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train.lua
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train.lua
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-- Copyright 2015 Carnegie Mellon University
--
-- Licensed under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License.
-- You may obtain a copy of the License at
--
-- http://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS,
-- WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-- See the License for the specific language governing permissions and
-- limitations under the License.
require 'optim'
require 'fbnn'
require 'image'
paths.dofile("OpenFaceOptim.lua")
local optimMethod = optim.adadelta
local optimState = {} -- Use for other algorithms like SGD
local optimator = OpenFaceOptim(model, optimState)
trainLogger = optim.Logger(paths.concat(opt.save, 'train.log'))
local batchNumber
local triplet_loss
function train()
print('==> doing epoch on training data:')
print("==> online epoch # " .. epoch)
batchNumber = 0
cutorch.synchronize()
-- set the dropouts to training mode
model:training()
model:cuda() -- get it back on the right GPUs.
local tm = torch.Timer()
triplet_loss = 0
local i = 1
while batchNumber < opt.epochSize do
-- queue jobs to data-workers
donkeys:addjob(
-- the job callback (runs in data-worker thread)
function()
local inputs, numPerClass = trainLoader:samplePeople(opt.peoplePerBatch,
opt.imagesPerPerson)
return sendTensor(inputs), sendTensor(numPerClass)
end,
-- the end callback (runs in the main thread)
trainBatch
)
if i % 5 == 0 then
donkeys:synchronize()
end
i = i + 1
end
donkeys:synchronize()
cutorch.synchronize()
triplet_loss = triplet_loss / batchNumber
trainLogger:add{
['avg triplet loss (train set)'] = triplet_loss,
}
print(string.format('Epoch: [%d][TRAINING SUMMARY] Total Time(s): %.2f\t'
.. 'average triplet loss (per batch): %.2f',
epoch, tm:time().real, triplet_loss))
print('\n')
collectgarbage()
local function sanitize(net)
net:apply(function (val)
for name,field in pairs(val) do
if torch.type(field) == 'cdata' then val[name] = nil end
if name == 'homeGradBuffers' then val[name] = nil end
if name == 'input_gpu' then val['input_gpu'] = {} end
if name == 'gradOutput_gpu' then val['gradOutput_gpu'] = {} end
if name == 'gradInput_gpu' then val['gradInput_gpu'] = {} end
if (name == 'output' or name == 'gradInput')
and torch.type(field) == 'torch.CudaTensor' then
cutorch.withDevice(field:getDevice(), function() val[name] = field.new() end)
end
end
end)
end
sanitize(model)
torch.save(paths.concat(opt.save, 'model_' .. epoch .. '.t7'),
model.modules[1]:float())
torch.save(paths.concat(opt.save, 'optimState_' .. epoch .. '.t7'), optimState)
collectgarbage()
end -- of train()
local inputsCPU = torch.FloatTensor()
local numPerClass = torch.FloatTensor()
local timer = torch.Timer()
function trainBatch(inputsThread, numPerClassThread)
if batchNumber >= opt.epochSize then
return
end
cutorch.synchronize()
timer:reset()
receiveTensor(inputsThread, inputsCPU)
receiveTensor(numPerClassThread, numPerClass)
-- inputs:resize(inputsCPU:size()):copy(inputsCPU)
local numImages = inputsCPU:size(1)
local embeddings = torch.Tensor(numImages, 128)
local singleNet = model.modules[1]
local beginIdx = 1
local inputs = torch.CudaTensor()
while beginIdx <= numImages do
local endIdx = math.min(beginIdx+opt.batchSize-1, numImages)
local range = {{beginIdx,endIdx}}
local sz = inputsCPU[range]:size()
inputs:resize(sz):copy(inputsCPU[range])
local reps = singleNet:forward(inputs):float()
embeddings[range] = reps
beginIdx = endIdx + 1
end
assert(beginIdx - 1 == numImages)
local numTrips = numImages - opt.peoplePerBatch
local as = torch.Tensor(numTrips, inputs:size(2),
inputs:size(3), inputs:size(4))
local ps = torch.Tensor(numTrips, inputs:size(2),
inputs:size(3), inputs:size(4))
local ns = torch.Tensor(numTrips, inputs:size(2),
inputs:size(3), inputs:size(4))
function dist(emb1, emb2)
local d = emb1 - emb2
return d:cmul(d):sum()
end
local tripIdx = 1
local shuffle = torch.randperm(numTrips)
local embStartIdx = 1
for i = 1,opt.peoplePerBatch do
local n = numPerClass[i]
for j = 1,n-1 do
local aIdx = embStartIdx
local pIdx = embStartIdx+j
as[shuffle[tripIdx]] = inputsCPU[aIdx]
ps[shuffle[tripIdx]] = inputsCPU[pIdx]
-- Select a semi-hard negative that has a distance
-- further away from the positive exemplar.
local posDist = dist(embeddings[aIdx], embeddings[pIdx])
local selNegIdx = embStartIdx
while selNegIdx >= embStartIdx and selNegIdx <= embStartIdx+n-1 do
selNegIdx = (torch.random() % numImages) + 1
end
local selNegDist = dist(embeddings[aIdx], embeddings[selNegIdx])
for k = 1,numImages do
if k < embStartIdx or k > embStartIdx+n-1 then
local negDist = dist(embeddings[aIdx], embeddings[k])
if posDist < negDist and negDist < selNegDist and negDist < alpha then
selNegDist = negDist
selNegIdx = k
end
end
end
ns[shuffle[tripIdx]] = inputsCPU[selNegIdx]
tripIdx = tripIdx + 1
end
embStartIdx = embStartIdx + n
end
assert(embStartIdx - 1 == numImages)
assert(tripIdx - 1 == numTrips)
local beginIdx = 1
local asCuda = torch.CudaTensor()
local psCuda = torch.CudaTensor()
local nsCuda = torch.CudaTensor()
-- Return early if the loss is 0 for `numZeros` iterations.
local numZeros = 4
local zeroCounts = torch.IntTensor(numZeros):zero()
local zeroIdx = 1
-- Return early if the loss shrinks too much.
-- local firstLoss = nil
-- TODO: Should be <=, but batches with just one image cause errors.
while beginIdx < numTrips do
local endIdx = math.min(beginIdx+opt.batchSize, numTrips)
local range = {{beginIdx,endIdx}}
local sz = as[range]:size()
asCuda:resize(sz):copy(as[range])
psCuda:resize(sz):copy(ps[range])
nsCuda:resize(sz):copy(ns[range])
local err, outputs = optimator:optimizeTriplet(optimMethod,
{asCuda, psCuda, nsCuda},
criterion)
cutorch.synchronize()
batchNumber = batchNumber + 1
print(('Epoch: [%d][%d/%d]\tTime %.3f\ttripErr %.2e'):format(
epoch, batchNumber, opt.epochSize, timer:time().real, err))
timer:reset()
triplet_loss = triplet_loss + err
-- Return early if the epoch is over.
if batchNumber >= opt.epochSize then
return
end
-- Return early if the loss is 0 for `numZeros` iterations.
zeroCounts[zeroIdx] = (err == 0.0) and 1 or 0 -- Boolean to int.
zeroIdx = (zeroIdx % numZeros) + 1
if zeroCounts:sum() == numZeros then
return
end
-- Return early if the loss shrinks too much.
-- if firstLoss == nil then
-- firstLoss = err
-- else
-- -- Triplets trivially satisfied if err=0
-- if err ~= 0 and firstLoss/err > 4 then
-- return
-- end
-- end
beginIdx = endIdx + 1
end
assert(beginIdx - 1 == numTrips or beginIdx == numTrips)
end