from models import PSNet as PSNet
import argparse
import time
import csv
import numpy as np
import torch
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import torch.optim
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import custom_transforms
from utils import tensor2array, save_checkpoint, save_path_formatter, adjust_learning_rate
from loss_functions import compute_errors_train
from logger import TermLogger, AverageMeter
from itertools import chain
from tensorboardX import SummaryWriter
from sequence_folders import SequenceFolder
parser = argparse.ArgumentParser(description='Structure from Motion Learner training on KITTI and CityScapes Dataset',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('data', metavar='DIR',
help='path to dataset')
parser.add_argument('--dataset-format', default='sequential', metavar='STR',
help='dataset format, stacked: stacked frames (from original TensorFlow code) \
sequential: sequential folders (easier to convert to with a non KITTI/Cityscape dataset')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers')
parser.add_argument('--epochs', default=10, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--epoch-size', default=0, type=int, metavar='N',
help='manual epoch size (will match dataset size if not set)')
parser.add_argument('-b', '--batch-size', default=16, type=int,
metavar='N', help='mini-batch size')
parser.add_argument('--lr', '--learning-rate', default=2e-4, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum for sgd, alpha parameter for adam')
parser.add_argument('--beta', default=0.999, type=float, metavar='M',
help='beta parameters for adam')
parser.add_argument('--weight-decay', '--wd', default=0, type=float,
metavar='W', help='weight decay')
parser.add_argument('--print-freq', default=10, type=int,
metavar='N', help='print frequency')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--pretrained-dps', dest='pretrained_dps', default=None, metavar='PATH',
help='path to pre-trained dispnet model')
parser.add_argument('--seed', default=0, type=int, help='seed for random functions, and network initialization')
parser.add_argument('--log-summary', default='progress_log_summary.csv', metavar='PATH',
help='csv where to save per-epoch train and valid stats')
parser.add_argument('--log-full', default='progress_log_full.csv', metavar='PATH',
help='csv where to save per-gradient descent train stats')
parser.add_argument('--log-output', action='store_true', help='will log dispnet outputs and warped imgs at validation step')
parser.add_argument('--ttype', default='train.txt', type=str, help='Text file indicates input data')
parser.add_argument('--ttype2', default='val.txt', type=str, help='Text file indicates input data')
parser.add_argument('-f', '--training-output-freq', type=int, help='frequence for outputting dispnet outputs and warped imgs at training for all scales if 0 will not output',
metavar='N', default=100)
parser.add_argument('--nlabel', type=int ,default=64, help='number of label')
parser.add_argument('--mindepth', type=float ,default=0.5, help='minimum depth')
n_iter = 0
def main():
global n_iter
args = parser.parse_args()
save_path = save_path_formatter(args, parser)
args.save_path = 'checkpoints'/save_path
print('=> will save everything to {}'.format(args.save_path))
args.save_path.makedirs_p()
torch.manual_seed(args.seed)
training_writer = SummaryWriter(args.save_path)
output_writers = []
if args.log_output:
for i in range(3):
output_writers.append(SummaryWriter(args.save_path/'valid'/str(i)))
# Data loading code
normalize = custom_transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])
