Added Onlinetrainer class.

3 files changed, 231 insertions, 0 deletions

diff --git a/OnlineTrainer.lua b/OnlineTrainer.lua
new file mode 100644
index 0000000..9dd81f2
--- /dev/null
+++ b/OnlineTrainer.lua
@@ -0,0 +1,229 @@
+local OnlineTrainer, parent = torch.class('nn.OnlineTrainer','nn.Trainer')
+
+function OnlineTrainer:__init(...)
+   parent.__init(self)
+   -- unpack args
+   xlua.unpack_class(self, {...},
+      'OnlineTrainer', 
+
+      'A general-purpose online trainer class.\n'
+         .. 'Provides 4 user hooks to perform extra work after each sample, or each epoch:\n'
+         .. '> trainer = nn.OnlineTrainer(...) \n'
+         .. '> trainer.hookTrainSample = function(trainer, sample) ... end \n'
+         .. '> trainer.hookTrainEpoch = function(trainer) ... end \n'
+         .. '> trainer.hookTestSample = function(trainer, sample) ... end \n'
+         .. '> trainer.hookTestEpoch = function(trainer) ... end \n'
+         .. '> ',
+
+      {arg='module', type='nn.Module', help='a module to train', req=true},
+      {arg='criterion', type='nn.Criterion', help='a criterion to estimate the error'},
+      {arg='preprocessor', type='nn.Module', help='a preprocessor to prime the data before the module'},
+      {arg='optimizer', type='nn.Optimization', help='an optimization method'},
+
+      {arg='maxEpoch', type='number', help='maximum number of epochs', default=50},
+      {arg='dispProgress', type='boolean', help='display a progress bar during training/testing', default=true},
+      {arg='save', type='string', help='path to save networks and log training'},
+      {arg='timestamp', type='boolean', help='if true, appends a timestamp to each network saved', default=false}
+   )
+   -- private params
+   self.trainOffset = 0
+   self.testOffset = 0
+end
+
+function OnlineTrainer:log()
+   -- save network
+   local filename = self.save
+   os.execute('mkdir -p ' .. sys.dirname(filename))
+   if self.timestamp then
+      -- use a timestamp to store all networks uniquely
+      filename = filename .. '-' .. os.date("%Y_%m_%d_%X")
+   else
+      -- if no timestamp, just store the previous one
+      if sys.filep(filename) then
+         os.execute('mv ' .. filename .. ' ' .. filename .. '.old')
+      end
+   end
+   print('<trainer> saving network to '..filename)
+   local file = torch.DiskFile(filename,'w')
+   self.module:write(file)
+   file:close()
+end
+
+function OnlineTrainer:train(dataset)
+   self.epoch = self.epoch or 1
+   local module = self.module
+   local criterion = self.criterion
+   self.trainset = dataset
+
+   local shuffledIndices = {}
+   if not self.shuffleIndices then
+      for t = 1,dataset:size() do
+         shuffledIndices[t] = t
+      end
+   else
+      shuffledIndices = lab.randperm(dataset:size())
+   end
+
+   local parameters = nnx.getParameters(module)
+   local gradParameters = nnx.getGradParameters(module)
+
+   while true do
+      print('<trainer> on training set:')
+      print("<trainer> stochastic gradient descent epoch # " .. self.epoch)
+
+      module:zeroGradParameters()
+
+      self.time = sys.clock()
+      self.currentError = 0
+      for t = 1,dataset:size() do
+         -- disp progress
+         if self.dispProgress then
+            xlua.progress(t, dataset:size())
+         end
+
+         -- load new sample
+         local sample = dataset[self.trainOffset + shuffledIndices[t]]
+         local input = sample[1]
+         local target = sample[2]
+         local sample_x = sample.x
+         local sample_y = sample.y
+
+         -- optional preprocess (no learning is done for that guy)
+         if self.preprocessor then input = self.preprocessor:forward(input) end
+
+         -- forward through model and criterion 
+         -- (if no criterion, it is assumed to be contained in the model)
+         local modelOut, error
+         if criterion then
+            modelOut = module:forward(input)
+            error = criterion:forward(modelOut, target)
+         else
+            modelOut, error = module:forward(input, target, sample_x, sample_y)
+         end
+
+         -- accumulate error
+         self.currentError = self.currentError + error
+
+         -- reset gradients
+         module:zeroGradParameters()
