mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-10-26 23:34:13 +01:00
rewrite model spec data structure
This commit is contained in:
kba
parent
146658f026
commit
d94285b3ea
1 changed files with 306 additions and 147 deletions
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@ -1,154 +1,329 @@
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from copy import deepcopy
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from dataclasses import dataclass
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import json
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import logging
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from pathlib import Path
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from typing import Dict, Literal, Optional, Tuple, List, Type, TypeVar, Union
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from copy import deepcopy
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from typing import Dict, Optional, Set, Tuple, List, Type, TypeVar, Union
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from keras.layers import StringLookup
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from keras.models import Model, load_model
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from keras.models import Model as KerasModel, load_model
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from transformers import TrOCRProcessor, VisionEncoderDecoderModel
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from eynollah.patch_encoder import PatchEncoder, Patches
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SomeEynollahModel = Union[VisionEncoderDecoderModel, TrOCRProcessor, Model, List]
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AnyModel = Union[VisionEncoderDecoderModel, TrOCRProcessor, KerasModel, List]
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T = TypeVar('T')
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# Dict mapping model_category to dict mapping variant (default is '') to Path
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DEFAULT_MODEL_VERSIONS: Dict[str, Dict[str, str]] = {
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# NOTE: This needs to change whenever models change
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ZENODO = "https://zenodo.org/records/17295988/files"
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MODELS_VERSION = "v0_7_0"
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"enhancement": {
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'': "eynollah-enhancement_20210425"
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},
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def dist_url(dist_name: str) -> str:
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return f'{ZENODO}/models_{dist_name}_${MODELS_VERSION}.zip'
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"binarization": {
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'': "eynollah-binarization_20210425"
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},
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@dataclass
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class EynollahModelSpec():
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"""
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Describing a single model abstractly.
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"""
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category: str
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# Relative filename to the models_eynollah directory in the dists
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filename: str
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# The smallest model distribution containing this model (link to Zenodo)
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dist: str
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type: Type[AnyModel]
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variant: str = ''
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help: str = ''
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"binarization_multi_1": {
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'': "saved_model_2020_01_16/model_bin1",
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},
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"binarization_multi_2": {
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'': "saved_model_2020_01_16/model_bin2",
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},
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"binarization_multi_3": {
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'': "saved_model_2020_01_16/model_bin3",
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},
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"binarization_multi_4": {
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'': "saved_model_2020_01_16/model_bin4",
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},
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"col_classifier": {
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'': "eynollah-column-classifier_20210425",
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},
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"page": {
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'': "model_eynollah_page_extraction_20250915",
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},
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# TODO: What is this commented out model?
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#?: "eynollah-main-regions-aug-scaling_20210425",
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# early layout
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"region": {
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'': "eynollah-main-regions-ensembled_20210425",
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'extract_only_images': "eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18",
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'light': "eynollah-main-regions_20220314",
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},
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# early layout, non-light, 2nd part
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"region_p2": {
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'': "eynollah-main-regions-aug-rotation_20210425",
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},
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# early layout, light, 1-or-2-column
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"region_1_2": {
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#'': "modelens_12sp_elay_0_3_4__3_6_n"
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#'': "modelens_earlylayout_12spaltige_2_3_5_6_7_8"
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#'': "modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18"
