honghuichao commented on issue #13759:
URL: https://github.com/apache/tvm/issues/13759#issuecomment-1379733000
this is my demo extract from my network. @masahi
```
import torch
from pdb import set_trace as d
import onnx
from tvm import relay
class TestIndexPut(torch.nn.Module):
def __init__(self):
super(TestIndexPut, self).__init__()
def
forward(self,batch_src_corr_points,indices0,indices1,src_corr_points):
batch_src_corr_points.index_put_([indices0, indices1],
src_corr_points)
return batch_src_corr_points
model = TestIndexPut()
dummy_batch_src_corr_points = torch.rand(136888, 3).float()
dummy_indices0 = torch.rand(50168,3).long()
dummy_indices1 = torch.rand(50168,3).long()
dummy_src_corr_points = torch.rand(50168,3).float()
dummy_output =
model(dummy_batch_src_corr_points,dummy_indices0,dummy_indices1,dummy_src_corr_points)
input_names=["dummy_batch_src_corr_points","dummy_indices0","dummy_indices1","dummy_src_corr_points"]
output_names=["batch_src_corr_points"]
dynamic_axes = {
"dummy_batch_src_corr_points":{0:"bscp_shape0",1:"bscp_shape1"},
"dummy_indices0":{0:"i0_shape0",1:"i0_shape1"},
"dummy_indices1":{0:"i1_shape0",1:"i1_shape1"},
"dummy_src_corr_points":{0:"o_shape0",1:"o_shape1"}
}
with torch.no_grad():
torch.onnx.export(model.eval(),
(dummy_batch_src_corr_points,dummy_indices0,dummy_indices1,dummy_src_corr_points),
'TestIndexPut.onnx',
input_names =
input_names,output_names=output_names,opset_version=11,dynamic_axes=dynamic_axes,operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK)
m = onnx.load('TestIndexPut.onnx')
relay.frontend.onnx.from_onnx(m)
```
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