train_transform = custom_transforms.Compose([
custom_transforms.RandomScaleCrop(),
custom_transforms.ArrayToTensor(),
normalize
])
valid_transform = custom_transforms.Compose([custom_transforms.ArrayToTensor(), normalize])
print("=> fetching scenes in '{}'".format(args.data))
train_set = SequenceFolder(
args.data,
transform=train_transform,
seed=args.seed,
ttype=args.ttype
)
val_set = SequenceFolder(
args.data,
transform=valid_transform,
seed=args.seed,
ttype=args.ttype2
)
print('{} samples found in {} train scenes'.format(len(train_set), len(train_set.scenes)))
print('{} samples found in {} valid scenes'.format(len(val_set), len(val_set.scenes)))
train_loader = torch.utils.data.DataLoader(
train_set, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers, pin_memory=True)
val_loader = torch.utils.data.DataLoader(
val_set, batch_size=args.batch_size, shuffle=False,
num_workers=args.workers, pin_memory=True)
if args.epoch_size == 0:
args.epoch_size = len(train_loader)
# create model
print("=> creating model")
dpsnet = PSNet(args.nlabel, args.mindepth).cuda()
if args.pretrained_dps:
print("=> using pre-trained weights for DPSNet")
weights = torch.load(args.pretrained_dps)
dpsnet.load_state_dict(weights['state_dict'])
else:
dpsnet.init_weights()
cudnn.benchmark = True
dpsnet = torch.nn.DataParallel(dpsnet)
print('=> setting adam solver')
parameters = chain(dpsnet.parameters())
optimizer = torch.optim.Adam(parameters, args.lr,
betas=(args.momentum, args.beta),
weight_decay=args.weight_decay)
with open(args.save_path/args.log_summary, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow(['train_loss', 'validation_loss'])
with open(args.save_path/args.log_full, 'w') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow(['train_loss'])
for epoch in range(args.epochs):
adjust_learning_rate(args, optimizer, epoch)
# train for one epoch
train_loss = train(args, train_loader, dpsnet, optimizer, args.epoch_size, training_writer)
errors, error_names = validate_with_gt(args, val_loader, dpsnet, epoch, output_writers)
error_string = ', '.join('{} : {:.3f}'.format(name, error) for name, error in zip(error_names, errors))
for error, name in zip(errors, error_names):
training_writer.add_scalar(name, error, epoch)
# Up to you to chose the most relevant error to measure your model's performance, careful some measures are to maximize (such as a1,a2,a3)
decisive_error = errors[0]
save_checkpoint(
args.save_path, {
'epoch': epoch + 1,
'state_dict': dpsnet.module.state_dict()
},
epoch)
with open(args.save_path/args.log_summary, 'a') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([train_loss, decisive_error])
def train(args, train_loader, dpsnet, optimizer, epoch_size, train_writer):
global n_iter
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter(precision=4)
# switch to train mode
dpsnet.train()
end = time.time()
for i, (tgt_img, ref_imgs, ref_poses, intrinsics, intrinsics_inv, tgt_depth) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
tgt_img_var = Variable(tgt_img.cuda())
ref_imgs_var = [Variable(img.cuda()) for img in ref_imgs]
ref_poses_var = [Variable(pose.cuda()) for pose in ref_poses]
intrinsics_var = Variable(intrinsics.cuda())
intrinsics_inv_var = Variable(intrinsics_inv.cuda())
tgt_depth_var = Variable(tgt_depth.cuda()).cuda()
# compute output
pose = torch.cat(ref_poses_var,1)
# get mask
mask = (tgt_depth_var <= args.nlabel*args.mindepth) & (tgt_depth_var >= args.mindepth) & (tgt_depth_var == tgt_depth_var)
mask.detach_()
depths = dpsnet(tgt_img_var, ref_imgs_var, pose, intrinsics_var, intrinsics_inv_var)
disps = [args.mindepth*args.nlabel/(depth) for depth in depths]
loss = 0.
