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arguments.py
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import argparse
import math
import torch
def get_args():
parser = argparse.ArgumentParser(description='Active-Neural-SLAM')
## General Arguments
parser.add_argument('--seed', type=int, default=1,
help='random seed (default: 1)')
parser.add_argument('--auto_gpu_config', type=int, default=1)
parser.add_argument('--total_num_scenes', type=str, default="auto")
parser.add_argument('-n', '--num_processes', type=int, default=1,
help="""how many training processes to use (default:4)
Overridden when auto_gpu_config=1
and training on gpus """)
parser.add_argument('--num_processes_per_gpu', type=int, default=11)
parser.add_argument('--num_processes_on_first_gpu', type=int, default=0)
parser.add_argument('--num_episodes', type=int, default=1000000,
help='number of training episodes (default: 1000000)')
parser.add_argument('--no_cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--eval', type=int, default=0,
help='1: evaluate models (default: 0)')
parser.add_argument('--train_global', type=int, default=1,
help="""0: Do not train the Global Policy
1: Train the Global Policy (default: 1)""")
parser.add_argument('--train_local', type=int, default=1,
help="""0: Do not train the Local Policy
1: Train the Local Policy (default: 1)""")
parser.add_argument('--train_slam', type=int, default=1,
help="""0: Do not train the Neural SLAM Module
1: Train the Neural SLAM Module (default: 1)""")
# Logging, loading models, visualization
parser.add_argument('--log_interval', type=int, default=10,
help="""log interval, one log per n updates
(default: 10) """)
parser.add_argument('--save_interval', type=int, default=1,
help="""save interval""")
parser.add_argument('-d', '--dump_location', type=str, default="./tmp/",
help='path to dump models and log (default: ./tmp/)')
parser.add_argument('--exp_name', type=str, default="exp1",
help='experiment name (default: exp1)')
parser.add_argument('--save_periodic', type=int, default=500000,
help='Model save frequency in number of updates')
parser.add_argument('--load_slam', type=str, default="0",
help="""model path to load,
0 to not reload (default: 0)""")
parser.add_argument('--load_global', type=str, default="0",
help="""model path to load,
0 to not reload (default: 0)""")
parser.add_argument('--load_local', type=str, default="0",
help="""model path to load,
0 to not reload (default: 0)""")
parser.add_argument('-v', '--visualize', type=int, default=1,
help='1:Render the frame (default: 0)')
parser.add_argument('--vis_type', type=int, default=1,
help='1: Show predicted map, 2: Show GT map')
parser.add_argument('--print_images', type=int, default=0,
help='1: save visualization as images')
parser.add_argument('--save_trajectory_data', type=str, default="0")
# Environment, dataset and episode specifications
parser.add_argument('-efw', '--env_frame_width', type=int, default=256, # 256 this is the resolution of the scene
help='Frame width (default:84)')
parser.add_argument('-efh', '--env_frame_height', type=int, default=256, # 256
help='Frame height (default:84)')
parser.add_argument('-fw', '--frame_width', type=int, default=256, # 128 for ANS, 256 for my method # this is the resolution for image
help='Frame width (default:84)')
parser.add_argument('-fh', '--frame_height', type=int, default=256, # 128 for ANS, 256 for my method
help='Frame height (default:84)')
parser.add_argument('-el', '--max_episode_length', type=int, default=1000,#1000
help="""Maximum episode length in seconds for
Doom (default: 180)""")
parser.add_argument("--sim_gpu_id", type=int, default=0,
help="gpu id on which scenes are loaded")
parser.add_argument("--task_config", type=str,
default="tasks/pointnav_gibson.yaml",#gibson # pointnav_gibson or pointnav_gibsontest #
help="path to config yaml containing task information")
parser.add_argument("--split", type=str, default="train",
help="dataset split (train | val | val_mini) ")
parser.add_argument('-na', '--noisy_actions', type=int, default=0)#1
parser.add_argument('-no', '--noisy_odometry', type=int, default=0)#1
parser.add_argument('--camera_height', type=float, default=1.25,#1.25
help="agent camera height in metres")
parser.add_argument('--hfov', type=float, default=90.0,#90.0
help="horizontal field of view in degrees")
parser.add_argument('--randomize_env_every', type=int, default=1000,
help="randomize scene in a thread every k episodes")
## Global Policy RL PPO Hyperparameters
parser.add_argument('--global_lr', type=float, default=2.5e-5,
help='global learning rate (default: 2.5e-5)')
parser.add_argument('--global_hidden_size', type=int, default=256,
help='local_hidden_size')
parser.add_argument('--eps', type=float, default=1e-5,
help='RL Optimizer epsilon (default: 1e-5)')
parser.add_argument('--alpha', type=float, default=0.99,
help='RL Optimizer alpha (default: 0.99)')
parser.add_argument('--gamma', type=float, default=0.99,
help='discount factor for rewards (default: 0.99)')
parser.add_argument('--use_gae', action='store_true', default=False,
help='use generalized advantage estimation')
parser.add_argument('--tau', type=float, default=0.95,
help='gae parameter (default: 0.95)')
parser.add_argument('--entropy_coef', type=float, default=0.001,
help='entropy term coefficient (default: 0.01)')
parser.add_argument('--value_loss_coef', type=float, default=0.5,
