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/*
* Copyright (c) 2021 Samsung Electronics Co., Ltd. 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.
*/
#ifndef LUCI_INTERPRETER_PAL_CONV2D_H
#define LUCI_INTERPRETER_PAL_CONV2D_H
#include <tensorflow/lite/kernels/internal/optimized/legacy_optimized_ops.h>
#include <tensorflow/lite/kernels/internal/reference/integer_ops/conv.h>
namespace luci_interpreter_pal
{
static inline void Conv(const tflite::ConvParams ¶ms, const tflite::RuntimeShape &input_shape,
const float *input_data, const tflite::RuntimeShape &filter_shape,
const float *filter_data, const tflite::RuntimeShape &bias_shape,
const float *bias_data, const tflite::RuntimeShape &output_shape,
float *output_data, const tflite::RuntimeShape &scratchpad_shape,
float *scratchpad_data)
{
(void)scratchpad_shape;
if (scratchpad_data)
{
const int32_t batches = tflite::MatchingDim(input_shape, 0, output_shape, 0);
const int32_t input_depth = tflite::MatchingDim(input_shape, 3, filter_shape, 3);
const int32_t output_height = output_shape.Dims(1);
const int32_t output_width = output_shape.Dims(2);
const int32_t filter_height = filter_shape.Dims(1);
const int32_t filter_width = filter_shape.Dims(2);
tflite::RuntimeShape im2col_shape{batches, output_height, output_width,
input_depth * filter_height * filter_width};
tflite::optimized_ops::Conv(params, input_shape, input_data, filter_shape, filter_data,
bias_shape, bias_data, output_shape, output_data, im2col_shape,
scratchpad_data);
}
else
tflite::reference_ops::Conv(params, input_shape, input_data, filter_shape, filter_data,
bias_shape, bias_data, output_shape, output_data,
tflite::RuntimeShape(), nullptr);
}
static inline void Conv(const tflite::ConvParams ¶ms, const tflite::RuntimeShape &input_shape,
const uint8 *input_data, const tflite::RuntimeShape &filter_shape,
const uint8 *filter_data, const tflite::RuntimeShape &bias_shape,
const int32 *bias_data, const tflite::RuntimeShape &output_shape,
uint8 *output_data, const tflite::RuntimeShape &scratchpad_shape,
uint8 *scratchpad_data)
{
// TODO This should only be done once (although it takes only a few microseconds).
// Also, the user should be able to adjust the number of threads.
auto gemmlowp_context = std::make_unique<gemmlowp::GemmContext>();
gemmlowp_context->set_max_num_threads(static_cast<int>(std::thread::hardware_concurrency()));
tflite::reference_ops::Conv(params, input_shape, input_data, filter_shape, filter_data,
bias_shape, bias_data, output_shape, output_data, scratchpad_shape,
scratchpad_data, gemmlowp_context.get());
}
static inline void ConvPerChannel(const tflite::ConvParams ¶ms, const int32_t *mult,
const int32_t *shifts, const tflite::RuntimeShape &input_shape,
const int8 *input_data, const tflite::RuntimeShape &filter_shape,
const int8 *filter_data, const tflite::RuntimeShape &bias_shape,
const int32 *bias_data, const tflite::RuntimeShape &output_shape,
int8 *output_data, const tflite::RuntimeShape &scratchpad_shape,
int8 *scratchpad_data)
{
(void)scratchpad_shape;
(void)scratchpad_data;
// TODO enable optimized version
tflite::reference_integer_ops::ConvPerChannel(params, mult, shifts, input_shape, input_data,
filter_shape, filter_data, bias_shape, bias_data,
output_shape, output_data);
}
static inline void SetupScratchpadTensor(luci_interpreter::Tensor *scratchpad,
const luci_interpreter::DataType &input_data_type,
const tflite::ConvParams ¶ms,
const tflite::RuntimeShape &input_shape,
const tflite::RuntimeShape &filter_shape,
const tflite::RuntimeShape &output_shape)
{
const int32_t filter_height = filter_shape.Dims(1);
const int32_t filter_width = filter_shape.Dims(2);
// Allocate tensor for scratchpad, if needed.
// The checks here should be aligned with the actual implementation.
const bool need_dilated_scratchpad =
params.dilation_height_factor != 1 || params.dilation_width_factor != 1;
const bool need_non_dilated_scratchpad = params.stride_height != 1 || params.stride_width != 1 ||
filter_height != 1 || filter_width != 1;
auto _need_scratchpad = input_data_type != luci_interpreter::DataType::S16 &&
(need_dilated_scratchpad || need_non_dilated_scratchpad);
if (_need_scratchpad)
{
const int32_t batches = tflite::MatchingDim(input_shape, 0, output_shape, 0);
const int32_t input_depth = tflite::MatchingDim(input_shape, 3, filter_shape, 3);
const int32_t output_height = output_shape.Dims(1);
const int32_t output_width = output_shape.Dims(2);
auto data_type_size = static_cast<int32_t>(luci_interpreter::getDataTypeSize(input_data_type));
int32_t scratchpad_size = batches * output_width * output_height * input_depth * filter_height *
filter_width * data_type_size;
luci_interpreter::Shape scratchpad_shape{scratchpad_size};
scratchpad->resize(scratchpad_shape);
}
else
{
scratchpad->set_allocatable(false);
}
}
} // namespace luci_interpreter_pal
#endif // LUCI_INTERPRETER_PAL_CONV2D_H
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