mirror of
https://github.com/vee1e/tflite-micro.git
synced 2026-09-01 17:57:27 +00:00
91 lines
3.4 KiB
C++
91 lines
3.4 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 "tensorflow/lite/kernels/internal/reference/broadcast_args.h"
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#include <stdint.h>
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#include "tensorflow/lite/c/common.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_context.h"
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namespace tflite {
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namespace {
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constexpr int kShape1Tensor = 0;
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constexpr int kShape2Tensor = 1;
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constexpr int kOutputTensor = 0;
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TfLiteStatus BroadcastArgsPrepare(TfLiteContext* context, TfLiteNode* node) {
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TF_LITE_ENSURE(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* shape1 =
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micro_context->AllocateTempInputTensor(node, kShape1Tensor);
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TfLiteTensor* shape2 =
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micro_context->AllocateTempInputTensor(node, kShape2Tensor);
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TfLiteTensor* output =
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micro_context->AllocateTempOutputTensor(node, kOutputTensor);
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TF_LITE_ENSURE(context,
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shape1->type == kTfLiteInt32 || shape1->type == kTfLiteInt64);
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TF_LITE_ENSURE_EQ(context, shape1->type, shape2->type);
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TF_LITE_ENSURE_EQ(context, shape1->type, output->type);
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// Ensures the shapes are 1D tensor.
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TF_LITE_ENSURE_EQ(context, NumDimensions(shape1), 1);
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TF_LITE_ENSURE_EQ(context, NumDimensions(shape2), 1);
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// Ensure the shape of the output tensor is compatible
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TF_LITE_ENSURE_EQ(context, NumDimensions(output), 1);
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micro_context->DeallocateTempTfLiteTensor(shape1);
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micro_context->DeallocateTempTfLiteTensor(shape2);
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micro_context->DeallocateTempTfLiteTensor(output);
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return kTfLiteOk;
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}
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TfLiteStatus BroadcastArgsEval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteEvalTensor* shape1 =
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micro::GetEvalInput(context, node, kShape1Tensor);
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const TfLiteEvalTensor* shape2 =
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micro::GetEvalInput(context, node, kShape2Tensor);
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TfLiteEvalTensor* output = micro::GetEvalOutput(context, node, kOutputTensor);
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if (output->type == kTfLiteInt32) {
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reference_ops::BroadcastArgs(
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micro::GetTensorShape(shape1), micro::GetTensorData<int32_t>(shape1),
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micro::GetTensorShape(shape2), micro::GetTensorData<int32_t>(shape2),
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micro::GetTensorShape(output), micro::GetTensorData<int32_t>(output));
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} else {
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reference_ops::BroadcastArgs(
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micro::GetTensorShape(shape1), micro::GetTensorData<int64_t>(shape1),
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micro::GetTensorShape(shape2), micro::GetTensorData<int64_t>(shape2),
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micro::GetTensorShape(output), micro::GetTensorData<int64_t>(output));
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}
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return kTfLiteOk;
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}
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} // namespace
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TFLMRegistration Register_BROADCAST_ARGS() {
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return tflite::micro::RegisterOp(nullptr, BroadcastArgsPrepare,
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BroadcastArgsEval);
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}
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} // namespace tflite
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