103 lines
3.8 KiB
C++
103 lines
3.8 KiB
C++
/* Copyright 2019 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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#ifndef TENSORFLOW_LITE_KERNELS_DEQUANTIZE_H_
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#define TENSORFLOW_LITE_KERNELS_DEQUANTIZE_H_
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#include <stdint.h>
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#include "third_party/eigen3/Eigen/Core"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/optimized/optimized_ops.h"
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#include "tensorflow/lite/kernels/internal/reference/dequantize.h"
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#include "tensorflow/lite/kernels/internal/reference/integer_ops/dequantize.h"
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#include "tensorflow/lite/kernels/internal/reference/reference_ops.h"
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#include "tensorflow/lite/kernels/internal/tensor.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/internal/types.h"
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namespace tflite {
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namespace ops {
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namespace builtin {
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namespace dequantize {
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// This file has two implementation of Dequantize.
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enum KernelType {
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kReference,
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kGenericOptimized,
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};
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template <KernelType kernel_type>
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TfLiteStatus DequantizeImpl(TfLiteContext* context, TfLiteNode* node,
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const TfLiteTensor* input, TfLiteTensor* output) {
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DequantizationParams op_params;
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op_params.zero_point = input->params.zero_point;
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op_params.scale = input->params.scale;
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switch (input->type) {
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case kTfLiteUInt8:
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if (kernel_type == kReference) {
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reference_ops::Dequantize(
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op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
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GetTensorShape(output), GetTensorData<float>(output));
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} else {
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optimized_ops::Dequantize(
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op_params, GetTensorShape(input), GetTensorData<uint8_t>(input),
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GetTensorShape(output), GetTensorData<float>(output));
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}
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break;
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case kTfLiteInt8:
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if (kernel_type == kReference) {
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reference_integer_ops::Dequantize<int8_t>(
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op_params, GetTensorShape(input), GetTensorData<int8_t>(input),
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GetTensorShape(output), GetTensorData<float>(output));
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} else {
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optimized_ops::Dequantize(
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op_params, GetTensorShape(input), GetTensorData<int8_t>(input),
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GetTensorShape(output), GetTensorData<float>(output));
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}
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break;
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case kTfLiteInt16:
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if (kernel_type == kReference) {
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reference_integer_ops::Dequantize<int16_t>(
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op_params, GetTensorShape(input), GetTensorData<int16_t>(input),
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GetTensorShape(output), GetTensorData<float>(output));
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} else {
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optimized_ops::Dequantize(
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op_params, GetTensorShape(input), GetTensorData<int16_t>(input),
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GetTensorShape(output), GetTensorData<float>(output));
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}
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break;
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case kTfLiteFloat16: {
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const Eigen::half* half_data = reinterpret_cast<const Eigen::half*>(
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GetTensorData<TfLiteFloat16>(input));
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reference_ops::Dequantize(GetTensorShape(input), half_data,
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GetTensorShape(output),
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GetTensorData<float>(output));
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break;
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}
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default:
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context->ReportError(context, "Type %d not supported.", input->type);
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return kTfLiteError;
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}
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return kTfLiteOk;
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}
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} // namespace dequantize
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} // namespace builtin
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} // namespace ops
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} // namespace tflite
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#endif // TENSORFLOW_LITE_KERNELS_DEQUANTIZE_H_
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