122 lines
4.9 KiB
C++
122 lines
4.9 KiB
C++
/* Copyright 2019 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/micro/kernels/conv.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/common.h"
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#include "tensorflow/lite/kernels/internal/quantization_util.h"
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#include "tensorflow/lite/kernels/internal/reference/conv.h"
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#include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.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/kernels/padding.h"
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#include "tensorflow/lite/micro/kernels/kernel_util.h"
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namespace tflite {
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namespace {
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void* Init(TfLiteContext* context, const char* buffer, size_t length) {
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TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
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return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
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}
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TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
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const TfLiteEvalTensor* input =
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tflite::micro::GetEvalInput(context, node, kConvInputTensor);
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const TfLiteEvalTensor* filter =
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tflite::micro::GetEvalInput(context, node, kConvWeightsTensor);
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const TfLiteEvalTensor* bias =
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(NumInputs(node) == 3)
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? tflite::micro::GetEvalInput(context, node, kConvBiasTensor)
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: nullptr;
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TfLiteEvalTensor* output =
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tflite::micro::GetEvalOutput(context, node, kConvOutputTensor);
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TFLITE_DCHECK(node->builtin_data != nullptr);
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const auto& params =
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*(reinterpret_cast<TfLiteConvParams*>(node->builtin_data));
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TFLITE_DCHECK(node->user_data != nullptr);
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const auto& data = *(static_cast<const OpDataConv*>(node->user_data));
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TF_LITE_ENSURE_EQ(context, input->type, output->type);
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TF_LITE_ENSURE_MSG(context, input->type == filter->type,
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"Hybrid models are not supported on TFLite Micro.");
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switch (input->type) { // Already know in/out types are same.
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case kTfLiteFloat32: {
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tflite::reference_ops::Conv(
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ConvParamsFloat(params, data), tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<float>(input),
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tflite::micro::GetTensorShape(filter),
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tflite::micro::GetTensorData<float>(filter),
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tflite::micro::GetTensorShape(bias),
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tflite::micro::GetTensorData<float>(bias),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<float>(output),
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tflite::micro::GetTensorShape(nullptr), nullptr);
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break;
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}
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case kTfLiteInt8: {
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reference_integer_ops::ConvPerChannel(
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ConvParamsQuantized(params, data), data.per_channel_output_multiplier,
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data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<int8_t>(input),
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tflite::micro::GetTensorShape(filter),
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tflite::micro::GetTensorData<int8_t>(filter),
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tflite::micro::GetTensorShape(bias),
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tflite::micro::GetTensorData<int32_t>(bias),
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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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}
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case kTfLiteUInt8: {
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reference_ops::Conv(ConvParamsQuantized(params, data),
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tflite::micro::GetTensorShape(input),
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tflite::micro::GetTensorData<uint8_t>(input),
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tflite::micro::GetTensorShape(filter),
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tflite::micro::GetTensorData<uint8_t>(filter),
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tflite::micro::GetTensorShape(bias),
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tflite::micro::GetTensorData<int32_t>(bias),
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tflite::micro::GetTensorShape(output),
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tflite::micro::GetTensorData<uint8_t>(output),
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tflite::micro::GetTensorShape(nullptr), nullptr,
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nullptr);
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break;
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}
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default:
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TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
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TfLiteTypeGetName(input->type), input->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
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TfLiteRegistration Register_CONV_2D() {
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return {/*init=*/Init,
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/*free=*/nullptr,
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/*prepare=*/ConvPrepare,
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/*invoke=*/Eval,
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/*profiling_string=*/nullptr,
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/*builtin_code=*/0,
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/*custom_name=*/nullptr,
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/*version=*/0};
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}
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} // namespace tflite
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