def prepare_models(root_dir: str = '/models'):
model_configs = Configs(models_dir=root_dir)
env_configs = EnvConfigs()
rec_name = env_configs.models.rec_name
det_name = env_configs.models.det_name
ga_name = env_configs.models.ga_name
mask_detector = env_configs.models.mask_detector
max_size = env_configs.defaults.max_size
if max_size is None:
max_size = [640, 640]
max_size = validate_max_size(max_size)
models = [
model for model in [det_name, rec_name, ga_name, mask_detector]
if model is not None
]
for model in models:
batch_size = 1
if model_configs.models[model].get('allow_batching'):
if model == det_name:
batch_size = env_configs.models.det_batch_size
else:
batch_size = env_configs.models.rec_batch_size
logging.info(f"Preparing '{model}' model...")
prepare_backend(model_name=model,
backend_name=env_configs.models.backend_name,
im_size=max_size,
force_fp16=env_configs.models.fp16,
max_batch_size=batch_size,
config=model_configs)
logging.info(f"'{model}' model ready!")
from fastapi.responses import UJSONResponse
from fastapi.openapi.docs import (
get_redoc_html,
get_swagger_ui_html,
get_swagger_ui_oauth2_redirect_html,
)
from modules.processing import Processing
from env_parser import EnvConfigs
__version__ = "0.5.9"
dir_path = os.path.dirname(os.path.realpath(__file__))
# Read runtime settings from environment variables
configs = EnvConfigs()
logging.basicConfig(
level=configs.log_level,
format='%(asctime)s %(levelname)s - %(message)s',
datefmt='[%H:%M:%S]',
)
processing = Processing(det_name=configs.models.det_name,
rec_name=configs.models.rec_name,
ga_name=configs.models.ga_name,
device=configs.models.device,
max_size=configs.defaults.max_size,
max_rec_batch_size=configs.models.rec_batch_size,
backend_name=configs.models.backend_name,
force_fp16=configs.models.fp16)