/
train_main.py
349 lines (263 loc) · 11.8 KB
/
train_main.py
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# AndreiN, 2019
# parts from https://github.com/lcswillems/torch-rl
import gym
import time
import datetime
import torch
import sys
from liftoff.config import read_config
from argparse import Namespace
import numpy as np
from typing import List
import utils
from models import get_model
from agents import get_agent
import environment
MAIN_CFG_ARGS = ["main", "env_cfg", "agent", "model"]
def add_to_cfg(cfg: Namespace, subgroups: List[str], new_arg: str, new_arg_value) -> None:
for arg in subgroups:
if hasattr(cfg, arg):
setattr(getattr(cfg, arg), new_arg, new_arg_value)
def post_process_args(args: Namespace) -> None:
args.mem = args.recurrence > args.min_mem
def extra_log_fields(header: list, log_keys: list) ->list:
unusable_fields = ['return_per_episode', 'reshaped_return_per_episode',
'num_frames_per_episode', 'num_frames']
extra_fields = []
for field in log_keys:
if field not in header and field not in unusable_fields:
extra_fields.append(field)
return extra_fields
def get_envs(full_args, env_wrapper, no_envs, master_make=False):
""" Minigrid action 6 is Done - useless"""
envs = []
args = full_args.main
actual_procs = args.actual_procs
add_to_cfg(full_args, MAIN_CFG_ARGS, "out_dir", full_args.out_dir)
# create env
env = gym.make(args.env)
env = env_wrapper(env)
# add env arguments
env.max_steps = full_args.env_cfg.max_episode_steps
env.no_stacked_frames = full_args.env_cfg.no_stacked_frames
env.seed(args.seed + 10000 * 0)
envs.append([env])
chunk_size = int(np.ceil((no_envs - 1) / float(actual_procs)))
for env_i in range(1, no_envs, chunk_size):
env_chunk = []
for i in range(env_i, min(env_i + chunk_size, no_envs)):
if master_make:
# create env
env = gym.make(args.env)
env = env_wrapper(env)
# add env arguments
env.max_steps = full_args.env_cfg.max_episode_steps
env.no_stacked_frames = full_args.env_cfg.no_stacked_frames
env.seed(args.seed + 10000 * i)
else:
env = [i, full_args, args]
env_chunk.append(env)
envs.append(env_chunk)
return envs, chunk_size
def print_keys(header: list, data: list, extra_logs: list = None) ->tuple:
basic_keys_format = \
"U {} | F {:06} | FPS {:04.0f} | D {} | rR:μσmM {:.2f} {:.2f} {:.2f} {:.2f} | " \
"F:μσmM {:.1f} {:.1f} {} {} | H {:.3f} | V {:.3f} | pL {:.3f} | vL {:.3f} | "\
"∇ {:.3f}"
printable_data = data[:17]
if extra_logs:
for field in extra_logs:
basic_keys_format += (" | " + field[1] + " {:." + field[2] + "} ")
printable_data.append(data[header.index(field[0])])
return basic_keys_format, printable_data
def run(full_args: Namespace) -> None:
# import torch.multiprocessing as mp
# mp.set_start_method('spawn')
args = full_args.main
agent_args = full_args.agent
model_args = full_args.model
env_args = full_args.env_cfg
extra_logs = getattr(full_args, "extra_logs", None)
if args.seed == 0:
args.seed = full_args.run_id + 1
max_eprews = args.max_eprews
post_process_args(agent_args)
post_process_args(model_args)
model_dir = getattr(args, "model_dir", full_args.out_dir)
print(model_dir)
# ==============================================================================================
# @ torc_rl repo original
# Define logger, CSV writer and Tensorboard writer
logger = utils.get_logger(model_dir)
csv_file, csv_writer = utils.get_csv_writer(model_dir)
tb_writer = None
if args.tb:
from tensorboardX import SummaryWriter
tb_writer = SummaryWriter(model_dir)
# Log command and all script arguments
logger.info("{}\n".format(" ".join(sys.argv)))
logger.info("{}\n".format(args))
# ==============================================================================================
# Set seed for all randomness sources
utils.seed(args.seed)
# ==============================================================================================
# Generate environments
envs = []
# Get environment wrapper
wrapper_method = getattr(full_args.env_cfg, "wrapper", None)
if wrapper_method is None:
def idem(x):
return x
env_wrapper = idem
else:
env_wrappers = [getattr(environment, w_p) for w_p in wrapper_method]
def env_wrapp(w_env):
for wrapper in env_wrappers[::-1]:
w_env = wrapper(w_env)
return w_env
env_wrapper = env_wrapp
actual_procs = getattr(args, "actual_procs", None)
master_make_envs = getattr(full_args.env_cfg, "master_make_envs", False)
if actual_procs:
# Split envs in chunks
no_envs = args.procs
envs, chunk_size = get_envs(full_args, env_wrapper, no_envs,
master_make=master_make_envs)
first_env = envs[0][0]
print(f"NO of envs / proc: {chunk_size}; No of processes {len(envs[1:])} + Master")
else:
for i in range(args.procs):
env = env_wrapper(gym.make(args.env))
env.max_steps = full_args.env_cfg.max_episode_steps
env.no_stacked_frames = full_args.env_cfg.no_stacked_frames
env.seed(args.seed + 10000 * i)
envs.append(env)
first_env = envs[0]
# Generate evaluation envs
eval_envs = []
if full_args.env_cfg.no_eval_envs > 0:
no_envs = full_args.env_cfg.no_eval_envs
