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9897e20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | 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 mediapy
import numpy as np
import torch
from eval_utils import load_policy, rollout, load_config, make_env, evaluate_policy
from pygpudrive.env.dataset import SceneDataLoader
from pygpudrive.datatypes.observation import LocalEgoState
import pdb
if __name__ == "__main__":
config = load_config("examples/experimental/config/hand_designed_experiments")
# Load original scenes
data_loader_orig = SceneDataLoader(
root=config.data_path_original,
batch_size=config.num_worlds,
dataset_size=config.dataset_size,
sample_with_replacement=False,
)
# Load altered scenes
data_loader_altered = SceneDataLoader(
root=config.data_path_altered,
batch_size=config.num_worlds,
dataset_size=config.dataset_size,
sample_with_replacement=False,
)
# Make env
env = make_env(config, data_loader_orig)
# 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 = evaluate_policy(
env=env,
policy=policy,
data_loader=data_loader_orig,
dataset_name="test",
deterministic=False,
render_sim_state=False,
)
df_perf_altered = evaluate_policy(
env=env,
policy=policy,
data_loader=data_loader_altered,
dataset_name="test",
deterministic=False,
render_sim_state=False,
)
# Concatenate all three dataframes with a new column to identify the scenario
df_perf_original['Class'] = 'Original'
df_perf_altered['Class'] = 'Altered'
df = pd.concat([df_perf_original, df_perf_altered])
metrics = ['goal_achieved_frac', 'collided_frac', 'off_road_frac', 'other_frac']
tab_agg_perf = df.groupby('Class')[metrics].agg(['mean', 'std'])
tab_agg_perf = tab_agg_perf * 100
tab_agg_perf = tab_agg_perf.round(1)
print('')
print(tab_agg_perf)
print('')
# 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_ood.csv", index=False)
logging.info(f"Saved results at {config.save_results_path}")
# # Make videos
# videos_dir = Path(f"videos/{config.cpt_name}/hand_designed")
# videos_dir.mkdir(parents=True, exist_ok=True)
# for data_loader in [data_loader_orig]: #data_loader_altered
# for batch in tqdm(
# data_loader,
# desc=f"Making videos",
# total=len(data_loader),
# colour="MAGENTA",
# ):
# env.swap_data_batch(batch)
# (
# goal_achieved_count,
# goal_achieved_frac,
# collided_count,
# collided_frac,
# off_road_count,
# off_road_frac,
# other_count,
# other_frac,
# controlled_agents_in_scene,
# sim_state_frames,
# agent_positions,
# episode_lengths,
# ) = rollout(
# env=env,
# policy=policy,
# device=config.device,
# deterministic=config.device,
# render_sim_state=config.render_sim_state,
# render_every_n_steps=1,
# zoom_radius=config.zoom_radius,
# )
# filenames = env.get_env_filenames()
# sim_state_arrays = {k: np.array(v) for k, v in sim_state_frames.items()}
# # Save videos locally
# for env_id, frames in sim_state_arrays.items():
# filename = filenames[env_id]
# video_path = videos_dir / f"{filename}.mp4"
# mediapy.write_video(
# str(video_path),
# frames,
# fps=15,
# )
# logging.info(f"Saved video to {video_path}")
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