MyCaffe  1.12.2.41
Deep learning software for Windows C# programmers.
LayerFactory.cs
1using MyCaffe.basecode;
2using MyCaffe.common;
3using MyCaffe.param;
4
8namespace MyCaffe.layers.beta
9{
13 public class LayerFactory : ILayerCreator
14 {
24 public Layer<double> CreateDouble(CudaDnn<double> cuda, Log log, LayerParameter p, CancelEvent evtCancel, IXDatabaseBase db)
25 {
26 switch (p.type)
27 {
28 case LayerParameter.LayerType.ACCURACY_DECODE:
29 return new AccuracyDecodeLayer<double>(cuda, log, p);
30
31 case LayerParameter.LayerType.ACCURACY_ENCODING:
32 return new AccuracyEncodingLayer<double>(cuda, log, p);
33
34 case LayerParameter.LayerType.ATTENTION:
35 return new AttentionLayer<double>(cuda, log, p);
36
37 case LayerParameter.LayerType.CONVOLUTION_OCTAVE:
38 return new ConvolutionOctaveLayer<double>(cuda, log, p);
39
40 case LayerParameter.LayerType.COPY:
41 return new CopyLayer<double>(cuda, log, p);
42
43 case LayerParameter.LayerType.DATA_SEQUENCE:
44 return new DataSequenceLayer<double>(cuda, log, p);
45
46 case LayerParameter.LayerType.DECODE:
47 return new DecodeLayer<double>(cuda, log, p);
48
49 case LayerParameter.LayerType.GATHER:
50 return new GatherLayer<double>(cuda, log, p);
51
52 case LayerParameter.LayerType.GLOBRES_NORM:
53 return new GlobResNormLayer<double>(cuda, log, p);
54
55 case LayerParameter.LayerType.INTERP:
56 return new InterpLayer<double>(cuda, log, p);
57
58 case LayerParameter.LayerType.KNN:
59 return new KnnLayer<double>(cuda, log, p);
60
61 case LayerParameter.LayerType.LSTM_ATTENTION:
62 return new LSTMAttentionLayer<double>(cuda, log, p);
63
64 case LayerParameter.LayerType.MEAN_ERROR_LOSS:
65 return new MeanErrorLossLayer<double>(cuda, log, p);
66
67 case LayerParameter.LayerType.MERGE:
68 return new MergeLayer<double>(cuda, log, p);
69
70 case LayerParameter.LayerType.MISH:
71 return new MishLayer<double>(cuda, log, p);
72
73 case LayerParameter.LayerType.NORMALIZATION1:
74 return new Normalization1Layer<double>(cuda, log, p);
75
76 case LayerParameter.LayerType.MODEL_DATA:
77 return new ModelDataLayer<double>(cuda, log, p, db, evtCancel);
78
79 case LayerParameter.LayerType.SERF:
80 return new SerfLayer<double>(cuda, log, p);
81
82 case LayerParameter.LayerType.TEXT_DATA:
83 return new TextDataLayer<double>(cuda, log, p);
84
85 case LayerParameter.LayerType.TRANSPOSE:
86 return new TransposeLayer<double>(cuda, log, p);
87
88 case LayerParameter.LayerType.TRIPLET_LOSS:
89 return new TripletLossLayer<double>(cuda, log, p);
90
91 case LayerParameter.LayerType.SQUEEZE:
92 return new SqueezeLayer<double>(cuda, log, p);
93
94 case LayerParameter.LayerType.UNSQUEEZE:
95 return new UnsqueezeLayer<double>(cuda, log, p);
96
97 case LayerParameter.LayerType.UNPOOLING1:
98 return new UnPoolingLayer1<double>(cuda, log, p);
99
100 case LayerParameter.LayerType.UNPOOLING:
101 return new UnPoolingLayer<double>(cuda, log, p);
102
103 default:
104 return null;
105 }
106 }
107
117 public Layer<float> CreateSingle(CudaDnn<float> cuda, Log log, LayerParameter p, CancelEvent evtCancel, IXDatabaseBase db)
118 {
119 switch (p.type)
120 {
121 case LayerParameter.LayerType.ACCURACY_DECODE:
122 return new AccuracyDecodeLayer<float>(cuda, log, p);
123
124 case LayerParameter.LayerType.ACCURACY_ENCODING:
125 return new AccuracyEncodingLayer<float>(cuda, log, p);
126
127 case LayerParameter.LayerType.ATTENTION:
128 return new AttentionLayer<float>(cuda, log, p);
129
130 case LayerParameter.LayerType.CONVOLUTION_OCTAVE:
131 return new ConvolutionOctaveLayer<float>(cuda, log, p);
132
133 case LayerParameter.LayerType.COPY:
134 return new CopyLayer<float>(cuda, log, p);
135
136 case LayerParameter.LayerType.DATA_SEQUENCE:
137 return new DataSequenceLayer<float>(cuda, log, p);
138
