adding functions to process expert data across locations and tracks in intersimple environment

src/data/expert_data.py

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+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
+from intersim.graphs import ConeVisibilityGraph
+import os
+opj = os.path.join
+
+def generate_expert_data(path: str='expert_data', loc: int = 0, track:int = 0, 
+                            mask_relstate: bool = False, regularize_actions: bool = False, 
+                            **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
+        mask_relstate (bool): whether to mask the relative states from the cone visibility graph
+        regularize_actions (bool): whether to regularize the action selection
+        kwargs: arguments for environment instantiation
+    """
+    
+    action_reg = 0.002 if regularize_actions else 0
+    
+    if not os.path.isdir(path):
+        os.makedirs(path)
+    filestr = opj(path,intersim.LOCATIONS[loc]+'_track%03i'%(track))
+    
+    svt, svt_path = get_svt(loc=loc, track=track) #base='InteractionSimulator'
+    osm = get_map_path(loc=loc)
+    print('SVT path: {}'.format(svt_path))
+    print('Map path: {}'.format(osm))
+    states, actions = SVT_to_stateactions(svt)
+
+    # animate from environment
+    if mask_relstate:
+        cvg = ConeVisibilityGraph(r=20, half_angle=120)
+        env = gym.make('intersim:intersim-v0', svt=svt, map_path=osm, 
+            min_acc=-np.inf, max_acc=np.inf, graph=cvg, mask_relstate=True, **kwargs)
+    else:
+        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)
+        if len(norms)>0:
+            max_devs.append(norms.max())
+
+        # propagate environment
+        ob, r, done, info = env.step(env.target_state(svt.simstate[i+1], mu=action_reg))
+        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)
+    actions = torch.stack(actions_taken)
+
+    # save observations and actions
+    pickle.dump(obs,open(filestr+'_raw_observations.pkl', 'wb'))
+    torch.save(actions, filestr+'_raw_actions.pt')
+    process_expert_observations(obs, actions, filestr)
+
+def process_expert_observations(obs, actions, filestr, remove_outliers=True, dtype=torch.float32):
+    """
+    Process the expert observations and save them as torch tensors
+    Args:
+        obs (list[dict]): lost of observations
+        actions (torch.Tensor): (T, nv, a) tensor of actions
+        filestr (str): base filename with which to save out observation tensors
+        remove_outliers (bool): whether to remove datapoints with acceleration above or below 5 m/s/s
+        dtype (torch.Type): type to convert data to
+    """
+    keys = ['ego_state', 'relative_state', 'path', 'action', 'next_ego_state', 'next_relative_state', 'next_path']
+    data = {key:[] for key in keys}
+    assert len(obs) == len(actions), 'non-matching action and observation lengths'
+    T = len(obs)
+    max_nv = 0
+    for t in range(T-1):
+        nni = ~torch.isnan(obs[t]['state'][:,0]) & ~torch.isnan(obs[t+1]['state'][:,0]) 
+        max_nv = max(max_nv,nni.count_nonzero())
+
+        # state
+        data['ego_state'].append(obs[t]['state'][nni])
+        data['relative_state'].append(obs[t]['relative_state'].index_select(0, 
+            nni.nonzero()[:,0]).index_select(1, nni.nonzero()[:,0]))
+        data['path'].append(torch.stack((obs[t]['paths'][0][nni], obs[t]['paths'][1][nni]), dim=-1))
+
+        # action
+        data['action'].append(actions[t][nni])
+
+        # next state
+        data['next_ego_state'].append(obs[t+1]['state'][nni])
+        data['next_relative_state'].append(obs[t+1]['relative_state'].index_select(0, 
+            nni.nonzero()[:,0]).index_select(1, nni.nonzero()[:,0]))
+        data['next_path'].append(torch.stack((obs[t+1]['paths'][0][nni], obs[t+1]['paths'][1][nni]), dim=-1))
+
+    
+
+    # pad second dimension of relative state
+    for i in range(len(data['relative_state'])):
+        nv1, nv2, d = data['relative_state'][i].shape
+        pad = torch.zeros(nv1, max_nv-nv2, d, dtype=dtype) * np.nan
+        data['relative_state'][i] = torch.cat((data['relative_state'][i], pad), dim=1)
+        data['next_relative_state'][i] = torch.cat((data['next_relative_state'][i], pad), dim=1)
+
+    # cat lists 
+    for key in keys:
+        data[key] = torch.cat(data[key]).type(dtype)
+
+    if remove_outliers:
+        non_outlier_indices = torch.nonzero(torch.abs(data['action'][:,0]) < 5)
+        for key in keys:
+            data[key] = data[key][non_outlier_indices[:,0]]    
+
+    # mandate equal length
+    lengths = [len(data[key]) for key in keys]
+    assert min(lengths) == max(lengths), 'dataset lengths unequal'
+
+    # save out data
+    for key in keys:
+        torch.save(data[key], filestr+'_'+key+'.pt')
+
+def load_expert_data(path='expert_data', loc: int = 0, track:int = 0):
+    """
+    Load expert data from processed files.
+    Args:
+        path (str): directory to save data
+        loc (int): location index
+        track (int): track index
+    Returns:
+        data (dict[torch.Tensor]): dict of data
+    """
+    # load observations and actions
+    filestr = opj(path, intersim.LOCATIONS[loc]+'_track%03i'%(track))
+    data = {}
+    keys = ['ego_state', 'relative_state', 'path', 'action', 'next_ego_state', 'next_relative_state', 'next_path']
+    for key in keys:
+        data[key] = torch.load(filestr+'_'+key+'.pt')
+    return data
+
+def load_expert_data_raw(path='expert_data', loc: int = 0, track:int = 0):
+    """
+    Load expert data from raw 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+'_raw_observations.pkl', 'rb'))
+    actions = torch.load(filestr+'_raw_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)')
+    parser.add_argument('--all-tracks', action='store_true',
+                       help='whether to process all tracks at location')
+    parser.add_argument('--graph', action='store_true',
+                       help='whether to mask the relative states based on a ConeVisibilityGraph')
+    parser.add_argument('--reg', action='store_true',
+                       help='whether to regularize actions in the action targeter')
+    parser.add_argument('-o', default='./expert_data', type=str,
+                       help='output folder')
+    args = parser.parse_args()
+
+    kwargs = {
+        'loc':args.loc, 
+        'track': args.track,
+        'path':args.o, 
+        'mask_relstate':args.graph, 
+        'regularize_actions': args.reg
+    }
+
+    if args.all_tracks:
+        for i in range(intersim.MAX_TRACKS):
+            kwargs['track'] = i
+            generate_expert_data(**kwargs)
+    else:
+        generate_expert_data(**kwargs)