Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
 Community
text
stringlengths
1
93.6k
args.n_classes = 100 if args.dataset == "cifar100" else 10
set_seed(args)
os.makedirs(args.log_dir, exist_ok=True)
configs = OrderedDict(sorted(vars(args).items(), key=lambda x: x[0]))
setup_logger(exp_prefix=args.exp_prefix, variant=configs, log_dir=args.log_dir)
with open(f"{args.log_dir}/params.txt", "w") as f:
json.dump(args.__dict__, f)
sys.stdout = open(f"{args.log_dir}/log.txt", "a")
main(args)
# <FILESEP>
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
import os, sys
import numpy as np
from tqdm import tqdm
from omegaconf import DictConfig
import pickle
import itertools
import torch
import hydra
import submitit
from accelerate import Accelerator, DistributedDataParallelKwargs
from detectron2.data import MetadataCatalog
from detectron2.evaluation import SemSegEvaluator
from detectron2.utils.comm import get_rank
from viewseg.dataset import collate_fn, get_viewseg_datasets
from viewseg.utils import single_gpu_prepare
CONFIG_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "configs")
# compatible with old training history
sys.modules['panonerf'] = sys.modules['viewseg']
@hydra.main(config_path=CONFIG_DIR, config_name="viewseg_replica_finetune")
def main(cfg: DictConfig):
try:
# Only needed when launching on cluster with slurm
job_env = submitit.JobEnvironment()
os.environ["LOCAL_RANK"] = str(job_env.local_rank)
os.environ["RANK"] = str(job_env.global_rank)
os.environ["WORLD_SIZE"] = str(job_env.num_tasks)
hostname_first_node = (
os.popen("scontrol show hostnames $SLURM_JOB_NODELIST").read().split("\n")[0]
)
print("[launcher] Using the following MASTER_ADDR: {}".format(hostname_first_node))
os.environ["MASTER_ADDR"] = hostname_first_node
os.environ["MASTER_PORT"] = "42918"
job_id = job_env.job_id
except RuntimeError:
print("Running locally")
job_id = ""
# Set the relevant seeds for reproducibility.
np.random.seed(cfg.seed)
torch.manual_seed(cfg.seed)
# Set up the accelerator for multigpu training
ddp_scaler = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(kwargs_handlers=[ddp_scaler])
device = accelerator.device
print("Device", accelerator.device)
# Resume from the checkpoint.
output_dir = os.path.join(hydra.utils.get_original_cwd(), 'checkpoints', cfg.experiment_name)
os.makedirs(output_dir, exist_ok=True)
if cfg.test.epoch == 'None':
checkpoint_path = os.path.join(output_dir, 'checkpoint.pth')
else:
checkpoint_path = os.path.join(output_dir, 'checkpoint_{}.pth'.format(cfg.test.epoch))
if not os.path.isfile(checkpoint_path):
raise ValueError(f"Model checkpoint {checkpoint_path} does not exist!")
loaded_data = torch.load(checkpoint_path)
# Do not load the cached xy grid.
# - this allows setting an arbitrary evaluation image size.
state_dict = loaded_data["model"]
state_dict = single_gpu_prepare(state_dict)
stats = pickle.loads(loaded_data["stats"])
print(f" => resuming from epoch {stats.epoch}.")
print("[detectron2] rank {}".format(get_rank()))
train_dataset, val_dataset, test_dataset = get_viewseg_datasets(
dataset_name=cfg.data.dataset_name,
image_size=cfg.data.image_size,
num_views=cfg.train.num_views,
load_depth=cfg.test.use_depth,
)
print("data split: {}".format(cfg.test.split))
if cfg.test.split == 'train':
test_dataset = train_dataset
elif cfg.test.split == 'val':
test_dataset = val_dataset
elif cfg.test.split == 'test':
pass
else:
raise NotImplementedError