You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

284 lines
10 KiB

# 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 .._utils.cli import (
add_simple_inference_args,
get_subcommand_args,
perform_simple_inference,
str2bool,
)
from .base import PaddleXPipelineWrapper, PipelineCLISubcommandExecutor
from .utils import create_config_from_structure
class FormulaRecognitionPipeline(PaddleXPipelineWrapper):
def __init__(
self,
doc_orientation_classify_model_name=None,
doc_orientation_classify_model_dir=None,
doc_orientation_classify_batch_size=None,
doc_unwarping_model_name=None,
doc_unwarping_model_dir=None,
doc_unwarping_batch_size=None,
use_doc_orientation_classify=None,
use_doc_unwarping=None,
layout_detection_model_name=None,
layout_detection_model_dir=None,
layout_threshold=None,
layout_nms=None,
layout_unclip_ratio=None,
layout_merge_bboxes_mode=None,
layout_detection_batch_size=None,
use_layout_detection=None,
formula_recognition_model_name=None,
formula_recognition_model_dir=None,
formula_recognition_batch_size=None,
**kwargs,
):
params = locals().copy()
params.pop("self")
params.pop("kwargs")
self._params = params
super().__init__(**kwargs)
@property
def _paddlex_pipeline_name(self):
return "formula_recognition"
def predict_iter(
self,
input,
*,
use_layout_detection=None,
use_doc_orientation_classify=None,
use_doc_unwarping=None,
layout_det_res=None,
layout_threshold=None,
layout_nms=None,
layout_unclip_ratio=None,
layout_merge_bboxes_mode=None,
**kwargs,
):
return self.paddlex_pipeline.predict(
input,
use_layout_detection=use_layout_detection,
use_doc_orientation_classify=use_doc_orientation_classify,
use_doc_unwarping=use_doc_unwarping,
layout_det_res=layout_det_res,
layout_threshold=layout_threshold,
layout_nms=layout_nms,
layout_unclip_ratio=layout_unclip_ratio,
layout_merge_bboxes_mode=layout_merge_bboxes_mode,
**kwargs,
)
def predict(
self,
input,
*,
use_layout_detection=None,
use_doc_orientation_classify=None,
use_doc_unwarping=None,
layout_det_res=None,
layout_threshold=None,
layout_nms=None,
layout_unclip_ratio=None,
layout_merge_bboxes_mode=None,
**kwargs,
):
return list(
self.predict_iter(
input,
use_layout_detection=use_layout_detection,
use_doc_orientation_classify=use_doc_orientation_classify,
use_doc_unwarping=use_doc_unwarping,
layout_det_res=layout_det_res,
layout_threshold=layout_threshold,
layout_nms=layout_nms,
layout_unclip_ratio=layout_unclip_ratio,
layout_merge_bboxes_mode=layout_merge_bboxes_mode,
**kwargs,
)
)
@classmethod
def get_cli_subcommand_executor(cls):
return FormulaRecognitionPipelineCLISubcommandExecutor()
def _get_paddlex_config_overrides(self):
STRUCTURE = {
"use_layout_detection": self._params["use_layout_detection"],
"SubModules.LayoutDetection.model_name": self._params[
"layout_detection_model_name"
],
"SubModules.LayoutDetection.model_dir": self._params[
"layout_detection_model_dir"
],
"SubModules.LayoutDetection.threshold": self._params["layout_threshold"],
"SubModules.LayoutDetection.layout_nms": self._params["layout_nms"],
"SubModules.LayoutDetection.layout_unclip_ratio": self._params[
"layout_unclip_ratio"
],
"SubModules.LayoutDetection.layout_merge_bboxes_mode": self._params[
"layout_merge_bboxes_mode"
],
"SubModules.LayoutDetection.batch_size": self._params[
"layout_detection_batch_size"
],
"SubModules.FormulaRecognition.model_name": self._params[
"formula_recognition_model_name"
],
"SubModules.FormulaRecognition.model_dir": self._params[
"formula_recognition_model_dir"
],
"SubModules.FormulaRecognition.batch_size": self._params[
"formula_recognition_batch_size"
],
"SubPipelines.DocPreprocessor.use_doc_orientation_classify": self._params[
"use_doc_orientation_classify"
],
"SubPipelines.DocPreprocessor.use_doc_unwarping": self._params[
"use_doc_unwarping"
],
"SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_name": self._params[
"doc_orientation_classify_model_name"
],
"SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.model_dir": self._params[
"doc_orientation_classify_model_dir"
],
"SubPipelines.DocPreprocessor.SubModules.DocOrientationClassify.batch_size": self._params[
"doc_orientation_classify_batch_size"
],
"SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_name": self._params[
"doc_unwarping_model_name"
],
"SubPipelines.DocPreprocessor.SubModules.DocUnwarping.model_dir": self._params[
"doc_unwarping_model_dir"
],
"SubPipelines.DocPreprocessor.SubModules.DocUnwarping.batch_size": self._params[
"doc_unwarping_batch_size"
],
}
return create_config_from_structure(STRUCTURE)
class FormulaRecognitionPipelineCLISubcommandExecutor(PipelineCLISubcommandExecutor):
@property
def subparser_name(self):
return "formula_recognition_pipeline"
def _update_subparser(self, subparser):
add_simple_inference_args(subparser)
subparser.add_argument(
"--doc_orientation_classify_model_name",
type=str,
help="Name of the document image orientation classification model.",
)
subparser.add_argument(
"--doc_orientation_classify_model_dir",
type=str,
help="Directory of the document image orientation classification model.",
)
subparser.add_argument(
"--doc_orientation_classify_batch_size",
type=int,
help="Batch size for document image orientation classification.",
)
subparser.add_argument(
"--doc_unwarping_model_name",
type=str,
help="Name of the document unwarping model.",
)
subparser.add_argument(
"--doc_unwarping_model_dir",
type=str,
help="Directory of the document unwarping model.",
)
subparser.add_argument(
"--doc_unwarping_batch_size",
type=int,
help="Batch size for document unwarping.",
)
subparser.add_argument(
"--use_doc_orientation_classify",
type=str2bool,
help="Use document image orientation classification.",
)
subparser.add_argument(
"--use_doc_unwarping",
type=str2bool,
help="Use document unwarping.",
)
subparser.add_argument(
"--layout_detection_model_name",
type=str,
help="Name of the layout detection model.",
)
subparser.add_argument(
"--layout_detection_model_dir",
type=str,
help="Directory of the layout detection model.",
)
subparser.add_argument(
"--layout_threshold",
type=float,
help="Threshold for layout detection.",
)
subparser.add_argument(
"--layout_nms",
type=str2bool,
help="Non-maximum suppression for layout detection.",
)
subparser.add_argument(
"--layout_unclip_ratio",
type=float,
help="Unclip ratio for layout detection.",
)
subparser.add_argument(
"--layout_merge_bboxes_mode",
type=str,
help="Mode for merging bounding boxes in layout detection.",
)
subparser.add_argument(
"--layout_detection_batch_size",
type=int,
help="Batch size for layout detection.",
)
subparser.add_argument(
"--use_layout_detection",
type=str2bool,
help="Use layout detection.",
)
subparser.add_argument(
"--formula_recognition_model_name",
type=str,
help="Name of the formula recognition model.",
)
subparser.add_argument(
"--formula_recognition_model_dir",
type=str,
help="Directory of the formula recognition model.",
)
subparser.add_argument(
"--formula_recognition_batch_size",
type=int,
help="Batch size for formula recognition.",
)
def execute_with_args(self, args):
params = get_subcommand_args(args)
perform_simple_inference(FormulaRecognitionPipeline, params)