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https://github.com/vee1e/tflite-micro.git
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2b7f86c86e
The Dequantize op only supports an output type of Float32. This change removes output_multiplier adjustments for Int32 outputs, as it cannot be reached due to an earlier check. It also eliminates logging for unsupported output types in DequantizeEval, as those would also be caught by an earlier check. BUG=http://b/230890286
58 lines
2.4 KiB
C++
58 lines
2.4 KiB
C++
/* Copyright 2021 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/c/builtin_op_data.h"
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#include "tensorflow/lite/c/common.h"
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#include "tensorflow/lite/kernels/internal/reference/dequantize.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/dequantize.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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namespace tflite {
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TfLiteStatus DequantizePrepare(TfLiteContext* context, TfLiteNode* node) {
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TFLITE_DCHECK(node->user_data != nullptr);
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DequantizeOpData* data = static_cast<DequantizeOpData*>(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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// TODO(b/140515557): Add cached dequant to improve hybrid model performance.
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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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TF_LITE_ENSURE(context, input->type == kTfLiteInt8 ||
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input->type == kTfLiteInt16 ||
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input->type == kTfLiteUInt8);
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TF_LITE_ENSURE(context, output->type == kTfLiteFloat32);
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data->quantization_params.zero_point = input->params.zero_point;
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data->quantization_params.scale = static_cast<double>(input->params.scale);
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data->output_zero_point = output->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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} // namespace tflite
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