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mapped groupedconv1d & unit test (#233) 

Signed-off-by: Chen Xin <jack.chen@verisilicon.com>

Co-authored-by: Chen Xin <jack.chen@verisilicon.com>
This commit is contained in:
chxin66 2021-12-06 19:20:13 +08:00 committed by GitHub
parent bd496219c8
commit dc31091db5
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GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 242 additions and 25 deletions
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16
include/tim/vx/ops/conv1d.h
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@ -36,22 +36,22 @@ class Conv1d : public Operation {
public:
Conv1d(Graph* graph, PadType padding, uint32_t stride,
uint32_t dilation, int32_t multiplier = 0,
DataLayout input_layout = DataLayout::WHCN,
DataLayout kernel_layout = DataLayout::WHIcOc);
DataLayout input_layout = DataLayout::WCN,
DataLayout kernel_layout = DataLayout::WIcOc);
Conv1d(Graph* graph, const std::array<uint32_t, 2>& pad,
uint32_t stride, uint32_t dilation, int32_t multiplier = 0,
DataLayout input_layout = DataLayout::WHCN,
DataLayout kernel_layout = DataLayout::WHIcOc);
DataLayout input_layout = DataLayout::WCN,
DataLayout kernel_layout = DataLayout::WIcOc);
Conv1d(Graph* graph, int32_t weights, PadType padding,
uint32_t ksize, uint32_t stride,
uint32_t dilation, int32_t multiplier = 0,
DataLayout input_layout = DataLayout::WHCN,
DataLayout kernel_layout = DataLayout::WHIcOc);
DataLayout input_layout = DataLayout::WCN,
DataLayout kernel_layout = DataLayout::WIcOc);
Conv1d(Graph* graph, int32_t weights, PadType padding,
uint32_t ksize, uint32_t stride, uint32_t dilation,
const std::array<uint32_t, 2>& pad, int32_t multiplier = 0,
DataLayout input_layout = DataLayout::WHCN,
DataLayout kernel_layout = DataLayout::WHIcOc);
DataLayout input_layout = DataLayout::WCN,
DataLayout kernel_layout = DataLayout::WIcOc);
DataLayout KernelDataLayout() { return kernel_layout_; }

82
include/tim/vx/ops/groupedconv1d.h Normal file
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@ -0,0 +1,82 @@
/****************************************************************************
*
* 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_GROUPEDCONV1D_H_
#define TIM_VX_OPS_GROUPEDCONV1D_H_
#include <array>
#include "tim/vx/operation.h"
namespace tim {
namespace vx {
namespace ops {
/**
* ## GroupedConv1d
*
* Performs a grouped 1-D convolution operation.
*
* Input:
* - input [WCN].
* - kernel [ WIcOc ] (Ic: Input Channels. Oc: Output Channels).Ic*group=C.
* - 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: Split conv to n group.
* - layout : WCN or CWN.
*/
class GroupedConv1d : public Operation {
public:
GroupedConv1d(Graph* graph, PadType padding,
uint32_t stride,
uint32_t dilation,
uint32_t group,
DataLayout input_layout = DataLayout::WCN,
DataLayout kernel_layout = DataLayout::WIcOc);
DataLayout KernelDataLayout() { return kernel_layout_; }
std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
protected:
const PadType padding_;
const uint32_t stride_;
const uint32_t dilation_;
const std::array<uint32_t, 2> pad_;
const uint32_t group_;
const DataLayout kernel_layout_;
};
} // namespace ops
} // namespace vx
} // namespace tim
#endif /* TIM_VX_OPS_GROUPED_CONV1D_H_ */

4
include/tim/vx/types.h
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@ -69,7 +69,9 @@ enum class DataLayout {
IcWHOc, /*TF*/
OcIcWH, /*TVM for classic conv2d in tflite model*/
IcOcWH, /*TVM for depthwise conv2d in tflite model*/
WHIcOc /*TIM-VX default*/
WHIcOc, /*TIM-VX default*/
WCN, /*for conv1d*/
WIcOc, /*for conv1d*/
};
} // namespace vx

