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Niccolo Dal Santo
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‎convmixer/convMixerLayers.m‎

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function lgraph = convMixerLayers(opts)
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% convMixerLayers Build ConvMixer architecture.
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%
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% lgraph = convMixerLayers() returns a LayerGraph object with a ConvMixer
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% architecture with default options as proposed in
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% https://openreview.net/forum?id=TVHS5Y4dNvM.
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%
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% lgraph = convMixerLayers(PARAM1=VAL1,PARAM2=VAL2,...) specifies optional
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% parameter name/value pairs for creating the layer graph:
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%
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% 'InputSize' - Size of the input images.
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%
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% 'NumClasses' - Number of classes the network predicts.
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%
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% 'KernelSize' - Size of the kernel for the depthwise
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% convolution.
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%
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% 'PatchSize' - Size of the pathes for the patch embedding
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% layer.
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%
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% 'Depth' - Number of repeated fully-convolutional
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% blocks.
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%
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% 'HiddenDimension' - Number of channels output by the patch
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% embedding.
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%
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% 'ConnectOutputLayer' - Determines whether to append a softmax and
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% classification output layers to the
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% returned LayerGraph object.
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%
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% Example:
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%
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% lgraph = convMixerLayers(InputSize=[28 28 1], Depth=5, NumClasses=10)
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% Copyright 2021 The MathWorks, Inc.
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arguments
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opts.InputSize = [227 227 3]
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opts.NumClasses = 1000
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opts.KernelSize = 9
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opts.PatchSize = 7
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opts.Depth = 20
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opts.HiddenDimension = 1536
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opts.ConnectOutputLayer logical = false
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end
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input_size = opts.InputSize;
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num_classes = opts.NumClasses;
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kernel_size = opts.KernelSize;
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patch_size = opts.PatchSize;
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depth = opts.Depth;
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hidden_dim = opts.HiddenDimension;
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connectOutputLayers = opts.ConnectOutputLayer;
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% First layer is a "path embedding". Seems to be this:
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patchEmbedding = convolution2dLayer(patch_size, hidden_dim, ...
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Stride=patch_size, ...
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Name="patchEmbedding", ...
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WeightsInitializer="glorot");
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% Make Layer Graph
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lgraph = layerGraph();
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start = [
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imageInputLayer(input_size,Normalization="none")
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patchEmbedding
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geluLayer(Name="gelu_0")
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batchNormalizationLayer(Name="batchnorm_0")
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];
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lgraph = addLayers(lgraph,start);
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for i = 1:depth
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convMixer = [
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groupedConvolution2dLayer(kernel_size,1,"channel-wise",Name="depthwiseConv_"+i,Padding="same",WeightsInitializer="glorot")
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geluLayer(Name="gelu_"+(2*i-1))
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batchNormalizationLayer(Name="batchnorm_"+(2*i-1))
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additionLayer(2,Name="addition_"+i)
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convolution2dLayer([1 1],hidden_dim,Name="pointwiseConv_"+i,WeightsInitializer="glorot")
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geluLayer(Name="gelu_"+2*i)
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batchNormalizationLayer(Name="batchnorm_"+2*i)
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];
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lgraph = addLayers(lgraph,convMixer);
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lgraph = connectLayers(lgraph,"batchnorm_"+2*(i-1),"depthwiseConv_"+i);
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lgraph = connectLayers(lgraph,"batchnorm_"+2*(i-1),"addition_"+i+"/in2");
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end
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gapFc = [
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globalAveragePooling2dLayer(Name="GAP")
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fullyConnectedLayer(num_classes)
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];
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lgraph = addLayers(lgraph,gapFc);
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lgraph = connectLayers(lgraph,"batchnorm_"+2*depth,"GAP");
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if connectOutputLayers
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lgraph = addLayers(lgraph, softmaxLayer('Name','softmax'));
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lgraph = addLayers(lgraph, classificationLayer('Name','classification'));
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lgraph = connectLayers(lgraph,'fc','softmax');
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lgraph = connectLayers(lgraph,'softmax','classification');
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end
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end
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function lgraph = convMixerLayers(opts)
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% convMixerLayers Build ConvMixer architecture.
