AndrewZhaoLuo commented on code in PR #11034:
URL: https://github.com/apache/tvm/pull/11034#discussion_r853331557
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python/tvm/relay/frontend/onnx.py:
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@@ -410,8 +410,16 @@ class Pool(OnnxOpConverter):
@classmethod
def _impl_v1(cls, inputs, attr, params):
+ data = inputs[0]
+ input_shape = infer_shape(data)
+ ndim = len(input_shape)
+
attr_cvt, data = cls._run_calculation(inputs, attr, params)
- return attr_cvt([data], attr, params)
+ out = attr_cvt([data], attr, params)
+
+ if ndim == 3 and len(attr["kernel_shape"]) == 2:
Review Comment:
Can you make this so if kernel_shape rank and ndim differ by 1 we assume
it's missing a batch dimension and runs below code? (same thing for
`_run_calculation`)
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