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cnn_init.m
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cnn_init.m
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% FILE: cnn_init.m
%
% This function initializes a well-known conv-net from scratch.
%
% INPUT: See below
%
% OUTPUT: net (initialized network)
function net = cnn_init(varargin)
opts.scale = 1 ;
opts.initBias = 0.1 ;
opts.weightDecay = 1 ;
%opts.weightInitMethod = 'xavierimproved' ;
opts.weightInitMethod = 'gaussian' ;
opts.model = 'alexnet' ;
opts.batchNormalization = false ;
opts.networkType = 'simplenn' ;
opts.cudnnWorkspaceLimit = 1024*1024*1204 ; % 1GB
opts = vl_argparse(opts, varargin) ;
% Define layers
switch opts.model
case 'alexnet'
net.meta.normalization.imageSize = [227, 227, 3] ;
net = alexnet(net, opts) ;
bs = 256 ;
case 'vgg-f'
net.meta.normalization.imageSize = [224, 224, 3] ;
net = vgg_f(net, opts) ;
bs = 256 ;
case 'vgg-m'
net.meta.normalization.imageSize = [224, 224, 3] ;
net = vgg_m(net, opts) ;
bs = 196 ;
case 'vgg-s'
net.meta.normalization.imageSize = [224, 224, 3] ;
net = vgg_s(net, opts) ;
bs = 128 ;
case 'vgg-vd-16'
net.meta.normalization.imageSize = [224, 224, 3] ;
net = vgg_vd(net, opts) ;
bs = 32 ;
case 'vgg-vd-19'
net.meta.normalization.imageSize = [224, 224, 3] ;
net = vgg_vd(net, opts) ;
bs = 24 ;
otherwise
fprintf('Unknown model: cannot initialize.\n');
net = [];
return;
end
% final touches
switch lower(opts.weightInitMethod)
case {'xavier', 'xavierimproved'}
net.layers{end}.weights{1} = net.layers{end}.weights{1} / 10 ;
end
net.layers{end+1} = struct('type', 'softmaxloss', 'name', 'loss') ;
% Meta parameters
net.meta.inputSize = net.meta.normalization.imageSize ;
net.meta.normalization.border = 256 - net.meta.normalization.imageSize(1:2) ;
net.meta.normalization.interpolation = 'bicubic' ;
net.meta.normalization.averageImage = [] ;
net.meta.normalization.keepAspect = true ;
net.meta.augmentation.rgbVariance = zeros(0,3) ;
net.meta.augmentation.transformation = 'stretch' ;
if ~opts.batchNormalization
lr = logspace(-2, -4, 60) ;
else
lr = logspace(-1, -4, 20) ;
end
net.meta.trainOpts.learningRate = lr ;
net.meta.trainOpts.numEpochs = numel(lr) ;
net.meta.trainOpts.batchSize = bs ;
net.meta.trainOpts.weightDecay = 0.0005 ;
% Fill in default values
net = vl_simplenn_tidy(net) ;
% Switch to DagNN if requested
switch lower(opts.networkType)
case 'simplenn'
% done
case 'dagnn'
net = dagnn.DagNN.fromSimpleNN(net, 'canonicalNames', true) ;
net.addLayer('top1err', dagnn.Loss('loss', 'classerror'), ...
{'prediction','label'}, 'top1err') ;
net.addLayer('top5err', dagnn.Loss('loss', 'topkerror', ...
'opts', {'topK',5}), ...
{'prediction','label'}, 'top5err') ;
otherwise
assert(false) ;
end
% --------------------------------------------------------------------
function net = add_block(net, opts, id, h, w, in, out, stride, pad, init_bias)
% --------------------------------------------------------------------
info = vl_simplenn_display(net) ;
fc = (h == info.dataSize(1,end) && w == info.dataSize(2,end)) ;
if fc
name = 'fc' ;
else
name = 'conv' ;
end
convOpts = {'CudnnWorkspaceLimit', opts.cudnnWorkspaceLimit} ;
net.layers{end+1} = struct('type', 'conv', 'name', sprintf('%s%s', name, id), ...
'weights', {{init_weight(opts, h, w, in, out, 'single'), zeros(out, 1, 'single')}}, ...
'stride', stride, ...
'pad', pad, ...
'learningRate', [1 2], ...
'weightDecay', [opts.weightDecay 0], ...
'opts', {convOpts}) ;
if opts.batchNormalization
net.layers{end+1} = struct('type', 'bnorm', 'name', sprintf('bn%s',id), ...
'weights', {{ones(out, 1, 'single'), zeros(out, 1, 'single'), zeros(out, 2, 'single')}}, ...
