Spaces:
Sleeping
Sleeping
File size: 6,562 Bytes
ba246bb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | import copy
import yaml
import gymnasium as gym
import numpy as np
import pandas as pd
import random
from tqdm import tqdm
from numpy import ndarray
from stable_baselines3 import PPO
from mesh_model.mesh_analysis.quadmesh_analysis import QuadMeshTopoAnalysis
from mesh_model.mesh_struct.mesh import Mesh
from mesh_model.mesh_struct.mesh_elements import Dart
from mesh_model.reader import read_gmsh, read_dataset, read_json
from mesh_model.writer import write_dataset
from view.mesh_plotter.create_plots import plot_test_results, plot_density
from view.mesh_plotter.mesh_plots import plot_dataset, plot_mesh, save_dataset_plot
from environment.actions.smoothing import smoothing_mean
from environment.quadmesh_env.wrappers import MeanRewardWrapper, WeightedRewardWrapper, CleanupWrapper
from environment import quadmesh_env
def testPolicy(
model,
n_eval_episodes: int,
config,
dataset: list[Mesh]
) -> pd.DataFrame:
"""
Tests policy on each mesh of a dataset with n_eval_episodes.
:param model: the model to test
:param n_eval_episodes: number of evaluation episodes on each mesh
:param config: configuration
:param dataset: list of mesh objects
:return: average length of evaluation episodes, number of wins,average reward per mesh, dataset with the modified meshes
"""
print('------------Testing policy-------------')
avg_length = np.zeros(len(dataset))
avg_mesh_rewards = np.zeros(len(dataset))
avg_normalized_return = np.zeros(len(dataset))
avg_mean_std = np.zeros(len(dataset))
list_normalized_return = np.zeros(n_eval_episodes)
nb_wins = np.zeros(len(dataset))
final_meshes = []
for i, mesh in tqdm(enumerate(dataset, 1)):
best_mesh = mesh
env = gym.make(
config["eval"]["eval_env_id"],
max_episode_steps=config["eval"]["max_episode_steps"],
learning_mesh = mesh,
n_darts_selected=config["eval"]["n_darts_selected"],
deep= config["eval"]["deep"],
action_restriction=config["eval"]["action_restriction"],
with_degree_obs=config["eval"]["with_quality_observation"],
render_mode = config["eval"]["render_mode"],
analysis_type=config["env"]["analysis_type"],
debug=False,
)
# env = CleanupWrapper(env)
for ne in range(n_eval_episodes):
terminated = False
truncated = False
ep_mesh_rewards: int = 0
ep_length: int = 0
cpt = 0
obs, info = env.reset(options={"mesh": copy.deepcopy(mesh)})
best_mesh_episode = mesh
mesh_init_score = info["mesh_score"]
mesh_ideal_score = info["mesh_ideal_score"]
best_mesh_episode_score = info["mesh_score"]
while terminated == False and truncated == False:
action, _states = model.predict(obs, deterministic=False)
if action is None:
env.terminal = True
break
obs, reward, terminated, truncated, info = env.step(action)
if info["mesh_score"]<best_mesh_episode_score:
best_mesh_episode = copy.deepcopy(info['mesh'])
best_mesh_episode_score = info["mesh_score"]
ep_mesh_rewards += info['mesh_reward']
if ep_mesh_rewards <= 0:
cpt +=1
if cpt > 30:
truncated = True
ep_length += 1
if terminated:
nb_wins[i-1] += 1
if isBetterMesh(best_mesh, best_mesh_episode, config["env"]["analysis_type"]):
best_mesh = copy.deepcopy(best_mesh_episode)
avg_length[i-1] += ep_length
avg_mesh_rewards[i-1] += ep_mesh_rewards
list_normalized_return[ne-1] = 0 if mesh_init_score == mesh_ideal_score else (mesh_init_score - best_mesh_episode_score) /(mesh_init_score- mesh_ideal_score)
avg_normalized_return[i-1] += 0 if mesh_init_score == mesh_ideal_score else (mesh_init_score - best_mesh_episode_score) /(mesh_init_score- mesh_ideal_score)
final_meshes.append(best_mesh)
avg_length[i-1] = avg_length[i-1]/n_eval_episodes
avg_mesh_rewards[i-1] = avg_mesh_rewards[i-1]/n_eval_episodes
avg_normalized_return[i-1] = avg_normalized_return[i-1]/n_eval_episodes
avg_mean_std[i-1] = np.std(list_normalized_return)
# Création du DataFrame
df_results = pd.DataFrame({
"mesh_id": range(len(dataset)),
"avg_length": avg_length,
"nb_wins": nb_wins,
"avg_mesh_rewards": avg_mesh_rewards,
"avg_normalized_return": avg_normalized_return,
"std_normalized_return": avg_mean_std,
"final_mesh": final_meshes
})
return df_results
def isBetterPolicy(actual_best_policy, policy_to_test):
if actual_best_policy is None:
return True
def isBetterMesh(best_mesh, actual_mesh, analysis_type):
ma_best_mesh = QuadMeshTopoAnalysis(best_mesh)
ma_actual_mesh = QuadMeshTopoAnalysis(actual_mesh)
if best_mesh is None or ma_best_mesh.global_score()[1] > ma_actual_mesh.global_score()[1]:
return True
else:
return False
if __name__ == '__main__':
# PARAMETERS CONFIGURATION
with open("environment/config.yaml", "r") as f:
config = yaml.safe_load(f)
print("------------Reading dataset---------------")
dataset = [read_gmsh("mesh_files/imr3.msh")]# read_dataset(config["dataset"]["exploit_dataset_dir"]) #[ read_gmsh("../mesh_files/bunny.msh")]
#plot_dataset(dataset)
print("------------Loading Model-----------------")
#Load the model
model = PPO.load("trained_models/full_dataset_ob36-v0.zip")
df_results = testPolicy(model, 50, config, dataset)
#plot_test_results(df_results["avg_mesh_rewards"], df_results["nb_wins"], df_results["avg_length"], df_results["avg_normalized_return"])
final_meshes = df_results["final_mesh"]
#plot_dataset(final_meshes)
for m in final_meshes:
smoothing_mean(m)
save_dataset_plot(final_meshes, "training/results_IMR/imr3_results.png")
# print(df_results[["mesh_id", "avg_normalized_return", "std_normalized_return"]])
df_results.drop(columns=["final_mesh"], inplace=True)
df_results = df_results.transpose()
df_results.to_csv("training/results_IMR/imr3_results.csv", index=True)
write_dataset("training/dataset/results/imr3_results", final_meshes) |