mirror of
https://github.com/vee1e/tflite-micro.git
synced 2026-09-01 17:57:27 +00:00
118 lines
4.2 KiB
C++
118 lines
4.2 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 "tensorflow/lite/kernels/internal/reference/arg_min_max.h"
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#include "tensorflow/lite/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/reference/comparisons.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/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/micro_log.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 kAxis = 1;
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constexpr int kOutputTensor = 0;
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template <typename T1, typename T2, typename T3>
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inline void ArgMinMaxHelper(const RuntimeShape& input1_shape,
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const T1* input1_data, const T3* input2_data,
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const RuntimeShape& output_shape, T2* output_data,
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bool is_arg_max) {
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// Use Greater/Less from comparisons.h (formerly from kernels/micro_utils.h
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// which was deprecated). Same as gtl::Greater but used here to reduce
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// dependencies and binary size for micro environment.
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if (is_arg_max) {
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reference_ops::ArgMinMax(input1_shape, input1_data, input2_data,
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output_shape, output_data,
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reference_ops::GreaterFn<T1>());
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} else {
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reference_ops::ArgMinMax(input1_shape, input1_data, input2_data,
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output_shape, output_data,
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reference_ops::LessFn<T1>());
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}
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node, bool is_arg_max) {
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const TfLiteEvalTensor* input =
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tflite::micro::GetEvalInput(context, node, kInputTensor);
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const TfLiteEvalTensor* axis =
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tflite::micro::GetEvalInput(context, node, kAxis);
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kOutputTensor);
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#define TF_LITE_ARG_MIN_MAX(data_type, axis_type, output_type) \
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ArgMinMaxHelper(tflite::micro::GetTensorShape(input), \
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tflite::micro::GetTensorData<data_type>(input), \
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tflite::micro::GetTensorData<axis_type>(axis), \
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tflite::micro::GetTensorShape(output), \
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tflite::micro::GetTensorData<output_type>(output), \
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is_arg_max)
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if (axis->type == kTfLiteInt32) {
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if (output->type == kTfLiteInt32) {
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switch (input->type) {
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case kTfLiteFloat32:
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TF_LITE_ARG_MIN_MAX(float, int32_t, int32_t);
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break;
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case kTfLiteInt8:
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TF_LITE_ARG_MIN_MAX(int8_t, int32_t, int32_t);
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break;
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default:
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MicroPrintf(
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"Only float32, uint8_t and int8_t are "
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"supported currently, got %s.",
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TfLiteTypeGetName(input->type));
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return kTfLiteError;
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}
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} else {
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MicroPrintf("Only int32_t are supported currently, got %s.",
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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} else {
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MicroPrintf("Only int32_t are supported currently, got %s.",
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TfLiteTypeGetName(axis->type));
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return kTfLiteError;
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}
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#undef TF_LITE_ARG_MIN_MAX
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return kTfLiteOk;
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}
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TfLiteStatus ArgMinEval(TfLiteContext* context, TfLiteNode* node) {
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return Eval(context, node, false);
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}
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TfLiteStatus ArgMaxEval(TfLiteContext* context, TfLiteNode* node) {
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return Eval(context, node, true);
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}
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} // namespace
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TFLMRegistration Register_ARG_MAX() {
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return tflite::micro::RegisterOp(nullptr, nullptr, ArgMaxEval);
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}
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TFLMRegistration Register_ARG_MIN() {
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return tflite::micro::RegisterOp(nullptr, nullptr, ArgMinEval);
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}
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} // namespace tflite
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