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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright (C) 2017 The Android Open Source Project
* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "FullyConnected.float.h"
#include "Assert.h"
#include "internal/Matrix.h"
#include "internal/Fused.h"
#include "internal/GEMM.h"
#include "internal/ActivationUtils.h"
// From optimized_ops.h in TensorFlow Lite
template <FusedActivationFunctionType Ac>
void FullyConnected(const float *input_data, const Dims<4> &input_dims, const float *weights_data,
const Dims<4> &weights_dims, const float *bias_data, const Dims<4> &bias_dims,
float *output_data, const Dims<4> &output_dims)
{
// TODO(b/62193649): this convoluted shape computation (determining
// input_rows from the weights_dims, then MapAsMatrixWithGivenNumberOfRows)
// is because the current --variable_batch hack consists in overwriting the
// 3rd dimension with the runtime batch size, as we don't keep track for each
// array of which dimension is the batch dimension in it.
// When that is fixed, this should become:
// const auto input_matrix_map =
// MapAsMatrixWithFirstDimAsRows(input_data, input_dims);
const int input_rows = ArraySize(weights_dims, 0);
const auto input_matrix_map =
MapAsMatrixWithGivenNumberOfRows(input_data, input_dims, input_rows);
const auto filter_matrix_map = MapAsMatrixWithFirstDimAsRows(weights_data, weights_dims);
auto output_matrix_map = MapAsMatrixWithFirstDimAsRows(output_data, output_dims);
Gemm(filter_matrix_map.transpose(), input_matrix_map, &output_matrix_map);
AddBiasAndEvalActivationFunction<Ac>(bias_data, bias_dims, output_data, output_dims);
}
bool fullyConnectedFloat32(const float *inputData, const Shape &inputShape,
const float *weightsData, const Shape &weightsShape,
const float *biasData, const Shape &biasShape, int32_t activation,
float *outputData, const Shape &outputShape)
{
#define ANDROID_NN_FULLY_CONNECTED(activation) \
FullyConnected<FusedActivationFunctionType::activation>( \
inputData, convertShapeToDims(inputShape), weightsData, convertShapeToDims(weightsShape), \
biasData, convertShapeToDims(biasShape), outputData, convertShapeToDims(outputShape))
ANDROID_NN_MACRO_DISPATCH(ANDROID_NN_FULLY_CONNECTED)
#undef ANDROID_NN_FULLY_CONNECTED
return true;
}
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