antimora opened a new issue #4587: [Feature] Support for 1D Convolution
URL: https://github.com/apache/incubator-tvm/issues/4587
 
 
   I was trying to import my ONNX model but it has failed with the following 
error:
   
   ```
   $ /usr/bin/python3 /workspace/trouble_onnx.py
   Traceback (most recent call last):
   
     File "/workspace/trouble_onnx.py", line 15, in <module>
       mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
   
     File "/workspace/python/tvm/relay/frontend/onnx.py", line 1553, in 
from_onnx
       mod, params = g.from_onnx(graph, opset)
   
     File "/workspace/python/tvm/relay/frontend/onnx.py", line 1381, in 
from_onnx
       op = self._convert_operator(op_name, inputs, attr, opset)
   
     File "/workspace/python/tvm/relay/frontend/onnx.py", line 1481, in 
_convert_operator
       sym = convert_map[op_name](inputs, attrs, self._params)
   
     File "/workspace/python/tvm/relay/frontend/onnx.py", line 246, in _impl_v1
       custom_check=dimension_constraint())(inputs[:2], attr, params)
   
     File "/workspace/python/tvm/relay/frontend/common.py", line 377, in 
__call__
       raise RuntimeError("Check failed: {}".format(msg))
   
   RuntimeError: Check failed: Only 2d kernel supported.
   ```
   
   Code is the latest version (master@1900041447cc7fd4f23a857c4839acbf74cd890c) 
as of now. 
   
   I noticed @tqchen closed [this related issue 
#3518](https://github.com/apache/incubator-tvm/issues/3518#event-2898116737) 
recently but not explanation was given. Could it be because  [this related 
PR](https://github.com/apache/incubator-tvm/pull/4476) was merged?
   
   I would really love if TVM adds 1D Conv support. It would be tremendous for 
speech processing projects.
   
   To help with debugging and development, I am attaching onnx model and graph 
image.
   
[trouble.zip](https://github.com/apache/incubator-tvm/files/4002130/trouble.zip)
   
   You can run the following python code to reproduce an error:
   
   ```python
   
   import onnx
   import numpy as np
   import tvm.relay as relay
   
   onnx_model = onnx.load('trouble.onnx')
   
   input1 = np.random.rand(1, 192, 100)
   input2 = np.random.rand(1)
   
   shape_dict = {'1': input1.shape, 'x.1': input2.shape}
   mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
   ```
   
   

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