mirror of
https://github.com/vee1e/tflite-micro.git
synced 2026-09-02 02:07:27 +00:00
b45012b52c
The RISC-V toolchain we're using failed to properly discern the TfLiteEvalTensor type for use with an assignment operator. It produced compiler errors for "no match for 'operator=' with operand types TfLiteEvalTensor and a brace-closed initializer list. This PR resolves this issue by explicitly using the TfLiteEvalTensor constructor for the initializer list. We also apply this approach to the TfLiteNode initialization as well, just for consistency. BUG=#2195
92 lines
3.7 KiB
Python
92 lines
3.7 KiB
Python
# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
""" Provides object representation for the model that is conducive to code
|
|
generation using templates. """
|
|
|
|
import abc
|
|
from typing import Optional
|
|
import string
|
|
import textwrap
|
|
|
|
from tflite_micro.codegen import utils
|
|
from tflite_micro.tensorflow.lite.python import schema_py_generated as schema_fb
|
|
|
|
|
|
class Operator(abc.ABC):
|
|
|
|
def __init__(self, operator: schema_fb.OperatorT):
|
|
self._operator: schema_fb.OperatorT = operator
|
|
self._inputs: utils.IntArray = utils.IntArray(self._operator.inputs)
|
|
self._outputs: utils.IntArray = utils.IntArray(self._operator.outputs)
|
|
self._intermediates: Optional[utils.IntArray] = utils.IntArray(
|
|
self._operator.intermediates) if self._operator.intermediates else None
|
|
|
|
def generate_c_node_data(self, type_name: str, node_name: str) -> str:
|
|
struct_template = string.Template("struct ${type_name} {\n"
|
|
"${body}"
|
|
"} ${node_name};")
|
|
body_template = string.Template("${inputs}\n"
|
|
"${outputs}\n"
|
|
"${intermediates}\n"
|
|
"${builtin_data}\n")
|
|
if self._intermediates:
|
|
intermediates = self._intermediates.generate_c_struct(
|
|
"Intermediates", "intermediates")
|
|
else:
|
|
intermediates = "// No intermediates"
|
|
|
|
body = body_template.substitute(
|
|
inputs=self._inputs.generate_c_struct("Inputs", "inputs"),
|
|
outputs=self._outputs.generate_c_struct("Outputs", "outputs"),
|
|
intermediates=intermediates,
|
|
builtin_data=self.generate_c_builtin_data())
|
|
|
|
return struct_template.substitute(type_name=type_name,
|
|
node_name=node_name,
|
|
body=textwrap.indent(body, " "))
|
|
|
|
def generate_c_node_init(self, tflite_node_name: str,
|
|
node_data_name: str) -> str:
|
|
init_template = string.Template(
|
|
"${tflite_node_name} = TfLiteNode{\n"
|
|
" .inputs ="
|
|
" reinterpret_cast<TfLiteIntArray*>(&${node_data_name}.inputs),\n"
|
|
" .outputs ="
|
|
" reinterpret_cast<TfLiteIntArray*>(&${node_data_name}.outputs),\n"
|
|
" .intermediates = ${intermediates},\n"
|
|
" .user_data = nullptr,\n"
|
|
" .builtin_data ="
|
|
" static_cast<void*>(&${node_data_name}.builtin_data),\n"
|
|
" .custom_initial_data = nullptr,\n"
|
|
" .custom_initial_data_size = 0};")
|
|
|
|
if self._intermediates:
|
|
intermediates = (
|
|
"reinterpret_cast<TfLiteIntArray*>(&{}.intermediates)".format(
|
|
self._intermediates))
|
|
else:
|
|
intermediates = "nullptr"
|
|
|
|
return init_template.substitute(tflite_node_name=tflite_node_name,
|
|
node_data_name=node_data_name,
|
|
intermediates=intermediates)
|
|
|
|
@property
|
|
def op_code_index(self) -> int:
|
|
return self._operator.opcodeIndex
|
|
|
|
@abc.abstractmethod
|
|
def generate_c_builtin_data(self) -> str:
|
|
raise NotImplementedError(f"Generating builtin data in {self.__name__}")
|