/
train_persp_joint_lsun.lua
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/
train_persp_joint_lsun.lua
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-- train script on perspective images
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))
gtMat = torch.zeros(sampsize, data.gt:size(2), data.gt:size(3), data.gt:size(4))
gt2Mat = torch.zeros(sampsize, data.gt2:size(2), data.gt2:size(3), data.gt2:size(4))
for i = 1, sampsize do
inputMat[{{i},{},{},{}}] = data.inp[{{count},{},{},{}}]
gtMat[{{i},{},{}, {}}] = data.gt[{{count},{},{}, {}}]
gt2Mat[{{i},{},{}, {}}] = data.gt2[{{count},{},{}, {}}]
count = count + 1
end
return inputMat, gtMat, gt2Mat, count
end
function getBatch(data, sampsize, count, idx)
-- select batch
inputMat = torch.zeros(sampsize, 3, 512, 512)
gtMat = torch.zeros(sampsize, 3, 512, 512)
gtMsk = torch.zeros(sampsize, 3, 512, 512)
gt2Mat = torch.zeros(sampsize, 8, 512, 512)
gt2Msk = torch.zeros(sampsize, 8, 512, 512)
for i = 1, sampsize do
dir = data.tr_name[idx[count]]
im = image.load(data.im_path..dir)
data_inp = im
ed = image.load(data.ed_path..dir)
data_gt = ed
jc = matio.load(data.jc_path..string.sub(dir, 1, #dir - 4)..'.mat')
data_gt2 = jc.cor:permute(3,1,2)
-- data augmentation
torch.seed() -- randomization
-- flip
local f_prob = torch.rand(1)
if f_prob[1]>0.5 then
data_inp = image.hflip(data_inp)
data_gt = image.hflip(data_gt)
jc = matio.load(data.jc_path2..string.sub(dir, 1, #dir - 4)..'.mat')
data_gt2 = jc.cor_f:permute(3,1,2)
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])
-- rotate
torch.seed()
local r_prob = torch.rand(1)
r_prob = r_prob[1]*1/9 - 1/18
data_inp = image.rotate(data_inp, r_prob, 'bilinear')
data_gt = image.rotate(data_gt, r_prob, 'bilinear')
data_gt2_1 = image.rotate(data_gt2[{{1,3},{},{}}], r_prob, 'bilinear')
data_gt2_2 = image.rotate(data_gt2[{{4,6},{},{}}], r_prob, 'bilinear')
data_gt2_3 = image.rotate(data_gt2[{{7},{},{}}], r_prob, 'bilinear')
data_gt2_4 = image.rotate(data_gt2[{{8},{},{}}], r_prob, 'bilinear')
data_gt2 = torch.cat({data_gt2_1, data_gt2_2, data_gt2_3, data_gt2_4}, 1)
msk = data_gt:gt(0)
msk2 = data_gt2:gt(0)
inputMat[{{i},{},{},{}}] = data_inp
gtMat[{{i},{},{}, {}}] = data_gt
gtMsk[{{i},{},{}, {}}] = msk
gt2Mat[{{i},{},{}, {}}] = data_gt2
gt2Msk[{{i},{},{}, {}}] = msk2
count = count + 1
if count > tr_size then
count = 1
idx = torch.randperm(tr_size)
end
end
return inputMat, gtMat, gtMsk, gt2Mat, gt2Msk, 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, gtMat, gt2Mat, valcount = getBatch_val(pano_val, 1, valcount)
------------------------------------------------------------
-- forward
inputMat = inputMat:cuda()
gtMat = gtMat:cuda()
gt2Mat = gt2Mat:cuda()
--print('forward')
output = model.core:forward(inputMat)
loss = model.criterion:forward(output[1], gtMat) + model.criterion_2:forward(output[2], gt2Mat)+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, gtMsk, gt2Mat, gt2Msk, count, idx = getBatch(pano_tr, sampleSize, count, idx)
------------------------------------------------------------
-- forward
inputMat = inputMat:cuda()
gtMat = gtMat:cuda()
gt2Mat = gt2Mat:cuda()
output = model.core:forward(inputMat)
loss = model.criterion:forward(output[1], gtMat) + model.criterion_2:forward(output[2], gt2Mat)+ loss
-- backward
loss_d_1 = model.criterion:backward(output[1], gtMat)
loss_d_2 = model.criterion_2:backward(output[2], gt2Mat)
gtMsk = torch.mul(gtMsk, 4)
gtMsk = gtMsk:cuda()
gtMsk_w = torch.cmul(loss_d_1, gtMsk)
loss_d_1 = torch.add(gtMsk_w, loss_d_1)
gt2Msk = torch.mul(gt2Msk, 4)
gt2Msk = gt2Msk:cuda()
gt2Msk_w = torch.cmul(loss_d_2, gt2Msk)
loss_d_2 = torch.add(gt2Msk_w, loss_d_2)
model.core:backward(inputMat, {loss_d_1, loss_d_2})
output = nil
loss_d_1 = nil
loss_d_2 = nil
gtMsk_w = nil
gt2Msk_w = nil
collectgarbage()
end
--print(loss_gt)
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 optim_stat
local iterations = 8000
local minValLoss = 1/0
count = 1
idx = torch.randperm(tr_size)
for i = 1, iterations do
model.core:training()
local _, loss = optim.adam(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/perspfull_joint_lsun.t7", params_save:double())
print("------- Model Saved --------")
params_save = nil
end
end
end