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#'': "modelens_1_2_4_5_early_lay_1_2_spaltige"
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#'': "model_3_eraly_layout_no_patches_1_2_spaltige"
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'': "modelens_e_l_all_sp_0_1_2_3_4_171024"
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},
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# full layout / no patches
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"region_fl_np": {
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#'': "modelens_full_lay_1_3_031124"
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#'': "modelens_full_lay_13__3_19_241024"
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#'': "model_full_lay_13_241024"
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#'': "modelens_full_lay_13_17_231024"
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#'': "modelens_full_lay_1_2_221024"
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#'': "eynollah-full-regions-1column_20210425"
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'': "modelens_full_lay_1__4_3_091124"
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},
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# full layout / with patches
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"region_fl": {
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#'': "eynollah-full-regions-3+column_20210425"
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#'': #"model_2_full_layout_new_trans"
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#'': "modelens_full_lay_1_3_031124"
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#'': "modelens_full_lay_13__3_19_241024"
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#'': "model_full_lay_13_241024"
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#'': "modelens_full_lay_13_17_231024"
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#'': "modelens_full_lay_1_2_221024"
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#'': "modelens_full_layout_24_till_28"
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#'': "model_2_full_layout_new_trans"
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'': "modelens_full_lay_1__4_3_091124",
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},
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"reading_order": {
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#'': "model_mb_ro_aug_ens_11"
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#'': "model_step_3200000_mb_ro"
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#'': "model_ens_reading_order_machine_based"
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#'': "model_mb_ro_aug_ens_8"
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#'': "model_ens_reading_order_machine_based"
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'': "model_eynollah_reading_order_20250824"
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},
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"textline": {
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#'light': "eynollah-textline_light_20210425"
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'light': "modelens_textline_0_1__2_4_16092024",
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#'': "modelens_textline_1_4_16092024"
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#'': "model_textline_ens_3_4_5_6_artificial"
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#'': "modelens_textline_1_3_4_20240915"
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#'': "model_textline_ens_3_4_5_6_artificial"
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#'': "modelens_textline_9_12_13_14_15"
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#'': "eynollah-textline_20210425"
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'': "modelens_textline_0_1__2_4_16092024"
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},
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"table": {
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'light': "modelens_table_0t4_201124",
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'': "eynollah-tables_20210319",
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},
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"ocr": {
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'tr': "model_eynollah_ocr_trocr_20250919",
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'': "model_eynollah_ocr_cnnrnn_20250930",
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},
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'trocr_processor': {
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'': 'microsoft/trocr-base-printed',
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'htr': "microsoft/trocr-base-handwritten",
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},
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'num_to_char': {
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'': 'characters_org.txt'
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},
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'characters': {
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'': 'characters_org.txt'
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},
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class EynollahModelSpecSet():
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"""
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List of all used models for eynollah.
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"""
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specs: List[EynollahModelSpec]
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def __init__(self, specs: List[EynollahModelSpec]) -> None:
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self.specs = specs
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self.categories: Set[str] = set([spec.category for spec in self.specs])
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self.variants: Dict[str, Set[str]] = {
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spec.category: set([x.variant for x in self.specs if x.category == spec.category])
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for spec in self.specs
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}
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self._index_category_variant: Dict[Tuple[str, str], EynollahModelSpec] = {
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(spec.category, spec.variant): spec
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for spec in self.specs
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}
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def asdict(self) -> Dict[str, Dict[str, str]]:
