DepthwiseConv2D.cc

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/*
 * Copyright (c) 2025 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.
 */
 
#include "../KernelGenerator.h"
#include "../Validator.h"
 
#include <AclKernelGen.h>
 
namespace onert::backend::acl_cl
{
 
void Validator::visit(const ir::operation::DepthwiseConv2D &) { _supported = true; }
 
void KernelGenerator::visit(const ir::operation::DepthwiseConv2D &node)
{
  using ir::operation::DepthwiseConv2D;
 
  const auto ofm_index{node.getOutputs().at(0)};
  const auto ifm_index{node.getInputs().at(DepthwiseConv2D::Input::INPUT)};
  const auto ker_index{node.getInputs().at(DepthwiseConv2D::Input::KERNEL)};
  const auto bias_index{node.getInputs().at(DepthwiseConv2D::Input::BIAS)};
 
  const auto ifm_shape = _ctx.at(ifm_index).shape().asFeature();
  const auto ofm_shape = _ctx.at(ofm_index).shape().asFeature();
  // Kernel format is [1, kernel_height, kernel_width, depth_out].
  const auto &ker_shape = _ctx.at(ker_index).shape();
  const auto ker_height = ker_shape.dim(1);
  const auto ker_width = ker_shape.dim(2);
 
  const auto stride = node.param().stride;
  const auto dilation = node.param().dilation;
  const auto padding =
    ir::calculatePadding(node.param().padding, ifm_shape, ofm_shape, stride, ker_width, ker_height,
                         dilation.width_factor, dilation.height_factor);
  const auto multiplier = node.param().multiplier;
  const auto activation = node.param().activation;
 
  auto ofm_tensor = _tensor_reg->getAclTensor(ofm_index);
  auto ifm_tensor = _tensor_reg->getAclTensor(ifm_index);
  auto ker_tensor = _tensor_reg->getAclTensor(ker_index);
  auto bias_tensor = _tensor_reg->getAclTensor(bias_index);
 
  const auto conv_info = acl_common::asPadStrideInfo(padding, stride);
  const auto act_info = acl_common::asActivationLayerInfo(activation);
  const auto dilation_info = acl_common::asDilation(dilation.width_factor, dilation.height_factor);
 
  auto fn = acl_common::generateLayer<arm_compute::CLDepthwiseConvolutionLayer>(
    ifm_tensor->handle(), ker_tensor->handle(), bias_tensor->handle(), ofm_tensor->handle(),
    conv_info, multiplier, act_info, dilation_info);
 
  _return_fn = acl_common::asAclFunction(std::move(fn));
}
 
} // namespace onert::backend::acl_cl