1
src/tim/vx/ops/README.md
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@ -101,6 +101,7 @@ shuffle_channel|SHUFFLECHANNEL|Mapped|[ANEURALNETWORKS_CHANNEL_SHUFFLE](https://
Gelu|GELU|Mapped|[tf.nn.gelu](https://tensorflow.google.cn/api_docs/python/tf/nn/gelu)
Svdf|SVDF|Mapped|[ANEURALNETWORKS_SVDF](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a7096de21038c1ce49d354a00cba7b552)
Erf|ERF|Mapped|[tf.math.erf](https://tensorflow.google.cn/api_docs/python/tf/math/erf)
GROUPED_CONV1D|Mapped|[tf.keras.layers.Conv1D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv1D)
||PROPOSAL| TBD |[Faster-RCNN Proposal Layer](https://github.com/intel/caffe/blob/master/examples/faster-rcnn/lib/rpn/proposal_layer.py)
||ROI_POOL|Planned 22Q1 |[ANEURALNETWORKS_ROI_POOLING](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a6736198af337b2efbdb0b6b64dee7fe4)
||ROI_ALIGN| TBD |[ANEURALNETWORKS_ROI_ALIGN](https://developer.android.com/ndk/reference/group/neural-networks#group___neural_networks_1ggaabbe492c60331b13038e39d4207940e0a2848b39dd4bfba78f2438fda0d9397a4)

15
src/tim/vx/ops/conv1d_test.cc
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@ -27,18 +27,19 @@
#include "test_utils.h"
#include "gtest/gtest.h"
TEST(Conv1d, shape_3_6_1_float_ksize_1_stride_1_weights_3_no_bias_whcn) {
TEST(Conv1d, shape_3_6_1_float_ksize_1_stride_1_weights_3_no_bias_wcn) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType io_shape({3, 6, 1});
tim::vx::ShapeType in_shape({3, 6, 1});
tim::vx::ShapeType param_shape({1,6,3});
tim::vx::ShapeType out_shape({3, 3, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::INPUT);
in_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
param_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
io_shape, tim::vx::TensorAttribute::OUTPUT);
out_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto weight_tensor = graph->CreateTensor(param_spec);
@ -78,7 +79,7 @@ TEST(Conv1d, shape_3_6_1_float_ksize_1_stride_1_weights_3_no_bias_whcn) {
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}
TEST(Conv1d, shape_6_2_1_uint8_ksize_6_stride_1_weights_2_whcn) {
TEST(Conv1d, shape_6_2_1_uint8_ksize_6_stride_1_weights_2_wcn) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
@ -144,7 +145,7 @@ TEST(Conv1d, shape_6_2_1_uint8_ksize_6_stride_1_weights_2_whcn) {
EXPECT_TRUE(ArraysMatch(golden, output, static_cast<uint8_t>(0)));
}
TEST(Conv1d, shape_6_2_1_uint8_ksize_3_stride_1_pad_1_weights_2_no_bias_whcn) {
TEST(Conv1d, shape_6_2_1_uint8_ksize_3_stride_1_pad_1_weights_2_no_bias_wcn) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
@ -199,7 +200,7 @@ TEST(Conv1d, shape_6_2_1_uint8_ksize_3_stride_1_pad_1_weights_2_no_bias_whcn) {
#if 0
// Fail case
// Internal impl conv1d don't support multiplier, need wait for the fix.
TEST(Conv1d, shape_7_2_1_uint8_ksize_3_stride_2_multiplier_1_whcn) {
TEST(Conv1d, shape_7_2_1_uint8_ksize_3_stride_2_multiplier_1_wcn) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();

