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/*
* Copyright (c) 2018 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.
*/
//
// Generated API
//
struct Network;
Network *Network_construct();
void Network_destruct(Network *net);
unsigned Network_input_count(const Network *);
const char *Network_input_name(const Network *, unsigned n);
unsigned Network_input_rank(const Network *, unsigned n);
unsigned Network_input_dim(const Network *, unsigned n, unsigned axis);
void Network_input_bind(Network *net, unsigned n, const void *ptr, unsigned len);
unsigned Network_output_count(const Network *net);
const char *Network_output_name(const Network *, unsigned n);
unsigned Network_output_rank(const Network *, unsigned n);
unsigned Network_output_dim(const Network *, unsigned n, unsigned axis);
void Network_output_bind(Network *net, unsigned n, void *ptr, unsigned len);
void Network_invoke(Network *net);
//
// nnkit backend
//
#include <nnkit/Backend.h>
#include <nnkit/TensorContext.h>
#include <nnkit/CmdlineArguments.h>
#include <nncc/core/ADT/tensor/LexicalLayout.h>
#include <nncc/core/ADT/tensor/Overlay.h>
#include <stdex/Memory.h>
using stdex::make_unique;
using namespace nncc::core::ADT;
namespace
{
class TensorContext final : public nnkit::TensorContext
{
public:
TensorContext() = default;
public:
void allocate(const std::string &name, const tensor::Shape &shape)
{
using nncc::core::ADT::tensor::num_elements;
auto blob = make_unique<std::vector<uint8_t>>();
blob->resize(num_elements(shape) * sizeof(float));
_names.emplace_back(name);
_shapes.emplace_back(shape);
_blobs.emplace_back(std::move(blob));
}
public:
uint8_t *base(uint32_t n) const { return _blobs.at(n)->data(); }
public:
uint32_t size(void) const override { return _blobs.size(); }
public:
std::string name(uint32_t n) const override { return _names.at(n); }
public:
tensor::Shape shape(uint32_t n) const override { return _shapes.at(n); }
public:
uint32_t size(uint32_t n) const { return _blobs.at(n)->size(); }
public:
// Float (fp32) tensor support
bool isFloatTensor(uint32_t n) const override { return true; }
void getMutableFloatTensor(uint32_t n, const TensorContext::TypedAccessor<float> &f) override
{
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::make_overlay;
auto base = reinterpret_cast<float *>(this->base(n));
auto view = make_overlay<float, LexicalLayout>(shape(n), base);
f(*this, n, view);
}
void getConstFloatTensor(uint32_t n, const TensorContext::TypedReader<float> &f) const override
{
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::make_overlay;
auto base = reinterpret_cast<float *>(this->base(n));
auto view = make_overlay<float, LexicalLayout>(shape(n), base);
f(*this, n, view);
}
private:
std::vector<std::string> _names;
std::vector<tensor::Shape> _shapes;
std::vector<std::unique_ptr<std::vector<uint8_t>>> _blobs;
};
class Backend final : public nnkit::Backend
{
public:
Backend()
{
_net = Network_construct();
// Allocate and bind inputs
for (uint32_t n = 0; n < Network_input_count(_net); ++n)
{
const uint32_t rank = Network_input_rank(_net, n);
const std::string name = Network_input_name(_net, n);
tensor::Shape shape;
shape.resize(rank);
for (uint32_t axis = 0; axis < rank; ++axis)
{
shape.dim(axis) = Network_input_dim(_net, n, axis);
}
_inputs.allocate(name, shape);
Network_input_bind(_net, n, reinterpret_cast<const void *>(_inputs.base(n)), _inputs.size(n));
}
// Allocate and bind outputs
for (uint32_t n = 0; n < Network_output_count(_net); ++n)
{
const uint32_t rank = Network_output_rank(_net, n);
const std::string name = Network_output_name(_net, n);
tensor::Shape shape;
shape.resize(rank);
for (uint32_t axis = 0; axis < rank; ++axis)
{
shape.dim(axis) = Network_output_dim(_net, n, axis);
}
_outputs.allocate(name, shape);
Network_output_bind(_net, n, reinterpret_cast<void *>(_outputs.base(n)), _outputs.size(n));
}
}
public:
~Backend() { Network_destruct(_net); }
public:
void prepare(const std::function<void(nnkit::TensorContext &)> &f) override { f(_inputs); }
void run(void) override { Network_invoke(_net); }
void teardown(const std::function<void(nnkit::TensorContext &)> &f) override { f(_outputs); }
private:
Network *_net;
private:
TensorContext _inputs;
TensorContext _outputs;
};
} // namespace
extern "C" std::unique_ptr<nnkit::Backend> make_backend(const nnkit::CmdlineArguments &args)
{
return make_unique<::Backend>();
}
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