import shutil from functools import partial from logging import getLogger from os import environ, path from pathlib import Path from typing import Dict, List, Optional, Tuple, Union import requests import torch from tqdm.auto import tqdm from yaml import safe_load logger = getLogger(__name__) ModelDict = Dict[str, Union[str, int]] LegacyModel = Tuple[str, str, Optional[bool], Optional[bool], Optional[int]] class ConversionContext: def __init__( self, model_path: Optional[str] = None, device: Optional[str] = None, cache_path: Optional[str] = None, half: Optional[bool] = False, opset: Optional[int] = None, token: Optional[str] = None, ) -> None: self.model_path = model_path or environ.get( "ONNX_WEB_MODEL_PATH", path.join("..", "models") ) self.cache_path = cache_path or path.join(self.model_path, ".cache") self.half = half self.opset = opset self.token = token if device is not None: self.training_device = device else: self.training_device = "cuda" if torch.cuda.is_available() else "cpu" self.map_location = torch.device(self.training_device) def download_progress(urls: List[Tuple[str, str]]): for url, dest in urls: dest_path = Path(dest).expanduser().resolve() dest_path.parent.mkdir(parents=True, exist_ok=True) if dest_path.exists(): logger.info("Destination already exists: %s", dest_path) return str(dest_path.absolute()) req = requests.get( url, stream=True, allow_redirects=True, headers={ "User-Agent": "onnx-web-api", }, ) if req.status_code != 200: req.raise_for_status() # Only works for 4xx errors, per SO answer raise RuntimeError( "Request to %s failed with status code: %s" % (url, req.status_code) ) total = int(req.headers.get("Content-Length", 0)) desc = "unknown" if total == 0 else "" req.raw.read = partial(req.raw.read, decode_content=True) with tqdm.wrapattr(req.raw, "read", total=total, desc=desc) as data: with dest_path.open("wb") as f: shutil.copyfileobj(data, f) return str(dest_path.absolute()) def tuple_to_correction(model: Union[ModelDict, LegacyModel]): if isinstance(model, list) or isinstance(model, tuple): name, source, *rest = model scale = rest[0] if len(rest) > 0 else 1 half = rest[0] if len(rest) > 0 else False opset = rest[0] if len(rest) > 0 else None return { "name": name, "source": source, "half": half, "opset": opset, "scale": scale, } else: return model def tuple_to_diffusion(model: Union[ModelDict, LegacyModel]): if isinstance(model, list) or isinstance(model, tuple): name, source, *rest = model single_vae = rest[0] if len(rest) > 0 else False half = rest[0] if len(rest) > 0 else False opset = rest[0] if len(rest) > 0 else None return { "name": name, "source": source, "half": half, "opset": opset, "single_vae": single_vae, } else: return model def tuple_to_upscaling(model: Union[ModelDict, LegacyModel]): if isinstance(model, list) or isinstance(model, tuple): name, source, *rest = model scale = rest[0] if len(rest) > 0 else 1 half = rest[0] if len(rest) > 0 else False opset = rest[0] if len(rest) > 0 else None return { "name": name, "source": source, "half": half, "opset": opset, "scale": scale, } else: return model model_formats = ["onnx", "pth", "ckpt", "safetensors"] model_formats_original = ["ckpt", "safetensors"] def source_format(model: Dict) -> Optional[str]: if "format" in model: return model["format"] if "source" in model: ext = path.splitext(model["source"]) if ext in model_formats: return ext return None class Config(object): """ Shim for pydantic-style config. """ def __init__(self, kwargs): self.__dict__.update(kwargs) for k, v in self.__dict__.items(): Config.config_from_key(self, k, v) def __iter__(self): for k in self.__dict__.keys(): yield k @classmethod def config_from_key(cls, target, k, v): if isinstance(v, dict): tmp = Config(v) setattr(target, k, tmp) else: setattr(target, k, v) def load_yaml(file: str) -> str: with open(file, "r") as f: data = safe_load(f.read()) return Config(data) safe_chars = "._-" def sanitize_name(name): return "".join(x for x in name if (x.isalnum() or x in safe_chars))