Add basic setup.py

interimit/expert_data.py

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