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Add map for DeConv1d (#62) 

Signed-off-by: zhao.xia <zhao.xia@verisilicon.com>
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Nightingale 2021-05-24 23:41:15 +08:00 committed by GitHub
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4 changed files with 279 additions and 1 deletions
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79
include/tim/vx/ops/deconv1d.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_DECONV1D_H_
#define TIM_VX_OPS_DECONV1D_H_
#include <array>
#include "tim/vx/operation.h"
namespace tim {
namespace vx {
namespace ops {
/**
* ## DeConv1d
*
* Performs the transpose of 1-D convolution operation.
*
* This operation is sometimes called "deconvolution1d" after Deconvolutional Networks,
* but is actually the transpose (gradient) of Conv2D rather than an actual deconvolution.
*
* - weights : the channel number for weight tensor.
* - ksize : the length for weight tensor.
* - padding : AUTO, VALID or SAME.
* - pad : pad value for each spatial axis.
* - stride : stride along each spatial axis.
* - output_padding : specifying the amount of padding along the height and width of
* the output tensor.
*/
class DeConv1d : public Operation {
public:
DeConv1d(Graph* graph, int32_t oc_count_, PadType pad_type,
uint32_t ksize,
uint32_t stride,
uint32_t output_padding);
DeConv1d(Graph* graph, int32_t oc_count_, PadType pad_type,
uint32_t ksize,
uint32_t stride,
uint32_t output_padding,
const std::array<uint32_t, 2>& pad,
uint32_t group = 1);
protected:
const uint32_t oc_count_; // output channel count
const PadType pad_type_;
const uint32_t ksize_;
const uint32_t stride_;
const uint32_t output_padding_;
const std::array<uint32_t, 2> pad_;
const uint32_t group_;
};
} // namespace ops
} // namespace vx
} // namespace tim
#endif /* TIM_VX_OPS_DECONV1D_H_ */

2
src/tim/vx/ops/README.md
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@ -148,7 +148,7 @@ Mish|MISH|Mapped|[tfa.activations.mish](https://tensorflow.google.cn/addons/api_
||PRE_PROCESS_YUV444|Unmapped ||PRE_PROCESS_YUV444|Unmapped
||PRE_PROCESS_NV12|Unmapped ||PRE_PROCESS_NV12|Unmapped
||SCATTER_ND|Unmapped|[tf.scatter_nd](https://tensorflow.google.cn/api_docs/python/tf/scatter_nd) ||SCATTER_ND|Unmapped|[tf.scatter_nd](https://tensorflow.google.cn/api_docs/python/tf/scatter_nd)
||DECONVOLUTION1D|Unmapped|[tf.nn.conv1d_transpose](https://tensorflow.google.cn/api_docs/python/tf/nn/conv1d_transpose) |DeConv1d|DECONVOLUTION1D|Mapped|[tf.nn.conv1d_transpose](https://tensorflow.google.cn/api_docs/python/tf/nn/conv1d_transpose)
||INTERP|Unmapped ||INTERP|Unmapped
||RESIZE_1D|Unmapped ||RESIZE_1D|Unmapped
||CONV_RELU|Deprecated ||CONV_RELU|Deprecated

66
src/tim/vx/ops/deconv1d.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/deconv1d.h"
#include <cassert>
#include "operation_private.h"
#include "type_utils.h"
#include "vsi_nn_pub.h"
namespace tim {
namespace vx {
namespace ops {
DeConv1d::DeConv1d(Graph* graph, int32_t oc_count, PadType pad_type,
uint32_t ksize, uint32_t stride, uint32_t output_padding)
: DeConv1d(graph, oc_count, pad_type, ksize, stride, output_padding,
{0, 0}) {
}
DeConv1d::DeConv1d(Graph* graph, int32_t oc_count, PadType pad_type,
uint32_t ksize, uint32_t stride, uint32_t output_padding,
const std::array<uint32_t, 2>& pad, uint32_t group)
: Operation(graph, VSI_NN_OP_DECONVOLUTION1D),
oc_count_(oc_count),
pad_type_(pad_type),
ksize_(ksize),
stride_(stride),
output_padding_(output_padding),
pad_(pad),
group_(group) {
this->impl()->node()->nn_param.deconvolution1d.ksize = ksize_;
this->impl()->node()->nn_param.deconvolution1d.stride = stride_;
this->impl()->node()->nn_param.deconvolution1d.pad_type = TranslatePadType(pad_type_);
this->impl()->node()->nn_param.deconvolution1d.weights = oc_count_;
this->impl()->node()->nn_param.deconvolution1d.group = group_;
this->impl()->node()->nn_param.deconvolution1d.output_padding = output_padding_;
this->impl()->node()->nn_param.deconvolution1d.pad[0] = pad_[0];
this->impl()->node()->nn_param.deconvolution1d.pad[1] = pad_[1];
}
} // namespace ops
} // namespace vx
} // namespace tim

