Implement metrics and write to tensorboard summary at test time

src/main.py

@@ -4,6 +4,7 @@ import gym
 import intersim
 import numpy as np
 from tqdm import tqdm
+from torch.utils.tensorboard import SummaryWriter
 
 from src import InteractionDatasetSingleAgent, metrics
 from intersim.utils import get_map_path, get_svt
@@ -63,7 +64,10 @@ def main(config, method='bc', train=False, test=False, loc=0, datadir='./expert_
 
         # run test metrics
         test_dataset = InteractionDatasetSingleAgent(output_dir=datadir, loc=loc, tracks=[track])
-        metrics(filestr, test_dataset, policy)
+        writer = SummaryWriter(filestr)
+        info = metrics(filestr, test_dataset, policy)
+        for k, m in info.items():
+            writer.add_scalar('test/{}'.format(k), m, 0)
 
 
 def simulate_policy(policy, loc=0, track=0, filestr='', nframes=float('inf'), graph=None):

src/metrics.py

@@ -12,7 +12,10 @@ def metrics(filestr: str, test_dataset, policy):
         filestr (str): base string to outputs of a simulation
         test_dataset: a dataset held for testing
         policy: policy
+    Returns:
+        info (dict): metrics in a dictionary
     """
+    info = {}
 
     # compute metrics using either
     # a) simulation files that were saved under the trained policy with prefix 'policy'
@@ -27,26 +30,41 @@ def metrics(filestr: str, test_dataset, policy):
 
     # count collisions (from function in intersim.collisions)
     n_collisions = collisions.count_collisions_trajectory(states, lengths, widths)
+    info['n_collisions'] = n_collisions
 
     # calculate average velocity
     avg_v = average_velocity(states)
+    info['average_velocity'] = avg_v
 
-    # calculate divergence between velocity distributions
-    
-    # calcuate divergence between acceleration distributions
+    # convert policy dtype between float32 and float64
     policy.policy = policy.policy.type(test_dataset[0]['state'].dtype)
     
     # generate actions in test dataset
     true_actions, pred_actions = [], []
+    true_velocities = []
     test_loader = DataLoader(test_dataset, batch_size=1024)
     with torch.no_grad():
         for (batch_idx, batch) in enumerate(test_loader):
             pred_actions.append(policy(batch))
             true_actions.append(batch['action'])
+            true_velocities.append(batch['state'][:,2])
 
-    true_actions, pred_actions = torch.cat(true_actions,dim=0), torch.cat(pred_actions,dim=0)
+    true_actions, pred_actions = torch.cat(true_actions,dim=0), torch.cat(pred_actions, dim=0)
     visualize_distribution(true_actions[:,0], pred_actions[:,0], filestr+'_action_viz') 
 
+    # calculate divergence between acceleration distributions
+    acceleration_kl = divergence(pred_actions, true_actions, type='kl', n_components=-1)
+    info['acceleration_kl'] = acceleration_kl
+
+    # calculate divergence between velocity distributions
+    sim_velocities = states[:,:,2]
+    sim_velocities = sim_velocities[~torch.isnan(sim_velocities)].flatten()
+    true_velocities = torch.cat(true_velocities, dim=0)
+    velocity_kl = divergence(sim_velocities, true_velocities, type='kl', n_components=-1)
+    info['velocity_kl'] = velocity_kl
+
+    return info
+
 
 def visualize_distribution(true, pred, filestr):
     """
@@ -68,11 +86,12 @@ def average_velocity(states):
     """
     Compute average of average velocity over all vehicles.
     Args:
-        states (torch.tensor): (T,nv,5) vehicle states
+        states (torch.tensor): (T,nv,5) vehicle states where T is the number of time steps and nv the number of vehicles
     Returns
         avg_v (float): average velocity
     """
     velocities = states[:,:,2]
+    # average velocity per vehicle
     vehicle_avg_v = nanmean(velocities, dim=0)
     arg_v = nanmean(vehicle_avg_v)
     return arg_v