'learningRate', [2 1 0.05], ...
'weightDecay', [0 0]) ;
end
net.layers{end+1} = struct('type', 'relu', 'name', sprintf('relu%s',id)) ;
% -------------------------------------------------------------------------
function weights = init_weight(opts, h, w, in, out, type)
% -------------------------------------------------------------------------
% See K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into
% rectifiers: Surpassing human-level performance on imagenet
% classification. CoRR, (arXiv:1502.01852v1), 2015.
switch lower(opts.weightInitMethod)
case 'gaussian'
sc = 0.01/opts.scale ;
weights = randn(h, w, in, out, type)*sc;
case 'xavier'
sc = sqrt(3/(h*w*in)) ;
weights = (rand(h, w, in, out, type)*2 - 1)*sc ;
case 'xavierimproved'
sc = sqrt(2/(h*w*out)) ;
weights = randn(h, w, in, out, type)*sc ;
otherwise
error('Unknown weight initialization method''%s''', opts.weightInitMethod) ;
end
% --------------------------------------------------------------------
function net = add_norm(net, opts, id)
% --------------------------------------------------------------------
if ~opts.batchNormalization
net.layers{end+1} = struct('type', 'normalize', ...
'name', sprintf('norm%s', id), ...
'param', [5 1 0.0001/5 0.75]) ;
end
% --------------------------------------------------------------------
function net = add_dropout(net, opts, id)
% --------------------------------------------------------------------
if ~opts.batchNormalization
net.layers{end+1} = struct('type', 'dropout', ...
'name', sprintf('dropout%s', id), ...
'rate', 0.5) ;
end
% --------------------------------------------------------------------
function net = alexnet(net, opts)
% --------------------------------------------------------------------
net.layers = {} ;
net = add_block(net, opts, '1', 11, 11, 3, 96, 4, 0) ;
net = add_norm(net, opts, '1') ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool1', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '2', 5, 5, 48, 256, 1, 2) ;
net = add_norm(net, opts, '2') ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool2', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '3', 3, 3, 256, 384, 1, 1) ;
net = add_block(net, opts, '4', 3, 3, 192, 384, 1, 1) ;
net = add_block(net, opts, '5', 3, 3, 192, 256, 1, 1) ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool5', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '6', 6, 6, 256, 4096, 1, 0) ;
net = add_dropout(net, opts, '6') ;
net = add_block(net, opts, '7', 1, 1, 4096, 4096, 1, 0) ;
net = add_dropout(net, opts, '7') ;
net = add_block(net, opts, '8', 1, 1, 4096, 1000, 1, 0) ;
net.layers(end) = [] ;
if opts.batchNormalization, net.layers(end) = [] ; end
% --------------------------------------------------------------------
function net = vgg_s(net, opts)
% --------------------------------------------------------------------
net.layers = {} ;
net = add_block(net, opts, '1', 7, 7, 3, 96, 2, 0) ;
net = add_norm(net, opts, '1') ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool1', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 3, ...
'pad', [0 2 0 2]) ;
net = add_block(net, opts, '2', 5, 5, 96, 256, 1, 0) ;
net = add_norm(net, opts, '2') ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool2', ...
'method', 'max', ...
'pool', [2 2], ...
'stride', 2, ...
'pad', [0 1 0 1]) ;
net = add_block(net, opts, '3', 3, 3, 256, 512, 1, 1) ;
net = add_block(net, opts, '4', 3, 3, 512, 512, 1, 1) ;
net = add_block(net, opts, '5', 3, 3, 512, 512, 1, 1) ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool5', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 3, ...
