finetune.py

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 torchvision import transforms
import pickle

import tensorboardX as tX

import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt


from models import *

parser = argparse.ArgumentParser(description='PSMNet')
parser.add_argument('--maxdisp', type=int ,default=192,
                    help='maxium disparity')
parser.add_argument('--logdir', default='log/runs', help='log directory')
parser.add_argument('--model', default='stackhourglass',
                    help='select model')
parser.add_argument('--datatype', default='2015',
                    help='datapath')
parser.add_argument('--datapath', default='C:/Users/darshanrobotics/Desktop/PSMNet-master/PSMNet-master/dataset/KITTI2015/data_scene_flow/training/',
                    help='datapath')
parser.add_argument('--epochs', type=int, default=150,
                    help='number of epochs to train')
parser.add_argument('--loadmodel', default='C:/Users/darshanrobotics/Desktop/PSMNet-master/PSMNet-master/pretrained_sceneflow.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()

writer = tX.SummaryWriter(log_dir=args.logdir, comment='PSMNet')
device = torch.device('cuda')
print(device)
torch.cuda.empty_cache()
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= 5, shuffle= True, num_workers= 0, drop_last=False)

TestImgLoader = torch.utils.data.DataLoader(
         DA.myImageFloder(test_left_img,test_right_img,test_left_disp, False), 
         batch_size= 5, shuffle= False, num_workers= 0, 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

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#
        disp_true = torch.squeeze(disp_true)
        pred_disp = torch.squeeze(pred_disp)
        print(pred_disp.shape)
        
        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()
    loss_list = []

    for epoch in range(1, args.epochs+1):
       total_train_loss = 0
       total_test_loss = 0
       adjust_learning_rate(optimizer,epoch)
     
           
    #try:
           ## 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
       avg_training_loss = total_train_loss/len(TrainImgLoader)
       print('epoch %d total training loss = %.3f' %(epoch, avg_training_loss))
       
       
               ## 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

       avg_test_loss =  total_test_loss/len(TestImgLoader)*100
       
       print('epoch %d total 3-px error in val = %.3f' %(epoch,  avg_test_loss))
       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))
       loss_list.append([epoch, avg_training_loss, avg_test_loss])

       #SAVE
       with open('loss_list', 'wb') as f:
           pickle.dump(loss_list,f)
           
       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()