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Add GroupedConv2d 

Signed-off-by: zhao.xia <zhao.xia@verisilicon.com>
This commit is contained in:
zhao.xia 2021-06-04 14:43:17 +08:00 committed by Kainan Cha
parent 353feca56a
commit f59f26412b
5 changed files with 467 additions and 40 deletions
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87
include/tim/vx/ops/groupedconv2d.h Normal file
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@ -0,0 +1,87 @@
/****************************************************************************
*
* 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_GROUPEDCONV2D_H_
#define TIM_VX_OPS_GROUPEDCONV2D_H_
#include <array>
#include "tim/vx/operation.h"
namespace tim {
namespace vx {
namespace ops {
/**
* ## GroupedConv2d
*
* Performs a grouped 2-D convolution operation.
*
* Input:
* - input [WHCN or CWHN].
* - kernel [ WHIcOc ] (Ic: Input Channels. Oc: Output Channels).
* - bias [ O ]. Optional.
*
* Attribute:
* - weights : the output channel number for weight tensor.
* - ksize : the height and width for weight tensor.
* - padding : AUTO, VALID or SAME.
* - pad : pad value for each spatial axis.
* - stride : stride along each spatial axis.
* - dilation : dilation value along each spatial axis of the filter.
* - group_number: Split conv to n group.
* - layout : WHCN or CWHN.
*/
class GroupedConv2d : public Operation {
public:
GroupedConv2d(Graph* graph, PadType padding,
const std::array<uint32_t, 2>& strides,
const std::array<uint32_t, 2>& dilation,
int32_t grouped_number,
DataLayout input_layout = DataLayout::WHCN,
DataLayout kernel_layout = DataLayout::WHIcOc);
GroupedConv2d(Graph* graph,
const std::array<uint32_t, 4>& pad,
const std::array<uint32_t, 2>& strides,
const std::array<uint32_t, 2>& dilation,
int32_t group_number,
DataLayout input_layout = DataLayout::WHCN,
DataLayout kernel_layout = DataLayout::WHIcOc);
DataLayout KernelDataLayout() { return kernel_layout_; }
protected:
const PadType padding_;
const std::array<uint32_t, 2> strides_;
const std::array<uint32_t, 2> dilation_;
const std::array<uint32_t, 4> pad_;
const int32_t group_number_;
const DataLayout kernel_layout_;
};
} // namespace ops
} // namespace vx
} // namespace tim
#endif /* TIM_VX_OPS_GROUPED_CONV2D_H_ */

40
src/tim/vx/internal/src/ops/vsi_nn_op_grouped_conv2d.c
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@ -207,45 +207,7 @@ static vsi_bool op_check
vsi_nn_tensor_t ** outputs
)
{
BEGIN_IO_TYPE_DECL(GROUPED_CONV2D, 3, 1)
IO_TYPE(D_F16, D_F16, D_NONE, D_F16)
IO_TYPE(D_F16, D_F16, D_F32, D_F16)
IO_TYPE(D_F16, D_F16, D_F16, D_F16)
IO_TYPE(D_F32, D_F32, D_F32, D_F32)
IO_TYPE(D_F32, D_F32, D_NONE, D_F32)
IO_TYPE(D_I16|Q_DFP, D_I16|Q_DFP, D_NONE, D_I16|Q_DFP)
IO_TYPE(D_I16|Q_DFP, D_I16|Q_DFP, D_I32, D_I16|Q_DFP)
IO_TYPE(D_I16|Q_DFP, D_I16|Q_DFP, D_I64, D_I16|Q_DFP)
IO_TYPE(D_I16|Q_DFP, D_I16|Q_DFP, D_I16|Q_DFP, D_I16|Q_DFP)
IO_TYPE(D_I8|Q_DFP, D_I8|Q_DFP, D_NONE, D_I8|Q_DFP)
IO_TYPE(D_I8|Q_DFP, D_I8|Q_DFP, D_I32, D_I8|Q_DFP)
IO_TYPE(D_U8|Q_ASYM, D_I8|Q_DFP, D_I32, D_U8|Q_ASYM)
IO_TYPE(D_U8|Q_ASYM, D_U8|Q_ASYM, D_NONE, D_U8|Q_ASYM)
IO_TYPE(D_U8|Q_ASYM, D_U8|Q_ASYM, D_I32, D_U8|Q_ASYM)
IO_TYPE(D_I8|Q_DFP, D_I8|Q_DFP, D_I32, D_I16|Q_DFP)
IO_TYPE(D_I8|Q_DFP, D_I8|Q_DFP, D_I32, D_U8|Q_ASYM)
IO_TYPE(D_I8|Q_DFP, D_I8|Q_DFP, D_I32, D_F16)
IO_TYPE(D_U8|Q_ASYM, D_U8|Q_ASYM, D_I32, D_I8|Q_DFP)
IO_TYPE(D_U8|Q_ASYM, D_U8|Q_ASYM, D_I32, D_I16|Q_DFP)
IO_TYPE(D_U8|Q_ASYM, D_U8|Q_ASYM, D_I32, D_F16)
