95 lines
3.2 KiB
C++
95 lines
3.2 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/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/reference_ops.h"
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#include "tensorflow/lite/kernels/internal/tensor.h"
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#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
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#include "tensorflow/lite/kernels/internal/types.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 fake_quant {
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// This file has reference implementation of FakeQuant.
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enum KernelType {
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kReference,
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};
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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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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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const auto* params =
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reinterpret_cast<TfLiteFakeQuantParams*>(node->builtin_data);
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if (params->narrow_range) {
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context->ReportError(
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context,
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"narrow_range FakeQuant is not currently supported at runtime. "
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"narrow_range is only meant to be applied to weights, not activations");
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return kTfLiteError;
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}
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OpContext op_context(context, node);
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TfLiteIntArray* output_dims = TfLiteIntArrayCopy(op_context.input->dims);
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op_context.output->type = op_context.input->type;
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return context->ResizeTensor(context, op_context.output, output_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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OpContext op_context(context, node);
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const auto* params =
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reinterpret_cast<TfLiteFakeQuantParams*>(node->builtin_data);
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tflite::FakeQuantParams op_params;
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op_params.num_bits = params->num_bits;
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op_params.minmax.min = params->min;
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op_params.minmax.max = params->max;
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reference_ops::FakeQuant(op_params, GetTensorShape(op_context.input),
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GetTensorData<float>(op_context.input),
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GetTensorShape(op_context.output),
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GetTensorData<float>(op_context.output));
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return kTfLiteOk;
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}
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} // namespace fake_quant
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TfLiteRegistration* Register_FAKE_QUANT_REF() {
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static TfLiteRegistration r = {nullptr, nullptr, fake_quant::Prepare,
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fake_quant::Eval<fake_quant::kReference>};
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return &r;
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}
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TfLiteRegistration* Register_FAKE_QUANT() { return Register_FAKE_QUANT_REF(); }
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} // namespace builtin
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} // namespace ops
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} // namespace tflite
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