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return {
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spec.category: {
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spec.variant: spec.filename
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}
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for spec in self.specs
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}
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def get(self, category: str, variant: str) -> EynollahModelSpec:
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if category not in self.categories:
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raise ValueError(f"Unknown category '{category}', must be one of {self.categories}")
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if variant not in self.variants[category]:
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raise ValueError(f"Unknown variant {variant} for {category}. Known variants: {self.variants[category]}")
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return self._index_category_variant[(category, variant)]
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DEFAULT_MODEL_SPECS = EynollahModelSpecSet([# {{{
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EynollahModelSpec(
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category="enhancement",
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variant='',
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filename="models_eynollah/eynollah-enhancement_20210425",
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dist=dist_url("enhancement"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="binarization",
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variant='',
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filename="models_eynollah/eynollah-binarization_20210425",
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dist=dist_url("binarization"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="binarization_multi_1",
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variant='',
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filename="models_eynollah/saved_model_2020_01_16/model_bin1",
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dist=dist_url("binarization"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="binarization_multi_2",
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variant='',
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filename="models_eynollah/saved_model_2020_01_16/model_bin2",
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dist=dist_url("binarization"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="binarization_multi_3",
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variant='',
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filename="models_eynollah/saved_model_2020_01_16/model_bin3",
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dist=dist_url("binarization"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="binarization_multi_4",
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variant='',
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filename="models_eynollah/saved_model_2020_01_16/model_bin4",
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dist=dist_url("binarization"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="col_classifier",
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variant='',
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filename="models_eynollah/eynollah-column-classifier_20210425",
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dist=dist_url("layout"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="page",
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variant='',
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filename="models_eynollah/model_eynollah_page_extraction_20250915",
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dist=dist_url("layout"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="region",
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variant='',
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filename="models_eynollah/eynollah-main-regions-ensembled_20210425",
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dist=dist_url("layout"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="region",
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variant='extract_only_images',
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filename="models_eynollah/eynollah-main-regions_20231127_672_org_ens_11_13_16_17_18",
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dist=dist_url("layout"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="region",
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variant='light',
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filename="models_eynollah/eynollah-main-regions_20220314",
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dist=dist_url("layout"),
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help="early layout",
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type=KerasModel,
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),
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EynollahModelSpec(
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category="region_p2",
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variant='',
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filename="models_eynollah/eynollah-main-regions-aug-rotation_20210425",
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dist=dist_url("layout"),
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help="early layout, non-light, 2nd part",
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type=KerasModel,
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),
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EynollahModelSpec(
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category="region_1_2",
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variant='',
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#filename="models_eynollah/modelens_12sp_elay_0_3_4__3_6_n",
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#filename="models_eynollah/modelens_earlylayout_12spaltige_2_3_5_6_7_8",
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#filename="models_eynollah/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18",
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#filename="models_eynollah/modelens_1_2_4_5_early_lay_1_2_spaltige",
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#filename="models_eynollah/model_3_eraly_layout_no_patches_1_2_spaltige",
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filename="models_eynollah/modelens_e_l_all_sp_0_1_2_3_4_171024",
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dist=dist_url("layout"),
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help="early layout, light, 1-or-2-column",
