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main.py
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main.py
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from __future__ import print_function
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
import time
import math
from dataloader import listflowfile as lt
from dataloader import SecenFlowLoader 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('--datapath', default='/media/jiaren/ImageNet/SceneFlowData/',
help='datapath')
parser.add_argument('--epochs', type=int, default=10,
help='number of epochs to train')
parser.add_argument('--loadmodel', default= None,
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)
all_left_img, all_right_img, all_left_disp, test_left_img, test_right_img, test_left_disp = lt.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:
print('Load pretrained model')
pretrain_dict = torch.load(args.loadmodel)
model.load_state_dict(pretrain_dict['state_dict'])
print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999))
def train(imgL,imgR, disp_L):
model.train()
if args.cuda:
imgL, imgR, disp_true = imgL.cuda(), imgR.cuda(), disp_L.cuda()
#---------
mask = disp_true < args.maxdisp
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(output,1)
loss = F.smooth_l1_loss(output[mask], disp_true[mask], size_average=True)
loss.backward()
optimizer.step()
return loss.data
def test(imgL,imgR,disp_true):
model.eval()
if args.cuda:
imgL, imgR, disp_true = imgL.cuda(), imgR.cuda(), disp_true.cuda()
#---------
mask = disp_true < 192
#----
if imgL.shape[2] % 16 != 0:
times = imgL.shape[2]//16
top_pad = (times+1)*16 -imgL.shape[2]
else:
top_pad = 0
if imgL.shape[3] % 16 != 0:
times = imgL.shape[3]//16
right_pad = (times+1)*16-imgL.shape[3]
else:
right_pad = 0
imgL = F.pad(imgL,(0,right_pad, top_pad,0))
imgR = F.pad(imgR,(0,right_pad, top_pad,0))
with torch.no_grad():
output3 = model(imgL,imgR)
output3 = torch.squeeze(output3)
if top_pad !=0:
img = output3[:,top_pad:,:]
else:
img = output3
if len(disp_true[mask])==0:
loss = 0
else:
loss = F.l1_loss(img[mask],disp_true[mask]) #torch.mean(torch.abs(img[mask]-disp_true[mask])) # end-point-error
return loss.data.cpu()
def adjust_learning_rate(optimizer, epoch):
lr = 0.001
print(lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def main():
start_full_time = time.time()
for epoch in range(0, args.epochs):
print('This is %d-th epoch' %(epoch))
total_train_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)))
#SAVE
savefilename = args.savemodel+'/checkpoint_'+str(epoch)+'.tar'
torch.save({
'epoch': epoch,
'state_dict': model.state_dict(),
'train_loss': total_train_loss/len(TrainImgLoader),
}, savefilename)
print('full training time = %.2f HR' %((time.time() - start_full_time)/3600))
#------------- TEST ------------------------------------------------------------
total_test_loss = 0
for batch_idx, (imgL, imgR, disp_L) in enumerate(TestImgLoader):
test_loss = test(imgL,imgR, disp_L)
print('Iter %d test loss = %.3f' %(batch_idx, test_loss))
total_test_loss += test_loss
print('total test loss = %.3f' %(total_test_loss/len(TestImgLoader)))
#----------------------------------------------------------------------------------
#SAVE test information
savefilename = args.savemodel+'testinformation.tar'
torch.save({
'test_loss': total_test_loss/len(TestImgLoader),
}, savefilename)
if __name__ == '__main__':
main()