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trainer.lua
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trainer.lua
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--
-- Copyright (c) 2016, Facebook, Inc.
-- All rights reserved.
--
-- This source code is licensed under the BSD-style license found in the
-- LICENSE file in the root directory of this source tree. An additional grant
-- of patent rights can be found in the PATENTS file in the same directory.
--
-- The training loop and learning rate schedule
--
local optim = require 'optim'
local M = {}
local Trainer = torch.class('Trainer', M)
function Trainer:__init(model, criterion, opt, optimState)
self.model = model
self.criterion = criterion
self.optimState = optimState or {
learningRate = opt.LR,
learningRateDecay = 0.0,
momentum = opt.momentum,
nesterov = opt.nesterov,
dampening = 0.0,
weightDecay = opt.weightDecay,
}
self.opt = opt
self.params, self.gradParams = model:getParameters()
print("-- Model parameters --")
print(self.params:size(1))
end
function Trainer:train(epoch, dataloader)
-- Trains the model for a single epoch
self.optimState.learningRate = self:learningRate(epoch)
local totalTime = 0
local timer = torch.Timer()
local dataTimer = torch.Timer()
local function feval()
return self.criterion.output, self.gradParams
end
local absCriterion = nn.AbsCriterion():cuda()
local trainSize = dataloader:size()
local top1Sum, top5Sum, lossSum, lossAbsSum = 0.0, 0.0, 0.0, 0.0
local N = 0
print('=> Training epoch # ' .. epoch)
-- set the batch norm to training mode
self.model:training()
for n, sample in dataloader:run() do
local dataTime = dataTimer:time().real
-- Copy input and target to the GPU
self:copyInputs(sample)
local output = self.model:forward(self.input):float()
local batchSize = output:size(1)
local loss = self.criterion:forward(self.model.output, self.target)
-- Average prediction for regression
local softmax = nn.SoftMax():cuda()
local outputSoft = softmax:forward(self.model.output)
local avgPred = outputSoft * torch.range(1,self.opt.nClasses):cuda()
local lossAbs = absCriterion:forward(avgPred, self.target)
self.model:zeroGradParameters()
self.criterion:backward(self.model.output, self.target)
self.model:backward(self.input, self.criterion.gradInput)
if self.opt.optimizer == 'sgd' then
optim.sgd(feval, self.params, self.optimState)
elseif self.opt.optimizer == 'adam' then
optim.adam(feval, self.params, self.optimState)
elseif self.opt.optimizer == 'rmsprop' then
optim.rmsprop(feval, self.params, self.optimState)
elseif self.opt.optimizer == 'adagrad' then
optim.adagrad(feval, self.params, self.optimState)
elseif self.opt.optimizer == 'lbfgs' then
optim.lbfgs(feval, self.params, self.optimState)
elseif self.opt.optimizer == 'nag' then
optim.nag(feval, self.params, self.optimState)
end
local top1, top5 = self:computeScore(output, sample.target, 1)
top1Sum = top1Sum + top1*batchSize
top5Sum = top5Sum + top5*batchSize
lossSum = lossSum + loss*batchSize
lossAbsSum = lossAbsSum + lossAbs*batchSize
N = N + batchSize
if self.opt.showFullOutput then
print((' | Epoch: [%d][%d/%d] Time %.3f Data %.3f Err %1.4f top1 %7.3f top5 %7.3f lossAbs %7.3f'):format(
epoch, n, trainSize, timer:time().real, dataTime, loss, top1, top5, lossAbs))
end
totalTime = totalTime + timer:time().real
-- check that the storage didn't get changed due to an unfortunate getParameters call
assert(self.params:storage() == self.model:parameters()[1]:storage())
timer:reset()
dataTimer:reset()
end
if self.opt.showFullOutput then
print((' * [Train] Finished epoch # %d top1: %7.3f top5: %7.3f loss: %7.3f lossAbs: %7.3f \n'):format(
epoch, top1Sum / N, top5Sum / N, lossSum / N, lossAbsSum / N))
end
print((' || Train total time: %2.5f'):format(totalTime))
return top1Sum / N, top5Sum / N, lossSum / N, lossAbsSum / N
end
function Trainer:test(epoch, dataloader)
