From 5ca849cc6271659c8fe4f8581cd38317c3436de5 Mon Sep 17 00:00:00 2001 From: David Huggins-Daines Date: Wed, 24 Jul 2024 23:18:17 -0400 Subject: [PATCH] =?UTF-8?q?Ajouter=20les=20r=C3=A8glements=20de=20Sainte-A?= =?UTF-8?q?gathe-des-Monts=20(#30)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix: supprimer des scripts inutilises * fix: reparer des erreurs dans les reglements de sainte-agathe * fix: ne pas reecrire un csv existant * chore: retrain * feat: telecharger sainte-agathe aussi * fix: lint --- .github/workflows/analyse.yml | 3 + TODO.md | 5 +- alexi/annotate.py | 6 +- alexi/download.py | 32 +- alexi/models/crf.joblib.gz | Bin 67209 -> 70672 bytes alexi/models/crf.vl.joblib.gz | Bin 65341 -> 72193 bytes alexi/models/crfseq.joblib.gz | Bin 27362 -> 26707 bytes data/patches/u51_patch1.csv | 852 ++++++++++++++++ data/patches/u51_patch2.csv | 807 +++++++++++++++ data/patches/u51_patch3.csv | 336 +++++++ data/patches/u51_patch4.csv | 674 +++++++++++++ data/patches/u53_patch1.csv | 1746 +++++++++++++++++++++++++++++++++ data/patches/u53_patch2.csv | 465 +++++++++ data/patches/u53_patch3.csv | 611 ++++++++++++ data/patches/u58_patch1.csv | 1001 +++++++++++++++++++ scripts/comparables.sh | 15 +- scripts/make_funsd_data.py | 220 ----- scripts/problem_pages.sh | 4 - scripts/retokenize.py | 44 - scripts/test_rnn.py | 120 --- scripts/test_rnn_voting.py | 107 -- scripts/train_crf.py | 12 +- scripts/train_layoutlm.py | 632 ------------ scripts/train_rnn.py | 320 ------ scripts/train_rnn_crf.py | 368 ------- 25 files changed, 6537 insertions(+), 1843 deletions(-) create mode 100644 data/patches/u51_patch1.csv create mode 100644 data/patches/u51_patch2.csv create mode 100644 data/patches/u51_patch3.csv create mode 100644 data/patches/u51_patch4.csv create mode 100644 data/patches/u53_patch1.csv create mode 100644 data/patches/u53_patch2.csv create mode 100644 data/patches/u53_patch3.csv create mode 100644 data/patches/u58_patch1.csv delete mode 100644 scripts/make_funsd_data.py delete mode 100644 scripts/problem_pages.sh delete mode 100644 scripts/retokenize.py delete mode 100644 scripts/test_rnn.py delete mode 100644 scripts/test_rnn_voting.py delete mode 100644 scripts/train_layoutlm.py delete mode 100644 scripts/train_rnn.py delete mode 100644 scripts/train_rnn_crf.py diff --git a/.github/workflows/analyse.yml b/.github/workflows/analyse.yml index 4f07579..b5f2a0b 100644 --- a/.github/workflows/analyse.yml +++ b/.github/workflows/analyse.yml @@ -52,12 +52,15 @@ jobs: fi done done + alexi -v download -u https://vsadm.ca/citoyens/reglementation/reglementation-durbanisme/index.html -o download/vsadm --all-pdf-links - name: Extract run: | alexi -v extract -m download/index.json download/*.pdf + alexi -v extract -m download/vsadm/index.json -o export/vsadm download/vsadm/*.pdf - name: Index run: | alexi -v index export + alexi -v index export/vsadm - name: Setup Pages uses: actions/configure-pages@v5 - name: Upload artifact diff --git a/TODO.md b/TODO.md index 6d080f6..998407c 100644 --- a/TODO.md +++ b/TODO.md @@ -3,7 +3,10 @@ DATA - Correct titles in zonage glossary - Correct extraction (see below, use RNN) of titles numbers etc -- Annotate multiple TOCs in Sainte-Agathe urbanisme +- Annotate multiple TOCs in Sainte-Agathe urbanisme DONE +- Add Sainte-Agathe to download DONE +- Add Sainte-Agathe to export (under /vsadm) DONE +- Do the same thing for Saint-Sauveur DERP LERNING ------------ diff --git a/alexi/annotate.py b/alexi/annotate.py index 1f2ff4a..811409b 100644 --- a/alexi/annotate.py +++ b/alexi/annotate.py @@ -140,6 +140,10 @@ def main(args: argparse.Namespace) -> None: with open(args.csv, "rt", encoding="utf-8-sig") as infh: iob = list(csv.DictReader(infh)) else: + args.csv = args.out.with_suffix(".csv") + if args.csv.exists(): + LOGGER.error("Fichier déjà existant: %s", args.csv) + return if args.segment_model is not None: crf = Segmenteur(args.segment_model) crf_n = crf @@ -155,7 +159,7 @@ def main(args: argparse.Namespace) -> None: else: segs = crf(feats) iob = list(spread_i(crf_s(segs))) - with open(args.out.with_suffix(".csv"), "wt") as outfh: + with open(args.csv, "wt") as outfh: write_csv(iob, outfh) annotate_pdf(args.doc, pages, iob, args.out.with_suffix(".pdf")) diff --git a/alexi/download.py b/alexi/download.py index d18f431..cfa3a2b 100644 --- a/alexi/download.py +++ b/alexi/download.py @@ -39,6 +39,12 @@ def add_arguments(parser: argparse.ArgumentParser) -> argparse.ArgumentParser: action="append", default=[], ) + parser.add_argument( + "--all-pdf-links", + action="store_true", + help="Télécharger les liens vers des PDF dans le document " + "sans égard à sa structure", + ) parser.add_argument( "section", help="Expression régulière pour sélectionner la section des documents", @@ -71,19 +77,25 @@ def main(args: argparse.Namespace) -> None: paths = [] with open(args.outdir / Path(u.path).name) as infh: soup = BeautifulSoup(infh, "lxml") - for h2 in soup.find_all("h2", string=re.compile(args.section, re.I)): - ul = h2.find_next("ul") - for li in ul.find_all("li"): - path = li.a["href"] - excluded = False - for r in excludes: - if r.search(path): - excluded = True - break - if not excluded: + if args.all_pdf_links: + for a in soup.find_all("a"): + path = a["href"] + if path.lower().endswith(".pdf"): paths.append(path) + else: + for h2 in soup.find_all("h2", string=re.compile(args.section, re.I)): + ul = h2.find_next("ul") + for li in ul.find_all("li"): + paths.append(li.a["href"]) urls = {} for p in paths: + excluded = False + for r in excludes: + if r.search(p): + excluded = True + break + if excluded: + continue up = urllib.parse.urlparse(p) if up.netloc: url = p diff --git a/alexi/models/crf.joblib.gz b/alexi/models/crf.joblib.gz index 57a0bb1ae343ddfbe33683da353ee711857b188c..f07deff9fee0e0da9d504d1c7862a328e3ce0ca5 100644 GIT binary patch literal 70672 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+O,I-Liste,les,25,612,792,GCDQNG+ArialMT,#000,526,540,357,368,19365,16,H4,Document;Sect;H4 +O,I-Liste,élévations,25,612,792,GCDQNG+ArialMT,#000,214,264,370,381,19378,16,H4,Document;Sect;H4 +O,I-Liste,en,25,612,792,GCDQNG+ArialMT,#000,267,279,370,381,19378,16,H4,Document;Sect;H4 +O,I-Liste,mètre,25,612,792,GCDQNG+ArialMT,#000,282,310,370,381,19378,16,H4,Document;Sect;H4 +O,I-Liste,;,25,612,792,GCDQNG+ArialMT,#000,313,317,370,381,19378,16,H4,Document;Sect;H4 +O,B-Liste,•,25,612,792,JZNDDA+SymbolMT,#000,196,201,391,402,19399,17,H4,Document;Sect;H4 +O,I-Liste,relief,25,612,792,GCDQNG+ArialMT,#000,214,238,392,403,19400,17,H4,Document;Sect;H4 +O,I-Liste,du,25,612,792,GCDQNG+ArialMT,#000,251,263,392,403,19400,17,H4,Document;Sect;H4 +O,I-Liste,sol,25,612,792,GCDQNG+ArialMT,#000,276,290,392,403,19400,17,H4,Document;Sect;H4 +O,I-Liste,exprimé,25,612,792,GCDQNG+ArialMT,#000,302,341,392,403,19400,17,H4,Document;Sect;H4 