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Usage:
Training:
python train.py --config-name=train_diffusion_lowdim_workspace
"""
import torch
import os
import sys
import hydra
from omegaconf import OmegaConf
import pathlib
from unified_video_action.workspace.base_workspace import BaseWorkspace
from omegaconf import open_dict
# allows arbitrary python code execution in configs using the ${eval:''} resolver
OmegaConf.register_new_resolver("eval", eval, replace=True)
import wandb
if "WANDB_API_KEY" in os.environ:
wandb.login(key=os.environ["WANDB_API_KEY"])
@hydra.main(
version_base=None,
config_path=str(
pathlib.Path(__file__).parent.joinpath("unified_video_action", "config")
),
)
def main(cfg: OmegaConf):
OmegaConf.resolve(cfg)
if cfg.model.policy.action_model_params.predict_action == False:
cfg.checkpoint.topk.monitor_key = "video_fvd"
cfg.checkpoint.topk.format_str = (
"epoch={epoch:04d}-video_fvd={video_fvd:.3f}.ckpt"
)
cfg.checkpoint.topk.mode = "min"
with open_dict(cfg):
cfg.n_gpus = torch.cuda.device_count()
cfg.model.policy.debug = cfg.training.debug
if cfg.training.debug:
cfg.dataloader.batch_size = 2
cfg.val_dataloader.batch_size = 2
cfg.dataloader.shuffle = False
cfg.val_dataloader.shuffle = False
if "env_runner" in cfg.task:
cfg.task.env_runner.max_steps = 20
if "dataloader_cfg" in cfg.task.dataset:
cfg.task.dataset.dataloader_cfg.batch_size = 2
cls = hydra.utils.get_class(cfg.model._target_)
workspace: BaseWorkspace = cls(cfg)
workspace.run()
if __name__ == "__main__":
print(sys.argv)
for arg in sys.argv:
if "local_rank" in arg: # For deepspeed compatibility
sys.argv.remove(arg)
main()
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