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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='batchSize', type='number',
help='[mini] batch size', default=1},
{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}
)
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
while true do
print('<trainer> on training set:')
print("<trainer> online epoch # " .. self.epoch .. ' [batchSize = ' .. self.batchSize .. ']')
self.time = sys.clock()
self.currentError = 0
for t = 1,dataset:size(),self.batchSize do
-- disp progress
if self.dispProgress then
xlua.progress(t, dataset:size())
end
-- create mini batch
local inputs = {}
local targets = {}
for i = t,math.min(t+self.batchSize-1,dataset:size()) do
-- load new sample
local sample = dataset[i]
local input = sample[1]
local target = sample[2]
-- optional preprocess (no learning is done for that guy)
if self.preprocessor then input = self.preprocessor:forward(input) end
-- store input/target
table.insert(inputs, input)
table.insert(targets, target)
end
-- optimize the model given current input/target set
local error = self.optimizer:forward(inputs, targets)
-- accumulate error
self.currentError = self.currentError + error
-- call user hook, if any
if self.hookTrainSample then
self.hookTrainSample(self, {inputs[#inputs], targets[#targets]})
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 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 criterion = self.criterion
self.currentError = 0
self.testset = dataset
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[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
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
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