eval_envs, chunk_size = get_envs(full_args, env_wrapper, no_envs, master_make=master_make_envs)
# Define obss preprocessor
max_image_value = full_args.env_cfg.max_image_value
normalize_img = full_args.env_cfg.normalize
obs_space, preprocess_obss = utils.get_obss_preprocessor(args.env,
first_env.observation_space,
model_dir,
max_image_value=max_image_value,
normalize=normalize_img)
# ==============================================================================================
# Load training status
try:
status = utils.load_status(model_dir)
except OSError:
status = {"num_frames": 0, "update": 0}
saver = utils.SaveData(model_dir, save_best=args.save_best, save_all=args.save_all)
model, agent_data, other_data = None, dict(), None
try:
# Continue from last point
model, agent_data, other_data = saver.load_training_data(best=False)
logger.info("Training data exists & loaded successfully\n")
except OSError:
logger.info("Could not load training data\n")
# ==============================================================================================
# Load Model
if model is None:
model = get_model(model_args, obs_space, first_env.action_space,
use_memory=model_args.use_memory,
no_stacked_frames=env_args.no_stacked_frames
)
logger.info(f"Model [{model_args.name}] successfully created\n")
# Print Model info
logger.info("{}\n".format(model))
if torch.cuda.is_available():
model.cuda()
logger.info("CUDA available: {}\n".format(torch.cuda.is_available()))
# ==============================================================================================
# Load Agent
algo = get_agent(full_args.agent, envs, model, agent_data,
preprocess_obss=preprocess_obss, reshape_reward=None, eval_envs=eval_envs)
has_evaluator = hasattr(algo, "evaluate") and full_args.env_cfg.no_eval_envs > 0
# ==============================================================================================
# Train model
crt_eprew = 0
if "eprew" in other_data:
crt_eprew = other_data["eprew"]
num_frames = status["num_frames"]
total_start_time = time.time()
update = status["update"]
update_start_time = time.time()
while num_frames < args.frames:
# Update model parameters
logs = algo.update_parameters()
num_frames += logs["num_frames"]
update += 1
if has_evaluator:
if update % args.eval_interval == 0:
algo.evaluate()
prev_start_time = update_start_time
update_start_time = time.time()
# Print logs
if update % args.log_interval == 0:
fps = logs["num_frames"] / (update_start_time - prev_start_time)
duration = int(time.time() - total_start_time)
return_per_episode = utils.synthesize(logs["return_per_episode"])
rreturn_per_episode = utils.synthesize(logs["reshaped_return_per_episode"])
num_frames_per_episode = utils.synthesize(logs["num_frames_per_episode"])
header = ["update", "frames", "FPS", "duration"]
data = [update, num_frames, fps, duration]
header += ["rreturn_" + key for key in rreturn_per_episode.keys()]
data += rreturn_per_episode.values()
header += ["num_frames_" + key for key in num_frames_per_episode.keys()]
data += num_frames_per_episode.values()
header += ["entropy", "value", "policy_loss", "value_loss"]
data += [logs["entropy"], logs["value"], logs["policy_loss"], logs["value_loss"]]
header += ["grad_norm"]
data += [logs["grad_norm"]]
# add log fields that are not in the standard log format (for example value_int)
extra_fields = extra_log_fields(header, list(logs.keys()))
header.extend(extra_fields)
data += [logs[field] for field in extra_fields]
# print to stdout the standard log fields + fields required in config
keys_format, printable_data = print_keys(header, data, extra_logs)
logger.info(keys_format.format(*printable_data))
header += ["return_" + key for key in return_per_episode.keys()]
data += return_per_episode.values()
if status["num_frames"] == 0:
csv_writer.writerow(header)
csv_writer.writerow(data)
csv_file.flush()
if args.tb:
for field, value in zip(header, data):
tb_writer.add_scalar(field, value, num_frames)
status = {"num_frames": num_frames, "update": update}
crt_eprew = list(rreturn_per_episode.values())[0]
# -- Save vocabulary and model
if args.save_interval > 0 and update % args.save_interval == 0:
# preprocess_obss.vocab.save()
saver.save_training_data(model, algo.get_save_data(), crt_eprew)
logger.info("Model successfully saved")
utils.save_status(status, model_dir)
if crt_eprew > max_eprews != 0:
print("Reached max return 0.93")
exit()
def main() -> None:
import os
""" Read configuration from disk (the old way)"""
# Reading args
full_args = read_config() # type: Namespace
args = full_args.main
if not hasattr(full_args, "run_id"):
full_args.run_id = 0
if hasattr(args, "model_dir"):
# Define run dir
os.environ["TORCH_RL_STORAGE"] = "results_dir"
suffix = datetime.datetime.now().strftime("%y-%m-%d-%H-%M-%S")
default_model_name = "{}_{}_seed{}_{}".format(args.env, args.algo, args.seed, suffix)
model_name = args.model or default_model_name
model_dir = utils.get_model_dir(model_name)
full_args.out_dir = model_dir
args.model_dir = model_dir
run(full_args)
if __name__ == "__main__":
main()