Deployment bug-fix

examples/cnn_train_dag.m

@@ -413,6 +413,7 @@
 % -------------------------------------------------------------------------
 sel = find(cellfun(@(x) isa(x,'dagnn.Loss'), {net.layers.block})) ;
 for i = 1:numel(sel)
+  if net.layers(sel(i)).block.ignoreAverage, continue; end;
   stats.(net.layers(sel(i)).outputs{1}) = net.layers(sel(i)).block.average ;
 end
 

examples/fast_rcnn/+dagnn/LossSmoothL1.m

@@ -34,6 +34,7 @@
       outputs{1} = inputs{3}(:)' * absDelta(:) ;
 
       % Accumulate loss statistics.
+      if obj.ignoreAverage, return; end;
       n = obj.numAveraged ;
       m = n + gather(sum(inputs{3}(:))) + 1e-9 ;
       obj.average = (n * obj.average + gather(outputs{1})) / m ;

matlab/+dagnn/Loss.m

@@ -1,6 +1,7 @@
 classdef Loss < dagnn.ElementWise
   properties
     loss = 'softmaxlog'
+    ignoreAverage = false
     opts = {}
   end
 
@@ -12,9 +13,14 @@
   methods
     function outputs = forward(obj, inputs, params)
       outputs{1} = vl_nnloss(inputs{1}, inputs{2}, [], 'loss', obj.loss, obj.opts{:}) ;
+      obj.accumulateAverage(inputs, outputs);
+    end
+
+    function accumulateAverage(obj, inputs, outputs)
+      if obj.ignoreAverage, return; end;
       n = obj.numAveraged ;
-      m = n + size(inputs{1},4) ;
-      obj.average = (n * obj.average + gather(outputs{1})) / m ;
+      m = n + size(inputs{1}, 4) ;
+      obj.average = bsxfun(@plus, n * obj.average, gather(outputs{1})) / m ;
       obj.numAveraged = m ;
     end
 

matlab/+dagnn/PDist.m

@@ -0,0 +1,49 @@
+%PDIST vl_nnpdist dagnn wrapper
+%  Accepts 2 or 3 inputs, where third input is used as variable
+%  'instanceWeights' parameter. Derivatives for the 3rd input are not
+%  computed.
+%  By default aggregates the element-wise loss.
+classdef PDist < dagnn.Loss
+  properties
+    p = 2;
+    aggregate = true;
+  end
+
+  methods
+    function outputs = forward(obj, inputs, params)
+      switch numel(inputs)
+        case 2
+          outputs{1} = vl_nnpdist(inputs{1}, inputs{2}, obj.p, [], ...
+            'aggregate', obj.aggregate, obj.opts{:}) ;
+        case 3
+          outputs{1} = vl_nnpdist(inputs{1}, inputs{2}, obj.p, [], ...
+            'aggregate', obj.aggregate, 'instanceWeights', inputs{3}, ...
+            obj.opts{:}) ;
+        otherwise
+          error('Invalid number of inputs');
+      end
+      obj.accumulateAverage(inputs, outputs);
+    end
+
+    function [derInputs, derParams] = backward(obj, inputs, params, derOutputs)
+      derInputs = cell(1, numel(inputs));
+      switch numel(inputs)
+        case 2
+          [derInputs{1}, derInputs{2}] = vl_nnpdist(inputs{1}, inputs{2}, ...
+            obj.p, derOutputs{1}, 'aggregate', obj.aggregate, obj.opts{:}) ;
+        case 3
+          [derInputs{1}, derInputs{2}] = vl_nnpdist(inputs{1}, inputs{2}, ...
+            obj.p, derOutputs{1}, 'aggregate', obj.aggregate, ...
+            'instanceWeights', inputs{3}, obj.opts{:}) ;
+        otherwise
+          error('Invalid number of inputs');
+      end
+      derParams = {} ;
+    end
+
+    function obj = PDist(varargin)
+      obj.load(varargin) ;
+      obj.loss = 'pdist';
+    end
+  end
+end