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+O,I-Liste,ou,35,612,792,GCDQNG+ArialMT,#000,271,283,144,155,27072,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"semi-creusée,",35,612,792,GCDQNG+ArialMT,#000,287,356,144,155,27072,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,incluant,35,612,792,GCDQNG+ArialMT,#000,360,398,144,155,27072,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l’érection,35,612,792,GCDQNG+ArialMT,#000,402,446,144,155,27072,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d’une,35,612,792,GCDQNG+ArialMT,#000,450,477,144,155,27072,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,construction,35,612,792,GCDQNG+ArialMT,#000,481,540,144,155,27072,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,donnant,35,612,792,GCDQNG+ArialMT,#000,198,238,156,167,27084,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ou,35,612,792,GCDQNG+ArialMT,#000,243,256,156,167,27084,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,empêchant,35,612,792,GCDQNG+ArialMT,#000,261,316,156,167,27084,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l’accès,35,612,792,GCDQNG+ArialMT,#000,321,355,156,167,27084,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,à,35,612,792,GCDQNG+ArialMT,#000,361,367,156,167,27084,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cette,35,612,792,GCDQNG+ArialMT,#000,372,396,156,167,27084,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,piscine,35,612,792,GCDQNG+ArialMT,#000,402,436,156,167,27084,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,et,35,612,792,GCDQNG+ArialMT,#000,442,451,156,167,27084,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l’installation,35,612,792,GCDQNG+ArialMT,#000,457,513,156,167,27084,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d’un,35,612,792,GCDQNG+ArialMT,#000,519,540,156,167,27084,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,plongeoir,35,612,792,GCDQNG+ArialMT,#000,198,243,169,180,27097,10,LBody,Document;Sect;L;LI;LBody +O,I-Liste,;,35,612,792,GCDQNG+ArialMT,#000,246,249,169,180,27097,10,LBody,Document;Sect;L;LI;LBody +O,B-Liste,16),35,612,792,GCDQNG+ArialMT,#000,162,178,190,201,27118,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,Toute,35,612,792,GCDQNG+ArialMT,#000,197,226,190,201,27118,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"construction,",35,612,792,GCDQNG+ArialMT,#000,235,297,190,201,27118,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,installation,35,612,792,GCDQNG+ArialMT,#000,307,359,190,201,27118,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ou,35,612,792,GCDQNG+ArialMT,#000,368,381,190,201,27118,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,remplacement,35,612,792,GCDQNG+ArialMT,#000,390,460,190,201,27118,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d’une,35,612,792,GCDQNG+ArialMT,#000,469,496,190,201,27118,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,piscine,35,612,792,GCDQNG+ArialMT,#000,506,540,190,201,27118,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"démontable,",35,612,792,GCDQNG+ArialMT,#000,198,259,203,214,27131,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,incluant,35,612,792,GCDQNG+ArialMT,#000,269,307,203,214,27131,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l’érection,35,612,792,GCDQNG+ArialMT,#000,317,361,203,214,27131,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d’une,35,612,792,GCDQNG+ArialMT,#000,371,398,203,214,27131,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,construction,35,612,792,GCDQNG+ArialMT,#000,408,468,203,214,27131,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,donnant,35,612,792,GCDQNG+ArialMT,#000,478,518,203,214,27131,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ou,35,612,792,GCDQNG+ArialMT,#000,528,540,203,214,27131,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,empêchant,35,612,792,GCDQNG+ArialMT,#000,198,252,216,227,27144,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l’accès,35,612,792,GCDQNG+ArialMT,#000,255,289,216,227,27144,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,à,35,612,792,GCDQNG+ArialMT,#000,292,298,216,227,27144,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cette,35,612,792,GCDQNG+ArialMT,#000,301,325,216,227,27144,14,LBody,Document;Sect;L;LI;LBody +O,I-Liste,piscine.,35,612,792,GCDQNG+ArialMT,#000,328,365,216,227,27144,14,LBody,Document;Sect;L;LI;LBody +O,B-Alinea,Note,35,612,792,GCDQNG+ArialMT,#000,198,221,237,248,27165,17,P,Document;Sect;P +O,I-Alinea,:,35,612,792,GCDQNG+ArialMT,#000,224,227,237,248,27165,17,P,Document;Sect;P +O,I-Alinea,une,35,612,792,GCDQNG+ArialMT,#000,231,250,237,248,27165,17,P,Document;Sect;P +O,I-Alinea,personne,35,612,792,GCDQNG+ArialMT,#000,253,299,237,248,27165,17,P,Document;Sect;P +O,I-Alinea,qui,35,612,792,GCDQNG+ArialMT,#000,303,318,237,248,27165,17,P,Document;Sect;P +O,I-Alinea,a,35,612,792,GCDQNG+ArialMT,#000,322,328,237,248,27165,17,P,Document;Sect;P +O,I-Alinea,obtenu,35,612,792,GCDQNG+ArialMT,#000,332,365,237,248,27165,17,P,Document;Sect;P +O,I-Alinea,un,35,612,792,GCDQNG+ArialMT,#000,369,381,237,248,27165,17,P,Document;Sect;P 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+O,I-TOC,Rénovation,152,612,792,GCDQNG+ArialMT,#000,117,173,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,ou,152,612,792,GCDQNG+ArialMT,#000,176,188,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,reconstruction,152,612,792,GCDQNG+ArialMT,#000,191,260,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,d'un,152,612,792,GCDQNG+ArialMT,#000,263,284,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,bâtiment,152,612,792,GCDQNG+ArialMT,#000,287,329,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,principal,152,612,792,GCDQNG+ArialMT,#000,332,373,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,sur,152,612,792,GCDQNG+ArialMT,#000,376,391,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,une,152,612,792,GCDQNG+ArialMT,#000,395,413,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,rive,152,612,792,GCDQNG+ArialMT,#000,416,434,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,.............,152,612,792,GCDQNG+ArialMT,#000,434,474,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,11-14,152,612,792,GCDQNG+ArialMT,#000,476,504,496,507,120088,82,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,11.5.2,152,612,792,GCDQNG+ArialMT,#000,72,103,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,Ouvrage,152,612,792,GCDQNG+ArialMT,#000,117,160,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,et,152,612,792,GCDQNG+ArialMT,#000,163,172,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,travaux,152,612,792,GCDQNG+ArialMT,#000,175,211,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,relatifs,152,612,792,GCDQNG+ArialMT,#000,214,246,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,à,152,612,792,GCDQNG+ArialMT,#000,249,256,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,la,152,612,792,GCDQNG+ArialMT,#000,259,267,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,végétation,152,612,792,GCDQNG+ArialMT,#000,270,321,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,sur,152,612,792,GCDQNG+ArialMT,#000,324,339,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,une,152,612,792,GCDQNG+ArialMT,#000,342,361,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,rive,152,612,792,GCDQNG+ArialMT,#000,364,381,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,.............................,152,612,792,GCDQNG+ArialMT,#000,384,474,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,11-15,152,612,792,GCDQNG+ArialMT,#000,476,504,512,523,120104,86,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,11.5.3,152,612,792,GCDQNG+ArialMT,#000,72,103,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,Autres,152,612,792,GCDQNG+ArialMT,#000,117,149,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,"constructions,",152,612,792,GCDQNG+ArialMT,#000,152,220,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,ouvrages,152,612,792,GCDQNG+ArialMT,#000,223,268,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,et,152,612,792,GCDQNG+ArialMT,#000,271,280,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,travaux,152,612,792,GCDQNG+ArialMT,#000,283,320,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,autorisés,152,612,792,GCDQNG+ArialMT,#000,323,367,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,sur,152,612,792,GCDQNG+ArialMT,#000,370,386,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,une,152,612,792