139 case LayerParameter.LayerType.DECODE:
140 return new DecodeLayer<float>(cuda, log, p);
141
142 case LayerParameter.LayerType.GATHER:
143 return new GatherLayer<float>(cuda, log, p);
144
145 case LayerParameter.LayerType.GLOBRES_NORM:
146 return new GlobResNormLayer<float>(cuda, log, p);
147
148 case LayerParameter.LayerType.INTERP:
149 return new InterpLayer<float>(cuda, log, p);
150
151 case LayerParameter.LayerType.KNN:
152 return new KnnLayer<float>(cuda, log, p);
153
154 case LayerParameter.LayerType.LSTM_ATTENTION:
155 return new LSTMAttentionLayer<float>(cuda, log, p);
156
157 case LayerParameter.LayerType.MEAN_ERROR_LOSS:
158 return new MeanErrorLossLayer<float>(cuda, log, p);
159
160 case LayerParameter.LayerType.MERGE:
161 return new MergeLayer<float>(cuda, log, p);
162
163 case LayerParameter.LayerType.MISH:
164 return new MishLayer<float>(cuda, log, p);
165
166 case LayerParameter.LayerType.NORMALIZATION1:
167 return new Normalization1Layer<float>(cuda, log, p);
168
169 case LayerParameter.LayerType.MODEL_DATA:
170 return new ModelDataLayer<float>(cuda, log, p, db, evtCancel);
171
172 case LayerParameter.LayerType.SERF:
173 return new SerfLayer<float>(cuda, log, p);
174
175 case LayerParameter.LayerType.TEXT_DATA:
176 return new TextDataLayer<float>(cuda, log, p);
177
178 case LayerParameter.LayerType.TRANSPOSE:
179 return new TransposeLayer<float>(cuda, log, p);
180
181 case LayerParameter.LayerType.TRIPLET_LOSS:
182 return new TripletLossLayer<float>(cuda, log, p);
183
184 case LayerParameter.LayerType.SQUEEZE:
185 return new SqueezeLayer<float>(cuda, log, p);
186
187 case LayerParameter.LayerType.UNSQUEEZE:
188 return new UnsqueezeLayer<float>(cuda, log, p);
189
190 case LayerParameter.LayerType.UNPOOLING1:
191 return new UnPoolingLayer1<float>(cuda, log, p);
192
193 case LayerParameter.LayerType.UNPOOLING:
194 return new UnPoolingLayer<float>(cuda, log, p);
195
196 default:
197 return null;
198 }
199 }
200 }
201}
MyCaffe.basecode.CancelEvent
The CancelEvent provides an extension to the manual cancel event that allows for overriding the manua...
Definition: CancelEvent.cs:17
MyCaffe.basecode.Log
The Log class provides general output in text form.
Definition: Log.cs:13
MyCaffe.common.CudaDnn
The CudaDnn object is the main interface to the Low-Level Cuda C++ DLL.
Definition: CudaDnn.cs:969
MyCaffe.layers.AttentionLayer
[DEPRECIATED] The AttentionLayer provides focus for LSTM based encoder/decoder models.
Definition: AttentionLayer.cs:31
MyCaffe.layers.CopyLayer
The CopyLayer copies the src bottom to the dst bottom. The layer has no output.
Definition: CopyLayer.cs:16
MyCaffe.layers.LSTMAttentionLayer
The LSTMAttentionLayer adds attention to the long-short term memory layer and is used in encoder/deco...
Definition: LSTMAttentionLayer.cs:45
MyCaffe.layers.Layer
An interface for the units of computation which can be composed into a Net.
Definition: Layer.cs:31
MyCaffe.layers.beta.AccuracyDecodeLayer
The AccuracyDecodeLayer compares the labels output by the DecodeLayer with the expected labels output...
Definition: AccuracyDecodeLayer.cs:18
MyCaffe.layers.beta.AccuracyEncodingLayer
The AccuracyEncodingLayer computes the classification accuracy for an encoding used in a classificati...
Definition: AccuracyEncodingLayer.cs:19
MyCaffe.layers.beta.ConvolutionOctaveLayer
The ConvolutionOctaveLayer processes high and low frequency portions of images using convolution.
Definition: ConvolutionOctaveLayer.cs:21
MyCaffe.layers.beta.DataSequenceLayer
DataSequence Layer - this caches inputs by label and then outputs data item tuplets that include an '...
Definition: DataSequenceLayer.cs:30
MyCaffe.layers.beta.DecodeLayer
The DecodeLayer decodes the label of a classification for an encoding produced by a Siamese Network o...
