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https://github.com/vee1e/tflite-micro.git
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ad83c8cf8c
`Implementation preserve_all_tensors and getTensor features in TFLM interpreter` [Design Doc](https://docs.google.com/document/d/13CB93tffg_dDnZYy1QkY3u88yJev4PtZeRY0MLH6n_w/edit?resourcekey=0-htWqjXWneKLXcD6o2SVNJw#heading=h.x9snb54sjlu9) * PreserveAllTensors is a flag / option being added to the TFLM interpreter that guarantees that post invocation all tensors will be available post invocation with there data untouched. * GetTensor() is an api being added to the interpreter that allows users to access any tensor in a model by providing the right index but this api is only available when the PreserveAllTensors flag is being used (all the data is guaranteed to be valid and untouched) *additionally this cl adds functionality for users to instantiate MicroAllocators with LinearMemoryPlanners vs the the default GreedyMemoryPlanner for MicroAllocator create methods that don't currently take a MemoryPlanner as an input [google3 cl](https://critique.corp.google.com/cl/543518092) BUG=[b/288141725](https://b.corp.google.com/288141725)
76 lines
3 KiB
C++
76 lines
3 KiB
C++
/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <pybind11/pybind11.h>
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#include <pybind11/stl.h>
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#include "python/tflite_micro/interpreter_wrapper.h"
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#include "python/tflite_micro/pybind11_lib.h"
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namespace py = pybind11;
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using tflite::InterpreterWrapper;
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PYBIND11_MODULE(_runtime, m) {
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m.doc() = "TFLite Micro Runtime Extension";
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py::enum_<tflite::InterpreterConfig>(m, "PythonInterpreterConfig")
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.value("kAllocationRecording",
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tflite::InterpreterConfig::kAllocationRecording)
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.value("kPreserveAllTensors",
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tflite::InterpreterConfig::kPreserveAllTensors);
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py::class_<InterpreterWrapper>(m, "InterpreterWrapper")
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.def(py::init([](const py::bytes& data,
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const std::vector<std::string>& registerers_by_name,
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size_t arena_size, int num_resource_variables,
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tflite::InterpreterConfig config) {
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return std::unique_ptr<InterpreterWrapper>(
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new InterpreterWrapper(data.ptr(), registerers_by_name, arena_size,
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num_resource_variables, config));
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}))
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.def("PrintAllocations", &InterpreterWrapper::PrintAllocations)
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.def("Invoke", &InterpreterWrapper::Invoke)
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.def("Reset", &InterpreterWrapper::Reset)
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.def(
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"SetInputTensor",
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[](InterpreterWrapper& self, py::handle& x, size_t index) {
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self.SetInputTensor(x.ptr(), index);
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},
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py::arg("x"), py::arg("index"))
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.def(
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"GetOutputTensor",
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[](InterpreterWrapper& self, size_t index) {
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return tflite::PyoOrThrow(self.GetOutputTensor(index));
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},
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py::arg("index"))
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.def(
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"GetInputTensorDetails",
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[](InterpreterWrapper& self, size_t index) {
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return tflite::PyoOrThrow(self.GetInputTensorDetails(index));
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},
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py::arg("index"))
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.def(
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"GetTensor",
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[](InterpreterWrapper& self, size_t tensor_index,
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size_t subgraph_index = 0) {
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return tflite::PyoOrThrow(
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self.GetTensor(tensor_index, subgraph_index));
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},
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py::arg("tensor_index"), py::arg("subgraph_index"))
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.def(
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"GetOutputTensorDetails",
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[](InterpreterWrapper& self, size_t index) {
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return tflite::PyoOrThrow(self.GetOutputTensorDetails(index));
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},
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py::arg("index"));
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}
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