128 lines
3.5 KiB
Python
128 lines
3.5 KiB
Python
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 gc
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import torch
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from .params import (
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SizeChart,
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)
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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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block_platforms: List[str] = [],
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default_platform: str = None,
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image_format: str = 'png',
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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.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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@classmethod
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def from_environ(cls):
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return ServerContext(
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bundle_path=environ.get('ONNX_WEB_BUNDLE_PATH',
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path.join('..', 'gui', 'out')),
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model_path=environ.get('ONNX_WEB_MODEL_PATH',
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path.join('..', 'models')),
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output_path=environ.get(
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'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=int(environ.get('ONNX_WEB_NUM_WORKERS', 1)),
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block_platforms=environ.get(
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'ONNX_WEB_BLOCK_PLATFORMS', '').split(','),
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default_platform=environ.get(
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'ONNX_WEB_DEFAULT_PLATFORM', None),
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image_format=environ.get(
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'ONNX_WEB_IMAGE_FORMAT', 'png'
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),
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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 environ.get('DEBUG') is not None
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def get_and_clamp_float(args: Any, key: str, default_value: float, max_value: float, min_value=0.0) -> 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(args: Any, key: str, default_value: int, max_value: int, min_value=1) -> 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():
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logger.debug('running garbage collection')
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gc.collect()
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torch.cuda.empty_cache()
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