test_agent.py

from tensorflow.python.ops.gen_math_ops import log
from agents.agent import Agent
# from agents.trainer import trainer
from agents.plotter import plotter as training_plotter

from environment.env_factory import env_factory
from configs.configs import get_configs
# For params tunning
import json
import os
from datetime import datetime
from copy import deepcopy

# import tensorflow as tf
# import numpy as np

# seed_value = 1234
# np.random.seed(seed_value)
# tf.random.set_seed(seed_value)

# import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

# import tensorflow as tf
# gpus = tf.config.experimental.list_physical_devices('GPU')
# if gpus:
#   # Restrict TensorFlow to only allocate 1GB of memory on the first GPU
#   try:
#     tf.config.experimental.set_virtual_device_configuration(
#         gpus[0],
#         [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=5120)])
#     logical_gpus = tf.config.experimental.list_logical_devices('GPU')
#     print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
#   except RuntimeError as e:
#     # Virtual devices must be set before GPUs have been initialized
#     print(e)

LOG_DIR = "./results/"

def runner(env_type="custom", env_name='ResourceV3', agent_name="tpc", agent_weights_dir='2021-05-28T13:17:36'):

    # Store the time of the script
    start_date = datetime.now().replace(microsecond=0).isoformat()
    log_dir = os.path.join(LOG_DIR, env_name, agent_weights_dir)

    # Read the configs
    agent_config, _, env_config, tester_config, _, _ = get_configs(
        'config', # File name where config are stored
        agent_name,
        log_dir
    )

    # Create the environment
    env, tester = env_factory(env_type, env_name, env_config)
    # If necessary load the dataset
    if not env.generate_request_on_the_fly:
        print('Loading Env. Dataset')
        env.load_dataset(os.path.join(log_dir, "env.txt"))

    # Add info about the environment
    agent_config = env.add_stats_to_agent_config(agent_config)
    
    # Create the agent
    agent = Agent('transformer', agent_config)
    # Load the weights    
    agent.load_weights(os.path.join(log_dir, 'model', 'actor'))

    # Test the agent
    print("\nTesting...")
    tester(env, agent, tester_config, os.path.join(log_dir, start_date))
    print('\nEnd... Goodbye!')

if  __name__ == "__main__": # pragma: no cover
    runner()