/
train_pano_box.lua
184 lines (143 loc) · 5.91 KB
/
train_pano_box.lua
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-- train script
require 'sys'
require 'image'
local matio = require 'matio'
sampleSize = opt.batchSize
numberOfPasses = opt.numPasses
function getBatch_val(data, sampsize, count)
-- select batch
inputMat = torch.zeros(sampsize, data.inp:size(2), data.inp:size(3), data.inp:size(4))
inputMat2 = torch.zeros(sampsize, data.inp2:size(2), data.inp2:size(3), data.inp2:size(4))
gtMat = torch.zeros(sampsize, 6)
for i = 1, sampsize do
inputMat[{{i},{},{},{}}] = data.inp[{{count},{},{},{}}]
inputMat2[{{i},{},{},{}}] = data.inp2[{{count},{},{},{}}]
gtMat[{{i},{}}] = data.gt[{{count},{1}, {1,6}}]
count = count + 1
end
return inputMat, inputMat2, gtMat, count
end
function getBatch(data, sampsize, count, idx)
-- select batch
inputMat = torch.zeros(sampsize, data.inp:size(2), data.inp:size(3), data.inp:size(4))
inputMat2 = torch.zeros(sampsize, data.inp2:size(2), data.inp2:size(3), data.inp2:size(4))
gtMat = torch.zeros(sampsize,6)
for i = 1, sampsize do
data_inp = data.inp[{{idx[count]},{},{},{}}]
data_inp2 = data.inp2[{{idx[count]},{},{},{}}]
data_gt = data.gt[{{idx[count]},{1}, {1,6}}]
-- data augmentation
torch.seed() -- randomization
-- rotate
torch.seed()
local r_prob = torch.add(torch.round(torch.mul(torch.rand(1),data.gt:size(2)-2)), 1)
if r_prob[1] > 0 then
data_inp = torch.cat(data_inp[{{},{},{},{data.inp:size(4) - r_prob[1]+1,data.inp:size(4)}}], data_inp[{{},{},{},{1,data.inp:size(4) - r_prob[1]}}], 4)
data_inp2 = torch.cat(data_inp2[{{},{},{},{data.inp2:size(4) - r_prob[1]+1,data.inp2:size(4)}}], data_inp2[{{},{},{},{1,data.inp2:size(4) - r_prob[1]}}], 4)
data_gt = data.gt[{{idx[count]},{r_prob[1]+1}, {1,6}}]
end
-- flip
local f_prob = torch.rand(1)
if f_prob[1]>0.5 then
data_inp = image.hflip(torch.reshape(data_inp, data.inp:size(2), data.inp:size(3), data.inp:size(4)))
data_inp2 = image.hflip(torch.reshape(data_inp2, data.inp2:size(2), data.inp2:size(3), data.inp2:size(4)))
data_inp = torch.reshape(data_inp, 1, data.inp:size(2), data.inp:size(3), data.inp:size(4))
data_inp2 = torch.reshape(data_inp2, 1, data.inp2:size(2), data.inp2:size(3), data.inp2:size(4))
data_gt = data.gt[{{idx[count]},{r_prob[1]+1}, {7,12}}]
end
-- gamma
--torch.seed()
--local g_prob = torch.add(torch.mul(torch.rand(1),1.5), 0.5)
--data_inp = torch.pow(data_inp, g_prob[1])
inputMat[{{i},{},{},{}}] = data_inp
inputMat2[{{i},{},{},{}}] = data_inp2
gtMat[{{i},{}}] = data_gt
count = count + 1
if count > tr_size then
count = 1
idx = torch.randperm(tr_size)
end
end
return inputMat, inputMat2, gtMat, count, idx
end
function getValLoss()
local valnumberOfPasses = torch.floor(pano_val.inp:size(1)/1)
local loss = 0
local valcount = 1
--local out
for i=1, valnumberOfPasses do
--------------------- get mini-batch -----------------------
inputMat, inputMat2, gtMat, valcount = getBatch_val(pano_val, 1, valcount)
------------------------------------------------------------
-- forward
inputMat = inputMat:cuda()
inputMat2 = inputMat2:cuda()
gtMat = gtMat:cuda()
--print('forward')
output = model.core:forward({inputMat, inputMat2})
loss = model.criterion:forward(output, gtMat) + loss
output = nil
collectgarbage()
end
loss = loss / valnumberOfPasses
return loss
end
-- do fwd/bwd and return loss, grad_params
function feval(x)
if x ~= params then
params:copy(x)
end
grad_params:zero()
local loss = 0
-- add for loop to increase mini-batch size
for i=1, numberOfPasses do
--------------------- get mini-batch -----------------------
--inputMat, gtMat, gtMask = getBatch_rand(pano_tr, sampleSize)
inputMat, inputMat2, gtMat, count, idx = getBatch(pano_tr, sampleSize, count, idx)
------------------------------------------------------------
-- forward
inputMat = inputMat:cuda()
inputMat2 = inputMat2:cuda()
gtMat = gtMat:cuda()
output = model.core:forward({inputMat, inputMat2})
loss = model.criterion:forward(output, gtMat) + loss
-- backward
loss_d_1 = model.criterion:backward(output, gtMat)
model.core:backward({inputMat,inputMat2}, loss_d_1)
output = nil
loss_d_1 = nil
collectgarbage()
end
grad_params:div(numberOfPasses)
-- clip gradient element-wise
grad_params:clamp(-10, 10)
return loss/numberOfPasses, grad_params
end
losses = {}
vallosses = {}
local optim_state = {opt.lr, opt.epsilon}
local iterations = 9000
local minValLoss = 1/0
count = 1
idx = torch.randperm(pano_tr.inp:size(1))
for i = 1, iterations do
model.core:training()
local _, loss = optim.adam(feval, params, optim_state)
--local _, loss = optim.rmsprop(feval, params, optim_state)
print(string.format("update param, loss = %6.8f, gradnorm = %6.4e", loss[1], grad_params:clone():norm()))
if i % 20 == 0 then
print(string.format("iteration %4d, loss = %6.8f, gradnorm = %6.4e", i, loss[1], grad_params:norm()))
model.core:evaluate()
valLoss, output = getValLoss()
vallosses[#vallosses + 1] = valLoss
print(string.format("validation loss = %6.8f", valLoss))
if minValLoss > valLoss then
minValLoss = valLoss
params_save = params:clone()
nn.utils.recursiveType(params_save, 'torch.DoubleTensor')
torch.save("./model/panofull_box.t7", params_save:double())
print("------- Model Saved --------")
end
losses[#losses + 1] = loss[1]
end
end