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src/tim/vx/ops/deconv1d_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/deconv1d.h"
#include "gtest/gtest.h"
TEST(DeConv1d, no_bias_layout_whcn_depthwise_shape_3_2_1) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape ({3, 2, 1}); //whcn
tim::vx::ShapeType kernel_shape({3, 2, 1}); //whc1 same as depthwise convolution
tim::vx::ShapeType output_shape({5, 2, 1}); //whcn
tim::vx::TensorSpec input_spec (tim::vx::DataType::FLOAT32, input_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec kernel_spec (tim::vx::DataType::FLOAT32, kernel_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 output_tensor = graph->CreateTensor(output_spec);
auto kernel_tensor = graph->CreateTensor(kernel_spec);
std::vector<float> input_data = { 3.0f, 9.0f, 3.0f,
7.0f, 5.0f, 9.0f, };
std::vector<float> kernel_data = { 9.0f, 0.0f, 1.0f,
3.0f, 0.0f, 0.0f, };
std::vector<float> golden = {
27.0f, 81.0f, 30.0f, 9.0f, 3.0f,
21.0f, 15.0f, 27.0f, 0.0f, 0.0f, };
std::vector<float> output_data(golden.size());
EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size()*4));
EXPECT_TRUE(kernel_tensor->CopyDataToTensor(kernel_data.data(), kernel_data.size()*4));
auto op = graph->CreateOperation<tim::vx::ops::DeConv1d>(
2, tim::vx::PadType::SAME, 3, 1, 1, std::array<uint32_t, 2>({0, 0}), 2);
(*op).BindInputs({input_tensor, kernel_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_data.data()));
EXPECT_EQ(golden, output_data) << "Result mismatch";
}
TEST(DeConv1d, layout_whcn_shape_3_1_1) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape ({3, 1, 1});
tim::vx::ShapeType kernel_shape({3, 1, 1});
tim::vx::ShapeType output_shape({5, 1, 1});
tim::vx::ShapeType bias_shape({1});
tim::vx::Quantization input_quant(tim::vx::QuantType::ASYMMETRIC, 1.0f, 0);
tim::vx::Quantization output_quant(tim::vx::QuantType::ASYMMETRIC, 1.0f, 2);
tim::vx::Quantization weight_quant(tim::vx::QuantType::ASYMMETRIC, 1.0f, 0);
tim::vx::Quantization bias_quant(tim::vx::QuantType::ASYMMETRIC, 1.0f, 0);
tim::vx::TensorSpec input_spec (
tim::vx::DataType::UINT8, input_shape, tim::vx::TensorAttribute::INPUT, input_quant);
tim::vx::TensorSpec kernel_spec (
tim::vx::DataType::UINT8, kernel_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 output_tensor = graph->CreateTensor(output_spec);
auto kernel_tensor = graph->CreateTensor(kernel_spec);
auto bias_tensor = graph->CreateTensor(bias_spec);
std::vector<uint8_t> input_data = {
3, 9, 3,
};
std::vector<uint8_t> kernel_data = {
9, 0, 1,
};
std::vector<int32_t> bias_data = {
-5,
};
std::vector<uint8_t> golden = {
24, 78, 27, 6, 0,
};
std::vector<uint8_t> output_data(golden.size());
EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size()));
EXPECT_TRUE(kernel_tensor->CopyDataToTensor(kernel_data.data(), kernel_data.size()));
EXPECT_TRUE(bias_tensor->CopyDataToTensor(bias_data.data(), bias_data.size() * sizeof(int32_t)));
auto op = graph->CreateOperation<tim::vx::ops::DeConv1d>(
1, tim::vx::PadType::SAME, 3, 1, 1,
std::array<uint32_t, 2>({0, 0,}),
1);
(*op).BindInputs({input_tensor, kernel_tensor, bias_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_data.data()));
EXPECT_EQ(golden, output_data) << "Result mismatch";
}