euntaik commented on a change in pull request #7400:
URL: https://github.com/apache/tvm/pull/7400#discussion_r573533135
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File path: python/tvm/relay/frontend/tflite.py
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@@ -3588,8 +3643,14 @@ def from_tflite(model, shape_dict, dtype_dict):
exp_tab = ExprTable()
for model_input in model_inputs:
model_input_name = get_tensor_name(subgraph, model_input)
- shape = shape_dict[model_input_name] if model_input_name in shape_dict
else None
- dtype = dtype_dict[model_input_name] if model_input_name in dtype_dict
else "float32"
+ if shape_dict:
+ shape = shape_dict[model_input_name] if model_input_name in
shape_dict else None
+ else:
+ shape = get_tensor_shape(subgraph, model_input)
+ if dtype_dict:
+ dtype = dtype_dict[model_input_name] if model_input_name in
dtype_dict else "float32"
+ else:
+ dtype = get_tensor_type(subgraph, model_input)
Review comment:
> We have a similar function, that collect the same information being
proposed here in TVMC. I agree we should move what is in there, to unify
functionality here.
Oh, it was there all along. I think I missed your code since I was loading
my models in a separate script to put the relay output into my compile passes.
> Can you have a look on the function I'm pointing here (below) and spot why
are they so different,
I don't see much difference except that your code accounts for models with
more than one subgraph.
> and in case you agree on what's the best approach, improve it here and
remove it there?
My rationale behind making and putting this code in the tflite.py file was:
1. use the data in the graph since it is already embedded in it.
2. place the code inside the frontend code since it is dependent on the
frontend.
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