1 //
2 // Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5
6 #include "Conv3dImpl.hpp"
7
8 namespace armnn
9 {
10
Convolve3d(const TensorShape & rInputShape,Decoder<float> & rInputDecoder,const TensorShape & rOutputShape,Encoder<float> & rOutputEncoder,const TensorShape & rFilterShape,Decoder<float> & rFilterDecoder,bool biasEnabled,Decoder<float> * pBiasDecoder,DataLayout dataLayout,unsigned int paddingTop,unsigned int paddingLeft,unsigned int paddingFront,unsigned int xStride,unsigned int yStride,unsigned int zStride,unsigned int xDilation,unsigned int yDilation,unsigned int zDilation)11 void Convolve3d(const TensorShape& rInputShape,
12 Decoder<float>& rInputDecoder,
13 const TensorShape& rOutputShape,
14 Encoder<float>& rOutputEncoder,
15 const TensorShape& rFilterShape,
16 Decoder<float>& rFilterDecoder,
17 bool biasEnabled,
18 Decoder<float>* pBiasDecoder,
19 DataLayout dataLayout,
20 unsigned int paddingTop,
21 unsigned int paddingLeft,
22 unsigned int paddingFront,
23 unsigned int xStride,
24 unsigned int yStride,
25 unsigned int zStride,
26 unsigned int xDilation,
27 unsigned int yDilation,
28 unsigned int zDilation)
29 {
30 if (biasEnabled && !pBiasDecoder)
31 {
32 throw InvalidArgumentException("Bias is enabled but the bias data is invalid");
33 }
34 const armnnUtils::DataLayoutIndexed dataLayoutIndexed(dataLayout);
35
36 const unsigned int channelsIndex = dataLayoutIndexed.GetChannelsIndex();
37 const unsigned int heightIndex = dataLayoutIndexed.GetHeightIndex();
38 const unsigned int widthIndex = dataLayoutIndexed.GetWidthIndex();
39 const unsigned int depthIndex = dataLayoutIndexed.GetDepthIndex();
40
41 const unsigned int inChannels = rInputShape[channelsIndex];
42 const unsigned int outChannels = rOutputShape[channelsIndex];
43
44 const unsigned int batchSize = rOutputShape[0];
45 const unsigned int outputHeight = rOutputShape[heightIndex];
46 const unsigned int outputWidth = rOutputShape[widthIndex];
47 const unsigned int outputDepth = rOutputShape[depthIndex];
48 const unsigned int inputHeight = rInputShape[heightIndex];
49 const unsigned int inputWidth = rInputShape[widthIndex];
50 const unsigned int inputDepth = rInputShape[depthIndex];
51
52 // Conv3d weights layout: [D,H,W,I,O]
53 const unsigned int filterDepth = rFilterShape[0];
54 const unsigned int filterHeight = rFilterShape[1];
55 const unsigned int filterWidth = rFilterShape[2];
56
57 const std::vector<float> inputVec = rInputDecoder.DecodeTensor(rInputShape);
58 const std::vector<float> filterVec = rFilterDecoder.DecodeTensor(rFilterShape);
59
60 const TensorShape biasShape{outChannels};
61 const std::vector<float> biasVec = biasEnabled ? pBiasDecoder->DecodeTensor(biasShape) : std::vector<float>();
62
63 for (unsigned int batchIdx = 0; batchIdx < batchSize; batchIdx++)
64 {
65 for (unsigned int zOutput = 0; zOutput < outputDepth; zOutput++)
66 {
67 for (unsigned int xOutput = 0; xOutput < outputWidth; xOutput++)
68 {
69 for (unsigned int yOutput = 0; yOutput < outputHeight; yOutput++)
70 {
71 for (unsigned int cOutput = 0; cOutput < outChannels; cOutput++)
72 {
73 // This loop goes over each output element.
74 float sum = 0.0f;
75
76 // Loop over each input channel.
77 for (unsigned int zFilter = 0; zFilter < filterDepth; zFilter++)
78 {
79 for (unsigned int yFilter = 0; yFilter < filterHeight; yFilter++)
80 {
81 for (unsigned int xFilter = 0; xFilter < filterWidth; xFilter++)
82 {
83 for (unsigned int cInput = 0; cInput < inChannels; cInput++)
84 {
85 // This loop goes over each input element for each output element.
86 unsigned int filterIndex = 0;
87
88 // Conv3d weights layout: [D,H,W,I,O]
89 // Keep this implementation, as using DataLayoutIndexed::GetIndex
90 // causes large performance regression.
91 filterIndex = zFilter * filterHeight * filterWidth * inChannels * outChannels +
92 yFilter * filterWidth * inChannels * outChannels +
93 xFilter * inChannels * outChannels +
94 cInput * outChannels +
95 cOutput;
96
97 unsigned int yInput = yOutput * yStride + yFilter * yDilation;
98 unsigned int xInput = xOutput * xStride + xFilter * xDilation;
99 unsigned int zInput = zOutput * zStride + zFilter * zDilation;
100
101 float inputValue;
102
103 // Check if we're in the padding.
104 if (yInput < paddingTop || yInput >= inputHeight + paddingTop ||
105 xInput < paddingLeft || xInput >= inputWidth + paddingLeft ||
106 zInput < paddingFront || zInput >= inputDepth + paddingFront)
107 {
108 inputValue = 0.0f;
109 }
110 else
111 {
112 unsigned int inputIndex = 0;
113
114 // Keep this implementation, as using DataLayoutIndexed::GetIndex
115 // causes large performance regression.
116 if (dataLayoutIndexed.GetDataLayout() == DataLayout::NDHWC)
117 {
118 inputIndex =
119 batchIdx * inputDepth * inputHeight * inputWidth * inChannels +
120 (zInput-paddingFront) * inputHeight * inputWidth * inChannels +
121 (yInput-paddingTop) * inputWidth * inChannels +
122 (xInput-paddingLeft) * inChannels +
123 cInput;
124 }
125 else
126 {
127 // NCDHW DataLayout
128 inputIndex =
129 batchIdx * inputDepth * inputHeight * inputWidth * inChannels +
130 inputDepth * inputHeight * inputWidth * cInput +
131 (zInput-paddingFront) * inputHeight * inputWidth +
132 (yInput-paddingTop) * inputWidth +
133 xInput-paddingLeft;
134 }
135
136 inputValue = inputVec[inputIndex];
137 }
138
139 sum += filterVec[filterIndex] * inputValue;
140 }
141 }
142 }
143 }
144
145 if (biasEnabled)
146 {
147 sum += biasVec[cOutput];
148 }
149
150 unsigned int outIdx;
151 if (dataLayoutIndexed.GetDataLayout() == DataLayout::NDHWC)
152 {
153 outIdx = batchIdx * outputDepth * outputHeight * outputWidth * outChannels +
154 zOutput * outputHeight * outputWidth * outChannels +
155 yOutput * outputWidth * outChannels +
156 xOutput * outChannels +
157 cOutput;
158 }
159 else
160 {
161 // NCDHW DataLayout
162 outIdx = batchIdx * outputDepth * outputHeight * outputWidth * outChannels +
163 cOutput * outputDepth * outputHeight * outputWidth +
164 zOutput * outputHeight * outputWidth +
165 yOutput * outputWidth +
166 xOutput;
167 }
168
169 rOutputEncoder[outIdx];
170 rOutputEncoder.Set(sum);
171 }
172 }
173 }
174 }
175 }
176 }
177
178 } // namespace armnn
179