colin
/
TIM-VX
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add Op MaxpoolWithArgmax 

Signed-off-by: Kainan Cha <kainan.zha@verisilicon.com>
This commit is contained in:
Kainan Cha 2021-05-26 00:20:39 +08:00
parent fae5cede7a
commit 18a928ee69
5 changed files with 255 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

66
include/tim/vx/ops/maxpoolwithargmax.h Normal file
View File

@ -0,0 +1,66 @@
/****************************************************************************
*
* 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_MAXPOOLWITHARGMAX_H_
#define TIM_VX_OPS_MAXPOOLWITHARGMAX_H_
#include <array>
#include "tim/vx/operation.h"
#include "tim/vx/types.h"
namespace tim {
namespace vx {
namespace ops {
/**
* ## MaxpoolWithArgmax
*
* Performs an 2-D Max pooling operation and return indices
*
* - padding : AUTO, VALID or SAME.
* - ksize : filter size.
* - stride : stride along each spatial axis.
* - round_type : CEILING or FLOOR.
*/
class MaxpoolWithArgmax : public Operation {
public:
MaxpoolWithArgmax(Graph* graph, PadType padding,
const std::array<uint32_t, 2>& ksize,
const std::array<uint32_t, 2>& stride,
RoundType round_type = RoundType::FLOOR,
DataLayout layout = DataLayout::WHCN);
protected:
const PadType padding_;
const std::array<uint32_t, 2> ksize_;
const std::array<uint32_t, 2> stride_;
const RoundType round_type_;
};
} // namespace ops
} // namespace vx
} // namespace tim
#endif /* TIM_VX_OPS_MAXPOOLWITHARGMAX_H_ */

8
src/tim/vx/internal/src/ops/vsi_nn_op_poolwithargmax.c
View File

@ -244,6 +244,14 @@ static vsi_bool op_setup
)
{
vsi_bool ret = TRUE;
vsi_nn_compute_padding(
inputs[0]->attr.size,
self->nn_param.pool.ksize,
self->nn_param.pool.stride,
NULL,
self->nn_param.pool.pad_type,
self->nn_param.pool.pad
);
if( VSI_NN_DIM_AUTO == outputs[0]->attr.dim_num )
{

2
src/tim/vx/ops/README.md
View File

@ -25,7 +25,7 @@ Reorg|REORG|Mapped|[darknet.reorg](https://github.com/pjreddie/darknet/blob/mast
||VARIABLE|Unmapped|[tf.variable](https://tensorflow.google.cn/api_docs/python/tf/Variable)
L2Normalization|L2_NORMALIZE|Mapped|[tf.math.l2_normalize](https://tensorflow.google.cn/api_docs/python/tf/math/l2_normalize)
FullyConnected|FCL2|Mapped|[tf.keras.layers.Dense](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/Dense)
||POOLWITHARGMAX|Unmapped|[tf.nn.max_pool_with_argmax](https://tensorflow.google.cn/api_docs/python/tf/nn/max_pool_with_argmax)
|MaxpoolWithArgmax|POOLWITHARGMAX|Mapped|[tf.nn.max_pool_with_argmax](https://tensorflow.google.cn/api_docs/python/tf/nn/max_pool_with_argmax)
ArgMax|ARGMAX|Mapped|[tf.math.argmax](https://tensorflow.google.cn/api_docs/python/tf/math/argmax)
Maximum|MAXIMUM|Mapped|[tf.math.maximum](https://tensorflow.google.cn/api_docs/python/tf/math/maximum)
||CROP|Unmapped

57
src/tim/vx/ops/maxpoolwithargmax.cc Normal file
View File

@ -0,0 +1,57 @@
/****************************************************************************
*
* 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/maxpoolwithargmax.h"
#include "operation_private.h"
#include "type_utils.h"
#include "vsi_nn_pub.h"
namespace tim {
namespace vx {
namespace ops {
MaxpoolWithArgmax::MaxpoolWithArgmax(Graph* graph, PadType padding,
const std::array<uint32_t, 2>& ksize,
const std::array<uint32_t, 2>& stride,
RoundType round_type,
DataLayout layout)
: Operation(graph, VSI_NN_OP_POOLWITHARGMAX, 1, 2, layout),
padding_(padding),
ksize_(ksize),
stride_(stride),
round_type_(round_type) {
this->impl()->node()->nn_param.pool.type = TranslatePoolType(PoolType::MAX);
this->impl()->node()->nn_param.pool.round_type =
TranslateRoundType(round_type_);
this->impl()->node()->nn_param.pool.ksize[0] = ksize_[0];
this->impl()->node()->nn_param.pool.ksize[1] = ksize_[1];
this->impl()->node()->nn_param.pool.stride[0] = stride_[0];
this->impl()->node()->nn_param.pool.stride[1] = stride_[1];
this->impl()->node()->nn_param.pool.pad_type = TranslatePadType(padding_);
this->SetRoundingPolicy(OverflowPolicy::SATURATE, RoundingPolicy::RTNE, round_type_);
}
} // namespace ops
} // namespace vx
} // namespace tim