,GCDQNG+ArialMT,#000,389,407,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,rive,152,612,792,GCDQNG+ArialMT,#000,410,428,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,..............,152,612,792,GCDQNG+ArialMT,#000,431,474,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,11-16,152,612,792,GCDQNG+ArialMT,#000,476,504,527,538,120119,90,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,11.5.4,152,612,792,GCDQNG+ArialMT,#000,72,103,543,554,120135,94,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,Interdiction,152,612,792,GCDQNG+ArialMT,#000,117,171,543,554,120135,94,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,d'altération,152,612,792,GCDQNG+ArialMT,#000,174,228,543,554,120135,94,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,de,152,612,792,GCDQNG+ArialMT,#000,231,243,543,554,120135,94,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,la,152,612,792,GCDQNG+ArialMT,#000,246,254,543,554,120135,94,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,végétation,152,612,792,GCDQNG+ArialMT,#000,258,308,543,554,120135,94,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,herbacée,152,612,792,GCDQNG+ArialMT,#000,311,357,543,554,120135,94,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,riveraine,152,612,792,GCDQNG+ArialMT,#000,360,403,543,554,120135,94,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,.......................,152,612,792,GCDQNG+ArialMT,#000,403,474,543,554,120135,94,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,11-17,152,612,792,GCDQNG+ArialMT,#000,476,504,543,554,120135,94,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,11.5.5,152,612,792,GCDQNG+ArialMT,#000,72,103,559,570,120151,98,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,Régénération,152,612,792,GCDQNG+ArialMT,#000,117,183,559,570,120151,98,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,de,152,612,792,GCDQNG+ArialMT,#000,186,199,559,570,120151,98,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,la,152,612,792,GCDQNG+ArialMT,#000,202,210,559,570,120151,98,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,bande,152,612,792,GCDQNG+ArialMT,#000,213,244,559,570,120151,98,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,de,152,612,792,GCDQNG+ArialMT,#000,247,259,559,570,120151,98,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,protection,152,612,792,GCDQNG+ArialMT,#000,262,311,559,570,120151,98,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,riveraine,152,612,792,GCDQNG+ArialMT,#000,313,356,559,570,120151,98,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,......................................,152,612,792,GCDQNG+ArialMT,#000,356,475,559,570,120151,98,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,11-18,152,612,792,GCDQNG+ArialMT,#000,476,504,559,570,120151,98,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,11.6,152,612,792,QZBYXM+Arial-BoldMT,#000,72,94,578,589,120170,102,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,Dispositions,152,612,792,QZBYXM+Arial-BoldMT,#000,117,183,578,589,120170,102,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,particulières,152,612,792,QZBYXM+Arial-BoldMT,#000,187,252,578,589,120170,102,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,applicables,152,612,792,QZBYXM+Arial-BoldMT,#000,255,315,578,589,120170,102,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,à,152,612,792,QZBYXM+Arial-BoldMT,#000,318,324,578,589,120170,102,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,proximité,152,612,792,QZBYXM+Arial-BoldMT,#000,328,377,578,589,120170,102,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,des,152,612,792,QZBYXM+Arial-BoldMT,#000,380,399,578,589,120170,102,Reference,Document;Sect;TOC;TOCI;Reference +O,I-TOC,lacs,152,612,792,QZBYXM+Arial-BoldMT,#000,402,424,578,589,120170,102,Reference,Document;Sect;TOC;TOCI;Reference 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+O,I-Liste,l'aire,213,612,792,GCDQNG+ArialMT,#000,431,454,227,238,168131,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,456,468,227,238,168131,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,471,540,227,238,168131,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,doit,213,612,792,GCDQNG+ArialMT,#000,198,216,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,être,213,612,792,GCDQNG+ArialMT,#000,225,244,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pavée,213,612,792,GCDQNG+ArialMT,#000,253,283,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ou,213,612,792,GCDQNG+ArialMT,#000,292,304,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,recouverte,213,612,792,GCDQNG+ArialMT,#000,313,365,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,au,213,612,792,GCDQNG+ArialMT,#000,375,387,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,plus,213,612,792,GCDQNG+ArialMT,#000,396,416,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,tard,213,612,792,GCDQNG+ArialMT,#000,425,444,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,6,213,612,792,GCDQNG+ArialMT,#000,453,459,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,mois,213,612,792,GCDQNG+ArialMT,#000,462,486,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,après,213,612,792,GCDQNG+ArialMT,#000,495,522,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,le,213,612,792,GCDQNG+ArialMT,#000,532,540,240,251,168144,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,parachèvement,213,612,792,GCDQNG+ArialMT,#000,198,274,252,263,168156,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,des,213,612,792,GCDQNG+ArialMT,#000,277,294,252,263,168156,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,travaux,213,612,792,GCDQNG+ArialMT,#000,297,333,252,263,168156,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,du,213,612,792,GCDQNG+ArialMT,#000,336,348,252,263,168156,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,bâtiment;,213,612,792,GCDQNG+ArialMT,#000,351,396,252,263,168156,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,en,213,612,792,GCDQNG+ArialMT,#000,399,411,252,263,168156,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cas,213,612,792,GCDQNG+ArialMT,#000,414,431,252,263,168156,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'impossibilité,213,612,792,GCDQNG+ArialMT,#000,434,502,252,263,168156,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'agir,213,612,792,GCDQNG+ArialMT,#000,505,531,252,263,168156,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,à,213,612,792,GCDQNG+ArialMT,#000,534,540,252,263,168156,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cause,213,612,792,GCDQNG+ArialMT,#000,198,227,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,du,213,612,792,GCDQNG+ArialMT,#000,231,243,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"climat,",213,612,792,GCDQNG+ArialMT,#000,246,278,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,un,213,612,792,GCDQNG+ArialMT,#000,282,294,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,délai,213,612,792,GCDQNG+ArialMT,#000,297,320,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,peut,213,612,792,GCDQNG+ArialMT,#000,324,345,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,être,213,612,792,GCDQNG+ArialMT,#000,349,368,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,accordé,213,612,792,GCDQNG+ArialMT,#000,371,410,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,jusqu'au,213,612,792,GCDQNG+ArialMT,#000,413,454,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,15,213,612,792,GCDQNG+ArialMT,#000,457,470,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,juin,213,612,792,GCDQNG+ArialMT,#000,473,490,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,suivant,213,612,792,GCDQNG+ArialMT,#000,493,528,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,le,213,612,792,GCDQNG+ArialMT,#000,532,540,265,276,168169,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,parachèvement,213,612,792,GCDQNG+ArialMT,#000,198,274,278,289,168182,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,du,213,612,792,GCDQNG+ArialMT,#000,277,289,278,289,168182,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,bâtiment,213,612,792,GCDQNG+ArialMT,#000,292,334,278,289,168182,12,LBody,Document;Sect;L;LI;LBody +O,I-Liste,principal.,213,612,792,GCDQNG+ArialMT,#000,338,381,278,289,168182,12,LBody,Document;Sect;L;LI;LBody +O,B-Liste,3),213,612,792,GCDQNG+ArialMT,#000,162,172,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,Dans,213,612,792,GCDQNG+ArialMT,#000,197,223,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,le,213,612,792,GCDQNG+ArialMT,#000,227,236,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cas,213,612,792,GCDQNG+ArialMT,#000,240,257,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,où,213,612,792,GCDQNG+ArialMT,#000,261,273,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l'aire,213,612,792,GCDQNG+ArialMT,#000,278,300,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,305,317,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,321,390,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,est,213,612,792,GCDQNG+ArialMT,#000,394,409,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,aménagée,213,612,792,GCDQNG+ArialMT,#000,413,465,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,à,213,612,792,GCDQNG+ArialMT,#000,469,475,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,titre,213,612,792,GCDQNG+ArialMT,#000,479,498,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'usage,213,612,792,GCDQNG+ArialMT,#000,502,540,299,311,168203,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"principal,",213,612,792,GCDQNG+ArialMT,#000,198,242,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,elle,213,612,792,GCDQNG+ArialMT,#000,245,263,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,doit,213,612,792,GCDQNG+ArialMT,#000,266,284,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,être,213,612,792,GCDQNG+ArialMT,#000,287,306,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pavée,213,612,792,GCDQNG+ArialMT,#000,310,340,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ou,213,612,792,GCDQNG+ArialMT,#000,343,355,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,recouverte,213,612,792,GCDQNG+ArialMT,#000,359,411,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,avant,213,612,792,GCDQNG+ArialMT,#000,414,441,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,que,213,612,792,GCDQNG+ArialMT,#000,445,463,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,la,213,612,792,GCDQNG+ArialMT,#000,466,475,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,demande,213,612,792,GCDQNG+ArialMT,#000,478,524,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,528,540,312,323,168216,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,certificat,213,612,792,GCDQNG+ArialMT,#000,198,239,325,336,168229,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'occupation,213,612,792,GCDQNG+ArialMT,#000,242,303,325,336,168229,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ne,213,612,792,GCDQNG+ArialMT,#000,307,319,325,336,168229,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,soit,213,612,792,GCDQNG+ArialMT,#000,322,339,325,336,168229,16,LBody,Document;Sect;L;LI;LBody +O,I-Liste,effectuée.,213,612,792,GCDQNG+ArialMT,#000,342,390,325,336,168229,16,LBody,Document;Sect;L;LI;LBody +O,B-Liste,4),213,612,792,GCDQNG+ArialMT,#000,162,172,346,357,168250,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,Pour,213,612,792,GCDQNG+ArialMT,#000,197,220,346,357,168250,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,une,213,612,792,GCDQNG+ArialMT,#000,233,251,346,357,168250,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,aire,213,612,792,GCDQNG+ArialMT,#000,264,282,346,357,168250,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,295,307,346,357,168250,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,319,388,346,357,168250,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,comprenant,213,612,792,GCDQNG+ArialMT,#000,401,459,346,357,168250,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,10,213,612,792,GCDQNG+ArialMT,#000,471,484,346,357,168250,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cases,213,612,792,GCDQNG+ArialMT,#000,487,515,346,357,168250,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,528,540,346,357,168250,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,198,267,359,370,168263,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,et,213,612,792,GCDQNG+ArialMT,#000,273,282,359,370,168263,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"plus,",213,612,792,GCDQNG+ArialMT,#000,288,311,359,370,168263,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l'aire,213,612,792,GCDQNG+ArialMT,#000,317,340,359,370,168263,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,346,359,359,370,168263,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,364,433,359,370,168263,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,doit,213,612,792,GCDQNG+ArialMT,#000,439,457,359,370,168263,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,être,213,612,792,GCDQNG+ArialMT,#000,463,482,359,370,168263,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,recouverte,213,612,792,GCDQNG+ArialMT,#000,488,540,359,370,168263,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"d'asphalte,",213,612,792,GCDQNG+ArialMT,#000,198,251,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,255,268,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,béton,213,612,792,GCDQNG+ArialMT,#000,272,300,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ou,213,612,792,GCDQNG+ArialMT,#000,304,316,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,321,333,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pavé,213,612,792,GCDQNG+ArialMT,#000,338,361,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,imbriqué,213,612,792,GCDQNG+ArialMT,#000,366,408,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,et,213,612,792,GCDQNG+ArialMT,#000,413,422,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,chacune,213,612,792,GCDQNG+ArialMT,#000,426,468,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,des,213,612,792,GCDQNG+ArialMT,#000,472,490,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cases,213,612,792,GCDQNG+ArialMT,#000,495,523,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,528,540,372,383,168276,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,198,267,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,doit,213,612,792,GCDQNG+ArialMT,#000,270,288,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,être,213,612,792,GCDQNG+ArialMT,#000,290,309,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,délimitée,213,612,792,GCDQNG+ArialMT,#000,312,356,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,par,213,612,792,GCDQNG+ArialMT,#000,359,375,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,une,213,612,792,GCDQNG+ArialMT,#000,377,396,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ligne,213,612,792,GCDQNG+ArialMT,#000,399,422,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,peinte,213,612,792,GCDQNG+ArialMT,#000,424,454,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,sur,213,612,792,GCDQNG+ArialMT,#000,457,473,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,le,213,612,792,GCDQNG+ArialMT,#000,475,484,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"pavé,",213,612,792,GCDQNG+ArialMT,#000,486,513,