If your final output is incorrect, the first step I would try is to see what it
should be in the original framework.
For example, if your model is in PyTorch, pass some input data, and save the
output.
Then, export to TVM, and pass it the same input data. If the output from TVM
is different
cause tvm leave no origin network layers infomation in the tvm graph, so how
can i use dump data compared with orgin network layers?
for exmaple, the bert-large has 2000+ ops, but which op related to origin layer
is hard to figure out.
when you face accuracy problem, you dump the data and com
You say that the output is not correct, can you provide a reproducible example
of what model you are running, and how? Perhaps there is an issue there.
To answer your question, [this part of the
documentation](https://tvm.apache.org/docs/arch/debugger.html#how-to-use-debugger)
takes you thro
> All the tensors will be saved as binary bytes in serialized format. The
> result binary bytes can be loaded by the API “load_params”.
how can we achive binary bytes in serialized format?
does "./_tvmdbg_device_CPU_0/output_tensors.params" contains all layers outputs?
then we get data, how to
Sure, you can see an example of how to use the `debug_executor` in [this part
of the
docs](https://tvm.apache.org/docs/arch/debugger.html#how-to-use-debugger).
The tensors will be dumped to a file, and you can then load this data using
code such as:
```python
data =
relay.load_param_dict(by
thx, could u give me some concrete example?
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Hi there, you can use the `debug_executor` to get the intermediate results.
You can see details [discussed in this forum
thread](https://discuss.tvm.apache.org/t/tvm-runtime-print-intermediate-outputs/10124).
You will need to have a ground truth to compare against, depending on what
framework
@tqchen Any Methods to debug to know which intermediate layer outputs is not
correct.
i seach the whole formu, but cant get answer.
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