59
src/tim/vx/ops/groupedconv1d.cc Normal file
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@ -0,0 +1,59 @@
/****************************************************************************
*
* 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/groupedconv1d.h"
#include "operation_private.h"
#include "type_utils.h"
#include "vsi_nn_pub.h"
namespace tim {
namespace vx {
namespace ops {
GroupedConv1d::GroupedConv1d(Graph* graph,
PadType padding,
const uint32_t stride,
const uint32_t dilation,
uint32_t group,
DataLayout input_layout, DataLayout kernel_layout)
: Operation(graph, VSI_NN_OP_GROUPED_CONV1D, 3, 1, input_layout),
padding_(padding), stride_(stride), dilation_(dilation),
pad_({0,0}), group_(group),
kernel_layout_(kernel_layout) {
this->impl()->node()->nn_param.grouped_conv1d.pad_type = TranslatePadType(padding_);
this->impl()->node()->nn_param.grouped_conv1d.stride = stride_;
this->impl()->node()->nn_param.grouped_conv1d.group = group_;
this->impl()->node()->nn_param.grouped_conv1d.dilation = dilation_;
}
std::shared_ptr<Operation> GroupedConv1d::Clone(
std::shared_ptr<Graph>& graph) const {
return graph->CreateOperation<GroupedConv1d>(
this->padding_, this->stride_, this->dilation_, this->group_, this->impl_->layout_,
this->kernel_layout_);
}
} // namespace ops
} // namespace vx
} // namespace tim

72
src/tim/vx/ops/groupedconv1d_test.cc Normal file
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@ -0,0 +1,72 @@
/****************************************************************************
*
* 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/groupedconv1d.h"
#include "test_utils.h"
#include "gtest/gtest.h"
TEST(GroupedConv1d, shape_6_2_1_float_ksize_6_stride_1_group_2_no_bias_wcn) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape({6, 2, 1});
tim::vx::ShapeType param_shape({6, 1, 2});
tim::vx::ShapeType out_shape({1, 2, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
in_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
param_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 weight_tensor = graph->CreateTensor(param_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {
-1, 0, 1, -1.5, 0.5, 1.5,
-2, -0.5, 2, -2.5, 0, 2.5,
};
std::vector<float> weight = {
-3, -2, -1.5, 1.5, 2, 3,
-2.5, -2, -1.5, 1.5, 2, 2.5,
};
std::vector<float> golden = {
4.75, 5.5,
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
EXPECT_TRUE(weight_tensor->CopyDataToTensor(weight.data(), weight.size() * sizeof(float)));
auto op = graph->CreateOperation<tim::vx::ops::GroupedConv1d>(tim::vx::PadType::VALID, 1, 1, 2);
(*op).BindInputs({input_tensor, weight_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_TRUE(ArraysMatch(golden, output, 1e-5f));
}

18
src/tim/vx/ops/groupedconv2d.cc
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@ -58,15 +58,15 @@ GroupedConv2d::GroupedConv2d(Graph* graph,
: 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];
this->impl()->node()->nn_param.conv2d.pad[0] = pad_[0];
this->impl()->node()->nn_param.conv2d.pad[1] = pad_[1];
this->impl()->node()->nn_param.conv2d.pad[2] = pad_[2];
this->impl()->node()->nn_param.conv2d.pad[3] = pad_[3];
this->impl()->node()->nn_param.grouped_conv2d.stride[0] = strides_[0];
this->impl()->node()->nn_param.grouped_conv2d.stride[1] = strides_[1];
this->impl()->node()->nn_param.grouped_conv2d.group = group_number_;
this->impl()->node()->nn_param.grouped_conv2d.dilation[0] = dilation_[0];
this->impl()->node()->nn_param.grouped_conv2d.dilation[1] = dilation_[1];
this->impl()->node()->nn_param.grouped_conv2d.pad[0] = pad_[0];
this->impl()->node()->nn_param.grouped_conv2d.pad[1] = pad_[1];
this->impl()->node()->nn_param.grouped_conv2d.pad[2] = pad_[2];
this->impl()->node()->nn_param.grouped_conv2d.pad[3] = pad_[3];
}
std::shared_ptr<Operation> GroupedConv2d::Clone(