IO_TYPE(D_U8|Q_ASYM, D_U8|Q_ASYM, D_U8|Q_ASYM, D_U8|Q_ASYM)
IO_TYPE(D_U8|Q_ASYM, D_U8|Q_ASYM, D_U8|Q_ASYM, D_I8|Q_DFP)
IO_TYPE(D_U8|Q_ASYM, D_U8|Q_ASYM, D_U8|Q_ASYM, D_I16|Q_DFP)
IO_TYPE(D_U8|Q_ASYM, D_U8|Q_ASYM, D_U8|Q_ASYM, D_F16)
IO_TYPE(D_BF16, D_BF16, D_F32, D_BF16)
IO_TYPE(D_BF16, D_BF16, D_F32, D_F32)
IO_TYPE(D_BF16, D_BF16, D_NONE, D_BF16)
END_IO_TYPE_DECL(GROUPED_CONV2D)
if (!VALIDATE_OP_IO_TYPES(GROUPED_CONV2D, self, inputs, self->input.num, outputs, self->output.num))
{
char* desc = generate_op_io_types_desc(inputs,
self->input.num, outputs, self->output.num);
VSILOGE("Inputs/Outputs data type not support: %s", desc);
destroy_op_io_types_desc(desc);
return FALSE;
}
return TRUE;
return vsi_nn_OpCheck(VSI_NN_OP_CONV2D, self, inputs, outputs);
} /* op_check() */
static vsi_bool op_setup

2
src/tim/vx/ops/README.md
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@ -95,7 +95,7 @@ ScatterND|SCATTER_ND|Mapped|[tf.scatter_nd](https://tensorflow.google.cn/api_doc
Unstack|UNSTACK|Mapped|[tf.unstack](https://tensorflow.google.cn/api_docs/python/tf/unstack)
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)
GroupedConv2d|GROUPED_CONV2D|Mapped|[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)
||ROI_POOL|Planned 21Q3|[ANEURALNETWORKS_ROI_POOLING](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a6736198af337b2efbdb0b6b64dee7fe4)
||ROI_ALIGN|Planned 21Q3|[ANEURALNETWORKS_ROI_ALIGN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a2848b39dd4bfba78f2438fda0d9397a4)

70
src/tim/vx/ops/groupedconv2d.cc Normal file
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@ -0,0 +1,70 @@
/****************************************************************************
*
* 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/groupedconv2d.h"
#include "operation_private.h"
#include "type_utils.h"
#include "vsi_nn_pub.h"
namespace tim {
namespace vx {
namespace ops {
GroupedConv2d::GroupedConv2d(Graph* graph,
PadType padding,
const std::array<uint32_t, 2>& strides,
const std::array<uint32_t, 2>& dilation,
int32_t group_number,
DataLayout input_layout, DataLayout kernel_layout)
: Operation(graph, VSI_NN_OP_GROUPED_CONV2D, 3, 1, input_layout),
padding_(padding), strides_(strides), dilation_(dilation),
pad_({0,0,0,0}), group_number_(group_number),
kernel_layout_(kernel_layout) {
this->impl()->node()->nn_param.conv2d.stride[0] = strides_[0];
this->impl()->node()->nn_param.conv2d.stride[1] = strides_[1];
this->impl()->node()->nn_param.conv2d.pad_type = TranslatePadType(padding_);
this->impl()->node()->nn_param.conv2d.group = group_number_;
this->impl()->node()->nn_param.conv2d.dilation[0] = dilation_[0];
this->impl()->node()->nn_param.conv2d.dilation[1] = dilation_[1];
}
GroupedConv2d::GroupedConv2d(Graph* graph,
const std::array<uint32_t, 4>& pad,
const std::array<uint32_t, 2>& strides,
const std::array<uint32_t, 2>& dilation,
int32_t group_number,
DataLayout input_layout, DataLayout kernel_layout)
: Operation(graph, VSI_NN_OP_GROUPED_CONV2D, 3, 1, input_layout),
padding_(PadType::AUTO), strides_(strides), dilation_(dilation), pad_(pad),
group_number_(group_number), kernel_layout_(kernel_layout) {
this->impl()->node()->nn_param.conv2d.stride[0] = strides_[0];
this->impl()->node()->nn_param.conv2d.stride[1] = strides_[1];
this->impl()->node()->nn_param.conv2d.group = group_number_;
this->impl()->node()->nn_param.conv2d.dilation[0] = dilation_[0];
this->impl()->node()->nn_param.conv2d.dilation[1] = dilation_[1];
}
} // namespace ops
} // namespace vx
} // namespace tim