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type=KerasModel,
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),
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EynollahModelSpec(
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category="region_fl_np",
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variant='',
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#'filename="models_eynollah/modelens_full_lay_1_3_031124",
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#'filename="models_eynollah/modelens_full_lay_13__3_19_241024",
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#'filename="models_eynollah/model_full_lay_13_241024",
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#'filename="models_eynollah/modelens_full_lay_13_17_231024",
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#'filename="models_eynollah/modelens_full_lay_1_2_221024",
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#'filename="models_eynollah/eynollah-full-regions-1column_20210425",
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filename="models_eynollah/modelens_full_lay_1__4_3_091124",
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dist=dist_url("layout"),
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help="full layout / no patches",
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type=KerasModel,
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),
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# FIXME: Why is region_fl and region_fl_np the same model?
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EynollahModelSpec(
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category="region_fl",
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variant='',
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# filename="models_eynollah/eynollah-full-regions-3+column_20210425",
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# filename="models_eynollah/model_2_full_layout_new_trans",
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# filename="models_eynollah/modelens_full_lay_1_3_031124",
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# filename="models_eynollah/modelens_full_lay_13__3_19_241024",
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# filename="models_eynollah/model_full_lay_13_241024",
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# filename="models_eynollah/modelens_full_lay_13_17_231024",
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# filename="models_eynollah/modelens_full_lay_1_2_221024",
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# filename="models_eynollah/modelens_full_layout_24_till_28",
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# filename="models_eynollah/model_2_full_layout_new_trans",
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filename="models_eynollah/modelens_full_lay_1__4_3_091124",
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dist=dist_url("layout"),
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help="full layout / with patches",
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type=KerasModel,
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),
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EynollahModelSpec(
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category="reading_order",
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variant='',
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#filename="models_eynollah/model_mb_ro_aug_ens_11",
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#filename="models_eynollah/model_step_3200000_mb_ro",
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#filename="models_eynollah/model_ens_reading_order_machine_based",
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#filename="models_eynollah/model_mb_ro_aug_ens_8",
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#filename="models_eynollah/model_ens_reading_order_machine_based",
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filename="models_eynollah/model_eynollah_reading_order_20250824",
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dist=dist_url("layout"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="textline",
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variant='',
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#filename="models_eynollah/modelens_textline_1_4_16092024",
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#filename="models_eynollah/model_textline_ens_3_4_5_6_artificial",
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#filename="models_eynollah/modelens_textline_1_3_4_20240915",
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#filename="models_eynollah/model_textline_ens_3_4_5_6_artificial",
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#filename="models_eynollah/modelens_textline_9_12_13_14_15",
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#filename="models_eynollah/eynollah-textline_20210425",
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filename="models_eynollah/modelens_textline_0_1__2_4_16092024",
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dist=dist_url("layout"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="textline",
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variant='light',
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#filename="models_eynollah/eynollah-textline_light_20210425",
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filename="models_eynollah/modelens_textline_0_1__2_4_16092024",
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dist=dist_url("layout"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="table",
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variant='',
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filename="models_eynollah/eynollah-tables_20210319",
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dist=dist_url("layout"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="table",
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variant='light',
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filename="models_eynollah/modelens_table_0t4_201124",
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dist=dist_url("layout"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="ocr",
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variant='',
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filename="models_eynollah/model_eynollah_ocr_cnnrnn_20250930",
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dist=dist_url("ocr"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="num_to_char",
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variant='',
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filename="models_eynollah/characters_org.txt",
