From 8efb48a6847c5cd166c561127ae6611150963ce3 Mon Sep 17 00:00:00 2001 From: Nikhil Raj Date: Fri, 19 May 2023 11:14:28 +0100 Subject: Update Doxygen docu for 23.05 Signed-off-by: Nikhil Raj Change-Id: I0a992286f14fa68fcc6e5eba31ac39fed003cbbe --- 23.05/_transpose_convolution2d_8cpp_source.xhtml | 303 +++++++++++++++++++++++ 1 file changed, 303 insertions(+) create mode 100644 23.05/_transpose_convolution2d_8cpp_source.xhtml (limited to '23.05/_transpose_convolution2d_8cpp_source.xhtml') diff --git a/23.05/_transpose_convolution2d_8cpp_source.xhtml b/23.05/_transpose_convolution2d_8cpp_source.xhtml new file mode 100644 index 0000000000..58e7a5f737 --- /dev/null +++ b/23.05/_transpose_convolution2d_8cpp_source.xhtml @@ -0,0 +1,303 @@ + + + + + + + + + + + + + +ArmNN: src/backends/reference/workloads/TransposeConvolution2d.cpp Source File + + + + + + + + + + + + + + + + +
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+ + + + ArmNN + + + +
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+  23.05 +
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+
TransposeConvolution2d.cpp
+
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+Go to the documentation of this file.
1 //
+
2 // Copyright © 2017 Arm Ltd. All rights reserved.
+
3 // SPDX-License-Identifier: MIT
+
4 //
+
5 
+
6 #include "TransposeConvolution2d.hpp"
+
7 
+
8 #include <armnnUtils/DataLayoutIndexed.hpp>
+
9 
+
10 namespace armnn
+
11 {
+
12 
+
13 using namespace armnnUtils;
+
14 
+
15 void TransposeConvolution2dImpl(const TransposeConvolution2dDescriptor& descriptor,
+
16  const TensorShape& inputShape,
+
17  Decoder<float>& inputDecoder,
+
18  const TensorShape& outputShape,
+
19  Encoder<float>& outputEncoder,
+
20  const TensorShape& weightsShape,
+
21  Decoder<float>& weightsDecoder,
+
22  Decoder<float>* biasesDecoder)
+
23 {
+
24  if (descriptor.m_BiasEnabled && !biasesDecoder)
+
25  {
+
26  throw InvalidArgumentException("Biases enabled but no bias data provided");
+
27  }
+
28  const DataLayoutIndexed dataLayoutIndexed(descriptor.m_DataLayout);
+
29  const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
+
30  const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
+
31  const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
+
32 
+
33  const unsigned int numBatches = inputShape[0];
+
34 
+
35  const unsigned int inputWidth = inputShape[widthIndex];
+
36  const unsigned int inputHeight = inputShape[heightIndex];
+
37  const unsigned int inputDepth = inputShape[channelsIndex];
+
38 
+
39  const unsigned int weightsHeight = weightsShape[heightIndex];
+
40  const unsigned int weightsWidth = weightsShape[widthIndex];
+
41  const unsigned int weightsDepth = weightsShape[channelsIndex];
+
42 
+
43  const unsigned int outputHeight = outputShape[heightIndex];
+
44  const unsigned int outputWidth = outputShape[widthIndex];
+
45  const unsigned int outputDepth = outputShape[channelsIndex];
+
46 
+
47  const unsigned int paddingLeft = descriptor.m_PadLeft;
+
48  const unsigned int paddingTop = descriptor.m_PadTop;
+
49 
+
50  const unsigned int strideX = descriptor.m_StrideX;
+
51  const unsigned int strideY = descriptor.m_StrideY;
+
52 
+
53  std::vector<float> outputBuffer(outputShape.GetNumElements(), 0);
+
54 
+
55  const std::vector<float> inputVec = inputDecoder.DecodeTensor(inputShape);
+
56  const std::vector<float> filterVec = weightsDecoder.DecodeTensor(weightsShape);
+
57 
+
58  for (unsigned int batch = 0u; batch < numBatches; ++batch)
+
59  {
+
60  for (unsigned int yInput = 0u; yInput < inputHeight; ++yInput)
+
61  {
+
62  for (unsigned int xInput = 0u; xInput < inputWidth; ++xInput)
+
63  {
+
64  unsigned int xOutputOrigin = xInput * strideX - paddingLeft;
+
65  unsigned int yOutputOrigin = yInput * strideY - paddingTop;
+
66 
+
67  for (unsigned int dOutput = 0u; dOutput < outputDepth; ++dOutput)
+
68  {
+
69  for (unsigned int yWeights = 0u; yWeights < weightsHeight; ++yWeights)
+
70  {
+
71  for (unsigned int xWeights = 0u; xWeights < weightsWidth; ++xWeights)
+
72  {
+
73  unsigned int yOutput = yOutputOrigin + yWeights;
+
74  unsigned int xOutput = xOutputOrigin + xWeights;
+
75 
+
76  if (yOutput < outputHeight && xOutput< outputWidth)
+
77  {
+
78  for (unsigned int dInput = 0u; dInput < inputDepth; dInput++)
+
79  {
+
80  unsigned int inputIndex;
+
81  unsigned int outputIndex;
+
82  unsigned int weightsIndex;
+
83 
+
84  if(descriptor.m_DataLayout == armnn::DataLayout::NHWC)
+
85  {
+
86  inputIndex = batch * inputHeight * inputWidth * inputDepth +
+
87  yInput * inputWidth * inputDepth +
+
88  xInput * inputDepth +
+
89  dInput;
+
90 
+
91  weightsIndex = dOutput * weightsHeight * weightsWidth * weightsDepth +