308
src/tim/vx/ops/groupedconv2d_test.cc Normal file
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@ -0,0 +1,308 @@
/****************************************************************************
*
* 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/groupedconv2d.h"
#include "gtest/gtest.h"
namespace {
template<typename T>
::testing::AssertionResult ArraysMatch(const std::vector<T>& expected,
const std::vector<T>& actual,
T abs_error){
for (size_t i = 0; i < expected.size(); ++i){
EXPECT_NEAR(expected[i], actual[i], abs_error) << "at index:" << i;
}
return ::testing::AssertionSuccess();
}
}
TEST(GroupedConv2d, shape_3_3_6_1_float_group_1_no_bias_whcn) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({3,3,6,1});
tim::vx::ShapeType param_shape({3,3,6,1});
tim::vx::ShapeType output_shape({1,1,1,1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
input_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
param_shape, tim::vx::TensorAttribute::CONSTANT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
output_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto weight_tensor = graph->CreateTensor(param_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {
-0.50, -0.50, -0.50,
0.00, 1.00, 0.00,
0.50, 0.50, 0.50,
-1.50, -1.00, -1.00,
-0.50, 1.00, 0.50,
1.00, 1.00, 1.50,
-2.50, -2.00, -2.00,
-1.50, 1.50, 1.50,
2.00, 2.00, 2.50,
-3.50, -3.00, -3.00,
-2.50, 2.50, 2.50,
3.00, 3.00, 3.50,
-4.50, -4.00, -4.00,
-3.50, 3.50, 3.50,
4.00, 4.00, 4.50,
-5.50, -5.00, -5.00,
-4.50, 4.50, 4.50,
5.00, 5.00, 5.50,
};
std::vector<float> weight = {
-0.50, 0.00, -0.50,
-0.50, 0.00, -0.50,
-0.50, 0.00, -0.50,
1.50, 1.00, -1.50,
1.50, 1.00, -1.50,
1.50, 1.00, -1.50,
-2.50, -2.00, -2.50,
-2.50, -2.00, -2.50,
-2.50, -2.00, -2.50,
3.50, 3.00, 3.50,
3.50, 3.00, 3.50,
3.50, 3.00, 3.50,
-4.50, -4.00, -4.50,
-4.50, -4.00, -4.50,
-4.50, -4.00, -4.50,
-5.50, -5.00, 5.50,
-5.50, -5.00, 5.50,
-5.50, -5.00, 5.50,
};
std::vector<float> golden = {
21.0
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
EXPECT_TRUE(weight_tensor->CopyDataToTensor(weight.data(), weight.size() * sizeof(float)));
std::array<uint32_t, 2> dilations = {1,1};
std::array<uint32_t, 2> strides = {1,1};
auto op = graph->CreateOperation<tim::vx::ops::GroupedConv2d>(
tim::vx::PadType::VALID, strides, dilations, 1);
(*op).BindInputs({input_tensor, weight_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(golden.size() * sizeof(float));
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}
TEST(GroupedConv2d, shape_3_3_6_1_float_group_2_whcn) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({3,3,6,1});
tim::vx::ShapeType weight_shape({3,3,3,2});
tim::vx::ShapeType bias_shape({2});
tim::vx::ShapeType output_shape({1,1,2,1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
input_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32,
weight_shape, tim::vx::TensorAttribute::CONSTANT);
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32,
bias_shape, tim::vx::TensorAttribute::CONSTANT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
output_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto weight_tensor = graph->CreateTensor(weight_spec);
auto bias_tensor = graph->CreateTensor(bias_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {
-0.50, -0.50, -0.50,
0.00, 1.00, 0.00,
0.50, 0.50, 0.50,
-1.50, -1.00, -1.00,
-0.50, 1.00, 0.50,
1.00, 1.00, 1.50,
-2.50, -2.00, -2.00,
-1.50, 1.50, 1.50,
2.00, 2.00, 2.50,
-3.50, -3.00, -3.00,
-2.50, 2.50, 2.50,
3.00, 3.00, 3.50,