for l, depth in enumerate(depths):
output = torch.squeeze(depth,1)
loss += F.smooth_l1_loss(output[mask], tgt_depth_var[mask], size_average=True) * pow(0.7, len(depths)-l-1)
if i > 0 and n_iter % args.print_freq == 0:
train_writer.add_scalar('total_loss', loss.item(), n_iter)
if args.training_output_freq > 0 and n_iter % args.training_output_freq == 0:
train_writer.add_image('train Input', tensor2array(tgt_img[0]), n_iter)
depth_to_show = tgt_depth_var.data[0].cpu()
depth_to_show[depth_to_show > args.nlabel*args.mindepth] = args.nlabel*args.mindepth
disp_to_show = (args.nlabel*args.mindepth/depth_to_show)
disp_to_show[disp_to_show > args.nlabel] = 0
train_writer.add_image('train Dispnet GT Normalized',
tensor2array(disp_to_show, max_value=args.nlabel, colormap='bone'),
n_iter)
train_writer.add_image('train Depth GT Normalized',
tensor2array(depth_to_show, max_value=args.nlabel*args.mindepth*0.3),
n_iter)
for k,scaled_depth in enumerate(depths):
train_writer.add_image('train Dispnet Output Normalized {}'.format(k),
tensor2array(disps[k].data[0].cpu(), max_value=args.nlabel, colormap='bone'),
n_iter)
train_writer.add_image('train Depth Output Normalized {}'.format(k),
tensor2array(depths[k].data[0].cpu(), max_value=args.nlabel*args.mindepth*0.3),
n_iter)
# record loss and EPE
losses.update(loss.item(), args.batch_size)
# compute gradient and do Adam step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
with open(args.save_path/args.log_full, 'a') as csvfile:
writer = csv.writer(csvfile, delimiter='\t')
writer.writerow([loss.item()])
if i % args.print_freq == 0:
print('Train: Time {} Data {} Loss {}'.format(batch_time, data_time, losses))
if i >= epoch_size - 1:
break
n_iter += 1
return losses.avg[0]
def validate_with_gt(args, val_loader, dpsnet, epoch, output_writers=[]):
batch_time = AverageMeter()
error_names = ['abs_rel', 'abs_diff', 'sq_rel', 'a1', 'a2', 'a3']
errors = AverageMeter(i=len(error_names))
log_outputs = len(output_writers) > 0
# switch to evaluate mode
dpsnet.eval()
end = time.time()
with torch.no_grad():
for i, (tgt_img, ref_imgs, ref_poses, intrinsics, intrinsics_inv, tgt_depth) in enumerate(val_loader):
tgt_img_var = Variable(tgt_img.cuda())
ref_imgs_var = [Variable(img.cuda()) for img in ref_imgs]
ref_poses_var = [Variable(pose.cuda()) for pose in ref_poses]
intrinsics_var = Variable(intrinsics.cuda())
intrinsics_inv_var = Variable(intrinsics_inv.cuda())
tgt_depth_var = Variable(tgt_depth.cuda())
pose = torch.cat(ref_poses_var,1)
output_depth = dpsnet(tgt_img_var, ref_imgs_var, pose, intrinsics_var, intrinsics_inv_var)
output_disp = args.nlabel*args.mindepth/(output_depth)
mask = (tgt_depth <= args.nlabel*args.mindepth) & (tgt_depth >= args.mindepth) & (tgt_depth == tgt_depth)
output = torch.squeeze(output_depth.data.cpu(),1)
if log_outputs and i % 100 == 0 and i/100 < len(output_writers):
index = int(i//100)
if epoch == 0:
output_writers[index].add_image('val Input', tensor2array(tgt_img[0]), 0)
depth_to_show = tgt_depth_var.data[0].cpu()
depth_to_show[depth_to_show > args.nlabel*args.mindepth] = args.nlabel*args.mindepth
disp_to_show = (args.nlabel*args.mindepth/depth_to_show)
disp_to_show[disp_to_show > args.nlabel] = 0
output_writers[index].add_image('val target Disparity Normalized', tensor2array(disp_to_show, max_value=args.nlabel, colormap='bone'), epoch)
output_writers[index].add_image('val target Depth Normalized', tensor2array(depth_to_show, max_value=args.nlabel*args.mindepth*0.3), epoch)
output_writers[index].add_image('val Dispnet Output Normalized', tensor2array(output_disp.data[0].cpu(), max_value=args.nlabel, colormap='bone'), epoch)
output_writers[index].add_image('val Depth Output', tensor2array(output_depth.data[0].cpu(), max_value=args.nlabel*args.mindepth*0.3), epoch)
errors.update(compute_errors_train(tgt_depth, output, mask))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('valid: Time {} Abs Error {:.4f} ({:.4f})'.format(batch_time, errors.val[0], errors.avg[0]))
return errors.avg, error_names
if __name__ == '__main__':
main()
