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test_example.py
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test_example.py
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import numpy as np
import torch
import pickle
from conformer_rl import utils
from conformer_rl.agents import PPORecurrentAgent
from conformer_rl.config import Config
from conformer_rl.environments import Task
from conformer_rl.models import RTGNRecurrent
from conformer_rl.molecule_generation.generate_alkanes import generate_branched_alkane
from conformer_rl.molecule_generation.generate_molecule_config import config_from_rdkit
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
import logging
logging.basicConfig(level=logging.DEBUG)
if __name__ == '__main__':
utils.set_one_thread()
mol_config = config_from_rdkit(generate_branched_alkane(10), num_conformers=4, calc_normalizers=True, save_file='10_alkane')
with open('10_alkane.pkl', 'rb') as file:
mol_config = pickle.load(file)
config = Config()
config.tag = 'test_example'
# config.network = RTGNRecurrent(6, 128, edge_dim=6, node_dim=5).to(device)
config.network = RTGNRecurrent(6, 128, edge_dim=6, node_dim=5).to(device)
# Batch Hyperparameters
config.rollout_length = 2
config.recurrence = 1
config.optimization_epochs = 1
config.max_steps = 24
config.save_interval = 8
config.eval_interval = 8
config.eval_episodes = 1
config.mini_batch_size = 4
# Coefficient Hyperparameters
lr = 5e-6 * np.sqrt(2)
config.optimizer_fn = lambda params: torch.optim.Adam(params, lr=lr, eps=1e-5)
# Task Settings
config.train_env = Task('GibbsScoreEnv-v0', concurrency=True, num_envs=2, seed=np.random.randint(0,1e5), mol_config=mol_config)
config.eval_env = Task('GibbsScorePruningEnv-v0', seed=np.random.randint(0,7e4), mol_config=mol_config)
config.curriculum = None
agent = PPORecurrentAgent(config)
agent.run_steps()