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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import warnings
warnings.filterwarnings('ignore', category=DeprecationWarning)
import os
os.environ['MKL_SERVICE_FORCE_INTEL'] = '1'
os.environ['MUJOCO_GL'] = 'egl'
from pathlib import Path
import hydra
import numpy as np
import torch
from dm_env import specs
import dmc
import utils
from logger import Logger
from numpy_replay_buffer import EfficientReplayBuffer
from video import TrainVideoRecorder, VideoRecorder
from utils import load_offline_dataset_into_buffer
torch.backends.cudnn.benchmark = True
def make_agent(obs_spec, action_spec, cfg):
cfg.obs_shape = obs_spec.shape
cfg.action_shape = action_spec.shape
return hydra.utils.instantiate(cfg)
class Workspace:
def __init__(self, cfg):
self.work_dir = Path.cwd()
print(f'workspace: {self.work_dir}')
self.cfg = cfg
utils.set_seed_everywhere(cfg.seed)
self.device = torch.device(cfg.device)
self.setup()
self.agent = make_agent(self.train_env.observation_spec(),
self.train_env.action_spec(),
self.cfg.agent)
self.timer = utils.Timer()
self._global_step = 0
self._global_episode = 0
def setup(self):
# create logger
self.logger = Logger(self.work_dir, use_tb=self.cfg.use_tb, offline=self.cfg.offline,
distracting_eval=self.cfg.eval_on_distracting, multitask_eval=self.cfg.eval_on_multitask)
# create envs
self.train_env = dmc.make(self.cfg.task_name, self.cfg.frame_stack,
self.cfg.action_repeat, self.cfg.seed, self.cfg.distracting_mode)
self.eval_env = dmc.make(self.cfg.task_name, self.cfg.frame_stack,
self.cfg.action_repeat, self.cfg.seed, self.cfg.distracting_mode)
# create replay buffer
data_specs = (self.train_env.observation_spec(),
self.train_env.action_spec(),
specs.Array((1,), np.float32, 'reward'),
specs.Array((1,), np.float32, 'discount'))
self.replay_buffer = EfficientReplayBuffer(self.cfg.replay_buffer_size,
self.cfg.batch_size,
self.cfg.nstep,
self.cfg.discount,
self.cfg.frame_stack,
data_specs)
self.video_recorder = VideoRecorder(
self.work_dir if self.cfg.save_video else None)
self.train_video_recorder = TrainVideoRecorder(
self.work_dir if self.cfg.save_train_video else None)
self.eval_on_distracting = self.cfg.eval_on_distracting
self.eval_on_multitask = self.cfg.eval_on_multitask
@property
def global_step(self):
return self._global_step
@property
def global_episode(self):
return self._global_episode
@property
def global_frame(self):
return self.global_step * self.cfg.action_repeat
def eval(self):
step, episode, total_reward = 0, 0, 0
eval_until_episode = utils.Until(self.cfg.num_eval_episodes)
while eval_until_episode(episode):
time_step = self.eval_env.reset()
self.video_recorder.init(self.eval_env, enabled=(episode == 0))
while not time_step.last():
with torch.no_grad(), utils.eval_mode(self.agent):
action = self.agent.act(time_step.observation,
self.global_step,
eval_mode=True)
time_step = self.eval_env.step(action)
self.video_recorder.record(self.eval_env)
total_reward += time_step.reward
step += 1
episode += 1
self.video_recorder.save(f'{self.global_frame}.mp4')
with self.logger.log_and_dump_ctx(self.global_frame, ty='eval') as log:
log('episode_reward', total_reward / episode)
log('episode_length', step * self.cfg.action_repeat / episode)
log('episode', self.global_episode)
log('step', self.global_step)
def eval_distracting(self, record_video):
distraction_modes = ['easy', 'medium', 'hard', 'fixed_easy', 'fixed_medium', 'fixed_hard']
if not hasattr(self, 'distracting_envs'):
self.distracting_envs = []
for distraction_mode in distraction_modes:
env = dmc.make(self.cfg.task_name, self.cfg.frame_stack,
self.cfg.action_repeat, self.cfg.seed, distracting_mode=distraction_mode)
self.distracting_envs.append(env)
for env, env_name in zip(self.distracting_envs, distraction_modes):
self.eval_single_env(env, env_name, record_video)
def eval_multitask(self, record_video):
multitask_modes = [f'len_{i}' for i in range(1, 11, 1)]
if not hasattr(self, 'multitask_envs'):
self.multitask_envs = []
for multitask_mode in multitask_modes:
env = dmc.make(self.cfg.task_name, self.cfg.frame_stack,
self.cfg.action_repeat, self.cfg.seed, multitask_mode=multitask_mode)
self.multitask_envs.append(env)
for env, env_name in zip(self.multitask_envs, multitask_modes):
self.eval_single_env(env, env_name, record_video)
def eval_single_env(self, env, env_name, save_video):
step, episode, total_reward = 0, 0, 0
eval_until_episode = utils.Until(self.cfg.num_eval_episodes)
while eval_until_episode(episode):
time_step = env.reset()
self.video_recorder.init(env, enabled=((episode == 0) and save_video))
while not time_step.last():
with torch.no_grad(), utils.eval_mode(self.agent):
action = self.agent.act(time_step.observation,
self.global_step,
eval_mode=True)
time_step = env.step(action)
self.video_recorder.record(env)
total_reward += time_step.reward
step += 1
episode += 1
self.video_recorder.save(f'{env_name}_{self.global_frame}.mp4')
self.logger.log(f'eval/{env_name}_episode_reward', total_reward / episode, self.global_frame)
