Suported specifying CRD_mode & DCR_mode in depthtospace (#362)
https://github.com/VeriSilicon/TIM-VX/issues/304 Signed-off-by: Chen Xin <jack.chen@verisilicon.com> Co-authored-by: Chen Xin <jack.chen@verisilicon.com>
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@ -47,13 +47,20 @@ namespace ops {
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class DepthToSpace : public DirectMapOp {
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public:
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enum depth2space_mode {
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CRD_mode = 0,
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DCR_mode = 1,
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};
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DepthToSpace(Graph* Graph, int block_size,
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DataLayout layout = DataLayout::WHCN);
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DepthToSpace(Graph* Graph, int block_size, depth2space_mode mode,
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DataLayout layout = DataLayout::WHCN);
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std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
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protected:
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int block_size_;
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depth2space_mode mode_;
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};
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} // namespace ops
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@ -30,15 +30,22 @@ namespace tim {
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namespace vx {
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namespace ops {
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DepthToSpace::DepthToSpace(Graph* graph, int block_size, DataLayout layout)
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: DirectMapOp(graph, VSI_NN_OP_DEPTH2SPACE, 0, 0, layout),
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block_size_(block_size) {
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DepthToSpace::DepthToSpace(Graph* Graph, int block_size, DataLayout layout)
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: DepthToSpace(Graph, block_size, CRD_mode, layout) {}
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DepthToSpace::DepthToSpace(Graph* Graph, int block_size, depth2space_mode mode,
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DataLayout layout)
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: DirectMapOp(Graph, VSI_NN_OP_DEPTH2SPACE, 0, 0, layout),
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block_size_(block_size),
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mode_(mode) {
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this->impl()->node()->nn_param.depth2space.block_size = block_size_;
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this->impl()->node()->nn_param.depth2space.mode =
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(vsi_nn_depth2space_mode_e)mode_;
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}
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std::shared_ptr<Operation> DepthToSpace::Clone(
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std::shared_ptr<Graph>& graph) const {
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return graph->CreateOperation<DepthToSpace>(this->block_size_,
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return graph->CreateOperation<DepthToSpace>(this->block_size_, this->mode_,
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this->impl_->layout_);
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}
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@ -0,0 +1,65 @@
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/****************************************************************************
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*
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* Copyright (c) 2021 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/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops/depth2space.h"
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#include "tim/vx/types.h"
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#include "test_utils.h"
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#include "gtest/gtest.h"
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TEST(DepthToSpace, shape_6_4_2_1_int32) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType in_shape({2, 1, 4, 1});
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tim::vx::ShapeType out_shape({4, 2, 1, 1});
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, in_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, out_shape,
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tim::vx::TensorAttribute::OUTPUT);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto output_tensor = graph->CreateTensor(output_spec);
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std::vector<float> in_data = {
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3, 5, 4, 8, 3, 5, 4, 8,
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};
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std::vector<float> golden = {
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3, 4, 5, 8, 3, 4, 5, 8,
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};
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EXPECT_TRUE(
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input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
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auto op = graph->CreateOperation<tim::vx::ops::DepthToSpace>(
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2, tim::vx::ops::DepthToSpace::DCR_mode);
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(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
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EXPECT_TRUE(graph->Compile());
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EXPECT_TRUE(graph->Run());
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std::vector<float> output(golden.size());
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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