Definition: DecodeLayer.cs:25
MyCaffe.layers.beta.GatherLayer
The GatherLayer extracts (gathers) data from specified indices along a given axis from the input and ...
Definition: GatherLayer.cs:19
MyCaffe.layers.beta.GlobResNormLayer
The GRNLayer performs an L2 normalization over the input data.
Definition: GlobResNormLayer.cs:21
MyCaffe.layers.beta.InterpLayer
The InterpLayer changes the spatial resolution by bi-linear interpolation.
Definition: InterpLayer.cs:23
MyCaffe.layers.beta.LayerFactory
The LayerFactor is responsible for creating all layers implemented in the MyCaffe....
Definition: LayerFactory.cs:14
MyCaffe.layers.beta.LayerFactory.CreateDouble
Layer< double > CreateDouble(CudaDnn< double > cuda, Log log, LayerParameter p, CancelEvent evtCancel, IXDatabaseBase db)
Create the layers when using the double base type.
Definition: LayerFactory.cs:24
MyCaffe.layers.beta.LayerFactory.CreateSingle
Layer< float > CreateSingle(CudaDnn< float > cuda, Log log, LayerParameter p, CancelEvent evtCancel, IXDatabaseBase db)
Create the layers when using the float base type.
Definition: LayerFactory.cs:117
MyCaffe.layers.beta.MeanErrorLossLayer
The MeanErrorLossLayer computes losses based on various different Mean Error methods as shown below....
Definition: MeanErrorLossLayer.cs:29
MyCaffe.layers.beta.MergeLayer
The MergeLayer merges two bottom blobs with a specified copy pattern and outputs a single blob result...
Definition: MergeLayer.cs:20
MyCaffe.layers.beta.MishLayer
The MishLayer provides a novel activation function that tends to work better than ReLU....
Definition: MishLayer.cs:26
MyCaffe.layers.beta.ModelDataLayer
The ModelDataLayer loads data from RawImageResults table for an encoder/decoder type model.
Definition: ModelDataLayer.cs:20
MyCaffe.layers.beta.Normalization1Layer
The Normalization1Layer performs an L2 normalization over the input data. This layer is initialized w...
Definition: Normalization1Layer.cs:21
MyCaffe.layers.beta.SerfLayer
The SerfLayer provides a novel activation function that tends to work better than ReLU.
Definition: SerfLayer.cs:22
MyCaffe.layers.beta.SqueezeLayer
The SqueezeLayer performs a squeeze operation where all single dimensions are removed.
Definition: SqueezeLayer.cs:17
MyCaffe.layers.beta.TextDataLayer
The TextDataLayer loads data from text data files for an encoder/decoder type model....
Definition: TextDataLayer.cs:20
MyCaffe.layers.beta.TransposeLayer
The TransposeLayer performs a permute and transpose operation similar to numpy.transpose.
Definition: TransposeLayer.cs:20
MyCaffe.layers.beta.TripletLossLayer
TripletLoss Layer - this is the triplet loss layer used to calculate the triplet loss and gradients u...
Definition: TripletLossLayer.cs:42
MyCaffe.layers.beta.UnsqueezeLayer
The UnsqueezeLayer performs an unsqueeze operation where a single dimension is inserted at each index...
Definition: UnsqueezeLayer.cs:17
MyCaffe.param.LayerParameter
Specifies the base parameter for all layers.
Definition: LayerParameter.cs:24
MyCaffe.param.LayerParameter.type
LayerType type
Specifies the type of this LayerParameter.
Definition: LayerParameter.cs:1874
MyCaffe.param.LayerParameter.LayerType
LayerType
Specifies the layer type.
Definition: LayerParameter.cs:110
MyCaffe.ILayerCreator
The ILayerCreator interface is implemented by each MyCaffe.layers.x layer extension dll and is used t...
Definition: Interfaces.cs:19
MyCaffe.basecode.IXDatabaseBase
The IXDatabaseBase interface defines the general interface to the in-memory database.
Definition: Interfaces.cs:444
MyCaffe.basecode
The MyCaffe.basecode contains all generic types used throughout MyCaffe.
Definition: Annotation.cs:12
MyCaffe.common
The MyCaffe.common namespace contains common MyCaffe classes.
Definition: BatchInput.cs:8
MyCaffe.layers.beta
The MyCaffe.layers.beta namespace contains all beta stage layers.
Definition: LayerFactory.cs:9
MyCaffe.param
The MyCaffe.param namespace contains parameters used to create models.
Definition: AttentionParameter.cs:9
MyCaffe
The MyCaffe namespace contains the main body of MyCaffe code that closesly tracks the C++ Caffe open-...
Definition: Annotation.cs:12