/
train_pano_edg.lua
179 lines (142 loc) · 5.61 KB
/
train_pano_edg.lua
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-- train script
require 'sys'
require 'image'
local matio = require 'matio'
local image = require 'image'
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))
for i = 1, sampsize do
inputMat[{{i},{},{},{}}] = data.inp[{{count},{},{},{}}]
data_gt = data.gt[{{count},{},{}, {}}]
gtMat[{{i},{},{}, {}}] = data_gt
count = count + 1
end
return inputMat, 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))
gtMat = torch.zeros(sampsize, data.gt:size(2), data.gt:size(3), data.gt:size(4))
gtMsk = torch.zeros(sampsize, data.gt:size(2), data.gt:size(3), data.gt:size(4))
for i = 1, sampsize do
data_inp = data.inp[{{idx[count]},{},{},{}}]
data_gt = data.gt[{{idx[count]},{},{}, {}}]
-- data augmentation
torch.seed() -- randomization
-- 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_gt = image.hflip(torch.reshape(data_gt, data.gt:size(2), data.gt:size(3), data.gt:size(4)))
data_inp = torch.reshape(data_inp, 1, data.inp:size(2), data.inp:size(3), data.inp:size(4))
data_gt = torch.reshape(data_gt, 1, data.gt:size(2), data.gt:size(3), data.gt:size(4))
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.add(torch.round(torch.mul(torch.rand(1),data.gt:size(4)-2)), 1)
data_inp = torch.cat(data_inp[{{},{},{},{r_prob[1]+1,data.inp:size(4)}}], data_inp[{{},{},{},{1,r_prob[1]}}], 4)
data_gt = torch.cat(data_gt[{{},{},{},{r_prob[1]+1,data.gt:size(4)}}], data_gt[{{},{},{},{1,r_prob[1]}}], 4)
msk = data_gt:gt(0)
inputMat[{{i},{},{},{}}] = data_inp
gtMat[{{i},{},{}, {}}] = data_gt
gtMsk[{{i},{},{}, {}}] = msk
count = count + 1
if count > tr_size then
count = 1
idx = torch.randperm(tr_size)
end
end
return inputMat, gtMat, gtMsk, 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, valcount = getBatch_val(pano_val, 1, valcount)
------------------------------------------------------------
-- forward
inputMat = inputMat:cuda()
gtMat = gtMat:cuda()
--print('forward')
output = model.core:forward(inputMat)
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, gtMat, gtMsk, count, idx = getBatch(pano_tr, sampleSize, count, idx)
------------------------------------------------------------
-- forward
inputMat = inputMat:cuda()
gtMat = gtMat:cuda()
output = model.core:forward(inputMat)
loss = model.criterion:forward(output, gtMat) + loss
-- backward
loss_d_1 = model.criterion:backward(output, gtMat)
gtMsk = torch.mul(gtMsk, 4)
gtMsk = gtMsk:cuda()
gtMsk_w = torch.cmul(loss_d_1, gtMsk)
loss_d = torch.add(gtMsk_w, loss_d_1)
model.core:backward(inputMat, loss_d)
output = nil
loss_d = nil
loss_d_1 = nil
collectgarbage()
end
grad_params:div(numberOfPasses)
-- clip gradient element-wise
grad_params:clamp(-10, 10)
return loss, grad_params
end
losses = {}
vallosses = {}
local optim_state = {opt.lr, opt.epsilon}
local iterations = 8000
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_edg.t7", params_save:double())
print("------- Model Saved --------")
end
losses[#losses + 1] = loss[1]
end
end