mirror of
https://github.com/vee1e/tflite-micro.git
synced 2026-09-02 10:17:54 +00:00
6fdade1aed
This PR updates remainder of the TFLM examples, all the kernels and integration tests to remove the usage of ErrorReporter as per the changes made to the TFLM Framework API in https://github.com/tensorflow/tflite-micro/pull/1415; replaces TF_LITE_REPORT_ERROR with MicroPrintf. BUG=http://b/192091017, http://b/245802069
239 lines
10 KiB
C++
239 lines
10 KiB
C++
/* Copyright 2020 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 <limits>
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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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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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#include "tensorflow/lite/micro/kernels/quantize.h"
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#include "tensorflow/lite/micro/micro_log.h"
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#include "tensorflow/lite/micro/micro_utils.h"
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namespace tflite {
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TfLiteStatus PrepareQuantizeReference(TfLiteContext* context,
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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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MicroContext* micro_context = GetMicroContext(context);
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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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// 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(
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context, input->type == kTfLiteFloat32 || input->type == kTfLiteInt32 ||
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input->type == kTfLiteInt16 || input->type == kTfLiteInt8 ||
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input->type == kTfLiteUInt8);
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TF_LITE_ENSURE(context, output->type == kTfLiteInt8 ||
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output->type == kTfLiteInt16 ||
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output->type == kTfLiteInt32 ||
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output->type == kTfLiteUInt8);
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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 == kTfLiteInt8 && output->type == kTfLiteUInt8) ||
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(input->type == kTfLiteUInt8 && output->type == kTfLiteInt8) ||
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(input->type == kTfLiteInt8 && output->type == kTfLiteInt16) ||
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(input->type == kTfLiteInt8 && output->type == kTfLiteInt32) ||
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(input->type == kTfLiteInt16 && output->type == kTfLiteInt16) ||
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(input->type == kTfLiteInt16 && output->type == kTfLiteInt32) ||
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(input->type == kTfLiteInt32 && output->type == kTfLiteInt8) ||
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(input->type == kTfLiteInt32 && output->type == kTfLiteInt16)) {
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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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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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TfLiteStatus EvalQuantizeReference(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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const TfLiteEvalTensor* input = tflite::micro::GetEvalInput(context, node, 0);
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TfLiteEvalTensor* output = tflite::micro::GetEvalOutput(context, node, 0);
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if (input->type == kTfLiteFloat32) {
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switch (output->type) {
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case kTfLiteInt8:
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reference_ops::AffineQuantize(
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data->quantization_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<float>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<int8_t>(output));
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break;
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case kTfLiteInt16:
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reference_ops::AffineQuantize(
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data->quantization_params, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<float>(input),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<int16_t>(output));
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return kTfLiteOk;
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default:
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MicroPrintf("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 (input->type == kTfLiteInt32) {
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size_t size = ElementCount(*input->dims);
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switch (output->type) {
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case kTfLiteInt8:
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reference_ops::Requantize(
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tflite::micro::GetTensorData<int32_t>(input), size,
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data->requantize_output_multiplier, data->requantize_output_shift,
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data->input_zero_point, data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int8_t>(output));
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break;
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case kTfLiteInt16:
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reference_ops::Requantize(
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tflite::micro::GetTensorData<int32_t>(input), size,
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data->requantize_output_multiplier, data->requantize_output_shift,
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data->input_zero_point, data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int16_t>(output));
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break;
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default:
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MicroPrintf("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 (input->type == kTfLiteInt16) {
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size_t size = ElementCount(*input->dims);
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switch (output->type) {
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case kTfLiteInt8:
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reference_ops::Requantize(
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tflite::micro::GetTensorData<int16_t>(input), size,
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data->requantize_output_multiplier, data->requantize_output_shift,
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data->input_zero_point, data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int8_t>(output));
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break;
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case kTfLiteInt16:
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reference_ops::Requantize(
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tflite::micro::GetTensorData<int16_t>(input), size,
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data->requantize_output_multiplier, data->requantize_output_shift,
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data->input_zero_point, data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int16_t>(output));
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return kTfLiteOk;
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case kTfLiteInt32:
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reference_ops::Requantize(
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tflite::micro::GetTensorData<int16_t>(input), size,
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data->requantize_output_multiplier, data->requantize_output_shift,
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data->input_zero_point, data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int32_t>(output));
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return kTfLiteOk;
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default:
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MicroPrintf("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 (input->type == kTfLiteInt8) {
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// Int8 to Int8 requantization, required if the input and output tensors
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// have different scales and/or zero points.
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size_t size = ElementCount(*input->dims);
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switch (output->type) {
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case kTfLiteInt8:
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reference_ops::Requantize(
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tflite::micro::GetTensorData<int8_t>(input), size,
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data->requantize_output_multiplier, data->requantize_output_shift,
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data->input_zero_point, data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int8_t>(output));
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break;
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case kTfLiteUInt8:
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reference_ops::Requantize(
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tflite::micro::GetTensorData<int8_t>(input), size,
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data->requantize_output_multiplier, data->requantize_output_shift,
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data->input_zero_point, data->quantization_params.zero_point,
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tflite::micro::GetTensorData<uint8_t>(output));
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break;
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case kTfLiteInt16:
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reference_ops::Requantize(
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tflite::micro::GetTensorData<int8_t>(input), size,
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data->requantize_output_multiplier, data->requantize_output_shift,
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data->input_zero_point, data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int16_t>(output));
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break;
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case kTfLiteInt32:
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reference_ops::Requantize(
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tflite::micro::GetTensorData<int8_t>(input), size,
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data->requantize_output_multiplier, data->requantize_output_shift,
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data->input_zero_point, data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int32_t>(output));
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break;
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default:
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MicroPrintf("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 (input->type == kTfLiteUInt8) {
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size_t size = ElementCount(*input->dims);
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switch (output->type) {
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case kTfLiteInt8:
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reference_ops::Requantize(
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tflite::micro::GetTensorData<uint8_t>(input), size,
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data->requantize_output_multiplier, data->requantize_output_shift,
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data->input_zero_point, data->quantization_params.zero_point,
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tflite::micro::GetTensorData<int8_t>(output));
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break;
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default:
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MicroPrintf("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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MicroPrintf("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 tflite
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