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cnn_resnet_fusion.m
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cnn_resnet_fusion.m
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function cnn_resnet_fusion(varargin)
%CNN_UCF101FUSION Demonstrates training a Two-Stream Fusion ConvNet on UCF101
% This module utilizes a pretrained VGG-VD-16 for rgb and flow
% on UCF101 data for training of the proposed architecture in our paper
%
% Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman
% "Convolutional Two-Stream Network Fusion for Video Action Recognition"
% in Proc. CVPR 2016
if ~isempty(gcp('nocreate')),
delete(gcp)
end
opts = cnn_setup_environment();
opts.train.gpus = [1];
opts.cudnnWorkspaceLimit = [1];
opts.dataSet = 'Remat';
addpath('network_surgery');
opts.dataDir = fullfile(opts.dataPath, opts.dataSet) ;
opts.splitDir = [opts.dataSet '_splits'];
opts.inputdim = [ 224, 224, 3] ;
opts.initMethod = '2sumAB';
opts.dropOutRatio = 0;
% opts.train.fuseFrom = 'temporal';
% opts.train.fuseInto = 'spatial';
opts.train.fuseFrom = 'spatial';
opts.train.fuseInto = 'temporal';
opts.train.removeFuseFrom = 0 ;
opts.backpropFuseFrom = 1 ;
opts.nSplit = 1 ;
addConv3D = 0 ;
addPool3D = 0 ;
doSum = 1;
doMax = 0;
opts.train.learningRate = 1*[1e-2*ones(1,3) 1e-3*ones(1,4) 1e-4*ones(1,1) 1e-5*ones(1,1) 1e-6*ones(1,2)] ;
opts.train.cheapResize = 0 ;
nFrames = 1;
model = ['test-angulargradient-res50-relu5-2x-resnet-split=' num2str(opts.nSplit) '-vgg-' opts.initMethod '-pred-3D=' num2str(addConv3D) ...
'-pool3D=' num2str(addPool3D) ...
'-fuseInto=' opts.train.fuseInto, ...
'-removeFuseFrom=' num2str( opts.train.removeFuseFrom )...
'-backpropFuseFrom=' num2str(opts.backpropFuseFrom), ...
'-nFrames=' num2str(nFrames), ...
'-dr' num2str(opts.dropOutRatio)];
if ~isempty(opts.train.gpus)
opts.train.memoryMapFile = fullfile(tempdir, 'ramdisk', ['matconvnet' num2str(opts.train.gpus(1)) '.bin']) ;
end
opts.train.fusionType = 'conv';
opts.train.fusionLayer = {'res5c_relu_spatial', 'res5c_relu_spatial'; };
% opts.train.fusionLayer = {'relu5_3', 'relu5_3'; };
opts.expDir = fullfile(opts.dataDir, [opts.dataSet '-' model]) ;
opts.modelA = fullfile(opts.modelPath, [opts.dataSet '-img-resnet-split' num2str(opts.nSplit) '-dr0.5.mat']) ;
opts.modelB = fullfile(opts.modelPath, [opts.dataSet '-TVL1flow-resnet-split' num2str(opts.nSplit) '-dr0.5.mat']) ;
opts.train.startEpoch = 1;
opts.train.epochStep = 1;
opts.train.epochFactor = 10;
opts.train.numEpochs = length(opts.train.learningRate) ;
[opts, varargin] = vl_argparse(opts, varargin) ;
opts.imdbPath = fullfile(opts.dataDir, [opts.dataSet '_split' num2str(opts.nSplit) 'imdb.mat']);
opts.train.batchSize = 196 ;
opts.train.numSubBatches = 32 / max(numel(opts.train.gpus),1); % lower this number if you have more GPU memory available
opts.train.saveAllPredScores = 0;
opts.train.denseEval = 1;
opts.train.plotDiagnostics = 0 ;
opts.train.continue = 1 ;
opts.train.prefetch = 1 ;
opts.train.expDir = opts.expDir ;
opts.train.numAugments = 1;
opts.train.frameSample = 'random';