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%
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% lgraph = convMixerLayers() returns a LayerGraph object with a ConvMixer
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% architecture with default options as proposed in
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% https://openreview.net/forum?id=TVHS5Y4dNvM.
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%
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% lgraph = convMixerLayers(PARAM1=VAL1,PARAM2=VAL2,...) specifies optional
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% parameter name/value pairs for creating the layer graph:
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%
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% 'InputSize' - Size of the input images.
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%
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% 'NumClasses' - Number of classes the network predicts.
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%
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% 'KernelSize' - Size of the kernel for the depthwise
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% convolution.
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%
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% 'PatchSize' - Size of the pathes for the patch embedding
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% layer.
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%
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% 'Depth' - Number of repeated fully-convolutional
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% blocks.
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%
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% 'HiddenDimension' - Number of channels output by the patch
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% embedding.
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%
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% 'ConnectOutputLayer' - Determines whether to append a softmax and
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% classification output layers to the
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% returned LayerGraph object.
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%
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% Example:
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%
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% lgraph = convMixerLayers(InputSize=[28 28 1], Depth=5, NumClasses=10)
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% Copyright 2021 The MathWorks, Inc.
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arguments
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opts.InputSize = [227 227 3]
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opts.NumClasses = 1000
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opts.KernelSize = 9
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opts.PatchSize = 7
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opts.Depth = 20
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opts.HiddenDimension = 1536
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opts.ConnectOutputLayer logical = false
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end
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input_size = opts.InputSize;
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num_classes = opts.NumClasses;
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kernel_size = opts.KernelSize;
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patch_size = opts.PatchSize;
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depth = opts.Depth;
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hidden_dim = opts.HiddenDimension;
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connectOutputLayers = opts.ConnectOutputLayer;
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% First layer is a "path embedding". Seems to be this:
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patchEmbedding = convolution2dLayer(patch_size, hidden_dim, ...
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Stride=patch_size, ...
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Name="patchEmbedding", ...
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WeightsInitializer="glorot");
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% Make Layer Graph
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lgraph = layerGraph();
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start = [
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imageInputLayer(input_size,Normalization="none")
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patchEmbedding
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geluLayer(Name="gelu_0")
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batchNormalizationLayer(Name="batchnorm_0")
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];
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lgraph = addLayers(lgraph,start);
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for i = 1:depth
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convMixer = [
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groupedConvolution2dLayer(kernel_size,1,"channel-wise",Name="depthwiseConv_"+i,Padding="same",WeightsInitializer="glorot")
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geluLayer(Name="gelu_"+(2*i-1))
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batchNormalizationLayer(Name="batchnorm_"+(2*i-1))
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additionLayer(2,Name="addition_"+i)
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convolution2dLayer([1 1],hidden_dim,Name="pointwiseConv_"+i,WeightsInitializer="glorot")
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geluLayer(Name="gelu_"+2*i)
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batchNormalizationLayer(Name="batchnorm_"+2*i)
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];
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lgraph = addLayers(lgraph,convMixer);
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lgraph = connectLayers(lgraph,"batchnorm_"+2*(i-1),"depthwiseConv_"+i);
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lgraph = connectLayers(lgraph,"batchnorm_"+2*(i-1),"addition_"+i+"/in2");
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end
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gapFc = [
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globalAveragePooling2dLayer(Name="GAP")
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fullyConnectedLayer(num_classes)
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];
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lgraph = addLayers(lgraph,gapFc);
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lgraph = connectLayers(lgraph,"batchnorm_"+2*depth,"GAP");
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if connectOutputLayers
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lgraph = addLayers(lgraph, softmaxLayer('Name','softmax'));
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lgraph = addLayers(lgraph, classificationLayer('Name','classification'));
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lgraph = connectLayers(lgraph,'fc','softmax');
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lgraph = connectLayers(lgraph,'softmax','classification');
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end
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end

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