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import shutil
import subprocess
import xml.etree.ElementTree as ElementTree
from functools import lru_cache
from typing import List, Optional
from onnxruntime import get_available_providers, set_default_logger_severity
import facefusion.choices
from facefusion.types import ExecutionDevice, ExecutionProvider, InferenceSessionProvider, ValueAndUnit
set_default_logger_severity(3)
def has_execution_provider(execution_provider : ExecutionProvider) -> bool:
return execution_provider in get_available_execution_providers()
def get_available_execution_providers() -> List[ExecutionProvider]:
inference_session_providers = get_available_providers()
available_execution_providers : List[ExecutionProvider] = []
for execution_provider, execution_provider_value in facefusion.choices.execution_provider_set.items():
if execution_provider_value in inference_session_providers:
index = facefusion.choices.execution_providers.index(execution_provider)
available_execution_providers.insert(index, execution_provider)
return available_execution_providers
def create_inference_session_providers(execution_device_id : str, execution_providers : List[ExecutionProvider]) -> List[InferenceSessionProvider]:
inference_session_providers : List[InferenceSessionProvider] = []
for execution_provider in execution_providers:
if execution_provider == 'cuda':
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_id': execution_device_id,
'cudnn_conv_algo_search': resolve_cudnn_conv_algo_search()
}))
if execution_provider == 'tensorrt':
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_id': execution_device_id,
'trt_engine_cache_enable': True,
'trt_engine_cache_path': '.caches',
'trt_timing_cache_enable': True,
'trt_timing_cache_path': '.caches',
'trt_builder_optimization_level': 5
}))
if execution_provider in [ 'directml', 'rocm' ]:
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_id': execution_device_id
}))
if execution_provider == 'openvino':
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'device_type': resolve_openvino_device_type(execution_device_id),
'precision': 'FP32'
}))
if execution_provider == 'coreml':
inference_session_providers.append((facefusion.choices.execution_provider_set.get(execution_provider),
{
'SpecializationStrategy': 'FastPrediction',
'ModelCacheDirectory': '.caches'
}))
if 'cpu' in execution_providers:
inference_session_providers.append(facefusion.choices.execution_provider_set.get('cpu'))
return inference_session_providers
def resolve_cudnn_conv_algo_search() -> str:
execution_devices = detect_static_execution_devices()
product_names = ('GeForce GTX 1630', 'GeForce GTX 1650', 'GeForce GTX 1660')
for execution_device in execution_devices:
if execution_device.get('product').get('name').startswith(product_names):
return 'DEFAULT'
return 'EXHAUSTIVE'
def resolve_openvino_device_type(execution_device_id : str) -> str:
if execution_device_id == '0':
return 'GPU'
if execution_device_id == '∞':
return 'MULTI:GPU'
return 'GPU.' + execution_device_id
def run_nvidia_smi() -> subprocess.Popen[bytes]:
commands = [ shutil.which('nvidia-smi'), '--query', '--xml-format' ]
return subprocess.Popen(commands, stdout = subprocess.PIPE)
@lru_cache(maxsize = None)
def detect_static_execution_devices() -> List[ExecutionDevice]:
return detect_execution_devices()
def detect_execution_devices() -> List[ExecutionDevice]:
execution_devices : List[ExecutionDevice] = []
try:
output, _ = run_nvidia_smi().communicate()
root_element = ElementTree.fromstring(output)
except Exception:
root_element = ElementTree.Element('xml')
for gpu_element in root_element.findall('gpu'):
execution_devices.append(
{
'driver_version': root_element.findtext('driver_version'),
'framework':
{
'name': 'CUDA',
'version': root_element.findtext('cuda_version')
},
'product':
{
'vendor': 'NVIDIA',
'name': gpu_element.findtext('product_name').replace('NVIDIA', '').strip()
},
'video_memory':
{
'total': create_value_and_unit(gpu_element.findtext('fb_memory_usage/total')),
'free': create_value_and_unit(gpu_element.findtext('fb_memory_usage/free'))
},
'temperature':
{
'gpu': create_value_and_unit(gpu_element.findtext('temperature/gpu_temp')),
'memory': create_value_and_unit(gpu_element.findtext('temperature/memory_temp'))
},
'utilization':
{
'gpu': create_value_and_unit(gpu_element.findtext('utilization/gpu_util')),
'memory': create_value_and_unit(gpu_element.findtext('utilization/memory_util'))
}
})
return execution_devices
def create_value_and_unit(text : str) -> Optional[ValueAndUnit]:
if ' ' in text:
value, unit = text.split()
return\
{
'value': int(value),
'unit': str(unit)
}
return None
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