PaddleOCR/paddleocr/_common_args.py

139 lines
4.6 KiB
Python

# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddlex.inference import PaddlePredictorOption
from paddlex.utils.device import get_default_device, parse_device
from ._constants import (
DEFAULT_CPU_THREADS,
DEFAULT_DEVICE,
DEFAULT_ENABLE_MKLDNN,
DEFAULT_MIN_SUBGRAPH_SIZE,
DEFAULT_PRECISION,
DEFAULT_USE_TENSORRT,
SUPPORTED_PRECISION_LIST,
)
from .utils.cli import str2bool
def parse_common_args(kwargs, *, default_enable_hpi):
default_vals = {
"device": DEFAULT_DEVICE,
"enable_hpi": default_enable_hpi,
"use_tensorrt": DEFAULT_USE_TENSORRT,
"min_subgraph_size": DEFAULT_MIN_SUBGRAPH_SIZE,
"precision": DEFAULT_PRECISION,
"enable_mkldnn": DEFAULT_ENABLE_MKLDNN,
"cpu_threads": DEFAULT_CPU_THREADS,
}
unknown_names = kwargs.keys() - default_vals.keys()
for name in unknown_names:
raise ValueError(f"Unknown argument: {name}")
kwargs = {**default_vals, **kwargs}
if kwargs["precision"] not in SUPPORTED_PRECISION_LIST:
raise ValueError(
f"Invalid precision: {kwargs['precision']}. Supported values are: {SUPPORTED_PRECISION_LIST}."
)
kwargs["use_pptrt"] = kwargs.pop("use_tensorrt")
kwargs["pptrt_min_subgraph_size"] = kwargs.pop("min_subgraph_size")
kwargs["pptrt_precision"] = kwargs.pop("precision")
return kwargs
def prepare_common_init_args(model_name, common_args):
device = common_args["device"]
if device is None:
device = get_default_device()
device_type, _ = parse_device(device)
init_kwargs = {"device": device}
init_kwargs["use_hpip"] = common_args["enable_hpi"]
pp_option = PaddlePredictorOption(model_name)
if device_type == "gpu":
if common_args["use_pptrt"]:
if common_args["pptrt_precision"] == "fp32":
pp_option.run_mode = "trt_fp32"
else:
assert common_args["pptrt_precision"] == "fp16", common_args[
"pptrt_precision"
]
pp_option.run_mode = "trt_fp16"
elif device_type == "cpu":
enable_mkldnn = common_args["enable_mkldnn"]
if enable_mkldnn is None:
from paddle.inference import Config
if hasattr(Config, "set_mkldnn_cache_capacity"):
enable_mkldnn = True
else:
enable_mkldnn = False
if enable_mkldnn:
pp_option.run_mode = "mkldnn"
pp_option.cpu_threads = common_args["cpu_threads"]
init_kwargs["pp_option"] = pp_option
return init_kwargs
def add_common_cli_args(parser, *, default_enable_hpi):
parser.add_argument(
"--device",
type=str,
default=DEFAULT_DEVICE,
help="Device to use for inference.",
)
parser.add_argument(
"--enable_hpi",
type=str2bool,
default=default_enable_hpi,
help="Enable the high performance inference.",
)
parser.add_argument(
"--use_tensorrt",
type=str2bool,
default=DEFAULT_USE_TENSORRT,
help="Whether to use the Paddle Inference TensorRT subgraph engine.",
)
parser.add_argument(
"--min_subgraph_size",
type=int,
default=DEFAULT_MIN_SUBGRAPH_SIZE,
help="Minimum subgraph size for TensorRT when using the Paddle Inference TensorRT subgraph engine.",
)
parser.add_argument(
"--precision",
type=str,
default=DEFAULT_PRECISION,
choices=SUPPORTED_PRECISION_LIST,
help="Precision for TensorRT when using the Paddle Inference TensorRT subgraph engine.",
)
parser.add_argument(
"--enable_mkldnn",
type=str2bool,
default=DEFAULT_ENABLE_MKLDNN,
help="Enable oneDNN (formerly MKL-DNN) acceleration for inference. By default, oneDNN will be used when available, except for models and pipelines that have known oneDNN issues.",
)
parser.add_argument(
"--cpu_threads",
type=int,
default=DEFAULT_CPU_THREADS,
help="Number of threads to use for inference on CPUs.",
)