from runner import Runner
from common.arguments import get_args
from common.utils import make_env
import numpy as np
import random
import torch
if __name__ == '__main__':
# get the params
args = get_args()
env, args = make_env(args)
runner = Runner(args, env)
if args.evaluate:
returns = runner.evaluate()
print('Average returns is', returns)
else:
runner.run()
import torch
import pickle
import matplotlib.pyplot as plt
from common.plot import plot
from common.train_test import train, test
from common.arguments import get_args
from common.data import save_model
from common.data import load_data
from common.data import load_model
from algos.DQN import DQNAgent
# ------------ environment ------------
env_id = "CartPole-v0"
env = gym.make(env_id)
config = get_args()
seed = config.seed
def seed_torch(seed):
torch.manual_seed(seed)
if torch.backends.cudnn.enabled:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
np.random.seed(seed)
seed_torch(seed)
env.seed(seed)
if _args.aicrowd_challenge:
from aicrowd import utils_pytorch as pyu, aicrowd_helpers
# Export the representation extractor
path_to_saved = pyu.export_model(pyu.RepresentationExtractor(model.model.encoder, 'mean'),
input_shape=(1, model.num_channels, model.image_size, model.image_size))
logging.info(f'A copy of the model saved in {path_to_saved}')
if on_aicrowd_server:
# AICrowd will handle the evaluation
aicrowd_helpers.register_progress(1.0)
aicrowd_helpers.submit()
else:
# Run evaluation locally
# The local_evaluation is implemented by aicrowd in the global namespace, so importing it suffices.
# todo: implement a modular version of local_evaluation
# noinspection PyUnresolvedReferences
from aicrowd import local_evaluation
if __name__ == "__main__":
_args = get_args(sys.argv[1:])
setup_logging(_args.verbose)
initialize_seeds(_args.seed)
# set the environment variables for dataset directory and name, and check if the root dataset directory exists.
set_environment_variables(_args.dset_dir, _args.dset_name)
assert os.path.exists(os.environ.get('DISENTANGLEMENT_LIB_DATA', '')), \
'Root dataset directory does not exist at: \"{}\"'.format(_args.dset_dir)
main(_args)
import common.arguments as arguments from common.utils import * check_exp_folder() args = arguments.get_args() seed = args.seed output_data_folder = get_output_data_folder() input_data_folder = get_input_data_folder() current_folder = get_current_folder()