'pad', [0 1 0 1]) ;
net = add_block(net, opts, '6', 6, 6, 512, 4096, 1, 0) ;
net = add_dropout(net, opts, '6') ;
net = add_block(net, opts, '7', 1, 1, 4096, 4096, 1, 0) ;
net = add_dropout(net, opts, '7') ;
net = add_block(net, opts, '8', 1, 1, 4096, 1000, 1, 0) ;
net.layers(end) = [] ;
if opts.batchNormalization, net.layers(end) = [] ; end
% --------------------------------------------------------------------
function net = vgg_m(net, opts)
% --------------------------------------------------------------------
net.layers = {} ;
net = add_block(net, opts, '1', 7, 7, 3, 96, 2, 0) ;
net = add_norm(net, opts, '1') ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool1', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '2', 5, 5, 96, 256, 2, 1) ;
net = add_norm(net, opts, '2') ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool2', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', [0 1 0 1]) ;
net = add_block(net, opts, '3', 3, 3, 256, 512, 1, 1) ;
net = add_block(net, opts, '4', 3, 3, 512, 512, 1, 1) ;
net = add_block(net, opts, '5', 3, 3, 512, 512, 1, 1) ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool5', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '6', 6, 6, 512, 4096, 1, 0) ;
net = add_dropout(net, opts, '6') ;
net = add_block(net, opts, '7', 1, 1, 4096, 4096, 1, 0) ;
net = add_dropout(net, opts, '7') ;
net = add_block(net, opts, '8', 1, 1, 4096, 1000, 1, 0) ;
net.layers(end) = [] ;
if opts.batchNormalization, net.layers(end) = [] ; end
% --------------------------------------------------------------------
function net = vgg_f(net, opts)
% --------------------------------------------------------------------
net.layers = {} ;
net = add_block(net, opts, '1', 11, 11, 3, 64, 4, 0) ;
net = add_norm(net, opts, '1') ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool1', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', [0 1 0 1]) ;
net = add_block(net, opts, '2', 5, 5, 64, 256, 1, 2) ;
net = add_norm(net, opts, '2') ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool2', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '3', 3, 3, 256, 256, 1, 1) ;
net = add_block(net, opts, '4', 3, 3, 256, 256, 1, 1) ;
net = add_block(net, opts, '5', 3, 3, 256, 256, 1, 1) ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool5', ...
'method', 'max', ...
'pool', [3 3], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '6', 6, 6, 256, 4096, 1, 0) ;
net = add_dropout(net, opts, '6') ;
net = add_block(net, opts, '7', 1, 1, 4096, 4096, 1, 0) ;
net = add_dropout(net, opts, '7') ;
net = add_block(net, opts, '8', 1, 1, 4096, 1000, 1, 0) ;
net.layers(end) = [] ;
if opts.batchNormalization, net.layers(end) = [] ; end
% --------------------------------------------------------------------
function net = vgg_vd(net, opts)
% --------------------------------------------------------------------
net.layers = {} ;
net = add_block(net, opts, '1_1', 3, 3, 3, 64, 1, 1) ;
net = add_block(net, opts, '1_2', 3, 3, 64, 64, 1, 1) ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool1', ...
'method', 'max', ...
'pool', [2 2], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '2_1', 3, 3, 64, 128, 1, 1) ;
net = add_block(net, opts, '2_2', 3, 3, 128, 128, 1, 1) ;
net.layers{end+1} = struct('type', 'pool', 'name', 'pool2', ...
'method', 'max', ...
'pool', [2 2], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '3_1', 3, 3, 128, 256, 1, 1) ;
net = add_block(net, opts, '3_2', 3, 3, 256, 256, 1, 1) ;
net = add_block(net, opts, '3_3', 3, 3, 256, 256, 1, 1) ;
if strcmp(opts.model, 'vgg-vd-19')
net = add_block(net, opts, '3_4', 3, 3, 256, 256, 1, 1) ;
end
net.layers{end+1} = struct('type', 'pool', 'name', 'pool3', ...
'method', 'max', ...
'pool', [2 2], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '4_1', 3, 3, 256, 512, 1, 1) ;
net = add_block(net, opts, '4_2', 3, 3, 512, 512, 1, 1) ;
net = add_block(net, opts, '4_3', 3, 3, 512, 512, 1, 1) ;
if strcmp(opts.model, 'vgg-vd-19')
net = add_block(net, opts, '4_4', 3, 3, 512, 512, 1, 1) ;
end
net.layers{end+1} = struct('type', 'pool', 'name', 'pool4', ...
'method', 'max', ...
'pool', [2 2], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '5_1', 3, 3, 512, 512, 1, 1) ;
net = add_block(net, opts, '5_2', 3, 3, 512, 512, 1, 1) ;
net = add_block(net, opts, '5_3', 3, 3, 512, 512, 1, 1) ;
if strcmp(opts.model, 'vgg-vd-19')
net = add_block(net, opts, '5_4', 3, 3, 512, 512, 1, 1) ;
end
net.layers{end+1} = struct('type', 'pool', 'name', 'pool5', ...
'method', 'max', ...
'pool', [2 2], ...
'stride', 2, ...
'pad', 0) ;
net = add_block(net, opts, '6', 7, 7, 512, 4096, 1, 0) ;
net = add_dropout(net, opts, '6') ;
net = add_block(net, opts, '7', 1, 1, 4096, 4096, 1, 0) ;
net = add_dropout(net, opts, '7') ;
net = add_block(net, opts, '8', 1, 1, 4096, 1000, 1, 0) ;
net.layers(end) = [] ;
if opts.batchNormalization, net.layers(end) = [] ; end