def train(self):
# predicates
train_until_step = utils.Until(self.cfg.num_train_frames,
self.cfg.action_repeat)
seed_until_step = utils.Until(self.cfg.num_seed_frames,
self.cfg.action_repeat)
eval_every_step = utils.Every(self.cfg.eval_every_frames,
self.cfg.action_repeat)
# only in distracting evaluation mode
eval_save_vid_every_step = utils.Every(self.cfg.eval_save_vid_every_step,
self.cfg.action_repeat)
episode_step, episode_reward = 0, 0
time_step = self.train_env.reset()
self.replay_storage.add(time_step)
self.train_video_recorder.init(time_step.observation)
metrics = None
while train_until_step(self.global_step):
if time_step.last():
self._global_episode += 1
self.train_video_recorder.save(f'{self.global_frame}.mp4')
# wait until all the metrics schema is populated
if metrics is not None:
# log stats
elapsed_time, total_time = self.timer.reset()
episode_frame = episode_step * self.cfg.action_repeat
with self.logger.log_and_dump_ctx(self.global_frame,
ty='train') as log:
log('fps', episode_frame / elapsed_time)
log('total_time', total_time)
log('episode_reward', episode_reward)
log('episode_length', episode_frame)
log('episode', self.global_episode)
log('buffer_size', len(self.replay_storage))
log('step', self.global_step)
# reset env
time_step = self.train_env.reset()
self.replay_storage.add(time_step)
self.train_video_recorder.init(time_step.observation)
# try to save snapshot
if self.cfg.save_snapshot:
self.save_snapshot()
episode_step = 0
episode_reward = 0
# try to evaluate
if eval_every_step(self.global_step):
self.logger.log('eval_total_time', self.timer.total_time(),
self.global_frame)
if self.eval_on_distracting:
self.eval_distracting(eval_save_vid_every_step(self.global_step))
if self.eval_on_multitask:
self.eval_multitask(eval_save_vid_every_step(self.global_step))
self.eval()
# sample action
with torch.no_grad(), utils.eval_mode(self.agent):
action = self.agent.act(time_step.observation,
self.global_step,
eval_mode=False)
# try to update the agent
if not seed_until_step(self.global_step):
metrics = self.agent.update(self.replay_iter, self.global_step)
self.logger.log_metrics(metrics, self.global_frame, ty='train')
# take env step
time_step = self.train_env.step(action)
episode_reward += time_step.reward
self.replay_storage.add(time_step)
self.train_video_recorder.record(time_step.observation)
episode_step += 1
self._global_step += 1
def train_offline(self, offline_dir):
# Open dataset, load as memory buffer
load_offline_dataset_into_buffer(Path(offline_dir), self.replay_buffer, self.cfg.frame_stack,
self.cfg.replay_buffer_size)
if self.replay_buffer.index == -1:
raise ValueError('No offline data loaded, check directory.')
# predicates
train_until_step = utils.Until(self.cfg.num_train_frames, 1)
eval_every_step = utils.Every(self.cfg.eval_every_frames, 1)
show_train_stats_every_step = utils.Every(self.cfg.show_train_stats_every_frames, 1)
# only in distracting evaluation mode
eval_save_vid_every_step = utils.Every(self.cfg.eval_save_vid_every_step,
self.cfg.action_repeat)
metrics = None
step = 0
while train_until_step(self.global_step):
if show_train_stats_every_step(self.global_step):
# wait until all the metrics schema is populated
if metrics is not None:
# log stats
elapsed_time, total_time = self.timer.reset()
with self.logger.log_and_dump_ctx(self.global_frame,
ty='train') as log:
log('fps', step / elapsed_time)
log('total_time', total_time)
log('buffer_size', len(self.replay_buffer))
log('step', self.global_step)
step = 0
# try to save snapshot
if self.cfg.save_snapshot:
self.save_snapshot()
step += 1
# try to evaluate
if eval_every_step(self.global_step):
self.logger.log('eval_total_time', self.timer.total_time(),
self.global_frame)
if self.eval_on_distracting:
self.eval_distracting(eval_save_vid_every_step(self.global_step))
if self.eval_on_multitask:
self.eval_multitask(eval_save_vid_every_step(self.global_step))
self.eval()
# try to update the agent
metrics = self.agent.update(self.replay_buffer, self.global_step)
if show_train_stats_every_step(self.global_step):
self.logger.log_metrics(metrics, self.global_frame, ty='train')
self._global_step += 1
def save_snapshot(self):
snapshot = self.work_dir / 'snapshot.pt'
keys_to_save = ['agent', 'timer', '_global_step', '_global_episode']
payload = {k: self.__dict__[k] for k in keys_to_save}
with snapshot.open('wb') as f:
torch.save(payload, f)
def load_snapshot(self):
snapshot = self.work_dir / 'snapshot.pt'
with snapshot.open('rb') as f:
payload = torch.load(f)
for k, v in payload.items():
self.__dict__[k] = v
@hydra.main(config_path='cfgs', config_name='config')
def main(cfg):
from train import Workspace as W
root_dir = Path.cwd()
workspace = W(cfg)
print(cfg)
snapshot = root_dir / 'snapshot.pt'
if snapshot.exists():
print(f'resuming: {snapshot}')
workspace.load_snapshot()
if cfg.offline:
workspace.train_offline(cfg.offline_dir)
else:
workspace.train()
if __name__ == '__main__':
main()
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