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validate.py
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validate.py
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import torch.multiprocessing as mp
import pickle
import torch
from typing import Tuple
# from model import Network
import numpy as np
import os
import wandb
import time
import random
from src.environment import Environment
from src.config import config
# NOTE: This validation script is designed to be run in a separate process.
from hydra.utils import instantiate
Network = instantiate({"_target_": config.model_target, "_partial_": True})
torch.manual_seed(config.test_seed)
np.random.seed(config.test_seed)
random.seed(config.test_seed)
# DEVICE = torch.device('cpu')
DEVICE = config.val_device
torch.set_num_threads(1)
def test_one_case(args):
env_set, network = args
env = Environment()
env.load(env_set[0], env_set[1], env_set[2])
obs, last_act, pos = env.observe()
done = False
network.reset()
step = 0
num_comm = 0
if env_set[0].shape[0] == 40:
max_episode_length = config.max_episode_length
elif env_set[0].shape[0] == 80:
max_episode_length = config.max_episode_length_80
else:
raise ValueError("Invalid map length")
while not done and env.steps < max_episode_length:
actions, _, _, _, comm_mask = network.step(torch.as_tensor(obs.astype(np.float32)).to(DEVICE),
torch.as_tensor(last_act.astype(np.float32)).to(DEVICE),
torch.as_tensor(pos.astype(int)).to(DEVICE))
(obs, last_act, pos), _, done, _ = env.step(actions)
step += 1
num_comm += np.sum(comm_mask)
arrived_num = np.sum(np.all(env.agents_pos == env.goals_pos, axis=1))
return np.array_equal(env.agents_pos, env.goals_pos), step, num_comm, arrived_num
def test_model(checkpoint_path, test_set: Tuple = tuple(config.val_test_set)):
'''
validate model in 'saved_models' folder
validation set path is './valid_set'
Each validation set has 16 cases
'''
# Context for faster inference
# https://pytorch.org/docs/stable/generated/torch.inference_mode.html
with torch.inference_mode():
network = Network()
network.eval()
network.to(DEVICE)
pool = mp.Pool(config.val_pool_parallel_cores)
state_dict = torch.load(checkpoint_path, map_location=DEVICE)
network.load_state_dict(state_dict)
network.eval()
network.share_memory()
print(f'----------validate model {checkpoint_path}----------')
success_rate, average_step, communication_times, average_arrived_agents, times = [], [], [], [], []
case_fmt = []
for case in test_set:
init_time = time.time()
print(f"valid set: {case[0]} length {case[1]} agents {case[2]} density")
with open('./valid_set/{}length_{}agents_{}density.pth'.format(case[0], case[1], case[2]), 'rb') as f:
tests = pickle.load(f)
tests = [(test, network) for test in tests]
ret = pool.map(test_one_case, tests)
success, steps, num_comm, arrived = zip(*ret)
print("success rate: {:.2f}%".format(sum(success)/len(success)*100))
print("average step: {}".format(sum(steps)/len(steps)))
# print("communication times: {}".format(sum(num_comm)/len(num_comm)))
print(f"average arrived agents : {sum(arrived)/len(arrived)} ")
print()
fmt = f'Agents#{case[1]}'
case_fmt.append(fmt)
success_rate.append(sum(success)/len(success)*100)
average_step.append(sum(steps)/len(steps))
communication_times.append(sum(num_comm)/len(num_comm))
average_arrived_agents.append(sum(arrived)/len(arrived))
times.append(time.time()-init_time)
return success_rate, average_step, communication_times, average_arrived_agents, case_fmt, times
def main(checkpoint_path: str, step: int = 0, run_id: str = 'test', name: str = config.name):
# Model file exists, proceed to test
print(f"Validating...")
a, b, c, d, e, times = test_model(checkpoint_path)
# Log the results
if config.use_wandb:
# Initialize wandb
# Note: run_id is used to resume a run
wandb.init(project=config.project, name=name+"-val", id=run_id+"-val", config=dict(config), resume="allow")
for i in range(len(a)):
wandb.log({f'val_success_rate/{e[i]}': a[i],
f'val_average_step/{e[i]}': b[i],
f'val_communication_times/{e[i]}': c[i],
f'val_average_arrived_agents/{e[i]}': d[i],
f"val_time/{e[i]}": times[i]},
step=step)
# wandb.finish()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', type=str, default='saved_models')
parser.add_argument('--step', type=int, default=0)
parser.add_argument('--run_id', type=str, default='test')
parser.add_argument('--name', type=str, default='test')
args = parser.parse_args()
main(args.checkpoint_path, args.step, args.run_id, args.name)