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cette,213,612,792,GCDQNG+ArialMT,#000,516,540,384,395,168288,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,disposition,213,612,792,GCDQNG+ArialMT,#000,198,250,397,408,168301,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ne,213,612,792,GCDQNG+ArialMT,#000,253,266,397,408,168301,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,s'appliquant,213,612,792,GCDQNG+ArialMT,#000,269,327,397,408,168301,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pas,213,612,792,GCDQNG+ArialMT,#000,331,348,397,408,168301,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,dans,213,612,792,GCDQNG+ArialMT,#000,352,376,397,408,168301,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,le,213,612,792,GCDQNG+ArialMT,#000,379,387,397,408,168301,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cas,213,612,792,GCDQNG+ArialMT,#000,391,408,397,408,168301,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'un,213,612,792,GCDQNG+ArialMT,#000,411,432,397,408,168301,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,changement,213,612,792,GCDQNG+ArialMT,#000,435,496,397,408,168301,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"d'usage,",213,612,792,GCDQNG+ArialMT,#000,499,540,397,408,168301,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,s'il,213,612,792,GCDQNG+ArialMT,#000,198,211,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,n'y,213,612,792,GCDQNG+ArialMT,#000,215,228,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,a,213,612,792,GCDQNG+ArialMT,#000,233,239,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pas,213,612,792,GCDQNG+ArialMT,#000,243,260,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'augmentation,213,612,792,GCDQNG+ArialMT,#000,265,339,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,du,213,612,792,GCDQNG+ArialMT,#000,344,356,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,nombre,213,612,792,GCDQNG+ArialMT,#000,360,397,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,401,414,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"cases,",213,612,792,GCDQNG+ArialMT,#000,418,449,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,à,213,612,792,GCDQNG+ArialMT,#000,454,460,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l'exception,213,612,792,GCDQNG+ArialMT,#000,464,516,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'un,213,612,792,GCDQNG+ArialMT,#000,520,540,410,421,168314,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,changement,213,612,792,GCDQNG+ArialMT,#000,198,259,422,433,168326,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'usage,213,612,792,GCDQNG+ArialMT,#000,262,300,422,433,168326,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,habitation,213,612,792,GCDQNG+ArialMT,#000,303,351,422,433,168326,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,à,213,612,792,GCDQNG+ArialMT,#000,354,360,422,433,168326,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,un,213,612,792,GCDQNG+ArialMT,#000,363,375,422,433,168326,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,autre,213,612,792,GCDQNG+ArialMT,#000,378,403,422,433,168326,20,LBody,Document;Sect;L;LI;LBody +O,I-Liste,usage.,213,612,792,GCDQNG+ArialMT,#000,406,439,422,433,168326,20,LBody,Document;Sect;L;LI;LBody +O,B-Liste,5),213,612,792,GCDQNG+ArialMT,#000,162,172,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,Dans,213,612,792,GCDQNG+ArialMT,#000,197,223,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,le,213,612,792,GCDQNG+ArialMT,#000,227,235,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cas,213,612,792,GCDQNG+ArialMT,#000,239,256,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'un,213,612,792,GCDQNG+ArialMT,#000,259,280,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,changement,213,612,792,GCDQNG+ArialMT,#000,283,344,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'usage,213,612,792,GCDQNG+ArialMT,#000,347,385,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,générant,213,612,792,GCDQNG+ArialMT,#000,389,432,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,une,213,612,792,GCDQNG+ArialMT,#000,436,454,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,augmentation,213,612,792,GCDQNG+ArialMT,#000,458,524,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,du,213,612,792,GCDQNG+ArialMT,#000,528,540,444,455,168348,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,nombre,213,612,792,GCDQNG+ArialMT,#000,198,235,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,238,251,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"cases,",213,612,792,GCDQNG+ArialMT,#000,254,285,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,la,213,612,792,GCDQNG+ArialMT,#000,289,297,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,totalité,213,612,792,GCDQNG+ArialMT,#000,300,332,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,336,348,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l'aire,213,612,792,GCDQNG+ArialMT,#000,351,374,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,377,389,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,392,461,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,doit,213,612,792,GCDQNG+ArialMT,#000,464,482,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,être,213,612,792,GCDQNG+ArialMT,#000,485,504,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pavée.,213,612,792,GCDQNG+ArialMT,#000,507,540,457,468,168361,24,LBody,Document;Sect;L;LI;LBody +O,B-Liste,6),213,612,792,GCDQNG+ArialMT,#000,162,172,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,Dans,213,612,792,GCDQNG+ArialMT,#000,197,223,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,le,213,612,792,GCDQNG+ArialMT,#000,230,239,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cas,213,612,792,GCDQNG+ArialMT,#000,245,263,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'un,213,612,792,GCDQNG+ArialMT,#000,270,290,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,agrandissement,213,612,792,GCDQNG+ArialMT,#000,297,375,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,excédant,213,612,792,GCDQNG+ArialMT,#000,382,427,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,25,213,612,792,GCDQNG+ArialMT,#000,434,446,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,%,213,612,792,GCDQNG+ArialMT,#000,449,459,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ou,213,612,792,GCDQNG+ArialMT,#000,466,478,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,plus,213,612,792,GCDQNG+ArialMT,#000,485,505,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,512,524,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,la,213,612,792,GCDQNG+ArialMT,#000,531,540,478,489,168382,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,superficie,213,612,792,GCDQNG+ArialMT,#000,198,245,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,totale,213,612,792,GCDQNG+ArialMT,#000,248,275,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,277,290,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,plancher,213,612,792,GCDQNG+ArialMT,#000,292,335,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,du,213,612,792,GCDQNG+ArialMT,#000,337,350,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,bâtiment,213,612,792,GCDQNG+ArialMT,#000,352,394,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"existant,",213,612,792,GCDQNG+ArialMT,#000,397,438,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,la,213,612,792,GCDQNG+ArialMT,#000,441,449,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,totalité,213,612,792,GCDQNG+ArialMT,#000,452,485,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,487,500,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l'aire,213,612,792,GCDQNG+ArialMT,#000,502,525,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,528,540,491,502,168395,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,198,267,503,514,168407,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,doit,213,612,792,GCDQNG+ArialMT,#000,270,288,503,514,168407,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,être,213,612,792,GCDQNG+ArialMT,#000,291,310,503,514,168407,28,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pavée.,213,612,792,GCDQNG+ArialMT,#000,313,346,503,514,168407,28,LBody,Document;Sect;L;LI;LBody +O,B-Liste,7),213,612,792,GCDQNG+ArialMT,#000,162,172,525,536,168429,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,Nonobstant,213,612,792,GCDQNG+ArialMT,#000,197,254,525,536,168429,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,les,213,612,792,GCDQNG+ArialMT,#000,258,272,525,536,168429,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,paragraphes,213,612,792,GCDQNG+ArialMT,#000,276,338,525,536,168429,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,"précédents,",213,612,792,GCDQNG+ArialMT,#000,342,400,525,536,168429,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l'obligation,213,612,792,GCDQNG+ArialMT,#000,404,456,525,536,168429,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,du,213,612,792,GCDQNG+ArialMT,#000,460,472,525,536,168429,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pavage,213,612,792,GCDQNG+ArialMT,#000,477,513,525,536,168429,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,n'est,213,612,792,GCDQNG+ArialMT,#000,517,540,525,536,168429,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pas,213,612,792,GCDQNG+ArialMT,#000,198,216,538,549,168442,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,requise,213,612,792,GCDQNG+ArialMT,#000,222,258,538,549,168442,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pour,213,612,792,GCDQNG+ArialMT,#000,265,287,538,549,168442,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,le,213,612,792,GCDQNG+ArialMT,#000,293,302,538,549,168442,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,309,378,538,549,168442,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ou,213,612,792,GCDQNG+ArialMT,#000,384,397,538,549,168442,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,la,213,612,792,GCDQNG+ArialMT,#000,403,412,538,549,168442,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,partie,213,612,792,GCDQNG+ArialMT,#000,418,446,538,549,168442,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,du,213,612,792,GCDQNG+ArialMT,#000,452,464,538,549,168442,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,471,540,538,549,168442,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,localisé,213,612,792,GCDQNG+ArialMT,#000,198,235,550,561,168454,32,LBody,Document;Sect;L;LI;LBody 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+O,I-Liste,une,213,612,792,GCDQNG+ArialMT,#000,460,478,550,561,168454,32,LBody,Document;Sect;L;LI;LBody +O,I-Liste,rue.,213,612,792,GCDQNG+ArialMT,#000,481,500,550,561,168454,32,LBody,Document;Sect;L;LI;LBody +O,B-Liste,8),213,612,792,GCDQNG+ArialMT,#000,162,172,572,583,168476,36,LBody,Document;Sect;L;LI;LBody +O,I-Liste,Toute,213,612,792,GCDQNG+ArialMT,#000,197,225,572,583,168476,36,LBody,Document;Sect;L;LI;LBody +O,I-Liste,aire,213,612,792,GCDQNG+ArialMT,#000,234,252,572,583,168476,36,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,261,273,572,583,168476,36,LBody,Document;Sect;L;LI;LBody +O,I-Liste,stationnement,213,612,792,GCDQNG+ArialMT,#000,282,351,572,583,168476,36,LBody,Document;Sect;L;LI;LBody +O,I-Liste,comprenant,213,612,792,GCDQNG+ArialMT,#000,359,417,572,583,168476,36,LBody,Document;Sect;L;LI;LBody +O,I-Liste,plus,213,612,792,GCDQNG+ArialMT,#000,426,446,572,583,168476,36,LBody,Document;Sect;L;LI;LBody 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+O,I-Liste,200,213,612,792,GCDQNG+ArialMT,#000,506,524,644,655,168548,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,m²,213,612,792,GCDQNG+ArialMT,#000,527,540,644,655,168548,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,ne,213,612,792,GCDQNG+ArialMT,#000,198,210,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,peut,213,612,792,GCDQNG+ArialMT,#000,215,236,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,être,213,612,792,GCDQNG+ArialMT,#000,241,260,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,drainée,213,612,792,GCDQNG+ArialMT,#000,265,301,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,vers,213,612,792,GCDQNG+ArialMT,#000,306,327,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,la,213,612,792,GCDQNG+ArialMT,#000,331,340,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,rue,213,612,792,GCDQNG+ArialMT,#000,345,361,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,sauf,213,612,792,GCDQNG+ArialMT,#000,365,386,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,dans,213,612,792,GCDQNG+ArialMT,#000,390,414,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,les,213,612,792,GCDQNG+ArialMT,#000,419,433,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cas,213,612,792,GCDQNG+ArialMT,#000,438,455,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,où,213,612,792,GCDQNG+ArialMT,#000,459,472,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,le,213,612,792,GCDQNG+ArialMT,#000,476,485,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,terrain,213,612,792,GCDQNG+ArialMT,#000,490,521,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,est,213,612,792,GCDQNG+ArialMT,#000,525,540,657,668,168561,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,adjacent,213,612,792,GCDQNG+ArialMT,#000,198,240,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,à,213,612,792,GCDQNG+ArialMT,#000,242,248,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,un,213,612,792,GCDQNG+ArialMT,#000,251,263,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,fossé,213,612,792,GCDQNG+ArialMT,#000,266,292,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,situé,213,612,792,GCDQNG+ArialMT,#000,295,318,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,dans,213,612,792,GCDQNG+ArialMT,#000,321,345,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,l'emprise,213,612,792,GCDQNG+ArialMT,#000,348,391,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,394,406,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,la,213,612,792,GCDQNG+ArialMT,#000,409,417,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,rue,213,612,792,GCDQNG+ArialMT,#000,420,436,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,;,213,612,792,GCDQNG+ArialMT,#000,439,442,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,elle,213,612,792,GCDQNG+ArialMT,#000,444,462,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,doit,213,612,792,GCDQNG+ArialMT,#000,464,482,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,être,213,612,792,GCDQNG+ArialMT,#000,485,504,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pourvu,213,612,792,GCDQNG+ArialMT,#000,506,540,670,681,168574,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'un,213,612,792,GCDQNG+ArialMT,#000,198,218,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,système,213,612,792,GCDQNG+ArialMT,#000,222,263,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,266,279,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,drainage,213,612,792,GCDQNG+ArialMT,#000,282,325,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,328,340,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,surface,213,612,792,GCDQNG+ArialMT,#000,344,380,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,composé,213,612,792,GCDQNG+ArialMT,#000,383,428,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,d'au,