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sample_refNet_initial.m
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sample_refNet_initial.m
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function [net] = sample_refNet_initial(varargin)
% sample code for initializing the refNet1 for mini-places challenge
% adapted from matconvnet-1.0-beta14/matconvnet-1.0-beta14/examples/cnn_imagenet_init.m
opts.scale = 1 ;
opts.initBias = 0.1 ;
opts.weightDecay = 1 ;
opts.weightInitMethod = 'gaussian' ;
opts.model = 'refNet1' ;
opts.batchNormalization = false ;
opts = vl_argparse(opts, varargin) ;
% Define layers
net.normalization.imageSize = [126, 126, 3] ;
switch opts.model
case 'refNet1'
net = refNet1(net, opts) ;
otherwise
error('Unknown model ''%s''', opts.model) ;
end
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') ;
net.normalization.border = 128 - net.normalization.imageSize(1:2) ;
net.normalization.interpolation = 'bicubic' ;
net.normalization.averageImage = [] ;
net.normalization.keepAspect = true ;
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
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]) ;
if opts.batchNormalization
net.layers{end+1} = struct('type', 'bnorm', 'name', sprintf('bn%d',id), ...
'weights', {{ones(out, 1, 'single'), zeros(out, 1, 'single')}}, ...
'learningRate', [2 1], ...
'weightDecay', [0 0]) ;
end
net.layers{end+1} = struct('type', 'relu', 'name', sprintf('relu%s',id)) ;
end
% -------------------------------------------------------------------------
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
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
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
end
% --------------------------------------------------------------------
function net = refNet1(net, opts)
% 3 convnet + 1 FC + 1 softmax
% --------------------------------------------------------------------
%% add_block(net, opts, id, h, w, in, out, stride, pad, init_bias)
net.layers = {} ;
net = add_block(net, opts, '1', 8, 8, 3, 64, 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, 32, 96, 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, 96, 128, 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, '4', 6, 6, 128, 512, 1, 0) ;
net = add_dropout(net, opts, '4') ;
net = add_block(net, opts, '5', 1, 1, 512, 100, 1, 0) ;
net.layers(end) = [] ;
if opts.batchNormalization, net.layers(end) = [] ; end
end