 # only support 2d
ValueError: Not implemented yet for count_include_pad or ceil_mode.
Kindly let us know the further steps to be taken
Thanks and Regards,
Shashank Nigam
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