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main_auxi_v0.py
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main_auxi_v0.py
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#!/usr/bin/env python
# coding=utf-8
'''
Author:Tai Lei
Date:Wed Sep 19 20:30:48 2018
Info:
References: https://github.com/pytorch/examples/tree/master/imagenet
'''
import argparse
import os
import random
import time
import datetime
import math
import logging
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim as optim
from tensorboardX import SummaryWriter
import numpy as np
import sys
sys.path.append(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
import auxi_ver0.auxi_net_v0 as resnet_carla
from auxi_ver0.carla_loader_db_auxi_v0 import CarlaH5Data
from auxi_ver0.helper_auxi_v0 import AverageMeter, save_checkpoint
parser = argparse.ArgumentParser(description='Carla CIL training')
parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
help='number of data loading workers (default: 4)')
parser.add_argument('--batch_size', default=200, type=int, metavar='N',
help='batch size of training')
parser.add_argument('--speed_weight', default=1, type=float,
help='speed weight')
parser.add_argument('--branch-weight', default=1, type=float,
help='branch weight')
parser.add_argument('--id', default="training", type=str)
parser.add_argument('--train-dir', default="/SSD1/datasets/carla/additional_db/gen_data_brake/",
type=str, metavar='PATH', help='training dataset')
parser.add_argument('--eval-dir', default="/SSD1/datasets/carla/additional_db/gen_data_brake_val/",
type=str, metavar='PATH',
help='evaluation dataset')
parser.add_argument('--epochs', default=100, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('--lr', '--learning-rate', default=1e-4, type=float,
metavar='LR', help='initial learning rate')
parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--evaluate-log', default="",
type=str, metavar='PATH',
help='path to log evaluation results (default: none)')
parser.add_argument('--world-size', default=1, type=int,
help='number of distributed processes')
parser.add_argument('--seed', default=None, type=int,
help='seed for initializing training. ')
parser.add_argument('--gpu', default=4, type=int,
help='GPU id to use.')
def output_log(output_str, logger=None):
"""
standard output and logging
"""
print("[{}]: {}".format(datetime.datetime.now(), output_str))
if logger is not None:
logger.critical("[{}]: {}".format(datetime.datetime.now(), output_str))
def log_args(logger):
'''
log args
'''
attrs = [(p, getattr(args, p)) for p in dir(args) if not p.startswith('_')]
for key, value in attrs:
output_log("{}: {}".format(key, value), logger=logger)
def main():
global args
args = parser.parse_args()
log_dir = os.path.join("./", "logs", args.id)
run_dir = os.path.join("./", "runs", args.id)
save_weight_dir = os.path.join("./save_models", args.id)
os.makedirs(log_dir, exist_ok=True)
os.makedirs(save_weight_dir, exist_ok=True)
logging.basicConfig(filename=os.path.join(log_dir, "carla_training.log"),
level=logging.ERROR)
tsbd = SummaryWriter(log_dir=run_dir)
log_args(logging)
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
output_log(
'You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.', logger=logging)
if args.gpu is not None:
output_log('You have chosen a specific GPU. This will completely '
'disable data parallelism.', logger=logging)
args.distributed = args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=0)
model = resnet_carla.resnet34_carla(True)
# criterion = EgoLoss()
criterion = nn.MSELoss()
tsbd.add_graph(model,
(torch.zeros(1, 3, 88, 200),
torch.zeros(1, 1)))
if args.gpu is not None:
model = model.cuda(args.gpu)
else:
model = torch.nn.DataParallel(model).cuda()
# TODO check other papers optimizers
optimizer = optim.Adam(
model.parameters(), args.lr, betas=(0.7, 0.85))
lr_scheduler = optim.lr_scheduler.StepLR(
optimizer, step_size=7, gamma=0.5)
# optionally resume from a checkpoint
if args.resume:
args.resume = os.path.join(save_weight_dir, args.resume)
if os.path.isfile(args.resume):
output_log("=> loading checkpoint '{}'".format(args.resume),
logging)
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['scheduler'])
output_log("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']), logging)
else:
output_log("=> no checkpoint found at '{}'".format(args.resume),
logging)
cudnn.benchmark = True
carla_data = CarlaH5Data(
train_folder=args.train_dir,
eval_folder=args.eval_dir,
batch_size=args.batch_size,
num_workers=args.workers)
train_loader = carla_data.loaders["train"]
eval_loader = carla_data.loaders["eval"]
best_prec = math.inf
if args.evaluate:
args.id = args.id+"_test"
