from __future__ import print_function
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable
import torch.nn.functional as F
import skimage
import skimage.io
import skimage.transform
import numpy as np
import time
import math
from dataloader import KITTIloader2015 as ls
from dataloader import KITTILoader as DA
from models import *
parser = argparse.ArgumentParser(description='PSMNet')
parser.add_argument('--maxdisp', type=int ,default=192,
help='maxium disparity')
parser.add_argument('--model', default='stackhourglass',
help='select model')
parser.add_argument('--datatype', default='2015',
help='datapath')
parser.add_argument('--datapath', default='/media/jiaren/ImageNet/data_scene_flow_2015/training/',
help='datapath')
parser.add_argument('--epochs', type=int, default=300,
help='number of epochs to train')
parser.add_argument('--loadmodel', default='./trained/submission_model.tar',
help='load model')
parser.add_argument('--savemodel', default='./',
help='save model')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.datatype == '2015':
from dataloader import KITTIloader2015 as ls
elif args.datatype == '2012':
from dataloader import KITTIloader2012 as ls
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = ls.dataloader(args.datapath)
TrainImgLoader = torch.utils.data.DataLoader(
DA.myImageFloder(all_left_img,all_right_img,all_left_disp, True),
batch_size= 12, shuffle= True, num_workers= 8, drop_last=False)
TestImgLoader = torch.utils.data.DataLoader(
DA.myImageFloder(test_left_img,test_right_img,test_left_disp, False),
batch_size= 8, shuffle= False, num_workers= 4, drop_last=False)
if args.model == 'stackhourglass':
model = stackhourglass(args.maxdisp)
elif args.model == 'basic':
model = basic(args.maxdisp)
else:
print('no model')
if args.cuda:
model = nn.DataParallel(model)
model.cuda()
if args.loadmodel is not None:
state_dict = torch.load(args.loadmodel)
model.load_state_dict(state_dict['state_dict'])
print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
optimizer = optim.Adam(model.parameters(), lr=0.1, betas=(0.9, 0.999))
def train(imgL,imgR,disp_L):
model.train()
imgL = Variable(torch.FloatTensor(imgL))
imgR = Variable(torch.FloatTensor(imgR))
disp_L = Variable(torch.FloatTensor(disp_L))
if args.cuda:
imgL, imgR, disp_true = imgL.cuda(), imgR.cuda(), disp_L.cuda()
#---------
mask = (disp_true > 0)
mask.detach_()
#----
optimizer.zero_grad()
if args.model == 'stackhourglass':
output1, output2, output3 = model(imgL,imgR)
output1 = torch.squeeze(output1,1)
output2 = torch.squeeze(output2,1)
output3 = torch.squeeze(output3,1)
loss = 0.5*F.smooth_l1_loss(output1[mask], disp_true[mask], size_average=True) + 0.7*F.smooth_l1_loss(output2[mask], disp_true[mask], size_average=True) + F.smooth_l1_loss(output3[mask], disp_true[mask], size_average=True)
elif args.model == 'basic':
output = model(imgL,imgR)
output = torch.squeeze(output3,1)
loss = F.smooth_l1_loss(output3[mask], disp_true[mask], size_average=True)
loss.backward()
optimizer.step()
return loss.data[0]
def test(imgL,imgR,disp_true):
model.eval()
imgL = Variable(torch.FloatTensor(imgL))
imgR = Variable(torch.FloatTensor(imgR))
if args.cuda:
imgL, imgR = imgL.cuda(), imgR.cuda()
with torch.no_grad():
output3 = model(imgL,imgR)
pred_disp = output3.data.cpu()
#computing 3-px error#
true_disp = disp_true
index = np.argwhere(true_disp>0)
disp_true[index[0][:], index[1][:], index[2][:]] = np.abs(true_disp[index[0][:], index[1][:], index[2][:]]-pred_disp[index[0][:], index[1][:], index[2][:]])
correct = (disp_true[index[0][:], index[1][:], index[2][:]] < 3)|(disp_true[index[0][:], index[1][:], index[2][:]] < true_disp[index[0][:], index[1][:], index[2][:]]*0.05)
torch.cuda.empty_cache()
return 1-(float(torch.sum(correct))/float(len(index[0])))
def adjust_learning_rate(optimizer, epoch):
if epoch <= 200:
lr = 0.001
else:
lr = 0.0001
print(lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def main():
max_acc=0
max_epo=0
start_full_time = time.time()
for epoch in range(1, args.epochs+1):
total_train_loss = 0
total_test_loss = 0
adjust_learning_rate(optimizer,epoch)
## training ##
for batch_idx, (imgL_crop, imgR_crop, disp_crop_L) in enumerate(TrainImgLoader):
start_time = time.time()
loss = train(imgL_crop,imgR_crop, disp_crop_L)
print('Iter %d training loss = %.3f , time = %.2f' %(batch_idx, loss, time.time() - start_time))
total_train_loss += loss
print('epoch %d total training loss = %.3f' %(epoch, total_train_loss/len(TrainImgLoader)))
## Test ##
for batch_idx, (imgL, imgR, disp_L) in enumerate(TestImgLoader):
test_loss = test(imgL,imgR, disp_L)
print('Iter %d 3-px error in val = %.3f' %(batch_idx, test_loss*100))
total_test_loss += test_loss
print('epoch %d total 3-px error in val = %.3f' %(epoch, total_test_loss/len(TestImgLoader)*100))
if total_test_loss/len(TestImgLoader)*100 > max_acc:
max_acc = total_test_loss/len(TestImgLoader)*100
max_epo = epoch
print('MAX epoch %d total test error = %.3f' %(max_epo, max_acc))
#SAVE
savefilename = args.savemodel+'finetune_'+str(epoch)+'.tar'
torch.save({
'epoch': epoch,
'state_dict': model.state_dict(),
'train_loss': total_train_loss/len(TrainImgLoader),
'test_loss': total_test_loss/len(TestImgLoader)*100,
}, savefilename)
print('full finetune time = %.2f HR' %((time.time() - start_full_time)/3600))
print(max_epo)
print(max_acc)
if __name__ == '__main__':
main()
