96 lines
3.8 KiB
C++
96 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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#include "tensorflow/lite/micro/kernels/quantize.h"
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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/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_utils.h"
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namespace tflite {
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namespace {
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
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return context->AllocatePersistentBuffer(context,
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sizeof(OpDataQuantizeReference));
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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auto* data = static_cast<OpDataQuantizeReference*>(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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const TfLiteTensor* input = GetInput(context, node, 0);
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TF_LITE_ENSURE(context, input != nullptr);
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TfLiteTensor* output = GetOutput(context, node, 0);
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TF_LITE_ENSURE(context, output != nullptr);
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// TODO(b/128934713): Add support for fixed-point per-channel quantization.
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// Currently this only support affine per-layer quantization.
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TF_LITE_ENSURE_EQ(context, output->quantization.type,
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kTfLiteAffineQuantization);
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const auto* affine_quantization =
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reinterpret_cast<TfLiteAffineQuantization*>(output->quantization.params);
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TF_LITE_ENSURE(context, affine_quantization);
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TF_LITE_ENSURE(context, affine_quantization->scale);
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TF_LITE_ENSURE(context, affine_quantization->scale->size == 1);
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TF_LITE_ENSURE(context, input->type == kTfLiteFloat32 ||
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input->type == kTfLiteInt16 ||
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input->type == kTfLiteInt8);
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TF_LITE_ENSURE(context, output->type == kTfLiteUInt8 ||
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output->type == kTfLiteInt8 ||
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output->type == kTfLiteInt16 ||
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output->type == kTfLiteInt32);
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if ((input->type == kTfLiteInt16 && output->type == kTfLiteInt8) ||
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(input->type == kTfLiteInt8 && output->type == kTfLiteInt8) ||
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(input->type == kTfLiteInt16 && output->type == kTfLiteInt16) ||
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(input->type == kTfLiteInt16 && output->type == kTfLiteInt32)) {
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double effective_scale = static_cast<double>(input->params.scale) /
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static_cast<double>(output->params.scale);
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QuantizeMultiplier(effective_scale, &data->requantize_output_multiplier,
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&data->requantize_output_shift);
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}
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data->quantization_params.zero_point = output->params.zero_point;
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data->quantization_params.scale = static_cast<double>(output->params.scale);
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data->input_zero_point = input->params.zero_point;
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return kTfLiteOk;
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}
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} // namespace
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TfLiteRegistration Register_QUANTIZE() {
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return {/*init=*/Init,
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/*free=*/nullptr,
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/*prepare=*/Prepare,
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/*invoke=*/EvalQuantizeReference,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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}
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} // namespace tflite
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