Open
Description
OpenVINO Version
2022.1.0
Operating System
Windows System
Device used for inference
CPU
Framework
ONNX
Model used
No response
Issue description
Hello!
I successfully converted a model from PyTorch to ONNX. However, I am currently trying to convert the ONNX model to OpenVINO. My ONNX model contains a Slice operator, and while converting to OpenVINO, I encounter the following error:
OpenVINO runtime found in: C:\Users\zhizhin\PycharmProjects\gtrcn_model_wenv\Lim_narabotki\venv\lib\site-packages\openvino
OpenVINO runtime version: 2022.1.0-7019-cdb9bec7210-releases/2022/1
Model Optimizer version: 2022.1.0-7019-cdb9bec7210-releases/2022/1
[ ERROR ] -------------------------------------------------
[ ERROR ] ----------------- INTERNAL ERROR ----------------
[ ERROR ] Unexpected exception happened.
[ ERROR ] Please contact Model Optimizer developers and forward the following information:
[ ERROR ] Check 'node->visit_attributes(visitor)' failed at C:\j\workspace\private-ci\ie\build-windows-vs2019@3\b\repos\openvino\src\core\src\pass\serialize.cpp:926:
Visitor API is not supported in v0::NullNode NullNode_9 () -> (dynamic...)
[ ERROR ] Traceback (most recent call last):
File "C:\Users\zhizhin\PycharmProjects\gtrcn_model_wenv\Lim_narabotki\venv\Lib\site-packages\openvino\tools\mo\main.py", line 533, in main
ret_code = driver(argv)
File "C:\Users\zhizhin\PycharmProjects\gtrcn_model_wenv\Lim_narabotki\venv\Lib\site-packages\openvino\tools\mo\main.py", line 493, in driver
ret_res = moc_emit_ir(ngraph_function, argv)
File "C:\Users\zhizhin\PycharmProjects\gtrcn_model_wenv\Lim_narabotki\venv\lib\site-packages\openvino\tools\mo\moc_frontend\serialize.py", line 44, in moc_emit_ir
serialize(ngraph_function, (orig_model_name + ".xml").encode('utf-8'), (orig_model_name + ".bin").encode('utf-8'))
RuntimeError: Check 'node->visit_attributes(visitor)' failed at C:\j\workspace\private-ci\ie\build-windows-vs2019@3\b\repos\openvino\src\core\src\pass\serialize.cpp:926:
Visitor API is not supported in v0::NullNode NullNode_9 () -> (dynamic...)
I also tried converting my Torch model to ONNX with the "dynamic_axes" parameter, but I also received error about Slice usage
I hope you can answer my question or tell me about workaround. I need to convert the model to OpenVINO exactly with version 2022.1.0.
Hope to hear from you soon!
Version of ONNX: 1.16.2
Version of Torch: 2.1.2+cu118
Version of OpenVINO and OpenVINO-dev: 2022.1.0
Step-by-step reproduction
import math
import torch.nn as nn
import torch
import onnx
from onnxsim import simplify
class DummyModel(nn.Module):
def __init__(self,
emb_dim,
emb_ks,
emb_hs,
n_head=4):
super().__init__()
self.emb_dim = emb_dim
self.emb_ks = emb_ks
self.emb_hs = emb_hs
self.n_head = n_head
def __getitem__(self, item):
return getattr(self, item)
def forward(self, x):
B, C, old_T, old_F = x.shape
T = math.ceil((old_T - self.emb_ks) / self.emb_hs) * self.emb_hs + self.emb_ks
F = math.ceil((old_F - self.emb_ks) / self.emb_hs) * self.emb_hs + self.emb_ks
x = nn.functional.pad(x, (0, F - old_F, 0, T - old_T))
inter_rnn = x[:, :, :, :old_F]
return inter_rnn
stream_model = DummyModel(emb_dim=64,
emb_hs=2,
emb_ks=4)
input_names = ["inp_noisy"]
output_names =["out_enh"]
onnx_name = "Dummy_Model.onnx"
torch.onnx.export(
stream_model,
args=(noisy),
input_names=input_names,
output_names=output_names,
f=onnx_name,
verbose=False,
opset_version=13,
)
onnx_model = onnx.load(onnx_name)
onnx.checker.check_model(onnx_model)
model_simp, check = simplify(onnx_model)
assert check, "Simplified ONNX model could not be validated"
onnx.save(model_simp, "simplified_Dummy_Model.onnx")
After conversion to ONNX, I am trying to convert my model to OpenVINO with: mo --input_model "simplified_Dummy_Model.onnx" --output_dir "path_to_output_dir"
ONNX GRAPH
Relevant log output
Issue submission checklist
- I'm reporting an issue. It's not a question.
- I checked the problem with the documentation, FAQ, open issues, Stack Overflow, etc., and have not found a solution.
- There is reproducer code and related data files such as images, videos, models, etc.