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[FRONTEND][TFLITE] get input tensor information from graph #7400

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merged 13 commits into from
Feb 15, 2021
Merged

[FRONTEND][TFLITE] get input tensor information from graph #7400

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3d4e491
Merge pull request #1 from apache/main
euntaik Feb 3, 2021
bd5a180
[FRONTEND][TFLITE] get input tensor information from graph
euntaik Feb 3, 2021
d4532d7
remove bare-except
euntaik Feb 3, 2021
d58353b
fix lint
euntaik Feb 3, 2021
e4621ec
delete empty line
euntaik Feb 3, 2021
86c70b7
comment change
euntaik Feb 4, 2021
7ec10c4
Merge branch 'main' of https://github.com/apache/tvm into tflite_fron…
euntaik Feb 8, 2021
c954a95
move some of the tflite frontend code from tvmc to tflite.py
euntaik Feb 10, 2021
dc91132
resolve conflicts
euntaik Feb 10, 2021
53e044a
update shape and dtype when user provided them
euntaik Feb 10, 2021
2ff9b43
remove unused var. pass user provided shape_dict
euntaik Feb 10, 2021
b80e798
remove duplicate code
euntaik Feb 10, 2021
bc89d27
Merge branch 'main' of https://github.com/apache/tvm into tflite_fron…
Feb 10, 2021
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67 changes: 64 additions & 3 deletions python/tvm/relay/frontend/tflite.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3539,7 +3539,62 @@ def get_tensor_name(subgraph, tensor_idx):
return subgraph.Tensors(tensor_idx).Name().decode("utf-8")


def from_tflite(model, shape_dict, dtype_dict):
def get_tensor_shape(subgraph, tensor_idx):
"""Get the tensor shape.
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  • minor: the name of the argument doesn't match with the actual argument
  • the types are not specified

Please review all docstring being introduced here for those items above.


Parameters
----------
subgraph:
tflite.Subgraph.Subgraph

tensor:
tensor index in subgraph

Returns
-------
tensor shape
"""
return tuple(subgraph.Tensors(tensor_idx).ShapeAsNumpy())


def get_tensor_type(subgraph, tensor_idx):
"""Get the tensor type.

Parameters
----------
subgraph:
tflite.Subgraph.Subgraph

tensor:
tensor index in subgraph

Returns
-------
tensor type in string
"""
from enum import Enum

class TensorType(Enum):
""" Enum defined in tensorflow lite """

FLOAT32 = 0
FLOAT16 = 1
INT32 = 2
UINT8 = 3
INT64 = 4
STRING = 5
BOOL = 6
INT16 = 7
COMPLEX64 = 8
INT8 = 9
FLOAT64 = 10
COMPLEX128 = 11
UINT64 = 12

return TensorType(subgraph.Tensors(tensor_idx).Type()).name.lower()


def from_tflite(model, shape_dict=None, dtype_dict=None):
"""Convert from tflite model into compatible relay Function.

Parameters
Expand Down Expand Up @@ -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)
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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.

Can you have a look on the function I'm pointing here (below) and spot why are they so different, and in case you agree on what's the best approach, improve it here and remove it there?

tvm/python/tvm/driver/tvmc/frontends.py

Lines 255 to 278 in 2999d03

@staticmethod
def _decode_type(n):
return TFLiteFrontend._tflite_m[n]
@staticmethod
def _input_type(model):
subgraph_count = model.SubgraphsLength()
assert subgraph_count > 0
shape_dict = {}
dtype_dict = {}
for subgraph_index in range(subgraph_count):
subgraph = model.Subgraphs(subgraph_index)
inputs_count = subgraph.InputsLength()
assert inputs_count >= 1
for input_index in range(inputs_count):
input_ = subgraph.Inputs(input_index)
assert subgraph.TensorsLength() > input_
tensor = subgraph.Tensors(input_)
input_shape = tuple(tensor.ShapeAsNumpy())
tensor_type = tensor.Type()
input_name = tensor.Name().decode("utf8")
shape_dict[input_name] = input_shape
dtype_dict[input_name] = TFLiteFrontend._decode_type(tensor_type)

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@euntaik euntaik Feb 10, 2021 •
edited
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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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Cool. I think we both agree that it is better to have the funcionality only in the tflite.py, and remove it from TVMC.

So I suggest we keep the one that accounts for many subgraphs, and move it from TVMC to the official frontend? If you agree, feel free to do it in this PR.

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Thanks, I will update the PR.

exp_tab.set_expr(model_input_name, _expr.var(model_input_name, shape=shape, dtype=dtype))

# op code in model
Expand Down