opts.train.nFramesPerVid = nFrames;
opts.train.augmentation = 'noCtr';
opts = vl_argparse(opts, varargin) ;
% -------------------------------------------------------------------------
% Database initialization
% -------------------------------------------------------------------------
if exist(opts.imdbPath)
imdb = load(opts.imdbPath) ;
imdb.flowDir = opts.flowDir;
else
imdb = cnn_Remat_setup_data('dataPath', opts.dataPath, 'flowDir',opts.flowDir, 'nSplit', opts.nSplit) ;
save(opts.imdbPath, '-struct', 'imdb', '-v6') ;
end
% -------------------------------------------------------------------------
% Network initialization
% -------------------------------------------------------------------------
netA = load(opts.modelA) ;
netB = load(opts.modelB) ;
if isfield(netA, 'net'), netA=netA.net;end
if isfield(netB, 'net'), netB=netB.net;end
% drop1 = struct('name', 'dropout1', 'type', 'dropout', 'rate' , opts.dropOutRatio) ;
% drop2 = struct('name', 'dropout2', 'type', 'dropout', 'rate' , opts.dropOutRatio) ;
% netA.layers = [netA.layers(1:15) drop1 netA.layers(16:17) drop2 netA.layers(18:end)] ;
% netB.layers = [netB.layers(1:15) drop1 netB.layers(16:17) drop2 netB.layers(18:end)] ;
% netA.layers=netA.layers(1:end-1);
% netB.layers=netB.layers(1:end-1);
% if ~isfield(netA, 'meta')
% netA = vl_simplenn_tidy(netA);
% netA = dagnn.DagNN.fromSimpleNN(netA,'canonicalNames', true) ;
% netA = dagnn.DagNN.loadobj(netA);
% netB = dagnn.DagNN.loadobj(netB);
% netA = netA.saveobj() ;
% end
% if ~isfield(netB, 'meta'),
% netB = vl_simplenn_tidy(netB);
% netB = dagnn.DagNN.fromSimpleNN(netB,'canonicalNames', true) ;
% netB = netB.saveobj() ;
% end
% f = find(strcmp({netA.layers(:).type}, 'dagnn.Loss'));
% netA.layers(f(1)-1).name = 'prediction';
% f = find(strcmp({netB.layers(:).type}, 'dagnn.Loss'));
% netB.layers(f(1)-1).name = 'prediction';
f = find(strcmp({netA.layers(:).type}, 'dagnn.Conv'));
netA.layers(f(end)).name = 'prediction';
f = find(strcmp({netB.layers(:).type}, 'dagnn.Conv'));
netB.layers(f(end)).name = 'prediction';
% f = find(strcmp({netA.layers(:).type}, 'dagnn.Loss'));
% netA.layers(f(1)-1).name = 'prediction';
% f = find(strcmp({netB.layers(:).type}, 'dagnn.Loss'));
% netB.layers(f(1)-1).name = 'prediction';
fusionLayerA = []; fusionLayerB = [];
if ~isempty(opts.train.fusionLayer)
for i=1:numel(netA.layers)
if isfield(netA.layers(i),'name') && any(strcmp(netA.layers(i).name,opts.train.fusionLayer(:,1)))
fusionLayerA = [fusionLayerA i];
end
end
for i=1:numel(netB.layers)
if isfield(netB.layers(i),'name') && any(strcmp(netB.layers(i).name,opts.train.fusionLayer(:,2)))
fusionLayerB = [fusionLayerB i];
end
end
end
netA.meta.normalization.averageImage = mean(mean(netA.meta.normalization.averageImage, 1), 2);
netB.meta.normalization.averageImage = mean(mean(netB.meta.normalization.averageImage, 1), 2);
netB.meta.normalization.averageImage = gather(cat(3,netB.meta.normalization.averageImage, netA.meta.normalization.averageImage));
% rename layers, params and vars
for x=1:numel(netA.layers)