-- Computes the top-1 and top-5 err on the validation set
local totalTime = 0
local timer = torch.Timer()
local dataTimer = torch.Timer()
local size = dataloader:size()
local nCrops = self.opt.tenCrop and 10 or 1
local top1Sum, top5Sum, lossSum, lossAbsSum = 0.0, 0.0, 0.0, 0.0
local N = 0
local absCriterion = nn.AbsCriterion():cuda()
self.model:evaluate()
for n, sample in dataloader:run() do
local dataTime = dataTimer:time().real
-- Copy input and target to the GPU
self:copyInputs(sample)
local output = self.model:forward(self.input):float()
local batchSize = output:size(1) / nCrops
local loss = self.criterion:forward(self.model.output, self.target)
-- Average prediction for regression
local softmax = nn.SoftMax():cuda()
local outputSoft = softmax:forward(self.model.output)
local avgPred = outputSoft * torch.range(1,self.opt.nClasses):cuda()
local lossAbs = absCriterion:forward(avgPred, self.target)
local top1, top5 = self:computeScore(output, sample.target, nCrops)
top1Sum = top1Sum + top1*batchSize
top5Sum = top5Sum + top5*batchSize
lossSum = lossSum + loss*batchSize
lossAbsSum = lossAbsSum + lossAbs*batchSize
N = N + batchSize
if self.opt.showFullOutput then
print((' | Test: [%d][%d/%d] Time %.3f Data %.3f top1 %7.3f (%7.3f) top5 %7.3f (%7.3f) loss %7.3f (%7.3f) lossAbs %7.3f (%7.3f)'):format(
epoch, n, size, timer:time().real, dataTime, top1, top1Sum / N, top5, top5Sum / N, loss, lossSum / N, lossAbs, lossAbsSum / N))
end
totalTime = totalTime + timer:time().real
timer:reset()
dataTimer:reset()
end
self.model:training()
if self.opt.showFullOutput then
print((' * [Test] Finished epoch # %d top1: %7.3f top5: %7.3f loss: %7.3f lossAbs: %7.3f \n'):format(
epoch, top1Sum / N, top5Sum / N, lossSum / N, lossAbsSum / N))
end
print((' || Test total time: %2.5f'):format(totalTime))
return top1Sum / N, top5Sum / N, lossSum / N, lossAbsSum / N
end
function Trainer:computeScore(output, target, nCrops)
if nCrops > 1 then
-- Sum over crops
output = output:view(output:size(1) / nCrops, nCrops, output:size(2))
--:exp()
:sum(2):squeeze(2)
end
-- Coputes the top1 and top5 error rate
local batchSize = output:size(1)
local _ , predictions = output:float():topk(5, 2, true, true) -- descending
-- Find which predictions match the target
local correct = predictions:eq(target:long():view(batchSize, 1):expandAs(predictions))
-- Top-1 score
local top1 = correct:narrow(2, 1, 1):sum() / batchSize
-- Top-5 score, if there are at least 5 classes
local len = math.min(5, correct:size(2))
local top5 = correct:narrow(2, 1, len):sum() / batchSize
return top1 * 100, top5 * 100
end
local function getCudaTensorType(tensorType)
if tensorType == 'torch.CudaHalfTensor' then
return cutorch.createCudaHostHalfTensor()
elseif tensorType == 'torch.CudaDoubleTensor' then
return cutorch.createCudaHostDoubleTensor()
else
return cutorch.createCudaHostTensor()
end
end
function Trainer:copyInputs(sample)
-- Copies the input to a CUDA tensor, if using 1 GPU, or to pinned memory,
-- if using DataParallelTable. The target is always copied to a CUDA tensor
self.input = self.input or (self.opt.nGPU == 1
and torch[self.opt.tensorType:match('torch.(%a+)')]()
or getCudaTensorType(self.opt.tensorType))
self.target = self.target or torch.CudaTensor()
self.input:resize(sample.input:size()):copy(sample.input)
self.target:resize(sample.target:size()):copy(sample.target)
end
function Trainer:learningRate(epoch)
-- Training schedule
-- local decay = math.floor((epoch - 1) / self.opt.LRDecayStep)
-- local newLR = self.opt.LR * math.pow(0.1, decay)
-- print('New LR: ' .. newLR)
-- return newLR
-- local decay = 0
-- if self.opt.optimizer == 'adam' then
-- decay = 1.0/math.sqrt(epoch)
-- return self.opt.LR * decay
-- else
-- decay = math.floor((epoch - 1) / self.opt.LRDecayStep)
-- return self.opt.LR * math.pow(0.1, decay)
-- end
return self.opt.LR
end
return M.Trainer