if not os.path.isfile(args.resume):
output_log("=> no checkpoint found at '{}'"
.format(args.resume), logging)
return
if args.evaluate_log == "":
output_log("=> please set evaluate log path with --evaluate-log <log-path>")
# TODO add test func
evaluate(eval_loader, model, criterion, 0, tsbd)
return
for epoch in range(args.start_epoch, args.epochs):
branch_losses, speed_losses, losses = \
train(train_loader, model, criterion, optimizer, epoch, tsbd)
#prec = evaluate(eval_loader, model, criterion, epoch, tsbd)
prec = 0
lr_scheduler.step()
# remember best prec@1 and save checkpoint
is_best = prec < best_prec
best_prec = min(prec, best_prec)
save_checkpoint(
{'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec': best_prec,
'scheduler': lr_scheduler.state_dict(),
'optimizer': optimizer.state_dict()},
args.id,
is_best,
os.path.join(
save_weight_dir,
"{}_{}.pth".format(epoch+1, args.id))
)
def acos_safe(x, eps=1e-4):
sign = torch.sign(x)
slope = np.arccos(1-eps) / eps
return torch.where(abs(x) <= 1-eps,
torch.acos(x),
torch.acos(sign * (1 - eps)) - slope*sign*(abs(x) - 1 + eps))
def get_predicted_wheel_location(steering_angle, v, time_stamp=0.1):
wheel_heading = steering_angle
wheel_traveled_dis = v * time_stamp
return [wheel_traveled_dis * torch.cos(wheel_heading), wheel_traveled_dis * torch.sin(wheel_heading)]
def get_predicted_steering(pred_x, pred_y, v, time_stamp=0.1):
# to prevent over range of arccos or arcsin
# eps = 1e-7
eps = 0.00001
cal_x = torch.clamp(pred_x / (v * time_stamp), -1 + eps, 1 - eps)
cal_y = torch.clamp(pred_y / (v * time_stamp), -1 + eps, 1 - eps)
steering_angle_x = torch.acos(cal_x)
steering_angle_y = torch.asin(cal_y)
return [steering_angle_x, steering_angle_y]
def get_predicted_velocity(pred_x, pred_y, steering_angle, time_stamp=0.1):
v_x = pred_x / (time_stamp * torch.cos(steering_angle))
v_y = pred_y / (time_stamp * torch.sin(steering_angle))
return [v_x, v_y]
def train(loader, model, criterion, optimizer, epoch, writer):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
branch_losses = AverageMeter()
speed_losses = AverageMeter()
# switch to train mode
model.train()
end = time.time()
step = epoch * len(loader)
for i, (img, speed, target, mask) in enumerate(loader):
data_time.update(time.time() - end)
# if args.gpu is not None:
img = img.cuda(args.gpu, non_blocking=True)
speed = speed.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, non_blocking=True)
mask = mask.cuda(args.gpu, non_blocking=True)
branches_out, pred_speed = model(img, speed)
mask_out = branches_out * mask
branch_loss = criterion(mask_out, target) * 4
speed_loss = criterion(pred_speed, speed)
loss = args.branch_weight * branch_loss + \
args.speed_weight * speed_loss
losses.update(loss.item(), args.batch_size)
branch_losses.update(branch_loss.item(), args.batch_size)
speed_losses.update(speed_loss.item(), args.batch_size)
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0 or i == len(loader):
writer.add_scalar('train/branch_loss', branch_losses.val, step+i)
writer.add_scalar('train/speed_loss', speed_losses.val, step+i)
writer.add_scalar('train/loss', losses.val, step+i)
output_log(
'Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Branch loss {branch_loss.val:.3f} ({branch_loss.avg:.3f})\t'
'Speed loss {speed_loss.val:.3f} ({speed_loss.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
.format(
epoch, i, len(loader), batch_time=batch_time,
data_time=data_time, branch_loss=branch_losses,
speed_loss=speed_losses, loss=losses), logging)
return branch_losses.avg, speed_losses.avg, losses.avg
def evaluate(loader, model, criterion, epoch, writer):
batch_time = AverageMeter()
losses = AverageMeter()
# switch to evaluate mode
model.eval()
step = epoch * len(loader)
with torch.no_grad():
end = time.time()
for i, (img, speed, target, mask) in enumerate(loader):
img = img.cuda(args.gpu, non_blocking=True)
speed = speed.cuda(args.gpu, non_blocking=True)
target = target.cuda(args.gpu, non_blocking=True)
mask = mask.cuda(args.gpu, non_blocking=True)
branches_out, pred_speed = model(img, speed)
mask_out = branches_out * mask
branch_loss = criterion(mask_out, target) * 4
speed_loss = criterion(pred_speed, speed)
loss = args.branch_weight * branch_loss + \
args.speed_weight * speed_loss
# measure accuracy and record loss
losses.update(loss.item(), args.batch_size)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# if i % args.print_freq == 0 or i == len(loader):
writer.add_scalar('eval/loss', losses.val, step+i)
output_log(
'Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
.format(
i, len(loader), batch_time=batch_time,
loss=losses), logging)
return losses.avg
if __name__ == '__main__':
main()