162 lines
5.0 KiB
Python
162 lines
5.0 KiB
Python
import gc
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import threading
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from logging import getLogger
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from os import environ, path
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from typing import Any, Dict, List, Optional, Union
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import torch
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from .params import DeviceParams, SizeChart
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from .server.model_cache import ModelCache
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logger = getLogger(__name__)
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class ServerContext:
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def __init__(
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self,
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bundle_path: str = ".",
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model_path: str = ".",
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output_path: str = ".",
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params_path: str = ".",
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cors_origin: str = "*",
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num_workers: int = 1,
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any_platform: bool = True,
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block_platforms: List[str] = [],
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default_platform: str = None,
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image_format: str = "png",
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cache: ModelCache = None,
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cache_path: str = None,
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show_progress: bool = True,
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optimizations: List[str] = [],
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) -> None:
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self.bundle_path = bundle_path
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self.model_path = model_path
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self.output_path = output_path
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self.params_path = params_path
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self.cors_origin = cors_origin
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self.num_workers = num_workers
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self.any_platform = any_platform
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self.block_platforms = block_platforms
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self.default_platform = default_platform
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self.image_format = image_format
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self.cache = cache or ModelCache(num_workers)
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self.cache_path = cache_path or path.join(model_path, ".cache")
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self.show_progress = show_progress
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self.optimizations = optimizations
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@classmethod
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def from_environ(cls):
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num_workers = int(environ.get("ONNX_WEB_NUM_WORKERS", 1))
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cache_limit = int(environ.get("ONNX_WEB_CACHE_MODELS", num_workers + 2))
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return cls(
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bundle_path=environ.get(
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"ONNX_WEB_BUNDLE_PATH", path.join("..", "gui", "out")
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),
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model_path=environ.get("ONNX_WEB_MODEL_PATH", path.join("..", "models")),
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output_path=environ.get("ONNX_WEB_OUTPUT_PATH", path.join("..", "outputs")),
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params_path=environ.get("ONNX_WEB_PARAMS_PATH", "."),
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# others
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cors_origin=environ.get("ONNX_WEB_CORS_ORIGIN", "*").split(","),
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num_workers=num_workers,
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any_platform=get_boolean(environ, "ONNX_WEB_ANY_PLATFORM", True),
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block_platforms=environ.get("ONNX_WEB_BLOCK_PLATFORMS", "").split(","),
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default_platform=environ.get("ONNX_WEB_DEFAULT_PLATFORM", None),
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image_format=environ.get("ONNX_WEB_IMAGE_FORMAT", "png"),
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cache=ModelCache(limit=cache_limit),
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show_progress=get_boolean(environ, "ONNX_WEB_SHOW_PROGRESS", True),
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optimizations=environ.get("ONNX_WEB_OPTIMIZATIONS", "").split(","),
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)
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def base_join(base: str, tail: str) -> str:
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tail_path = path.relpath(path.normpath(path.join("/", tail)), "/")
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return path.join(base, tail_path)
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def is_debug() -> bool:
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return get_boolean(environ, "DEBUG", False)
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def get_boolean(args: Any, key: str, default_value: bool) -> bool:
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return args.get(key, str(default_value)).lower() in ("1", "t", "true", "y", "yes")
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def get_and_clamp_float(
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args: Any, key: str, default_value: float, max_value: float, min_value=0.0
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) -> float:
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return min(max(float(args.get(key, default_value)), min_value), max_value)
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def get_and_clamp_int(
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args: Any, key: str, default_value: int, max_value: int, min_value=1
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) -> int:
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return min(max(int(args.get(key, default_value)), min_value), max_value)
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def get_from_list(args: Any, key: str, values: List[Any]) -> Optional[Any]:
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selected = args.get(key, None)
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if selected in values:
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return selected
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logger.warn("invalid selection: %s", selected)
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if len(values) > 0:
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return values[0]
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return None
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def get_from_map(args: Any, key: str, values: Dict[str, Any], default: Any) -> Any:
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selected = args.get(key, default)
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if selected in values:
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return values[selected]
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else:
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return values[default]
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def get_not_empty(args: Any, key: str, default: Any) -> Any:
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val = args.get(key, default)
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if val is None or len(val) == 0:
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val = default
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return val
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def get_size(val: Union[int, str, None]) -> SizeChart:
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if val is None:
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return SizeChart.auto
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if type(val) is int:
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return val
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if type(val) is str:
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for size in SizeChart:
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if val == size.name:
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return size
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return int(val)
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raise Exception("invalid size")
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def run_gc(devices: List[DeviceParams] = []):
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logger.debug(
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"running garbage collection with %s active threads", threading.active_count()
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)
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gc.collect()
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if torch.cuda.is_available():
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for device in devices:
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logger.debug("running Torch garbage collection for device: %s", device)
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with torch.cuda.device(device.torch_str()):
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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mem_free, mem_total = torch.cuda.mem_get_info()
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logger.debug(
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"remaining CUDA VRAM usage: %s of %s",
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(mem_total - mem_free),
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mem_total,
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)
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