mirror of
https://github.com/vee1e/tflite-micro.git
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74c4632d4d
Refactor init, prepare , and eval functions to be unique names for signal/ kernels BUG=b/327655885
123 lines
4.4 KiB
C++
123 lines
4.4 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 "signal/src/window.h"
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#include <stdint.h>
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/flatbuffer_utils.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/memory_helpers.h"
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#include "tensorflow/lite/micro/micro_utils.h"
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namespace tflite {
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namespace {
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constexpr int kInputTensor = 0;
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constexpr int kWeightsTensor = 1;
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constexpr int kOutputTensor = 0;
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// Indices into the init flexbuffer's vector.
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// The parameter's name is in the comment that follows.
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// Elements in the vectors are ordered alphabetically by parameter name.
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constexpr int kShiftIndex = 0; // 'shift'
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struct TFLMSignalWindowParams {
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int32_t shift;
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int32_t input_size;
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};
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void* WindowInit(TfLiteContext* context, const char* buffer, size_t length) {
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const uint8_t* buffer_t = reinterpret_cast<const uint8_t*>(buffer);
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auto* params =
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static_cast<TFLMSignalWindowParams*>(context->AllocatePersistentBuffer(
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context, sizeof(TFLMSignalWindowParams)));
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tflite::FlexbufferWrapper fbw(buffer_t, length);
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params->shift = fbw.ElementAsInt32(kShiftIndex);
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return params;
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}
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TfLiteStatus WindowPrepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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MicroContext* micro_context = GetMicroContext(context);
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TfLiteTensor* input =
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micro_context->AllocateTempInputTensor(node, kInputTensor);
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TF_LITE_ENSURE(context, input != nullptr);
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TfLiteTensor* weights =
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micro_context->AllocateTempInputTensor(node, kWeightsTensor);
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TF_LITE_ENSURE(context, weights != nullptr);
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TfLiteTensor* output =
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micro_context->AllocateTempOutputTensor(node, kOutputTensor);
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TF_LITE_ENSURE(context, output != nullptr);
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TF_LITE_ENSURE(context, NumDimensions(input) >= 1);
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TF_LITE_ENSURE_EQ(context, NumDimensions(weights), 1);
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TF_LITE_ENSURE_EQ(context, NumDimensions(input), NumDimensions(output));
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TF_LITE_ENSURE_TYPES_EQ(context, input->type, kTfLiteInt16);
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TF_LITE_ENSURE_TYPES_EQ(context, weights->type, kTfLiteInt16);
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TF_LITE_ENSURE_TYPES_EQ(context, output->type, kTfLiteInt16);
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auto* params = reinterpret_cast<TFLMSignalWindowParams*>(node->user_data);
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RuntimeShape input_shape = GetTensorShape(input);
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params->input_size = input_shape.FlatSize();
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micro_context->DeallocateTempTfLiteTensor(input);
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micro_context->DeallocateTempTfLiteTensor(weights);
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micro_context->DeallocateTempTfLiteTensor(output);
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return kTfLiteOk;
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}
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TfLiteStatus WindowEval(TfLiteContext* context, TfLiteNode* node) {
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auto* params = reinterpret_cast<TFLMSignalWindowParams*>(node->user_data);
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const TfLiteEvalTensor* input =
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tflite::micro::GetEvalInput(context, node, kInputTensor);
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const TfLiteEvalTensor* weights =
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tflite::micro::GetEvalInput(context, node, kWeightsTensor);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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const int16_t* input_data = tflite::micro::GetTensorData<int16_t>(input);
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const int16_t* weight_data = tflite::micro::GetTensorData<int16_t>(weights);
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int16_t* output_data = tflite::micro::GetTensorData<int16_t>(output);
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int weight_size = weights->dims->data[0];
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for (int i = 0; i < params->input_size; i += weight_size) {
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::tflm_signal::ApplyWindow(&input_data[i], weight_data, weight_size,
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params->shift, &output_data[i]);
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}
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return kTfLiteOk;
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}
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} // namespace
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// TODO(b/286250473): remove namespace once de-duped libraries
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namespace tflm_signal {
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TFLMRegistration* Register_WINDOW() {
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static TFLMRegistration r =
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tflite::micro::RegisterOp(WindowInit, WindowPrepare, WindowEval);
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return &r;
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}
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} // namespace tflm_signal
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} // namespace tflite
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