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/*
* Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/graph/nodes/DepthwiseConvolutionLayer.h"
#include "arm_compute/graph/Error.h"
#include "arm_compute/graph/NodeContext.h"
#include "arm_compute/graph/OperationRegistry.h"
#include "support/ToolchainSupport.h"
using namespace arm_compute::graph;
std::unique_ptr<arm_compute::IFunction> DepthwiseConvolutionLayer::instantiate_node(GraphContext &ctx, ITensorObject *input, ITensorObject *output)
{
ARM_COMPUTE_ERROR_ON_UNALLOCATED_TENSOR_OBJECT(input, output);
arm_compute::ITensor *in = input->tensor();
arm_compute::ITensor *out = output->tensor();
_target_hint = ctx.hints().target_hint();
if(_weights.tensor() == nullptr)
{
TensorShape weights_shape(_conv_width, _conv_height, input->tensor()->info()->tensor_shape().z());
TensorInfo info = TensorInfo(TensorShape(weights_shape), in->info()->num_channels(), in->info()->data_type(), in->info()->fixed_point_position());
info.set_quantization_info(_quant_info);
_weights.set_info(std::move(info));
}
if(_biases.has_accessor() && _biases.tensor() == nullptr)
{
DataType dt = in->info()->data_type();
_biases.set_info(TensorInfo(TensorShape(in->info()->dimension(2)), in->info()->num_channels(), is_data_type_quantized_asymmetric(dt) ? DataType::S32 : dt, in->info()->fixed_point_position()));
}
bool weights_is_loaded = _weights.tensor() != nullptr;
bool biases_is_loaded = _biases.has_accessor() ? _biases.tensor() != nullptr : true;
_weights.set_target(_target_hint);
if(_biases.has_accessor())
{
_biases.set_target(_target_hint);
}
// Create node context
NodeContext node_ctx(OperationType::DepthwiseConvolutionLayer);
node_ctx.set_target(_target_hint);
node_ctx.add_input(in);
node_ctx.add_input(_weights.tensor());
if(_biases.has_accessor())
{
node_ctx.add_input(_biases.tensor());
}
node_ctx.add_output(out);
node_ctx.add_parameter<PadStrideInfo>("ConvolutionInfo", _conv_info);
node_ctx.add_parameter<bool>("Optimized3x3", _opt3x3);
// Configure operation
auto func = OperationRegistry::get().find_operation(OperationType::DepthwiseConvolutionLayer, _target_hint)->configure(node_ctx);
// Fill tensors
if(!weights_is_loaded)
{
_weights.allocate_and_fill_if_needed();
}
if(!biases_is_loaded)
{
_biases.allocate_and_fill_if_needed();
}
// Get function
return func;
}
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