| import importlib |
| import random |
| from functools import lru_cache |
| from time import sleep, time |
| from typing import List |
|
|
| from onnxruntime import InferenceSession |
|
|
| from facefusion import logger, process_manager, state_manager, translator |
| from facefusion.app_context import detect_app_context |
| from facefusion.common_helper import is_windows |
| from facefusion.execution import create_inference_providers, has_execution_provider |
| from facefusion.exit_helper import fatal_exit |
| from facefusion.filesystem import get_file_name, is_file |
| from facefusion.time_helper import calculate_end_time |
| from facefusion.types import DownloadSet, ExecutionProvider, InferencePool, InferencePoolSet, InferenceProvider |
|
|
| INFERENCE_POOL_SET : InferencePoolSet =\ |
| { |
| 'cli': {}, |
| 'ui': {} |
| } |
|
|
|
|
| def get_inference_pool(module_name : str, model_names : List[str], model_source_set : DownloadSet) -> InferencePool: |
| while process_manager.is_checking(): |
| sleep(0.5) |
| execution_device_ids = state_manager.get_item('execution_device_ids') |
| execution_providers = state_manager.get_item('execution_providers') |
| app_context = detect_app_context() |
|
|
| for execution_device_id in execution_device_ids: |
| inference_context = get_inference_context(module_name, model_names, execution_device_id, execution_providers) |
|
|
| if app_context == 'cli' and INFERENCE_POOL_SET.get('ui').get(inference_context): |
| INFERENCE_POOL_SET['cli'][inference_context] = INFERENCE_POOL_SET.get('ui').get(inference_context) |
| if app_context == 'ui' and INFERENCE_POOL_SET.get('cli').get(inference_context): |
| INFERENCE_POOL_SET['ui'][inference_context] = INFERENCE_POOL_SET.get('cli').get(inference_context) |
| if not INFERENCE_POOL_SET.get(app_context).get(inference_context): |
| inference_providers = resolve_static_inference_providers(module_name, execution_device_id) |
| INFERENCE_POOL_SET[app_context][inference_context] = create_inference_pool(model_source_set, inference_providers) |
|
|
| current_inference_context = get_inference_context(module_name, model_names, random.choice(execution_device_ids), execution_providers) |
| return INFERENCE_POOL_SET.get(app_context).get(current_inference_context) |
|
|
|
|
| def create_inference_pool(model_source_set : DownloadSet, inference_providers : List[InferenceProvider]) -> InferencePool: |
| inference_pool : InferencePool = {} |
|
|
| for model_name in model_source_set.keys(): |
| model_path = model_source_set.get(model_name).get('path') |
| if is_file(model_path): |
| inference_pool[model_name] = create_inference_session(model_path, inference_providers) |
|
|
| return inference_pool |
|
|
|
|
| def clear_inference_pool(module_name : str, model_names : List[str]) -> None: |
| execution_device_ids = state_manager.get_item('execution_device_ids') |
| execution_providers = state_manager.get_item('execution_providers') |
| app_context = detect_app_context() |
|
|
| if is_windows() and has_execution_provider('directml'): |
| INFERENCE_POOL_SET[app_context].clear() |
|
|
| for execution_device_id in execution_device_ids: |
| inference_context = get_inference_context(module_name, model_names, execution_device_id, execution_providers) |
| if INFERENCE_POOL_SET.get(app_context).get(inference_context): |
| del INFERENCE_POOL_SET[app_context][inference_context] |
|
|
|
|
| def create_inference_session(model_path : str, inference_providers : List[InferenceProvider]) -> InferenceSession: |
| model_file_name = get_file_name(model_path) |
| start_time = time() |
|
|
| try: |
| inference_session = InferenceSession(model_path, providers = inference_providers) |
| logger.debug(translator.get('loading_model_succeeded').format(model_name = model_file_name, seconds = calculate_end_time(start_time)), __name__) |
| return inference_session |
|
|
| except Exception: |
| logger.error(translator.get('loading_model_failed').format(model_name = model_file_name), __name__) |
| fatal_exit(1) |
|
|
|
|
| def get_inference_context(module_name : str, model_names : List[str], execution_device_id : int, execution_providers : List[ExecutionProvider]) -> str: |
| inference_context = '.'.join([ module_name ] + model_names + [ str(execution_device_id) ] + list(execution_providers)) |
| return inference_context |
|
|
|
|
| @lru_cache() |
| def resolve_static_inference_providers(module_name : str, execution_device_id : int) -> List[InferenceProvider]: |
| module = importlib.import_module(module_name) |
| execution_providers = state_manager.get_item('execution_providers') |
|
|
| if hasattr(module, 'override_inference_providers'): |
| override_inference_providers = getattr(module, 'override_inference_providers')() |
|
|
| if override_inference_providers: |
| return override_inference_providers |
|
|
| if hasattr(module, 'adjust_inference_providers'): |
| adjust_inference_providers = getattr(module, 'adjust_inference_providers')() |
|
|
| if adjust_inference_providers: |
| inference_providers = create_inference_providers(execution_device_id, execution_providers) |
|
|
| for adjust_inference_provider in adjust_inference_providers: |
| for inference_provider in inference_providers: |
| if inference_provider[0] == adjust_inference_provider[0] and inference_provider[1]: |
| inference_provider[1].update(adjust_inference_provider[1]) |
|
|
| return inference_providers |
|
|
| return create_inference_providers(execution_device_id, execution_providers) |
|
|