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test.py
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test.py
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import logging
import argparse
import os
import sys
from matplotlib import pyplot as plt
import torch
import torch.nn as nn
from rl.networks.envs import make_vec_envs
from rl.evaluation import evaluate
from rl.networks.model import Policy
from crowd_sim import *
def main():
"""
The main function for testing a trained model
"""
# the following parameters will be determined for each test run
parser = argparse.ArgumentParser('Parse configuration file')
# the model directory that we are testing
parser.add_argument('--model_dir', type=str, default='trained_models/GST_predictor_rand')
# render the environment or not
parser.add_argument('--visualize', default=True, action='store_true')
# if -1, it will run 500 different cases; if >=0, it will run the specified test case repeatedly
parser.add_argument('--test_case', type=int, default=-1)
# model weight file you want to test
parser.add_argument('--test_model', type=str, default='41665.pt')
# whether to save trajectories of episodes
parser.add_argument('--render_traj', default=False, action='store_true')
# whether to save slide show of episodes
parser.add_argument('--save_slides', default=False, action='store_true')
test_args = parser.parse_args()
if test_args.save_slides:
test_args.visualize = True
from importlib import import_module
model_dir_temp = test_args.model_dir
if model_dir_temp.endswith('/'):
model_dir_temp = model_dir_temp[:-1]
# import arguments.py from saved directory
# if not found, import from the default directory
try:
model_dir_string = model_dir_temp.replace('/', '.') + '.arguments'
model_arguments = import_module(model_dir_string)
get_args = getattr(model_arguments, 'get_args')
except:
print('Failed to get get_args function from ', test_args.model_dir, '/arguments.py')
from arguments import get_args
algo_args = get_args()
# import config class from saved directory
# if not found, import from the default directory
try:
model_dir_string = model_dir_temp.replace('/', '.') + '.configs.config'
model_arguments = import_module(model_dir_string)
Config = getattr(model_arguments, 'Config')
except:
print('Failed to get Config function from ', test_args.model_dir)
from crowd_nav.configs.config import Config
env_config = config = Config()
# configure logging and device
# print test result in log file
log_file = os.path.join(test_args.model_dir,'test')
if not os.path.exists(log_file):
os.mkdir(log_file)
if test_args.visualize:
log_file = os.path.join(test_args.model_dir, 'test', 'test_visual.log')
else:
log_file = os.path.join(test_args.model_dir, 'test', 'test_' + test_args.test_model + '.log')
file_handler = logging.FileHandler(log_file, mode='w')
stdout_handler = logging.StreamHandler(sys.stdout)
level = logging.INFO
logging.basicConfig(level=level, handlers=[stdout_handler, file_handler],
format='%(asctime)s, %(levelname)s: %(message)s', datefmt="%Y-%m-%d %H:%M:%S")
logging.info('robot FOV %f', config.robot.FOV)
logging.info('humans FOV %f', config.humans.FOV)
torch.manual_seed(algo_args.seed)
torch.cuda.manual_seed_all(algo_args.seed)
if algo_args.cuda:
if algo_args.cuda_deterministic:
# reproducible but slower
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
else:
# not reproducible but faster
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
torch.set_num_threads(1)
device = torch.device("cuda" if algo_args.cuda else "cpu")
logging.info('Create other envs with new settings')
# set up visualization
if test_args.visualize:
fig, ax = plt.subplots(figsize=(7, 7))
ax.set_xlim(-6.5, 6.5) # 6
ax.set_ylim(-6.5, 6.5)
ax.axes.xaxis.set_visible(False)
ax.axes.yaxis.set_visible(False)
# ax.set_xlabel('x(m)', fontsize=16)
# ax.set_ylabel('y(m)', fontsize=16)
plt.ion()
plt.show()
else:
ax = None
load_path=os.path.join(test_args.model_dir,'checkpoints', test_args.test_model)
print(load_path)
# create an environment
env_name = algo_args.env_name
eval_dir = os.path.join(test_args.model_dir,'eval')
if not os.path.exists(eval_dir):
os.mkdir(eval_dir)
env_config.render_traj = test_args.render_traj
env_config.save_slides = test_args.save_slides
env_config.save_path = os.path.join(test_args.model_dir, 'social_eval', test_args.test_model[:-3])
envs = make_vec_envs(env_name, algo_args.seed, 1,
algo_args.gamma, eval_dir, device, allow_early_resets=True,
config=env_config, ax=ax, test_case=test_args.test_case, pretext_wrapper=config.env.use_wrapper)
if config.robot.policy not in ['orca', 'social_force']:
# load the policy weights
actor_critic = Policy(
envs.observation_space.spaces,
envs.action_space,
base_kwargs=algo_args,
base=config.robot.policy)
actor_critic.load_state_dict(torch.load(load_path, map_location=device))
actor_critic.base.nenv = 1
# allow the usage of multiple GPUs to increase the number of examples processed simultaneously
nn.DataParallel(actor_critic).to(device)
else:
actor_critic = None
test_size = config.env.test_size
# call the evaluation function
evaluate(actor_critic, envs, 1, device, test_size, logging, config, algo_args, test_args.visualize)
if __name__ == '__main__':
main()