mirror of
https://github.com/vee1e/tflite-micro.git
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78 lines
3.1 KiB
C++
78 lines
3.1 KiB
C++
/* Copyright 2021 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/c/common.h"
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#include "tensorflow/lite/kernels/internal/quantization_util.h"
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#include "tensorflow/lite/kernels/internal/reference/leaky_relu.h"
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#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
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#include "tensorflow/lite/kernels/internal/types.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/kernels/leaky_relu.h"
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namespace tflite {
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// Input/output tensor index.
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const int kInputTensor = 0;
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const int kOutputTensor = 0;
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TfLiteStatus CalculateOpDataLeakyRelu(TfLiteContext* context,
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TfLiteNode* node) {
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MicroContext* micro_context = GetMicroContext(context);
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TF_LITE_ENSURE_EQ(context, NumInputs(node), 1);
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TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
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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* 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_TYPES_EQ(context, input->type, output->type);
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if (output->type == kTfLiteInt8 || output->type == kTfLiteInt16) {
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LeakyReluOpData* data = static_cast<LeakyReluOpData*>(node->user_data);
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const auto* params =
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static_cast<TfLiteLeakyReluParams*>(node->builtin_data);
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data->input_zero_point = input->params.zero_point;
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data->output_zero_point = output->params.zero_point;
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int output_shift_alpha;
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double alpha_multiplier = static_cast<double>(
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input->params.scale * params->alpha / output->params.scale);
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QuantizeMultiplier(alpha_multiplier, &data->output_multiplier_alpha,
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&output_shift_alpha);
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data->output_shift_alpha = static_cast<int32_t>(output_shift_alpha);
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int output_shift_identity;
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double identity_multiplier =
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static_cast<double>(input->params.scale / output->params.scale);
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QuantizeMultiplier(identity_multiplier, &data->output_multiplier_identity,
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&output_shift_identity);
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data->output_shift_identity = static_cast<int32_t>(output_shift_identity);
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}
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micro_context->DeallocateTempTfLiteTensor(input);
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micro_context->DeallocateTempTfLiteTensor(output);
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return kTfLiteOk;
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}
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TfLiteStatus LeakyReluPrepare(TfLiteContext* context, TfLiteNode* node) {
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return CalculateOpDataLeakyRelu(context, node);
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}
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} // namespace tflite
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