91 lines
3.7 KiB
C++
91 lines
3.7 KiB
C++
/****************************************************************************
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*
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* Copyright (c) 2020-2023 Vivante Corporation
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*
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* Permission is hereby granted, free of charge, to any person obtaining a
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* copy of this software and associated documentation files (the "Software"),
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* to deal in the Software without restriction, including without limitation
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* the rights to use, copy, modify, merge, publish, distribute, sublicense,
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* and/or sell copies of the Software, and to permit persons to whom the
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* Software is furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in
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* all copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
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* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
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* DEALINGS IN THE SOFTWARE.
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*
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*****************************************************************************/
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#include "tim/vx/ops/deconv.h"
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#include <cassert>
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#include "builtin_op_impl.h"
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#include "type_utils.h"
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#include "vsi_nn_pub.h"
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namespace tim {
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namespace vx {
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namespace ops {
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DeConv2d::DeConv2d(Graph* graph, int32_t oc_count, PadType pad_type,
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const std::array<uint32_t, 2>& ksize,
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const std::array<uint32_t, 2>& stride,
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const std::array<uint32_t, 2>& output_padding,
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DataLayout input_layout,
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DataLayout kernel_layout)
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: DeConv2d(graph, oc_count, pad_type, ksize, stride, output_padding,
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{0, 0, 0, 0}, 1, input_layout, kernel_layout) {}
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DeConv2d::DeConv2d(Graph* graph, int32_t oc_count, PadType pad_type,
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const std::array<uint32_t, 2>& ksize,
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const std::array<uint32_t, 2>& stride,
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const std::array<uint32_t, 2>& output_padding,
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const std::array<uint32_t, 4>& pad,
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const uint32_t group,
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DataLayout input_layout,
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DataLayout kernel_layout)
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: BuiltinOp(graph, VSI_NN_OP_DECONVOLUTION, 0, 0, input_layout),
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oc_count_(oc_count),
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pad_type_(pad_type),
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ksize_(ksize),
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stride_(stride),
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output_padding_(output_padding),
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pad_(pad),
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group_(group),
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kernel_layout_(kernel_layout) {
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// TODO(Sven): only support depthwise usage
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assert((group == 1U) || group == static_cast<uint32_t>(oc_count));
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this->impl()->node()->nn_param.deconv.ksize[0] = ksize_[0];
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this->impl()->node()->nn_param.deconv.ksize[1] = ksize_[1];
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this->impl()->node()->nn_param.deconv.stride[0] = stride_[0];
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this->impl()->node()->nn_param.deconv.stride[1] = stride_[1];
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this->impl()->node()->nn_param.deconv.pad_type = TranslatePadType(pad_type_);
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this->impl()->node()->nn_param.deconv.weights = oc_count_;
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this->impl()->node()->nn_param.deconv.group = group_;
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this->impl()->node()->nn_param.deconv.output_padding[0] = output_padding_[0];
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this->impl()->node()->nn_param.deconv.output_padding[1] = output_padding_[1];
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this->impl()->node()->nn_param.deconv.pad[0] = pad_[0];
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this->impl()->node()->nn_param.deconv.pad[1] = pad_[1];
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this->impl()->node()->nn_param.deconv.pad[2] = pad_[2];
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this->impl()->node()->nn_param.deconv.pad[3] = pad_[3];
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}
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std::shared_ptr<Operation> DeConv2d::Clone(
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std::shared_ptr<Graph>& graph) const {
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return graph->CreateOperation<DeConv2d>(
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this->oc_count_, this->pad_type_, this->ksize_, this->stride_,
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this->output_padding_, this->pad_, this->group_, this->impl_->layout_,
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this->kernel_layout_);
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}
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} // namespace ops
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} // namespace vx
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} // namespace tim
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