125 lines
3.9 KiB
C++
125 lines
3.9 KiB
C++
/* Copyright 2018 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/dequantize.h"
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#include <stddef.h>
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/optimized/neon_check.h"
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#include "tensorflow/lite/kernels/kernel_util.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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struct OpContext {
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OpContext(TfLiteContext* context, TfLiteNode* node) {
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input = GetInput(context, node, 0);
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output = GetOutput(context, node, 0);
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}
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const TfLiteTensor* input;
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TfLiteTensor* output;
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};
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struct OpData {
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// This boolean value is only used when the input tensor is constant.
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bool float_dequantized_weights_initialized;
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};
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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auto* op_data = new OpData();
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op_data->float_dequantized_weights_initialized = false;
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return op_data;
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}
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void Free(TfLiteContext* context, void* buffer) {
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delete reinterpret_cast<OpData*>(buffer);
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}
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TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
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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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OpContext op_context(context, node);
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TF_LITE_ENSURE(context, op_context.input->type == kTfLiteUInt8 ||
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op_context.input->type == kTfLiteInt8 ||
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op_context.input->type == kTfLiteInt16 ||
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op_context.input->type == kTfLiteFloat16);
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if (op_context.input->type == kTfLiteInt16) {
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TF_LITE_ENSURE_EQ(context, op_context.input->params.zero_point, 0);
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}
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op_context.output->type = kTfLiteFloat32;
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// If the input tensor is constant, we can persist the dequantized value in
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// the output tensor. Otherwise we run dequantize upon each eval.
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if (IsConstantTensor(op_context.input)) {
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op_context.output->allocation_type = kTfLiteArenaRwPersistent;
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}
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return context->ResizeTensor(context, op_context.output,
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TfLiteIntArrayCopy(op_context.input->dims));
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}
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template <KernelType kernel_type>
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
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OpContext op_context(context, node);
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if (IsConstantTensor(op_context.input) &&
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op_data->float_dequantized_weights_initialized) {
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return kTfLiteOk;
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}
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auto status = DequantizeImpl<kernel_type>(context, node, op_context.input,
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op_context.output);
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if (status != kTfLiteOk) {
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return status;
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}
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if (IsConstantTensor(op_context.input)) {
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op_data->float_dequantized_weights_initialized = true;
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}
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return kTfLiteOk;
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}
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} // namespace dequantize
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TfLiteRegistration* Register_DEQUANTIZE_OPT() {
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static TfLiteRegistration r = {
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dequantize::Init, dequantize::Free, dequantize::Prepare,
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dequantize::Eval<dequantize::kGenericOptimized>};
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return &r;
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}
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TfLiteRegistration* Register_DEQUANTIZE_REF() {
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static TfLiteRegistration r = {dequantize::Init, dequantize::Free,
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dequantize::Prepare,
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dequantize::Eval<dequantize::kReference>};
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return &r;
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}
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TfLiteRegistration* Register_DEQUANTIZE() {
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#ifdef USE_NEON
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return Register_DEQUANTIZE_OPT();
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#else
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return Register_DEQUANTIZE_REF();
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#endif
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}
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} // namespace builtin
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} // namespace ops
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} // namespace tflite
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