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Add tile 

Signed-off-by: zhao.xia <zhao.xia@verisilicon.com>
This commit is contained in:
zhao.xia 2021-06-03 17:01:15 +08:00 committed by Kainan Cha
parent bd9c5df70a
commit 353feca56a
4 changed files with 199 additions and 1 deletions
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51
include/tim/vx/ops/tile.h Normal file
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/****************************************************************************
*
* Copyright (c) 2021 Vivante Corporation
*
* 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.
*
*****************************************************************************/
#ifndef TIM_VX_OPS_TILE_H_
#define TIM_VX_OPS_TILE_H_
#include "tim/vx/operation.h"
namespace tim {
namespace vx {
namespace ops {
/**
* ## Tile
*
* Constructs a tensor by tiling a given tensor.
* - multiples : Must be one of the following types: int32, int64.
* Length must be the same as the number of dimensions in input.
*/
class Tile : public Operation {
public:
Tile(Graph* graph, const std::vector<int32_t>& multiples);
protected:
const std::vector<int32_t> multiples_;
};
} // namespace ops
} // namespace vx
} // namespace tim
#endif /* TIM_VX_OPS_TILE_H_ */

2
src/tim/vx/ops/README.md
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@ -93,7 +93,7 @@ ScatterND|SCATTER_ND|Mapped|[tf.scatter_nd](https://tensorflow.google.cn/api_doc
||MOMENTS|Planned 21Q2|[tf.moments](https://tensorflow.google.cn/api_docs/python/tf/nn/moments)
||MATRIXMUL|Planned 21Q2|[tf.experimental.numpy.matmul](https://www.tensorflow.org/api_docs/python/tf/experimental/numpy/matmul)
Unstack|UNSTACK|Mapped|[tf.unstack](https://tensorflow.google.cn/api_docs/python/tf/unstack)
||TILE|Planned 21Q2|[tf.tile](https://tensorflow.google.cn/api_docs/python/tf/tile)
Tile|TILE|Mapped|[tf.tile](https://tensorflow.google.cn/api_docs/python/tf/tile)
||TOPK|Planned 21Q2|[tf.math.top_k](https://tensorflow.google.cn/api_docs/python/tf/math/top_k)
||GROUPED_CONV2D|Planned 21Q2|[ANEURALNETWORKS_GROUPED_CONV_2D](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a847acf8d9f3d2343328c3dbe6d447c50)
||PROPOSAL|Planned 21Q3|[Faster-RCNN Proposal Layer](https://github.com/intel/caffe/blob/master/examples/faster-rcnn/lib/rpn/proposal_layer.py)

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src/tim/vx/ops/tile.cc Normal file
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/****************************************************************************
*
* Copyright (c) 2021 Vivante Corporation
*
* 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 "tim/vx/ops/tile.h"
#include "operation_private.h"
#include "vsi_nn_pub.h"
namespace tim {
namespace vx {
namespace ops {
Tile::Tile(Graph* graph, const std::vector<int32_t>& multiples)
: Operation(graph, VSI_NN_OP_TILE, 1, 1), multiples_(multiples) {
this->impl()->node()->nn_param.tile.multiples = multiples_.data();
this->impl()->node()->nn_param.tile.multiples_num = multiples_.size();
}
} // namespace ops
} // namespace vx
} // namespace tim

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src/tim/vx/ops/tile_test.cc Normal file
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/****************************************************************************
*
* Copyright (c) 2021 Vivante Corporation
*
* 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 "tim/vx/context.h"
#include "tim/vx/graph.h"
#include "tim/vx/ops/tile.h"
#include "gtest/gtest.h"
TEST(Tile, shape_3_2_float_multiples_2_1) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({3,2});
tim::vx::ShapeType output_shape({6,2});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
input_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
output_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {
1,2,3,
4,5,6,
};
std::vector<float> golden = {
1,2,3,1,2,3,
4,5,6,4,5,6,
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(
in_data.data(), in_data.size() * sizeof(float)));
std::vector<int32_t> multiples = {2,1};
auto op = graph->CreateOperation<tim::vx::ops::Tile>(multiples);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(golden.size());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}
TEST(Tile, shape_3_2_1_int8_multiples_2_2_1) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({3,2,1});
tim::vx::ShapeType output_shape({6,4,1});
tim::vx::Quantization quant(tim::vx::QuantType::ASYMMETRIC, 0.5, 0);
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8,
input_shape, tim::vx::TensorAttribute::INPUT, quant);
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
output_shape, tim::vx::TensorAttribute::OUTPUT, quant);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<uint8_t> in_data = {
2,4,6,
8,10,12,
};
std::vector<uint8_t> golden = {
2,4,6,2,4,6,
8,10,12,8,10,12,
2,4,6,2,4,6,
8,10,12,8,10,12,
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
std::vector<int32_t> multiples = {2,2,1};
auto op = graph->CreateOperation<tim::vx::ops::Tile>(multiples);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<uint8_t> output(golden.size());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_EQ(golden, output);
}