-4.50, -4.00, -4.00,
-3.50, 3.50, 3.50,
4.00, 4.00, 4.50,
-5.50, -5.00, -5.00,
-4.50, 4.50, 4.50,
5.00, 5.00, 5.50,
};
std::vector<float> weight = {
-0.50, 0.00, -0.50,
-0.50, 0.00, -0.50,
-0.50, 0.00, -0.50,
1.50, 1.00, -1.50,
1.50, 1.00, -1.50,
1.50, 1.00, -1.50,
-2.50, -2.00, -2.50,
-2.50, -2.00, -2.50,
-2.50, -2.00, -2.50,
3.50, 3.00, 3.50,
3.50, 3.00, 3.50,
3.50, 3.00, 3.50,
-4.50, -4.00, -4.50,
-4.50, -4.00, -4.50,
-4.50, -4.00, -4.50,
-5.50, -5.00, 5.50,
-5.50, -5.00, 5.50,
-5.50, -5.00, 5.50,
};
std::vector<float> bias = {
-1.25, 1.25,
};
std::vector<float> golden = {
-6.25, 27.25,
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
EXPECT_TRUE(weight_tensor->CopyDataToTensor(weight.data(), weight.size() * sizeof(float)));
EXPECT_TRUE(bias_tensor->CopyDataToTensor(bias.data(), bias.size() * sizeof(float)));
std::array<uint32_t, 2> dilations = {1,1};
std::array<uint32_t, 2> strides = {1,1};
auto op = graph->CreateOperation<tim::vx::ops::GroupedConv2d>(
tim::vx::PadType::VALID, strides, dilations, 2);
(*op).BindInputs({input_tensor, weight_tensor, bias_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(golden.size() * sizeof(float));
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}
TEST(GroupedConv2d, shape_3_3_6_1_uint8_group_6_whcn) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape({3,3,6,1});
tim::vx::ShapeType weight_shape({2,2,1,6});
tim::vx::ShapeType bias_shape({6});
tim::vx::ShapeType output_shape({2,2,6,1});
tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 0.5, 10);
tim::vx::Quantization weight_quant(tim::vx::QuantType::ASYMMETRIC, 0.5, 9);
tim::vx::Quantization bias_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 0);
tim::vx::Quantization output_quant(tim::vx::QuantType::ASYMMETRIC, 0.25, 85);
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8,
input_shape, tim::vx::TensorAttribute::INPUT,
input_quant);
tim::vx::TensorSpec weight_spec(tim::vx::DataType::UINT8,
weight_shape, tim::vx::TensorAttribute::CONSTANT,
weight_quant);
tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32,
bias_shape, tim::vx::TensorAttribute::CONSTANT,
bias_quant);
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8,
output_shape, tim::vx::TensorAttribute::OUTPUT,
output_quant);
auto input_tensor = graph->CreateTensor(input_spec);
auto weight_tensor = graph->CreateTensor(weight_spec);
auto bias_tensor = graph->CreateTensor(bias_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<uint8_t> in_data = {
9, 9, 9,
10, 12, 10,
11, 11, 11,
7, 8, 8,
9, 12, 11,
12, 12, 13,
5, 6, 6,
7, 13, 13,
14, 14, 15,
3, 4, 4,
5, 15, 15,
16, 16, 17,
1, 2, 2,
3, 17, 17,
18, 18, 19,
3, 0, 0,
1, 19, 19,
16, 4, 3,
};
std::vector<uint8_t> weight = {
8, 9,
8, 9,
12, 11,
12, 11,
4, 5,
4, 5,
16, 15,
16, 15,
0, 17,
0, 17,
6, 5,
6, 13,
};
std::vector<int32_t> bias = {
-24,-20,-16, 16, -4, 20,
};
std::vector<uint8_t> golden = {
62, 62,
60, 60,
53, 62,
75, 74,
113, 74,
33, 44,
11, 94,
179,150,
217, 90,
73, 0,
229,108,
111,126,
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size()));
EXPECT_TRUE(weight_tensor->CopyDataToTensor(weight.data(), weight.size()));
EXPECT_TRUE(bias_tensor->CopyDataToTensor(bias.data(), bias.size()));
std::array<uint32_t, 2> dilations = {1,1};
std::array<uint32_t, 2> strides = {2,2};
std::array<uint32_t, 4> pad = {0,1,0,1};
auto op = graph->CreateOperation<tim::vx::ops::GroupedConv2d>(pad, strides, dilations, 6);
(*op).BindInputs({input_tensor, weight_tensor, bias_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);
}