if isfield(netA.layers(x), 'name'), netA.layers(x).name = [netA.layers(x).name '_spatial'] ; end
end
for x=1:numel(netB.layers)
if isfield(netB.layers(x), 'name'), netB.layers(x).name = [netB.layers(x).name '_temporal']; end
end
netA = dagnn.DagNN.loadobj(netA);
for i = 1:numel(netA.vars), if~strcmp(netA.vars(i).name,'label'), netA.renameVar(netA.vars(i).name, [netA.vars(i).name '_spatial']); end; end;
for i = 1:numel(netA.params), netA.renameParam(netA.params(i).name, [netA.params(i).name '_spatial']); end;
netB = dagnn.DagNN.loadobj(netB);
for i = 1:numel(netB.vars), if~strcmp(netB.vars(i).name,'label'), netB.renameVar(netB.vars(i).name, [netB.vars(i).name '_temporal']); end;end;
for i = 1:numel(netB.params), netB.renameParam(netB.params(i).name, [netB.params(i).name '_temporal']); end;
% inject conv fusion layer
if addConv3D & any(~cellfun(@isempty,(strfind(opts.train.fusionLayer, 'prediction'))))
if strcmp(opts.train.fuseInto,'temporal')
[ netB ] = insert_conv_layers( netB, fusionLayerB(end), 'initMethod', opts.initMethod );
else
[ netA ] = insert_conv_layers( netA, fusionLayerA(end), 'initMethod', opts.initMethod );
end
end
if ~addConv3D && ~doSum &&~doMax
if strcmp(opts.train.fuseInto,'temporal')
[ netB ] = insert_conv_layers( netB, fusionLayerB, 'initMethod', opts.initMethod );
else
[ netA ] = insert_conv_layers( netA, fusionLayerA, 'initMethod', opts.initMethod );
end
end
if opts.train.removeFuseFrom,
switch opts.train.fuseFrom
case 'spatial'
netA.layers = netA.layers(1:fusionLayerA(end)); netA.rebuild;
case'temporal'
netB.layers = netB.layers(1:fusionLayerB(end)); netB.rebuild;
end
end
% merge nets
netA = netA.saveobj() ;
netB = netB.saveobj() ;
net.layers = [netA.layers netB.layers] ;
net.params = [netA.params netB.params] ;
net.meta = netB.meta;
net = dagnn.DagNN.loadobj(net);
clear netA netB;
net = dagnn.DagNN.setLrWd(net, 'convFiltersLRWD', [1 1], 'convBiasesLRWD', [2 0], ...
'fusionFiltersLRWD', [1 1], 'fusionBiasesLRWD', [2 0], ...
'filtersLRWD' , [1 1], 'biasesLRWD' , [2 0] ) ;
for i = 1:size(opts.train.fusionLayer,1)
if strcmp(opts.train.fuseInto,'spatial')
i_fusion = find(~cellfun('isempty', strfind({net.layers.name}, ...
[opts.train.fusionLayer{i,1} '_' opts.train.fuseInto])));
else
i_fusion = find(~cellfun('isempty', strfind({net.layers.name}, ...
[opts.train.fusionLayer{i,2} '_' opts.train.fuseInto])));
end
name_concat = [opts.train.fusionLayer{i,2} '_concat'];
if doSum
block = dagnn.Sum() ;
net.addLayerAt(i_fusion(end), name_concat, block, ...
[net.layers(strcmp({net.layers.name},[opts.train.fusionLayer{i,1} '_spatial'])).outputs ...
net.layers(strcmp({net.layers.name},[opts.train.fusionLayer{i,2} '_temporal'])).outputs], ...
name_concat) ;
elseif doMax
block = dagnn.Max() ;
net.addLayerAt(i_fusion(end), name_concat, block, ...
[net.layers(strcmp({net.layers.name},[opts.train.fusionLayer{i,1} '_spatial'])).outputs ...
net.layers(strcmp({net.layers.name},[opts.train.fusionLayer{i,2} '_temporal'])).outputs], ...
name_concat) ;
else
block = dagnn.Concat() ;
net.addLayerAt(i_fusion(end), name_concat, block, ...
