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Running
on
Zero
Running
on
Zero
File size: 4,144 Bytes
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import warnings
warnings.filterwarnings('ignore', category=DeprecationWarning)
import io
import os
from tqdm import tqdm
os.environ['MKL_SERVICE_FORCE_INTEL'] = '1'
from pathlib import Path
from collections import OrderedDict
import hydra
import numpy as np
import torch
import tools.utils as utils
from tools.replay import load_episode
torch.backends.cudnn.benchmark = True
if os.name == "nt":
import msvcrt
def portable_lock(fp):
fp.seek(0)
msvcrt.locking(fp, msvcrt.LK_LOCK, 1)
def portable_unlock(fp):
fp.seek(0)
msvcrt.locking(fp, msvcrt.LK_UNLCK, 1)
else:
import fcntl
def portable_lock(fp):
fcntl.flock(fp, fcntl.LOCK_EX | fcntl.LOCK_NB)
def portable_unlock(fp):
fcntl.flock(fp, fcntl.LOCK_UN)
class Locker:
def __init__(self, lock_name):
# e.g. lock_name = "./lockfile.lck"
self.lock_name = lock_name
def __enter__(self,):
open_mode = os.O_RDWR | os.O_CREAT | os.O_TRUNC
self.fd = os.open(self.lock_name, open_mode)
portable_lock(self.fd)
def __exit__(self, _type, value, tb):
portable_unlock(self.fd)
os.close(self.fd)
try:
os.remove(self.lock_name)
except:
pass
class Workspace:
def __init__(self, cfg, savedir=None, workdir=None,):
self.workdir = Path.cwd() if workdir is None else workdir
print(f'workspace: {self.workdir}')
assert int(cfg.viclip_encode) == 1, "encoding only one (video or img)"
if cfg.viclip_encode:
self.key_to_add = 'clip_video'
self.key_to_process = getattr(cfg, 'key_to_process', 'observation')
self.cfg = cfg
self.device = torch.device(cfg.device)
# create envs
task = cfg.task
self.task = task
img_size = cfg.img_size
import envs.main as envs
self.train_env = envs.make(task, cfg.obs_type, cfg.action_repeat, cfg.seed, img_size=img_size, viclip_encode=cfg.viclip_encode, device='cuda')
self.dataset_path = Path(cfg.dataset_dir)
self.timer = utils.Timer()
self._global_step = 0
self._global_episode = 0
def process(self):
filenames = sorted(self.dataset_path.glob('**/*.npz'))
print(f"Found {len(filenames)} files")
episodes_to_process = {}
for idx, fname in tqdm(enumerate(filenames)):
lockname = str(fname.absolute()) + ".lck"
try:
with Locker(lockname):
episode = load_episode(fname)
# validate before continuing
if type(episode[self.key_to_add]) == np.ndarray and episode[self.key_to_add].size > 1 and episode[self.key_to_add].shape[0] == episode[self.key_to_process].shape[0]:
continue
else:
del episode[self.key_to_add]
add_data = self.train_env.process_episode(episode[self.key_to_process]) # .cpu().numpy()
if idx == 0:
print(add_data.shape)
episode[self.key_to_add] = add_data
# save episode
with io.BytesIO() as f1:
np.savez_compressed(f1, **episode)
f1.seek(0)
with fname.open('wb') as f2:
f2.write(f1.read())
except BlockingIOError:
print(f"File busy: {str(fname)}")
continue
def start_processing(cfg, savedir, workdir):
from process_dataset import Workspace as W
root_dir = Path.cwd()
cfg.workdir = str(root_dir)
workspace = W(cfg, savedir, workdir)
workspace.root_dir = root_dir
snapshot = workspace.root_dir / 'last_snapshot.pt'
if snapshot.exists():
print(f'resuming: {snapshot}')
workspace.load_snapshot(workspace.root_dir)
workspace.process()
@hydra.main(config_path='.', config_name='process_dataset')
def main(cfg):
start_processing(cfg, None, None)
if __name__ == '__main__':
main() |