onnx-web/api/onnx_web/utils.py

import gc
import threading
from logging import getLogger
from os import environ, path
from typing import Any, Dict, List, Optional, Union

import torch

from .params import DeviceParams, SizeChart

logger = getLogger(__name__)


def base_join(base: str, tail: str) -> str:
    tail_path = path.relpath(path.normpath(path.join("/", tail)), "/")
    return path.join(base, tail_path)


def is_debug() -> bool:
    return get_boolean(environ, "DEBUG", False)


def get_boolean(args: Any, key: str, default_value: bool) -> bool:
    return args.get(key, str(default_value)).lower() in ("1", "t", "true", "y", "yes")


def get_and_clamp_float(
    args: Any, key: str, default_value: float, max_value: float, min_value=0.0
) -> float:
    return min(max(float(args.get(key, default_value)), min_value), max_value)


def get_and_clamp_int(
    args: Any, key: str, default_value: int, max_value: int, min_value=1
) -> int:
    return min(max(int(args.get(key, default_value)), min_value), max_value)


def get_from_list(args: Any, key: str, values: List[Any]) -> Optional[Any]:
    selected = args.get(key, None)
    if selected in values:
        return selected

    logger.warn("invalid selection: %s", selected)
    if len(values) > 0:
        return values[0]

    return None


def get_from_map(args: Any, key: str, values: Dict[str, Any], default: Any) -> Any:
    selected = args.get(key, default)
    if selected in values:
        return values[selected]
    else:
        return values[default]


def get_not_empty(args: Any, key: str, default: Any) -> Any:
    val = args.get(key, default)

    if val is None or len(val) == 0:
        val = default

    return val


def get_size(val: Union[int, str, None]) -> Union[int, SizeChart]:
    if val is None:
        return SizeChart.auto

    if type(val) is int:
        return val

    if type(val) is str:
        for size in SizeChart:
            if val == size.name:
                return size

        return int(val)

    raise ValueError("invalid size")


def run_gc(devices: List[DeviceParams] = None):
    logger.debug(
        "running garbage collection with %s active threads", threading.active_count()
    )
    gc.collect()

    if torch.cuda.is_available() and devices is not None:
        for device in [d for d in devices if d.device.startswith("cuda")]:
            logger.debug("running Torch garbage collection for device: %s", device)
            with torch.cuda.device(device.torch_str()):
                torch.cuda.empty_cache()
                torch.cuda.ipc_collect()
                mem_free, mem_total = torch.cuda.mem_get_info()
                logger.debug(
                    "remaining CUDA VRAM usage: %s of %s",
                    (mem_total - mem_free),
                    mem_total,
                )