-
Notifications
You must be signed in to change notification settings - Fork 2
/
createUnet.m
146 lines (104 loc) · 6.11 KB
/
createUnet.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
function lgraph = createUnet()
% EDIT: modify these parameters for your application.
encoderDepth = 4;
initialEncoderNumChannels = 64;
inputTileSize = [256 256 2];
convFilterSize = [3 3];
inputNumChannels = 2;
numClasses = 2;
inputlayer = imageInputLayer(inputTileSize,'Name','ImageInputTile');
[encoder, finalNumChannels] = iCreateEncoder(encoderDepth, convFilterSize, initialEncoderNumChannels, inputNumChannels);
firstConv = createAndInitializeConvLayer(convFilterSize, finalNumChannels, ...
2*finalNumChannels, 'Bridge-Conv-1');
firstReLU = reluLayer('Name','Bridge-ReLU-1');
secondConv = createAndInitializeConvLayer(convFilterSize, 2*finalNumChannels, ...
2*finalNumChannels, 'Bridge-Conv-2');
secondReLU = reluLayer('Name','Bridge-ReLU-2');
encoderDecoderBridge = [firstConv; firstReLU; secondConv; secondReLU];
dropOutLayer = dropoutLayer(0.5,'Name','Bridge-DropOut');
encoderDecoderBridge = [encoderDecoderBridge; dropOutLayer];
initialDecoderNumChannels = finalNumChannels;
upConvFilterSize = 2;
[decoder, finalDecoderNumChannels] = iCreateDecoder(encoderDepth, upConvFilterSize, convFilterSize, initialDecoderNumChannels);
layers = [inputlayer; encoder; encoderDecoderBridge; decoder];
finalConv = convolution2dLayer(1,numClasses,...
'BiasL2Factor',0,...
'Name','Final-ConvolutionLayer');
finalConv.Weights = randn(1,1,finalDecoderNumChannels,numClasses);
finalConv.Bias = zeros(1,1,numClasses);
smLayer = softmaxLayer('Name','Softmax-Layer');
pixelClassLayer = pixelClassificationLayer('Name','Segmentation-Layer');
layers = [layers; finalConv; smLayer; pixelClassLayer];
lgraph = layerGraph(layers);
for depth = 1:encoderDepth
startLayer = sprintf('Encoder-Stage-%d-ReLU-2',depth);
endLayer = sprintf('Decoder-Stage-%d-DepthConcatenation/in2',encoderDepth-depth + 1);
lgraph = connectLayers(lgraph,startLayer, endLayer);
end
end
%--------------------------------------------------------------------------
function [encoder, finalNumChannels] = iCreateEncoder(encoderDepth, convFilterSize, initialEncoderNumChannels, inputNumChannels)
encoder = [];
for stage = 1:encoderDepth
% Double the layer number of channels at each stage of the encoder.
encoderNumChannels = initialEncoderNumChannels * 2^(stage-1);
if stage == 1
firstConv = createAndInitializeConvLayer(convFilterSize, inputNumChannels, encoderNumChannels, ['Encoder-Stage-' num2str(stage) '-Conv-1']);
else
firstConv = createAndInitializeConvLayer(convFilterSize, encoderNumChannels/2, encoderNumChannels, ['Encoder-Stage-' num2str(stage) '-Conv-1']);
end
firstReLU = reluLayer('Name',['Encoder-Stage-' num2str(stage) '-ReLU-1']);
secondConv = createAndInitializeConvLayer(convFilterSize, encoderNumChannels, encoderNumChannels, ['Encoder-Stage-' num2str(stage) '-Conv-2']);
secondReLU = reluLayer('Name',['Encoder-Stage-' num2str(stage) '-ReLU-2']);
encoder = [encoder;firstConv; firstReLU; secondConv; secondReLU];
if stage == encoderDepth
dropOutLayer = dropoutLayer(0.5,'Name',['Encoder-Stage-' num2str(stage) '-DropOut']);
encoder = [encoder; dropOutLayer];
end
maxPoolLayer = maxPooling2dLayer(2, 'Stride', 2, 'Name',['Encoder-Stage-' num2str(stage) '-MaxPool']);
encoder = [encoder; maxPoolLayer];
end
finalNumChannels = encoderNumChannels;
end
%--------------------------------------------------------------------------
function [decoder, finalDecoderNumChannels] = iCreateDecoder(encoderDepth, upConvFilterSize, convFilterSize, initialDecoderNumChannels)
decoder = [];
for stage = 1:encoderDepth
% Half the layer number of channels at each stage of the decoder.
decoderNumChannels = initialDecoderNumChannels / 2^(stage-1);
upConv = createAndInitializeUpConvLayer(upConvFilterSize, 2*decoderNumChannels, decoderNumChannels, ['Decoder-Stage-' num2str(stage) '-UpConv']);
upReLU = reluLayer('Name',['Decoder-Stage-' num2str(stage) '-UpReLU']);
% Input feature channels are concatenated with deconvolved features within the decoder.
depthConcatLayer = depthConcatenationLayer(2, 'Name', ['Decoder-Stage-' num2str(stage) '-DepthConcatenation']);
firstConv = createAndInitializeConvLayer(convFilterSize, 2*decoderNumChannels, decoderNumChannels, ['Decoder-Stage-' num2str(stage) '-Conv-1']);
firstReLU = reluLayer('Name',['Decoder-Stage-' num2str(stage) '-ReLU-1']);
secondConv = createAndInitializeConvLayer(convFilterSize, decoderNumChannels, decoderNumChannels, ['Decoder-Stage-' num2str(stage) '-Conv-2']);
secondReLU = reluLayer('Name',['Decoder-Stage-' num2str(stage) '-ReLU-2']);
decoder = [decoder; upConv; upReLU; depthConcatLayer; firstConv; firstReLU; secondConv; secondReLU];
end
finalDecoderNumChannels = decoderNumChannels;
end
%--------------------------------------------------------------------------
function convLayer = createAndInitializeConvLayer(convFilterSize, inputNumChannels, outputNumChannels, layerName)
convLayer = convolution2dLayer(convFilterSize,outputNumChannels,...
'Padding', 'same',...
'BiasL2Factor',0,...
'Name',layerName);
% He initialization is used
convLayer.Weights = sqrt(2/((convFilterSize(1)*convFilterSize(2))*inputNumChannels)) ...
* randn(convFilterSize(1),convFilterSize(2), inputNumChannels, outputNumChannels);
convLayer.Bias = zeros(1,1,outputNumChannels);
convLayer.BiasLearnRateFactor = 2;
end
%--------------------------------------------------------------------------
function upConvLayer = createAndInitializeUpConvLayer(UpconvFilterSize, inputNumChannels, outputNumChannels, layerName)
upConvLayer = transposedConv2dLayer(UpconvFilterSize, outputNumChannels,...
'Stride',2,...
'BiasL2Factor',0,...
'Name',layerName);
% The transposed conv filter size is a scalar
upConvLayer.Weights = sqrt(2/((UpconvFilterSize^2)*inputNumChannels)) ...
* randn(UpconvFilterSize,UpconvFilterSize,outputNumChannels,inputNumChannels);
upConvLayer.Bias = zeros(1,1,outputNumChannels);
upConvLayer.BiasLearnRateFactor = 2;
end