59 lines
2.4 KiB
C++
59 lines
2.4 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/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/reference/dequantize.h"
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#include "tensorflow/lite/kernels/internal/reference/quantize.h"
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#include "tensorflow/lite/kernels/internal/reference/requantize.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/dequantize.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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namespace tflite {
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TfLiteStatus DequantizePrepare(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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DequantizeOpData* data = static_cast<DequantizeOpData*>(node->user_data);
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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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MicroContext* micro_context = GetMicroContext(context);
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// TODO(b/140515557): Add cached dequant to improve hybrid model performance.
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TfLiteTensor* input = micro_context->AllocateTempInputTensor(node, 0);
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TF_LITE_ENSURE(context, input != nullptr);
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TfLiteTensor* output = micro_context->AllocateTempOutputTensor(node, 0);
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TF_LITE_ENSURE(context, output != nullptr);
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TF_LITE_ENSURE(context, input->type == kTfLiteInt8 ||
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input->type == kTfLiteInt16 ||
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input->type == kTfLiteUInt8);
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TF_LITE_ENSURE(context, output->type == kTfLiteFloat32);
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data->quantization_params.zero_point = input->params.zero_point;
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data->quantization_params.scale = static_cast<double>(input->params.scale);
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data->output_zero_point = output->params.zero_point;
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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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} // namespace tflite
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