132 lines
4.9 KiB
C++
132 lines
4.9 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/internal/reference/dequantize.h"
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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/quantization_util.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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namespace tflite {
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namespace ops {
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namespace micro {
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namespace dequantize {
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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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// TODO(b/140515557): Add cached dequant to improve hybrid model performance.
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TfLiteTensor* input = &context->tensors[node->inputs->data[0]];
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TfLiteTensor* output = &context->tensors[node->outputs->data[0]];
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TF_LITE_ENSURE(context, input->type == kTfLiteUInt8 ||
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input->type == kTfLiteInt8 ||
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input->type == kTfLiteInt16);
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TF_LITE_ENSURE(
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context, output->type == kTfLiteFloat32 || output->type == kTfLiteInt32);
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return kTfLiteOk;
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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TfLiteTensor* input = &context->tensors[node->inputs->data[0]];
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TfLiteTensor* output = &context->tensors[node->outputs->data[0]];
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tflite::DequantizationParams op_params;
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op_params.zero_point = input->params.zero_point;
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op_params.scale = static_cast<double>(input->params.scale);
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if (output->type == kTfLiteFloat32) {
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switch (input->type) {
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case kTfLiteUInt8:
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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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break;
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case kTfLiteInt8:
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reference_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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break;
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case kTfLiteInt16:
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reference_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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break;
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default:
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TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
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TfLiteTypeGetName(input->type),
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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} else if (output->type == kTfLiteInt32) {
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switch (input->type) {
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// TODO(b/148749335): DequantizeInteger and Requantize are hacks here.
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case kTfLiteInt16: {
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reference_ops::DequantizeInteger(
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op_params, GetTensorShape(input), GetTensorData<int16_t>(input),
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GetTensorShape(output), GetTensorData<int32_t>(output));
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break;
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}
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case kTfLiteInt8: {
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int32_t output_multiplier;
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int output_shift;
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const double effective_output_scale =
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static_cast<double>(input->params.scale) /
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static_cast<double>(output->params.scale);
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QuantizeMultiplier(effective_output_scale, &output_multiplier,
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&output_shift);
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int flat_size =
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MatchingFlatSize(GetTensorShape(input), GetTensorShape(output));
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reference_ops::Requantize(
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GetTensorData<int8_t>(input), flat_size, output_multiplier,
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output_shift, input->params.zero_point, output->params.zero_point,
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GetTensorData<int32_t>(output));
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break;
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}
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default:
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TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
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TfLiteTypeGetName(input->type),
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TfLiteTypeGetName(output->type));
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return kTfLiteError;
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}
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} else {
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TF_LITE_KERNEL_LOG(context, "Input %s, output %s not supported.",
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TfLiteTypeGetName(input->type),
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TfLiteTypeGetName(output->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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TfLiteRegistration* Register_DEQUANTIZE() {
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static TfLiteRegistration r = {};
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r.prepare = dequantize::Prepare;
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r.invoke = dequantize::Eval;
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return &r;
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}
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} // namespace micro
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} // namespace ops
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} // namespace tflite
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