[net.layers(strcmp({net.layers.name},[opts.train.fusionLayer{i,1} '_spatial'])).outputs ...
net.layers(strcmp({net.layers.name},[opts.train.fusionLayer{i,2} '_temporal'])).outputs], ...
name_concat) ;
end
% set input for fusion layer
net.layers(i_fusion(end)+2).inputs{1} = name_concat;
end
% set inputs
net.addVar('input_flow')
net.vars(net.getVarIndex('input_flow')).fanout = net.vars(net.getVarIndex('input_flow')).fanout + 1 ;
% i_conv1= find(~cellfun('isempty', strfind({net.layers.name},'conv1_1_temporal')));
i_conv1= find(~cellfun('isempty', strfind({net.layers.name},'conv1_spatial_temporal')));
net.layers(i_conv1(1)).inputs = {'input_flow'};
net.renameVar(net.vars(1).name, 'input');
if addConv3D
block = dagnn.Conv3D() ;
params(1).name = 'conv3Df' ;
in = 4096 ;
% in = size(net.params(net.getParamIndex('conv5_3f_spatial')).value,4) + ...
% size(net.params(net.getParamIndex('conv5_3f_temporal')).value,4) ;
out = 2048;
kernel = eye(in/2,out,'single');
kernel = cat(1, .25 * kernel, .75 * kernel);
kernel = permute(kernel, [4 5 3 1 2]);
sigma = 1;
[X,Y,Z] = ndgrid(-1:1, -1:1, -1:1);
G3 = exp( -((X.*X)/(sigma*sigma) + (Y.*Y)/(sigma*sigma) + (Z.*Z)/(sigma*sigma))/2 );
G3 = G3./sum(G3(:));
kernel = bsxfun(@times, kernel, G3);
params(1).value = kernel;
params(2).name = 'conv3Db' ;
params(2).value = zeros(1, out ,'single') ;
pads = size(kernel); pads = ceil(pads(1:3) / 2) - 1;
block.pad = [pads(1),pads(1), pads(2),pads(2), pads(3),pads(3)];
block.stride = [1 1 1];
block.size = size(kernel);
i_relu5 = find(~cellfun('isempty', strfind({net.layers.name},name_concat)));
% i_relu5 = find(~cellfun('isempty', strfind({net.layers.name},'relu5_3_concat')));
net.addLayerAt(i_relu5, 'conv53D', block, ...
[net.layers(i_relu5).outputs ], ...
'conv3D5', {params.name}) ;
net.params(net.getParamIndex(params(1).name)).value = params(1).value ;
net.params(net.getParamIndex(params(2).name)).value = params(2).value ;
% net.params(net.getParamIndex(params(1).name)).learningRate = 1;
net.params(net.getParamIndex(params(2).name)).learningRate = 2;
net.params(net.getParamIndex(params(2).name)).weightDecay = 0;
block = dagnn.ReLU() ;
net.addLayerAt(i_relu5+1, 'relu3D5', block, ...
[net.layers(i_relu5+1).outputs ], ...
'relu3D5') ;
pool5_layer = find(~cellfun('isempty', strfind({net.layers.name},['pool5_' opts.train.fuseInto])));
net.layers(pool5_layer(1)).inputs = {'relu3D5'};
end
if addPool3D
block = dagnn.AvePooling3D() ;
block.method = 'avg' ;
i_pool5 = find(~cellfun('isempty', strfind({net.layers.name},['pool5_spatial_' opts.train.fuseInto])));
i_pool5 = (i_pool5(1));
block.poolSize = [1 1 nFrames];
block.pad = [net.layers(i_pool5).block.pad 0,0];
block.stride = [net.layers(i_pool5).block.stride 2];
net.addLayerAt(i_pool5, ['pool3D5_' opts.train.fuseInto], block, ...
[net.layers(i_pool5).inputs], ...