help='value loss coefficient (default: 0.5)')
parser.add_argument('--max_grad_norm', type=float, default=0.5,
help='max norm of gradients (default: 0.5)')
parser.add_argument('--num_global_steps', type=int, default=40,
help='number of forward steps in A2C (default: 5)')
parser.add_argument('--ppo_epoch', type=int, default=4,
help='number of ppo epochs (default: 4)')
parser.add_argument('--num_mini_batch', type=str, default="auto",
help='number of batches for ppo (default: 32)')
parser.add_argument('--clip_param', type=float, default=0.2,
help='ppo clip parameter (default: 0.2)')
parser.add_argument('--use_recurrent_global', type=int, default=0,
help='use a recurrent global policy')
# Local Policy
parser.add_argument('--local_optimizer', type=str,
default='adam,lr=0.0001')
parser.add_argument('--num_local_steps', type=int, default=25,
help="""Number of steps the local can
perform between each global instruction""")
parser.add_argument('--local_hidden_size', type=int, default=512,
help='local_hidden_size')
parser.add_argument('--short_goal_dist', type=int, default=1,
help="""Maximum distance between the agent
and the short term goal""")
parser.add_argument('--local_policy_update_freq', type=int, default=5)
parser.add_argument('--use_recurrent_local', type=int, default=1,
help='use a recurrent local policy')
parser.add_argument('--use_deterministic_local', type=int, default=0,
help="use classical deterministic local policy")
# Neural SLAM Module
parser.add_argument('-pe', '--use_pose_estimation', type=int, default=2)
parser.add_argument('--goals_size', type=int, default=2)
parser.add_argument('-pt', '--pretrained_resnet', type=int, default=1)
parser.add_argument('--slam_optimizer', type=str, default='adam,lr=0.0001')
parser.add_argument('-sbs', '--slam_batch_size', type=int, default=72)#72
parser.add_argument('-sit', '--slam_iterations', type=int, default=10)
parser.add_argument('-sms', '--slam_memory_size', type=int, default=500000)
parser.add_argument('--proj_loss_coeff', type=float, default=1.0)
parser.add_argument('--pose_loss_coeff', type=float, default=10000.0)
parser.add_argument('--exp_loss_coeff', type=float, default=1.0)
parser.add_argument('--global_downscaling', type=int, default=2)
parser.add_argument('--map_pred_threshold', type=float, default=0.5)
parser.add_argument('--vision_range', type=int, default=60)#64 when run the main.py 60~3m 40~2m
parser.add_argument('--obstacle_boundary', type=int, default=5)
parser.add_argument('--map_resolution', type=int, default=5)
parser.add_argument('--du_scale', type=int, default=2)
parser.add_argument('--map_size_cm', type=int, default=4800) # 2400 ANS 4800 others
parser.add_argument('-ot', '--obs_threshold', type=float, default=1)
parser.add_argument('-ct', '--collision_threshold', type=float, default=0.20)
parser.add_argument('-nl', '--noise_level', type=float, default=1.0)
# parse arguments
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
if args.cuda:
if args.auto_gpu_config:
num_gpus = torch.cuda.device_count()
if args.total_num_scenes != "auto":
args.total_num_scenes = int(args.total_num_scenes)
elif "gibson" in args.task_config and \
"train" in args.split:
args.total_num_scenes = 72
elif "gibson" in args.task_config and \
"val_mt" in args.split:
args.total_num_scenes = 14
elif "gibson" in args.task_config and \
"val" in args.split:
args.total_num_scenes = 1
else:
assert False, "Unknown task config, please specify" + \
" total_num_scenes"
# Automatically configure number of training threads based on
# number of GPUs available and GPU memory size
total_num_scenes = args.total_num_scenes
gpu_memory = 10 # 1000
for i in range(num_gpus):
gpu_memory = min(gpu_memory,
torch.cuda.get_device_properties(i).total_memory \
/1024/1024/1024)
if i==0:
assert torch.cuda.get_device_properties(i).total_memory \
/1024/1024/1024 > 10.0, "Insufficient GPU memory"
num_processes_per_gpu = int(gpu_memory/1.4)
num_processes_on_first_gpu = int((gpu_memory - 10.0)/1.4)
if num_gpus == 1:
args.num_processes_on_first_gpu = num_processes_on_first_gpu
args.num_processes_per_gpu = 0
args.num_processes = num_processes_on_first_gpu
else:
total_threads = num_processes_per_gpu * (num_gpus - 1) \
+ num_processes_on_first_gpu
num_scenes_per_thread = math.ceil(total_num_scenes/total_threads)
num_threads = math.ceil(total_num_scenes/num_scenes_per_thread)
args.num_processes_per_gpu = min(num_processes_per_gpu,
math.ceil(num_threads//(num_gpus-1)))
args.num_processes_on_first_gpu = max(0,
num_threads - args.num_processes_per_gpu*(num_gpus - 1))
args.num_processes = num_threads
args.sim_gpu_id = 1
print("Auto GPU config:")
print("Number of processes: {}".format(args.num_processes))
print("Number of processes on GPU 0: {}".format(
args.num_processes_on_first_gpu))
print("Number of processes per GPU: {}".format(
args.num_processes_per_gpu))
if args.eval == 1:
if args.train_global:
print("WARNING: Training Global Policy during evaluation")
if args.train_local:
print("WARNING: Training Local Policy during evaluation")
if args.train_slam:
print("WARNING: Training Neural SLAM module during evaluation")
assert args.short_goal_dist >= 1, "args.short_goal_dist >= 1"
if args.use_deterministic_local:
args.train_local = 0
if args.num_mini_batch == "auto":
print('args.num_process {}'.format(args.num_processes))
args.num_mini_batch = args.num_processes // 2
else:
args.num_mini_batch = int(args.num_mini_batch)
return args