+
+         -- backward through model
+         -- (if no criterion, it is assumed that derror is internally generated)
+         if criterion then
+            local derror = criterion:backward(module.output, target)
+            module:backward(input, derror)
+         else
+            module:backward(input)
+         end
+
+         -- update parameters in the model
+         self.optimizer:forward(parameters, gradParameters)
+
+         -- call user hook, if any
+         if self.hookTrainSample then
+            self.hookTrainSample(self, sample)
+         end
+      end
+
+      self.currentError = self.currentError / dataset:size()
+      print("<trainer> current error = " .. self.currentError)
+
+      self.time = sys.clock() - self.time
+      self.time = self.time / dataset:size()
+      print("<trainer> time to learn 1 sample = " .. (self.time*1000) .. 'ms')
+
+      if self.hookTrainEpoch then
+         self.hookTrainEpoch(self)
+      end
+
+      if self.save then self:log() end
+
+      self.epoch = self.epoch + 1
+
+      if dataset.infiniteSet then
+         self.trainOffset = self.trainOffset + dataset:size()
+      end
+
+      if self.maxEpoch > 0 and self.epoch > self.maxEpoch then
+         print("<trainer> you have reached the maximum number of epochs")
+         break
+      end
+   end
+end
+
+
+function OnlineTrainer:test(dataset)
+   print('<trainer> on testing Set:')
+
+   local module = self.module
+   local shuffledIndices = {}
+   local criterion = self.criterion
+   self.currentError = 0
+   self.testset = dataset
+
+   local shuffledIndices = {}
+   if not self.shuffleIndices then
+      for t = 1,dataset:size() do
+         shuffledIndices[t] = t
+      end
+   else
+      shuffledIndices = lab.randperm(dataset:size())
+   end
+   
+   self.time = sys.clock()
+   for t = 1,dataset:size() do
+      -- disp progress
+      if self.dispProgress then
+         xlua.progress(t, dataset:size())
+      end
+
+      -- get new sample
+      local sample = dataset[self.testOffset + shuffledIndices[t]]
+      local input = sample[1]
+      local target = sample[2]
+      
+      -- test sample through current model
+      if self.preprocessor then input = self.preprocessor:forward(input) end
+      if criterion then
+         self.currentError = self.currentError + 
+	    criterion:forward(module:forward(input), target)
+      else
+         local _,error = module:forward(input, target)
+         self.currentError = self.currentError + error
+      end
+
+      -- user hook
+      if self.hookTestSample then
+         self.hookTestSample(self, sample)
+      end
+   end
+
+   self.currentError = self.currentError / dataset:size()
+   print("<trainer> test current error = " .. self.currentError)
+
+   self.time = sys.clock() - self.time
+   self.time = self.time / dataset:size()
+   print("<trainer> time to test 1 sample = " .. (self.time*1000) .. 'ms')
+
+   if self.hookTestEpoch then
+      self.hookTestEpoch(self)
+   end
+
+   if dataset.infiniteSet then
+      self.testOffset = self.testOffset + dataset:size()
+   end
+
+   return self.currentError
+end
+
+function OnlineTrainer:write(file)
+   parent.write(self,file)
+   file:writeObject(self.module)
+   file:writeObject(self.criterion)
+end
+
+function OnlineTrainer:read(file)
+   parent.read(self,file)
+   self.module = file:readObject()
+   self.criterion = file:readObject()
+end
diff --git a/init.lua b/init.lua
index 3fc2ac1..f1b138e 100644
--- a/init.lua
+++ b/init.lua
@@ -103,6 +103,7 @@ torch.include('nnx', 'SGDOptimization.lua')
 
 -- trainers:
 torch.include('nnx', 'Trainer.lua')
+torch.include('nnx', 'OnlineTrainer.lua')
 torch.include('nnx', 'StochasticTrainer.lua')
 
 -- datasets:
diff --git a/nnx-1.0-1.rockspec b/nnx-1.0-1.rockspec
index ba2120c..abcb5da 100644
--- a/nnx-1.0-1.rockspec
+++ b/nnx-1.0-1.rockspec
@@ -77,6 +77,7 @@ build = {
          install_files(/lua/nnx SuperCriterion.lua)
          install_files(/lua/nnx SpatialCriterion.lua)
          install_files(/lua/nnx Trainer.lua)
+         install_files(/lua/nnx OnlineTrainer.lua)
          install_files(/lua/nnx StochasticTrainer.lua)
          install_files(/lua/nnx DataSet.lua)
          install_files(/lua/nnx DataList.lua)