95 lines
3.3 KiB
Python
95 lines
3.3 KiB
Python
import torch
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import pickle
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import gym
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import numpy as np
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import intersim
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from intersim.utils import get_map_path, get_svt, SVT_to_stateactions
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from intersim import collisions
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import os
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opj = os.path.join
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def generate_expert_data(path: str='expert_data', loc: int = 0, track:int = 0, **kwargs):
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"""
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Function to save (joint) states and observations from simulated frame
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Args:
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path (str): directory to save data
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loc (int): location index
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track (int): track index
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kwargs: arguments for environment instantiation
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"""
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if not os.path.isdir(path):
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os.mkdir(path)
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filestr = opj(path,intersim.LOCATIONS[loc]+'_track%03i'%(track))
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svt, svt_path = get_svt(base='InteractionSimulator', loc=loc, track=track)
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osm = get_map_path(base='InteractionSimulator', loc=loc)
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print('SVT path: {}'.format(svt_path))
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print('Map path: {}'.format(osm))
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states, actions = SVT_to_stateactions(svt)
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# animate from environment
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env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, **kwargs,
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min_acc=-np.inf, max_acc=np.inf)
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env.reset()
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done = False
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obs, actions_taken, max_devs = [], [], []
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i = 0
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while not done and i < len(actions):
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# check state deviation
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env_state = env.projected_state
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nni = ~torch.isnan(env_state[:,0])
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norms = torch.norm(env_state[nni,:2]-states[i,nni,:2], dim=1)
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max_devs.append(norms.max())
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# propagate environment
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ob, r, done, info = env.step(env.target_state(svt.simstate[i+1]))
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obs.append(ob)
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actions_taken.append(info['action_taken'])
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i += 1
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print("Maximum environment deviation from track: %f m" %(max(max_devs)))
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# check for collisions
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x = torch.stack([ob['state'] for ob in obs])
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cols = collisions.check_collisions_trajectory(x, svt.lengths, svt.widths)
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assert ~torch.any(cols), 'Error: Collisions found at indices {}'.format(cols.nonzero(as_tuple=True))
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# shift actions
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actions_taken.pop(0)
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obs.pop(-1)
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# save observations and actions
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pickle.dump(obs,open(filestr+'_observations.pkl', 'wb'))
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torch.save(torch.stack(actions_taken), filestr+'_actions.pt')
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def load_expert_data(path='expert_data', loc: int = 0, track:int = 0):
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"""
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Load expert data from file.
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Args:
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path (str): directory to save data
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loc (int): location index
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track (int): track index
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Returns:
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obs (list[Observations]): list of observations
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actions (list[torch.tensor]): list of corresponding actions taken in observations
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"""
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# load observations and actions
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filestr = opj(path, intersim.LOCATIONS[loc]+'_track%03i'%(track))
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obs = pickle.load(open(filestr+'_observations.pkl', 'rb'))
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actions = torch.load(filestr+'_actions.pt')
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actions = list(torch.unbind(actions))
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return obs, actions
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='Save Expert Trajectories')
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parser.add_argument('--loc', default=0, type=int,
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help='location (default 0)')
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parser.add_argument('--track', default=0, type=int,
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help='track number (default 0)')
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args = parser.parse_args()
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generate_expert_data(loc=args.loc,track=args.track) |