+
92  yWeights * weightsWidth * weightsDepth +
+
93  xWeights * weightsDepth +
+
94  dInput;
+
95 
+
96  outputIndex = batch * outputHeight * outputWidth * outputDepth +
+
97  yOutput * outputWidth * outputDepth +
+
98  xOutput * outputDepth +
+
99  dOutput;
+
100  }
+
101  else
+
102  {
+
103  inputIndex = batch * inputDepth * inputHeight * inputWidth +
+
104  dInput * inputHeight * inputWidth +
+
105  yInput * inputWidth +
+
106  xInput;
+
107 
+
108  weightsIndex = dOutput * weightsDepth * weightsHeight * weightsWidth +
+
109  dInput * weightsHeight * weightsWidth +
+
110  yWeights * weightsWidth +
+
111  xWeights;
+
112 
+
113  outputIndex = batch * outputDepth * outputHeight * outputWidth +
+
114  dOutput * outputHeight * outputWidth +
+
115  yOutput * outputWidth +
+
116  xOutput;
+
117  }
+
118 
+
119  outputBuffer[outputIndex] += inputVec[inputIndex] * filterVec[weightsIndex];
+
120  }
+
121  }
+
122  }
+
123  }
+
124 
+
125  }
+
126  }
+
127  }
+
128  }
+
129 
+
130  // Apply bias (if enabled)
+
131  if (descriptor.m_BiasEnabled)
+
132  {
+
133  outputEncoder[0];
+
134  Decoder<float>& rBiasesDecoder = *biasesDecoder;
+
135 
+
136  for (unsigned int batch = 0u; batch < numBatches; ++batch)
+
137  {
+
138  for (unsigned int dOutput = 0u; dOutput < outputDepth; ++dOutput)
+
139  {
+
140  rBiasesDecoder[dOutput];
+
141  for (unsigned int yOutput = 0u; yOutput < outputHeight; ++yOutput)
+
142  {
+
143  for (unsigned int xOutput = 0u; xOutput < outputWidth; ++xOutput)
+
144  {
+
145  const unsigned int outputIndex =
+
146  dataLayoutIndexed.GetIndex(outputShape, batch, dOutput, yOutput, xOutput);
+
147  outputBuffer[outputIndex] += rBiasesDecoder.Get();
+
148  }
+
149  }
+
150  }
+
151  }
+
152  }
+
153  outputEncoder[0];
+
154  for (float output : outputBuffer)
+
155  {
+
156  outputEncoder.Set(output);
+
157  ++outputEncoder;
+
158  }
+
159 }
+
160 
+
161 } // namespace armnn
+
+
+
armnnUtils::DataLayoutIndexed::GetIndex
unsigned int GetIndex(const armnn::TensorShape &shape, unsigned int batchIndex, unsigned int channelIndex, unsigned int heightIndex, unsigned int widthIndex) const
Definition: DataLayoutIndexed.hpp:28
+ +
armnn::TransposeConvolution2dDescriptor::m_PadLeft
uint32_t m_PadLeft
Padding left value in the width dimension.
Definition: Descriptors.hpp:1436
+
armnn::TransposeConvolution2dDescriptor::m_PadTop
uint32_t m_PadTop
Padding top value in the height dimension.
Definition: Descriptors.hpp:1440
+ +
armnn::TensorShape::GetNumElements
unsigned int GetNumElements() const
Function that calculates the tensor elements by multiplying all dimension size which are Specified.
Definition: Tensor.cpp:181
+ +
armnn::TransposeConvolution2dDescriptor::m_BiasEnabled
bool m_BiasEnabled
Enable/disable bias.
Definition: Descriptors.hpp:1448
+
armnnUtils::DataLayoutIndexed::GetWidthIndex
unsigned int GetWidthIndex() const
Definition: DataLayoutIndexed.hpp:25
+
armnn
Copyright (c) 2021 ARM Limited and Contributors.
Definition: 01_00_quick_start.dox:6
+
armnnUtils::DataLayoutIndexed::GetChannelsIndex
unsigned int GetChannelsIndex() const
Definition: DataLayoutIndexed.hpp:23
+ + + + +
armnn::Encoder::Set
virtual void Set(IType right)=0
+
armnn::TransposeConvolution2dDescriptor::m_DataLayout
DataLayout m_DataLayout
The data layout to be used (NCHW, NHWC).
Definition: Descriptors.hpp:1450
+
armnn::TransposeConvolution2dDescriptor
A TransposeConvolution2dDescriptor for the TransposeConvolution2dLayer.
Definition: Descriptors.hpp:1407
+
armnnUtils::DataLayoutIndexed
Provides access to the appropriate indexes for Channels, Height and Width based on DataLayout.
Definition: DataLayoutIndexed.hpp:17
+
armnnUtils::DataLayoutIndexed::GetHeightIndex
unsigned int GetHeightIndex() const
Definition: DataLayoutIndexed.hpp:24
+
armnn::TransposeConvolution2dDescriptor::m_StrideX
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
Definition: Descriptors.hpp:1444
+
armnn::Decoder::Get
virtual IType Get() const =0
+
armnn::TransposeConvolution2dDescriptor::m_StrideY
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
Definition: Descriptors.hpp:1446
+
armnn::TransposeConvolution2dImpl
void TransposeConvolution2dImpl(const TransposeConvolution2dDescriptor &descriptor, const TensorShape &inputShape, Decoder< float > &inputDecoder, const TensorShape &outputShape, Encoder< float > &outputEncoder, const TensorShape &weightsShape, Decoder< float > &weightsDecoder, Decoder< float > *biasesDecoder)
Definition: TransposeConvolution2d.cpp:15
+
armnn::Decoder::DecodeTensor
virtual std::vector< float > DecodeTensor(const TensorShape &tensorShape, bool isDepthwise=false)=0
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