[net.layers(i_pool5).outputs]) ;
net.removeLayer(['pool5_spatial_' opts.train.fuseInto], 0) ;
% net.layers(i_pool5).inputs=net.layers(i_pool5+1).outputs;
% net.layers(i_pool5).inputIndexes=net.layers(i_pool5+1).outputIndexes;
i_pool5 = find(~cellfun('isempty', strfind({net.layers.name},['pool5_spatial_' opts.train.fuseFrom ])));
if ~isempty(i_pool5)
i_pool5=i_pool5(1);
block = dagnn.AvePooling3D() ;
block.poolSize = [1 1 nFrames];
block.pad = [net.layers(i_pool5).block.pad 0,0];
block.stride = [net.layers(i_pool5).block.stride 2];
net.addLayerAt(i_pool5, ['pool3D5_' opts.train.fuseFrom], block, ...
[net.layers(i_pool5).inputs], ...
[net.layers(i_pool5).outputs]) ;
% net.layers(i_pool5).inputs=net.layers(i_pool5+1).outputs;
% net.layers(i_pool5).inputIndexes=net.layers(i_pool5+1).outputIndexes;
net.removeLayer(['pool5_spatial_' opts.train.fuseFrom ], 0) ;
end
end
if addConv3D || addPool3D
opts.train.augmentation = 'noCtr';
opts.train.frameSample = 'temporalStrideRandom';
opts.train.nFramesPerVid = nFrames * 1;
opts.train.temporalStride = 5:15;
opts.train.valmode = 'temporalStrideRandom';
opts.train.numValFrames = nFrames * 1 ;
opts.train.saveAllPredScores = 0 ;
opts.train.denseEval = 1;
end
net.meta.normalization.rgbVariance = [];
opts.train.train = find(ismember(imdb.images.set, [1])) ;
opts.train.train = repmat(opts.train.train,1,opts.train.epochFactor);
% opts.train.backpropDepth = ['res5c_relu_spatial_temporal';'res5c_relu_spatial_temporal';'res5c_relu_spatial_temporal';'res5c_relu_spatial_temporal';'res5c_relu_spatial_temporal';];
opts.train.backpropDepth = ['res5c_relu_temporal';'res5c_relu_temporal';'res3d_relu_temporal';'res3d_relu_temporal';'res3d_relu_temporal';'res3d_relu_temporal';'conv1_relu_temporal'];%'relu3_temporal';'relu3_temporal';
% opts.train.backpropDepth = ['relu5_spatial';'relu5_spatial';'relu3_spatial';'relu3_spatial';'relu1_spatial';'relu1_spatial'];
opts.train.backpropDepth = cellstr(opts.train.backpropDepth);
for l = 1:numel(net.layers)
if isa(net.layers(l).block, 'dagnn.DropOut')
net.layers(l).block.rate = opts.dropOutRatio;
end
end
%get ride of dropout layers
% for l = 1:numel(net.layers)
% if isa(net.layers(l).block, 'dagnn.DropOut')
% % net.layers(l).block.rate = opts.dropOutRatio;
% net.layers(l-1).inputs=net.layers(l).inputs;
% net.layers(l-1).inputIndexes=net.layers(l).inputIndexes;
% %net.removeLayer(net.layers(l).name,0);
%
% end
% end
net.layers(~cellfun('isempty', strfind({net.layers(:).name}, 'err'))) = [] ;
% net.layers(~cellfun('isempty', strfind({net.layers(:).name}, 'drop'))) = [] ;
net.rebuild() ;
opts.train.derOutputs = {} ;
for l=1:numel(net.layers)
if isa(net.layers(l).block, 'dagnn.Loss') && isempty(strfind(net.layers(l).block.loss, 'err'))
if opts.backpropFuseFrom || ~isempty(strfind(net.layers(l).name, opts.train.fuseInto ))
fprintf('setting derivative for layer %s \n', net.layers(l).name);
opts.train.derOutputs = [opts.train.derOutputs, net.layers(l).outputs, {1}] ;
end
net.addLayer(['err1_' net.layers(l).name(end-7:end) ], dagnn.Loss('loss', 'classerror'), ...
net.layers(l).inputs, 'error') ;
end
end
net.print('MaxNumColumns', 5, 'Layers','*','variables','') ;
net.conserveMemory = 1 ;
fn = getBatchWrapper_ucf101_rgbflow(net.meta.normalization, opts.numFetchThreads, opts.train) ;
[info] = cnn_train_dag(net, imdb, fn, opts.train) ;