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dist=dist_url("ocr"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="characters",
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variant='',
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filename="models_eynollah/characters_org.txt",
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dist=dist_url("ocr"),
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type=List,
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),
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EynollahModelSpec(
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category="ocr",
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variant='tr',
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filename="models_eynollah/model_eynollah_ocr_trocr_20250919",
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dist=dist_url("trocr"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="trocr_processor",
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variant='',
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filename="models_eynollah/microsoft/trocr-base-printed",
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dist=dist_url("trocr"),
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type=KerasModel,
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),
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EynollahModelSpec(
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category="trocr_processor",
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variant='htr',
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filename="models_eynollah/microsoft/trocr-base-handwritten",
|
||||
dist=dist_url("trocr"),
|
||||
type=TrOCRProcessor,
|
||||
),
|
||||
|
||||
])# }}}
|
||||
|
||||
class EynollahModelZoo():
|
||||
"""
|
||||
Wrapper class that handles storage and loading of models for all eynollah runners.
|
||||
"""
|
||||
model_basedir: Path
|
||||
model_versions: dict
|
||||
specs: EynollahModelSpecSet
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -157,10 +332,10 @@ class EynollahModelZoo():
|
|||
) -> None:
|
||||
self.model_basedir = Path(basedir)
|
||||
self.logger = logging.getLogger('eynollah.model_zoo')
|
||||
self.model_versions = deepcopy(DEFAULT_MODEL_VERSIONS)
|
||||
self.specs = deepcopy(DEFAULT_MODEL_SPECS)
|
||||
if model_overrides:
|
||||
self.override_models(*model_overrides)
|
||||
self._loaded: Dict[str, SomeEynollahModel] = {}
|
||||
self._loaded: Dict[str, AnyModel] = {}
|
||||
|
||||
def override_models(
|
||||
self,
|
||||
|
|
@ -170,39 +345,24 @@ class EynollahModelZoo():
|
|||
Override the default model versions
|
||||
"""
|
||||
for model_category, model_variant, model_filename in model_overrides:
|
||||
if model_category not in DEFAULT_MODEL_VERSIONS:
|
||||
raise ValueError(f"Unknown model_category '{model_category}', must be one of {DEFAULT_MODEL_VERSIONS.keys()}")
|
||||
if model_variant not in DEFAULT_MODEL_VERSIONS[model_category]:
|
||||
raise ValueError(f"Unknown variant {model_variant} for {model_category}. Known variants: {DEFAULT_MODEL_VERSIONS[model_category].keys()}")
|
||||
self.logger.warning(
|
||||
"Overriding default model %s ('%s' variant) from %s to %s",
|
||||
model_category,
|
||||
model_variant,
|
||||
DEFAULT_MODEL_VERSIONS[model_category][model_variant],
|
||||
model_filename
|
||||
)
|
||||
self.model_versions[model_category][model_variant] = model_filename
|
||||
spec = self.specs.get(model_category, model_variant)
|
||||
self.logger.warning("Overriding filename for model spec %s to %s", spec, model_filename)
|
||||
self.specs.get(model_category, model_variant).filename = model_filename
|
||||
|
||||
def model_path(
|
||||
self,
|
||||
model_category: str,
|
||||
model_variant: str = '',
|
||||
model_filename: str = '',
|
||||
absolute: bool = True,
|
||||
) -> Path:
|
||||
"""
|
||||
Translate model_{type,variant,filename} tuple into an absolute (or relative) Path
|
||||
Translate model_{type,variant} tuple into an absolute (or relative) Path
|
||||
"""
|
||||
if model_category not in DEFAULT_MODEL_VERSIONS:
|
||||
raise ValueError(f"Unknown model_category '{model_category}', must be one of {DEFAULT_MODEL_VERSIONS.keys()}")
|
||||
if model_variant not in DEFAULT_MODEL_VERSIONS[model_category]:
|
||||
raise ValueError(f"Unknown variant {model_variant} for {model_category}. Known variants: {DEFAULT_MODEL_VERSIONS[model_category].keys()}")
|
||||
if not model_filename:
|
||||
model_filename = DEFAULT_MODEL_VERSIONS[model_category][model_variant]
|
||||
if not Path(model_filename).is_absolute() and absolute:
|
||||
model_path = Path(self.model_basedir).joinpath(model_filename)
|
||||
spec = self.specs.get(model_category, model_variant)
|
||||
if not Path(spec.filename).is_absolute() and absolute:
|
||||
model_path = Path(self.model_basedir).joinpath(spec.filename)
|
||||
else:
|
||||
model_path = Path(model_filename)
|
||||
model_path = Path(spec.filename)
|
||||
return model_path
|
||||
|
||||
def load_models(
|
||||
|
|
@ -224,12 +384,11 @@ class EynollahModelZoo():
|
|||
self,
|
||||
model_category: str,
|
||||
model_variant: str = '',
|
||||
model_filename: str = '',
|
||||
) -> SomeEynollahModel:
|
||||
) -> AnyModel:
|
||||
"""
|
||||
Load any model
|
||||
"""
|
||||
model_path = self.model_path(model_category, model_variant, model_filename)
|
||||
model_path = self.model_path(model_category, model_variant)
|
||||
if model_path.suffix == '.h5' and Path(model_path.stem).exists():
|
||||
# prefer SavedModel over HDF5 format if it exists
|
||||
model_path = Path(model_path.stem)
|
||||
|
|
@ -259,17 +418,17 @@ class EynollahModelZoo():
|
|||
assert isinstance(ret, model_type)
|
||||
return ret # type: ignore # FIXME: convince typing that we're returning generic type
|
||||
|
||||
def _load_ocr_model(self, variant: str) -> SomeEynollahModel:
|
||||
def _load_ocr_model(self, variant: str) -> AnyModel:
|
||||
"""
|
||||
Load OCR model
|
||||
"""
|
||||
ocr_model_dir = Path(self.model_basedir, self.model_versions["ocr"][variant])
|
||||
ocr_model_dir = self.model_path('ocr', variant)
|
||||
if variant == 'tr':
|
||||
return VisionEncoderDecoderModel.from_pretrained(ocr_model_dir)
|
||||
else:
|
||||
ocr_model = load_model(ocr_model_dir, compile=False)
|
||||
assert isinstance(ocr_model, Model)
|
||||
return Model(
|
||||
assert isinstance(ocr_model, KerasModel)
|
||||
return KerasModel(
|
||||
ocr_model.get_layer(name = "image").input, # type: ignore
|
||||
ocr_model.get_layer(name = "dense2").output) # type: ignore
|
||||
|
||||
|
|
@ -295,7 +454,7 @@ class EynollahModelZoo():
|
|||
def __str__(self):
|
||||
return str(json.dumps({
|
||||
'basedir': str(self.model_basedir),
|
||||
'versions': self.model_versions,
|
||||
'versions': self.specs,
|
||||
}, indent=2))
|
||||
|
||||
def shutdown(self):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue