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added MaxPool3d op

This commit is contained in:
Feiyue Chen 2022-09-23 12:31:39 +08:00 committed by Sven
parent 8b8d09aea3
commit 9cb37b920f
4 changed files with 263 additions and 1 deletions
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77
include/tim/vx/ops/max_pool3d.h Normal file
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/****************************************************************************
*
* Copyright (c) 2022 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.
*
*****************************************************************************/
#ifdef VSI_FEAT_OP_MAX_POOL3D
#ifndef TIM_VX_OPS_MAX_POOL3D_H_
#define TIM_VX_OPS_MAX_POOL3D_H_
#include "tim/vx/builtin_op.h"
#include "tim/vx/types.h"
#include <array>
namespace tim {
namespace vx {
namespace ops {
/**
* ## Max_pool3d
*
* Applies a 3D max pooling over an input Tensor which can be regarded as a composition of 3D planes.
*
* Input:
* - input [WHDCN]
* - kernel [ WHD ]
*
* Attribute:
* - round_type : CEILING or FLOOR
* - ksize : the height and width for kernel tensor.
* - stride : stride along each spatial axis.
* - pad : pad value for each spatial axis. (left, right, top, bottom, front, rear).
* - pad_type : AUTO, VALID or SAME.
*
*/
class MaxPool3d : public BuiltinOp {
public:
MaxPool3d(Graph* Graph, RoundType round_type,
const std::array<uint32_t, 3>& ksize,
const std::array<uint32_t, 3>& stride,
const std::array<uint32_t, 6>& pad,
PadType pad_type,
DataLayout layout = DataLayout::WHDCN);
std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
protected:
const RoundType round_type_;
const std::array<uint32_t, 3> ksize_;
const std::array<uint32_t, 3> stride_;
const std::array<uint32_t, 6> pad_;
const PadType pad_type_;
};
} // namespace ops
} // namespace vx
} // namespace tim
#endif /* TIM_VX_OPS_MAX_POOL3D_H_ */
#endif //(VSI_FEAT_OP_MAX_POOL3D)

3
src/tim/vx/ops/README.md
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@ -113,10 +113,11 @@ GroupedConv1d|GROUPED_CONV1D|Mapped|[tf.keras.layers.Conv1D](https://tensorflow.
|TopK|TOPK|Mapped (limited support)|[tf.math.top_k](https://tensorflow.google.cn/api_docs/python/tf/math/top_k)
|GRUCell|GRUCELL_OVXLIB|Mapped|[tf.keras.layers.GRUCell](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/GRUCell?hl=en)
|UnidirectionalSequenceGRU|GRU_OVXLIB|Mapped|[tf.keras.layers.GRU](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/GRUCell?hl=en)
Mod|MOD|Mapped|[Onnx.mod](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Mod)
Mod|MOD|Mapped|[Onnx.Mod](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Mod)
Selu|SELU|Mapped|[tf.keras.activations.selu](https://www.tensorflow.org/api_docs/python/tf/keras/activations/selu)
Celu|CELU|Mapped|[Onnx.celu](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Celu)
Rcp|RCP|Mapped|[tf.math.reciprocal](https://www.tensorflow.org/api_docs/python/tf/math/reciprocal)
MaxPool3d|MAX_POOL3D|Mapped|[Onnx.MaxPool](https://github.com/onnx/onnx/blob/main/docs/Operators.md#MaxPool)
|UnidirectionalSequenceRNN|UNIDIRECTIONAL_SEQUENCE_RNN|Planned 22Q3|[ANEURALNETWORKS_UNIDIRECTIONAL_SEQUENCE_RNN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0ae11aa1d461d2abaa117f6ee2cb503dd8)
|BidirectionalSequenceRNN|BIDIRECTIONAL_SEQUENCE_RNN|Planned 22Q3|[ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_RNN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a487fc5ae247de828f13e62b99f259f3c)
|BidirectionalSequenceLSTM|BIDIRECTIONAL_SEQUENCE_LSTM|Mapped|[ANEURALNETWORKS_BIDIRECTIONAL_SEQUENCE_LSTM](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a492a71cb7aa50b9a1a834a3cb269d778)

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src/tim/vx/ops/max_pool3d.cc Normal file
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/****************************************************************************
*
* Copyright (c) 2022 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.
*
*****************************************************************************/
#ifdef VSI_FEAT_OP_MAX_POOL3D
#include "tim/vx/ops/max_pool3d.h"
#include "type_utils.h"
#include "builtin_op_impl.h"
#include "vsi_nn_pub.h"
namespace tim {
namespace vx {
namespace ops {
MaxPool3d::MaxPool3d(Graph* Graph, RoundType round_type,
const std::array<uint32_t, 3>& ksize,
const std::array<uint32_t, 3>& stride,
const std::array<uint32_t, 6>& pad,
PadType pad_type,
DataLayout layout)
: BuiltinOp(Graph, VSI_NN_OP_MAX_POOL3D, 1, 1, layout),
round_type_(round_type),
ksize_(ksize),
stride_(stride),
pad_(pad),
pad_type_(pad_type){
this->impl()->node()->nn_param.max_pool3d.round_type = TranslateRoundType(round_type_);
this->impl()->node()->nn_param.max_pool3d.ksize[0] = ksize_[0];
this->impl()->node()->nn_param.max_pool3d.ksize[1] = ksize_[1];
this->impl()->node()->nn_param.max_pool3d.ksize[2] = ksize_[2];
this->impl()->node()->nn_param.max_pool3d.stride[0] = stride_[0];
this->impl()->node()->nn_param.max_pool3d.stride[1] = stride_[1];
this->impl()->node()->nn_param.max_pool3d.stride[2] = stride_[2];
for (int i = 0; i < 6; i++){this->impl()->node()->nn_param.max_pool3d.pad[i] = pad_[i];}
this->impl()->node()->nn_param.max_pool3d.pad_type = TranslatePadType(pad_type_);
}
std::shared_ptr<Operation> MaxPool3d::Clone(std::shared_ptr<Graph>& graph) const {
return graph->CreateOperation<MaxPool3d>(
this->round_type_, this->ksize_, this->stride_, this->pad_, this->pad_type_);
}
} // namespace ops
} // namespace vx
} // namespace tim
#endif //(VSI_FEAT_OP_MAX_POOL3D)

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src/tim/vx/ops/max_pool3d_test.cc Normal file
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/****************************************************************************
*
* Copyright (c) 2022 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.
*
*****************************************************************************/
#ifdef VSI_FEAT_OP_MAX_POOL3D
#include "tim/vx/ops/max_pool3d.h"
#include "tim/vx/context.h"
#include "tim/vx/graph.h"
#include "gtest/gtest.h"
TEST(MaxPool3d, shape_3_2_2_2_1_fp32_kernel_2_2_2_stride_1_1_1_VALID) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape({3, 2, 2, 2, 1});//whdcn
tim::vx::ShapeType out_shape({2, 1, 1, 2, 1});//whdcn
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, in_shape,
tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, out_shape,
tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {
0, 1, 2,
3, 4 ,5, // depth0 channel0
6, 7, 8,
9, 10, 11, // depth1 channel0
12, 13, 14,
15, 16, 17,// depth0 channel1
18, 19, 20,
21, 22, 23 // depth1 channel1
};
std::vector<float> golden = {
10,11,
22,23
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
auto round_type = tim::vx::RoundType::FLOOR;
std::array<uint32_t, 3> ksize = {2, 2, 2}; //whd
std::array<uint32_t, 3> stride = {1, 1, 1}; //whd
std::array<uint32_t, 6> pad = {0, 0, 0, 0, 0, 0};
auto op = graph->CreateOperation<tim::vx::ops::MaxPool3d>(
round_type, ksize, stride, pad, tim::vx::PadType::VALID);
(*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(MaxPool3d, shape_4_2_2_1_1_fp32_kernel_2_2_2_stride_1_1_1_SAME) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape({4,2,2,1,1}); //whdcn
tim::vx::ShapeType out_shape({4,2,2,1,1}); //whdcn
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, in_shape,
tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, out_shape,
tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {
0, 6, 2, 4, 2, 5, 4, 3, 3, 2, 10, 7, 3, 2, 2, 4
};
std::vector<float> golden = {
6, 10, 10, 7, 5, 5, 4, 4, 3, 10,
10, 7, 3, 2, 4, 4
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * 4));
auto round_type = tim::vx::RoundType::FLOOR;
std::array<uint32_t, 3> ksize = {2, 2, 2};
std::array<uint32_t, 3> stride = {1, 1, 1};
std::array<uint32_t, 6> pad = {0, 0, 0, 0, 0, 0};
auto op = graph->CreateOperation<tim::vx::ops::MaxPool3d>(
round_type, ksize, stride, pad, tim::vx::PadType::SAME);
(*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);
}
# endif //(VSI_FEAT_OP_MAX_POOL3D)