123
src/tim/vx/ops/maxpoolwithargmax_test.cc Normal file
View File

@ -0,0 +1,123 @@
/****************************************************************************
*
* 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/maxpoolwithargmax.h"
#include "gtest/gtest.h"
TEST(MaxpoolWithArgmax, shape_3_3_1_fp32_kernel_2_stride_2) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape({3, 3, 1});
tim::vx::ShapeType out_shape({2, 2, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
in_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec_indices(tim::vx::DataType::UINT8,
out_shape, tim::vx::TensorAttribute::OUTPUT);
tim::vx::TensorSpec output_spec_values(tim::vx::DataType::FLOAT32,
out_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor_indices = graph->CreateTensor(output_spec_indices);
auto output_tensor_values = graph->CreateTensor(output_spec_values);
std::vector<float> in_data = {
1, 2, 3,
4, 5, 6,
7, 8, 9 };
std::vector<float> values_golden = {
5, 6,
8, 9 };
std::vector<uint8_t> indices_golden = {
3, 2,
1, 0 };
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()*4));
std::array<uint32_t, 2> ksize = {2, 2};
std::array<uint32_t, 2> stride = {2, 2};
auto op = graph->CreateOperation<tim::vx::ops::MaxpoolWithArgmax>(
tim::vx::PadType::VALID, ksize, stride);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor_values, output_tensor_indices});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output_values(4);
std::vector<uint8_t> output_indices(4);
EXPECT_TRUE(output_tensor_values->CopyDataFromTensor(output_values.data()));
EXPECT_TRUE(output_tensor_indices->CopyDataFromTensor(output_indices.data()));
EXPECT_EQ(values_golden, output_values);
EXPECT_EQ(indices_golden, output_indices);
}
TEST(MaxpoolWithArgmax, shape_4_4_1_uint8_kernel_2_stride_2) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape({4, 4, 1});
tim::vx::ShapeType out_shape({2, 2, 1});
tim::vx::Quantization io_quant(tim::vx::QuantType::ASYMMETRIC, 1, 0);
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8,
in_shape, tim::vx::TensorAttribute::INPUT, io_quant);
tim::vx::TensorSpec output_spec_indices(tim::vx::DataType::UINT8,
out_shape, tim::vx::TensorAttribute::OUTPUT);
tim::vx::TensorSpec output_spec_values(tim::vx::DataType::UINT8,
out_shape, tim::vx::TensorAttribute::OUTPUT, io_quant);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor_indices = graph->CreateTensor(output_spec_indices);
auto output_tensor_values = graph->CreateTensor(output_spec_values);
std::vector<uint8_t> in_data = {
1, 2, 3, 3,
4, 5, 6, 6,
7, 8, 9, 9,
10, 11, 12, 12 };
std::vector<uint8_t> values_golden = {
5, 6,
11, 12};
std::vector<uint8_t> indices_golden = {
3, 2,
3, 2};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
std::array<uint32_t, 2> ksize = {2, 2};
std::array<uint32_t, 2> stride = {2, 2};
auto op = graph->CreateOperation<tim::vx::ops::MaxpoolWithArgmax>(
tim::vx::PadType::VALID, ksize, stride);
(*op).BindInputs({input_tensor}).BindOutputs({output_tensor_values, output_tensor_indices});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<uint8_t> output_values(4);
std::vector<uint8_t> output_indices(4);
EXPECT_TRUE(output_tensor_values->CopyDataFromTensor(output_values.data()));
EXPECT_TRUE(output_tensor_indices->CopyDataFromTensor(output_indices.data()));
EXPECT_EQ(values_golden, output_values);
EXPECT_EQ(indices_golden, output_indices);
}