import os import logging from PIL import Image from tqdm import tqdm import pandas as pd from pathlib import Path import matplotlib.pyplot as plt import numpy as np import torch from eval_utils import load_policy, rollout, load_config, make_env from pygpudrive.env.dataset import SceneDataLoader from pygpudrive.datatypes.observation import LocalEgoState import pdb def test_policy_robustness( env, policy, data_loader, config, remove_random_agents=False, remove_controlled_agents=True, plot_trajectories=False ): res_dict = { "scene": [], "goal_achieved": [], "collided": [], "off_road": [], "not_goal_nor_crashed": [], "controlled_agents_in_scene": [], } for batch in tqdm( data_loader, desc=f"Processing; remove_random_agents = {remove_random_agents}", total=len(data_loader), colour="red", ): # Set new data batch with simulator env.swap_data_batch(batch) # Sanity check: plot scenes before removing agents sim_state_figs_before = env.vis.plot_simulator_state( env_indices=[0, 1], time_steps=[0, 0], ) sim_state_figs_before[0].savefig(f"sim_state_before_0.png") sim_state_figs_before[1].savefig(f"sim_state_before_1.png") if remove_random_agents: env.remove_agents_by_id( config.perc_to_rmv_per_scene, remove_controlled_agents = remove_controlled_agents ) # Check after sim_state_figs_after = env.vis.plot_simulator_state( env_indices=[0, 1], time_steps=[0, 0], ) sim_state_figs_after[0].savefig(f"sim_state_after_0.png") sim_state_figs_after[1].savefig(f"sim_state_after_1.png") # Rollout policy ( goal_achieved, collided, off_road, controlled_agents_in_scene, not_goal_nor_crashed, _, agent_positions, ) = rollout( env=env, policy=policy, device=config.device, deterministic=config.deterministic, render_sim_state=config.render_sim_state, return_agent_positions=plot_trajectories ) # Save last timestep rollout _ = env.reset() last_sim_state = env.vis.plot_simulator_state( env_indices=[0, 1], time_steps=[-1, -1], agent_positions=agent_positions ) last_sim_state[0].savefig(f"last_sim_state_0.png") last_sim_state[1].savefig(f"last_sim_state_1.png") # Store results for the current batch scenario_names = [Path(path).stem for path in batch] res_dict["scene"].extend(scenario_names) res_dict["goal_achieved"].extend(goal_achieved.cpu().numpy()) res_dict["collided"].extend(collided.cpu().numpy()) res_dict["off_road"].extend(off_road.cpu().numpy()) res_dict["not_goal_nor_crashed"].extend( not_goal_nor_crashed.cpu().numpy() ) res_dict["controlled_agents_in_scene"].extend( controlled_agents_in_scene.cpu().numpy() ) df_res = pd.DataFrame(res_dict) if remove_random_agents: df_res["deleted_agents"] = True else: df_res["deleted_agents"] = False return df_res if __name__ == "__main__": config = load_config("examples/experimental/config/scene_manipulation_config") train_loader = SceneDataLoader( root=config.train_path, batch_size=config.num_worlds, dataset_size=config.dataset_size, sample_with_replacement=False, ) # Make env env = make_env(config, train_loader) # Load policy policy = load_policy( path_to_cpt=config.cpt_path, model_name=config.cpt_name, device=config.device, env=env, ) # Run tests df_perf_original = test_policy_robustness( env=env, policy=policy, data_loader=train_loader, config=config, remove_random_agents=False, plot_trajectories=True ) df_perf_perturbed_controlled = test_policy_robustness( env=env, policy=policy, data_loader=train_loader, config=config, remove_random_agents=True, plot_trajectories=True ) df_perf_perturbed_static = test_policy_robustness( env=env, policy=policy, data_loader=train_loader, config=config, remove_random_agents=True, remove_controlled_agents=False, plot_trajectories=True ) # Concatenate all three dataframes with a new column to identify the scenario df_perf_original['Class'] = 'Original' df_perf_perturbed_controlled['Class'] = 'Removed controlled' df_perf_perturbed_static['Class'] = 'Removed other' df = pd.concat([df_perf_original, df_perf_perturbed_controlled, df_perf_perturbed_static]) # # Calculate mean values for each metric grouped by deleted_agents # metrics = ['goal_achieved', 'collided', 'off_road', 'not_goal_nor_crashed'] # # Convert boolean columns to float for averaging # for col in metrics: # df[col] = df[col].astype(float) # # Now calculate means # means_by_group = df.groupby('scenario')[metrics].mean() # # Set up the plot # fig, ax = plt.subplots(figsize=(12, 6)) # x = np.arange(len(metrics)) # width = 0.25 # # Plot bars for each group # ax.bar(x - width, means_by_group.loc['Original'], width, # label='Original', color='skyblue') # ax.bar(x, means_by_group.loc['Removed Controlled'], width, # label=f'Removed {int(config.perc_to_rmv_per_scene*100)}% Controlled', color='lightcoral') # ax.bar(x + width, means_by_group.loc['Removed Uncontrolled'], width, # label=f'Removed {int(config.perc_to_rmv_per_scene*100)}% Uncontrolled', color='lightgreen') # # Customize the plot # ax.set_ylabel('Proportion') # ax.set_title('Policy Performance Comparison') # ax.set_xticks(x) # ax.set_xticklabels(metrics, rotation=45) # ax.legend() # # Adjust layout and save # plt.tight_layout() # plt.savefig(f"{config.save_results_path}/metrics_comparison_{int(config.perc_to_rmv_per_scene*100)}pct.png") # plt.close() # Save if not os.path.exists(config.save_results_path): os.makedirs(config.save_results_path) df.to_csv(f"{config.save_results_path}/combined_results_{int(config.perc_to_rmv_per_scene*100)}pct.csv", index=False) logging.info(f"Saved results at {config.save_results_path}")