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import argparse
import json
import os
from glob import glob
from mmengine.config import Config
from torch.utils.tensorboard import SummaryWriter
def parse_args(training=False):
parser = argparse.ArgumentParser()
# model config
parser.add_argument("config", help="model config file path")
parser.add_argument("--seed", default=42, type=int, help="generation seed")
parser.add_argument("--ckpt-path", type=str, help="path to model ckpt; will overwrite cfg.ckpt_path if specified")
parser.add_argument("--batch-size", default=None, type=int, help="batch size")
# ======================================================
# Inference
# ======================================================
if not training:
# prompt
parser.add_argument("--prompt-path", default=None, type=str, help="path to prompt txt file")
parser.add_argument("--save-dir", default=None, type=str, help="path to save generated samples")
# hyperparameters
parser.add_argument("--num-sampling-steps", default=None, type=int, help="sampling steps")
parser.add_argument("--cfg-scale", default=None, type=float, help="balance between cond & uncond")
else:
parser.add_argument("--wandb", default=None, type=bool, help="enable wandb")
parser.add_argument("--load", default=None, type=str, help="path to continue training")
parser.add_argument("--data-path", default=None, type=str, help="path to data csv")
return parser.parse_args()
def merge_args(cfg, args, training=False):
if args.ckpt_path is not None:
cfg.model["from_pretrained"] = args.ckpt_path
args.ckpt_path = None
if not training:
if args.cfg_scale is not None:
cfg.scheduler["cfg_scale"] = args.cfg_scale
args.cfg_scale = None
if "multi_resolution" not in cfg:
cfg["multi_resolution"] = False
for k, v in vars(args).items():
if k in cfg and v is not None:
cfg[k] = v
return cfg
def parse_configs(training=False):
args = parse_args(training)
cfg = Config.fromfile(args.config)
cfg = merge_args(cfg, args, training)
return cfg
def create_experiment_workspace(cfg):
"""
This function creates a folder for experiment tracking.
Args:
args: The parsed arguments.
Returns:
exp_dir: The path to the experiment folder.
"""
# Make outputs folder (holds all experiment subfolders)
os.makedirs(cfg.outputs, exist_ok=True)
experiment_index = len(glob(f"{cfg.outputs}/*"))
# Create an experiment folder
model_name = cfg.model["type"].replace("/", "-")
exp_name = f"{experiment_index:03d}-F{cfg.num_frames}S{cfg.frame_interval}-{model_name}"
exp_dir = f"{cfg.outputs}/{exp_name}"
os.makedirs(exp_dir, exist_ok=True)
return exp_name, exp_dir
def save_training_config(cfg, experiment_dir):
with open(f"{experiment_dir}/config.txt", "w") as f:
json.dump(cfg, f, indent=4)
def create_tensorboard_writer(exp_dir):
tensorboard_dir = f"{exp_dir}/tensorboard"
os.makedirs(tensorboard_dir, exist_ok=True)
writer = SummaryWriter(tensorboard_dir)
return writer
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