213,612,792,GCDQNG+ArialMT,#000,432,452,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,moins,213,612,792,GCDQNG+ArialMT,#000,455,485,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,un,213,612,792,GCDQNG+ArialMT,#000,488,501,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,puisard,213,612,792,GCDQNG+ArialMT,#000,504,540,682,693,168586,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,198,210,695,706,168599,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,45,213,612,792,GCDQNG+ArialMT,#000,215,227,695,706,168599,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,cm,213,612,792,GCDQNG+ArialMT,#000,230,245,695,706,168599,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,249,261,695,706,168599,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,diamètre,213,612,792,GCDQNG+ArialMT,#000,266,309,695,706,168599,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,pour,213,612,792,GCDQNG+ArialMT,#000,313,335,695,706,168599,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,chaque,213,612,792,GCDQNG+ArialMT,#000,339,376,695,706,168599,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,4,213,612,792,GCDQNG+ArialMT,#000,380,386,695,706,168599,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,000,213,612,792,GCDQNG+ArialMT,#000,389,407,695,706,168599,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,m²,213,612,792,GCDQNG+ArialMT,#000,410,423,695,706,168599,40,LBody,Document;Sect;L;LI;LBody +O,I-Liste,de,213,612,792,GCDQNG+ArialMT,#000,428,440,695,706,168599,40,LBody,Document;Sect;L;LI;LBody 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https://www.val-morin.ca/reglementation_formulaires/reglementation_urbanisme.php -wget --no-check-certificate --timestamping --recursive --level=1 https://www.valdavid.com/services-aux-citoyens/urbanisme-et-permis/reglements-durbanisme/ -wget --no-check-certificate --timestamping --recursive --level=1 https://www.piedmont.ca/fr/administration-municipale/reglements-municipaux/ -wget --no-check-certificate --timestamping --recursive --level=1 https://www.vss.ca/services-aux-citoyens/services/reglementation-durbanisme -wget --no-check-certificate --timestamping --recursive --level=1 https://www.sadl.qc.ca/reglements-urbanisme/ -wget --no-check-certificate --timestamping --recursive --level=1 https://www.morinheights.com/Reglements -wget --no-check-certificate --timestamping --recursive --level=1 https://lacmasson.com/services-aux-citoyens/plan-durbanisme +wget --accept pdf --no-check-certificate --timestamping --recursive --level=1 https://vsadm.ca/citoyens/reglementation/reglementation-durbanisme/ +wget --accept pdf --no-check-certificate --timestamping --recursive --level=1 https://www.val-morin.ca/reglementation_formulaires/reglementation_urbanisme.php +wget --accept pdf --no-check-certificate --timestamping --recursive --level=1 https://www.valdavid.com/services-aux-citoyens/urbanisme-et-permis/reglements-durbanisme/ +wget --accept pdf --no-check-certificate --timestamping --recursive --level=1 https://www.piedmont.ca/fr/administration-municipale/reglements-municipaux/ +wget --accept pdf --no-check-certificate --timestamping --recursive --level=1 https://www.vss.ca/services-aux-citoyens/services/reglementation-durbanisme +wget --accept pdf --no-check-certificate --timestamping --recursive --level=1 https://www.sadl.qc.ca/reglements-urbanisme/ +wget --accept pdf --no-check-certificate --timestamping --recursive --level=1 https://www.morinheights.com/Reglements +wget --accept pdf --no-check-certificate --timestamping --recursive --level=1 https://lacmasson.com/services-aux-citoyens/plan-durbanisme diff --git a/scripts/make_funsd_data.py b/scripts/make_funsd_data.py deleted file mode 100644 index 64d289f..0000000 --- a/scripts/make_funsd_data.py +++ /dev/null @@ -1,220 +0,0 @@ -""" -Convert our data to FUNSD style (IOB boxes and PNG images) -""" - -import argparse -import itertools -import logging -from collections import Counter -from pathlib import Path -from typing import Iterable, Iterator - -import pdfplumber -from sklearn.model_selection import KFold -from tokenizers import Tokenizer - -from alexi import segment -from alexi.format import line_breaks -from alexi.types import T_obj - -LOGGER = logging.getLogger(Path(__file__).stem) - - -def resegment(page: list[T_obj], max_seq_length: int) -> Iterator[list[T_obj]]: - """Refaire la segmentation d'une page pour respecter les limites de tokens""" - if len(page) < max_seq_length: - yield page - return - # Find the mode of line spacing - # Try to break where there is a larger line break - lines = list(line_breaks(page)) - spacing = Counter( - int(b[0]["top"]) - int(a[0]["bottom"]) for a, b in itertools.pairwise(lines) - ) - line_gap = spacing.most_common(1)[0][0] - LOGGER.info("line gap %d", line_gap) - # Total count of output (for validation) - page_length = 0 - # Length and extent of current "paragraph" - tcount = start = cur = 0 - for idx, line in enumerate(lines): - LOGGER.info( - "line %d start %d cur %d tcount %d => %d", - idx, - start, - cur, - tcount, - tcount + len(line), - ) - tcount += len(line) - # Try to output paragraphs, fall back to lines, then if - # necessary fall back to sub-lines (shouldn't be necessary) - while tcount > max_seq_length: - if cur > start: - seg = list(itertools.chain.from_iterable(lines[start:cur])) - LOGGER.info("output paragraph %d:%d (%d tokens)", start, cur, len(seg)) - assert len(seg) <= max_seq_length - page_length += len(seg) - yield seg - start = cur - tcount = sum(len(x) for x in lines[start : idx + 1]) - elif idx > cur: - seg = list(itertools.chain.from_iterable(lines[start:idx])) - assert len(seg) <= max_seq_length - LOGGER.info("output lines %d:%d (%d tokens)", start, idx, len(seg)) - page_length += len(seg) - yield seg - start = cur = idx - tcount = len(line) - else: - LOGGER.warning( - "Very long line %d (%d tokens), must split to %d", - idx, - tcount, - max_seq_length, - ) - assert idx == start - assert idx == cur - for start in range(0, len(line), max_seq_length): - seg = line[start : start + max_seq_length] - page_length += len(seg) - yield seg - start = cur = idx + 1 - tcount = 0 - line_line_gap = int(line[0]["top"]) - int(lines[idx - 1][0]["bottom"]) - if line_line_gap > line_gap + 1: - LOGGER.info( - "break at line %d line gap %d", - idx, - line_line_gap, - ) - cur = idx - if start < len(lines): - seg = list(itertools.chain.from_iterable(lines[start:])) - assert len(seg) <= max_seq_length - page_length += len(seg) - yield seg - assert page_length == len(page) - - -def write_fold( - pages: Iterable[T_obj], - tokenizer: Tokenizer, - max_seq_length: int, - outbase: Path, - imgdir: Path | None = None, -) -> set[str]: - txtpath = outbase.with_suffix(".txt") - boxpath = txtpath.with_stem(f"{txtpath.stem}_box") - imgpath = txtpath.with_stem(f"{txtpath.stem}_image") - if imgdir is not None: - imgdir.mkdir(parents=True, exist_ok=True) - labels = set() - path = pngpath = None - with open(txtpath, "wt") as txtfh, open(boxpath, "wt") as boxfh, open( - imgpath, "wt" - ) as imgfh: - for page in pages: - if page[0]["path"] != path: - path = Path(page[0]["path"]) - pageno = int(page[0]["page"]) - pdfpath = path.with_suffix(".pdf") - if imgdir is not None: - pdf = pdfplumber.open(pdfpath) - ipage = pdf.pages[page - 1] - pngpath = imgdir / f"{pdfpath.stem}-{pageno}.png" - ipage.to_image().save(pngpath) - else: - pngpath = Path(f"{pdfpath.stem}-{pageno}.png") - page_width = int(page[0]["page_width"]) - page_height = int(page[0]["page_height"]) - maxdim = max(page_width, page_height) - mediabox = " ".join( - str(int(f / maxdim * 1000)) for f in (page_width, page_height) - ) - tpage = list(segment.retokenize(page, tokenizer)) - LOGGER.info("page: %d tokens", len(tpage)) - tseg = list(resegment(tpage, max_seq_length)) - for seg in tseg: - LOGGER.info("seg: %d tokens", len(seg)) - # Detokenize here since that's what the current training - # code wants (even though it's going to tokenize it - # again...) - for word in segment.detokenize(seg, tokenizer): - label = segment.tonly(0, word) - labels.add(label) - bbox = [ - int(float(word[f]) / maxdim * 1000) - for f in "x0 top x1 bottom".split() - ] - assert all(x <= 1000 for x in bbox) - box = " ".join(str(f) for f in bbox) - print("\t".join((word["text"], label)), file=txtfh) - print( - "\t".join((word["text"], box)), - file=boxfh, - ) - print( - "\t".join((word["text"], box, mediabox, pngpath.name)), - file=imgfh, - ) - for fh in txtfh, boxfh, imgfh: - print(file=fh) - return labels - - -def main(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "-o", - "--outdir", - default="data", - help="Repertoire pour fichiers de sortie", - type=Path, - ) - parser.add_argument( - "-x", - "--cross-validation-folds", - default=4, - type=int, - help="Nombre de partitions de validation croisée.", - ) - parser.add_argument("-d", "--imgdir", help="Repertoire pour images", type=Path) - parser.add_argument("--seed", default=1381, type=int, help="Graine aléatoire") - parser.add_argument( - "--max-seq-length", - default=512, - type=int, - help="Longueur maximale des sequences", - ) - parser.add_argument("csvs", nargs="+", help="Fichiers CSV", type=Path) - args = parser.parse_args() - logging.basicConfig(level=logging.INFO) - # Need to split inside pages to respect the 512-token limit, so we - # will tokenize to find out where we need to split - tokenizer = Tokenizer.from_pretrained("microsoft/layoutlm-base-uncased") - pages = list(segment.split_pages(segment.filter_tab(segment.load(args.csvs)))) - kf = KFold(n_splits=4, shuffle=True, random_state=args.seed) - for fold, (train_idx, dev_idx) in enumerate(kf.split(pages)): - foldir = args.outdir / f"fold{fold + 1}" - foldir.mkdir(parents=True, exist_ok=True) - labels = write_fold( - (pages[x] for x in train_idx), - tokenizer, - args.max_seq_length - 3, - foldir / "train", - ) - devlabels = write_fold( - (pages[x] for x in dev_idx), - tokenizer, - args.max_seq_length - 3, - foldir / "test", - ) - labels.update(devlabels) - with open(foldir / "labels.txt", "wt") as outfh: - for label in labels: - print(label, file=outfh) - - -if __name__ == "__main__": - main() diff --git a/scripts/problem_pages.sh b/scripts/problem_pages.sh deleted file mode 100644 index 414a7b5..0000000 --- a/scripts/problem_pages.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/sh - -alexi convert --pages 10,11,12,13,14,15,16,17 ville.sainte-adele.qc.ca/upload/documents/Rgl-1314-2021-Z-en-vigueur-20230614.pdf | alexi crf - > zonage_sections.csv -alexi convert --pages 189,190,205,206,214,222,223,231 ville.sainte-adele.qc.ca/upload/documents/Rgl-1314-2021-Z-en-vigueur-20230614.pdf | alexi crf - > zonage_zones.csv diff --git a/scripts/retokenize.py b/scripts/retokenize.py deleted file mode 100644 index 8d0ba0f..0000000 --- a/scripts/retokenize.py +++ /dev/null @@ -1,44 +0,0 @@ -"""Refaire la tokenisation en alignant les etiquettes et traits sur -les nouveaux segments.""" - -# To implement: -# -# - retokenize: CSV/IOB -> CSV/IOB -# - detokenize: CSV/IOB -> CSV/IOB -# -# This should improve existing CRF as well! - - -import argparse -import csv -import sys -import tokenizers - -from alexi.convert import write_csv -from alexi.segment import retokenize, detokenize - - -def main(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "-t", "--tokenizer", help="Nom du tokenisateur", default="camembert-base" - ) - parser.add_argument( - "-d", "--detokenize", help="Inverser la retokenisation", action="store_true" - ) - parser.add_argument( - "csv", - help="Fichier CSV à traiter", - type=argparse.FileType("rt"), - ) - args = parser.parse_args() - tokenizer = tokenizers.Tokenizer.from_pretrained(args.tokenizer) - reader = csv.DictReader(args.csv) - if args.detokenize: - write_csv(detokenize(reader, tokenizer), sys.stdout) - else: - write_csv(retokenize(reader, tokenizer), sys.stdout) - - -if __name__ == "__main__": - main() diff --git a/scripts/test_rnn.py b/scripts/test_rnn.py deleted file mode 100644 index 96a1687..0000000 --- a/scripts/test_rnn.py +++ /dev/null @@ -1,120 +0,0 @@ -"""Tester un RNN""" - -import argparse -import json -import logging -from pathlib import Path - -import torch -from sklearn_crfsuite import metrics -from torch.utils.data import DataLoader -from tokenizers import Tokenizer - -from alexi.segment import ( - load, - load_rnn_data, - pad_collate_fn_predict, - RNN, - RNNCRF, - bio_transitions, - retokenize, -) - -from allennlp_light.modules.conditional_random_field import ConditionalRandomField - - -def make_argparse(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "-m", "--model", help="Fichier modele", default="rnn.pt", type=Path - ) - parser.add_argument( - "--device", default="cuda:0", help="Device pour rouler la prediction" - ) - parser.add_argument( - "--decode", action="store_true", help="Appliquer l'algorithme Viterbi" - ) - parser.add_argument( - "--all-labels", action="store_true", help="Evaluater toutes les classes" - ) - parser.add_argument( - "-t", "--tokenize", action="store_true", help="Tokeniser les mots" - ) - parser.add_argument("csvs", nargs="+", help="Fichiers CSV de test", type=Path) - return parser - - -def main(): - parser = make_argparse() - args = parser.parse_args() - logging.basicConfig(level=logging.INFO) - - with open(args.model.with_suffix(".json"), "rt") as infh: - config = json.load(infh) - id2label = config["id2label"] - feat2id = config["feat2id"] - tokenizer = None - words = load(args.csvs) - if args.tokenize: - tokenizer = Tokenizer.from_pretrained("camembert-base") - words = retokenize(words, tokenizer, drop=True) - all_data = load_rnn_data( - words, feat2id, id2label, config["features"], config["labels"] - ) - ordering, sorted_test_data = zip( - *sorted(enumerate(all_data), reverse=True, key=lambda x: len(x[1][0])) - ) - test_loader = DataLoader( - sorted_test_data, - batch_size=32, - collate_fn=pad_collate_fn_predict, - ) - device = torch.device(args.device) - if "crf" in args.model.name: - model = RNNCRF(**config) - else: - model = RNN(**config) - if args.decode: - crf = ConditionalRandomField( - num_tags=len(id2label), - constraints=bio_transitions(id2label), - include_start_end_transitions=False, - ) - - model.load_state_dict(torch.load(args.model)) - model.eval() - model.to(device) - predictions = [] - lengths = [len(tokens) for tokens, _ in sorted_test_data] - for batch in test_loader: - if "crf" in args.model.name: - _logits, labels, _mask = model(*(t.to(device) for t in batch)) - predictions.extend(labels) - else: - features, vectors, mask = batch - out = model(*(t.to(device) for t in (features, vectors, mask))) - out = out.transpose(1, -1).cpu() # WTF - if args.decode: - tags = crf.viterbi_tags(out, mask) - predictions.extend(sequence for sequence, _ in tags) - else: - for length, row in zip(lengths, out.argmax(-1).cpu()): - predictions.append(row[:length]) - del lengths[: len(out)] - y_pred = [[id2label[x] for x in page] for page in predictions] - y_true = [[id2label[x] for x in page] for _, page in sorted_test_data] - if args.all_labels: - eval_labels = sorted(id2label) - else: - eval_labels = sorted( - ["O", *(c for c in id2label if c.startswith("B-"))], - key=lambda name: (name[1:], name[0]), - ) - report = metrics.flat_classification_report( - y_true, y_pred, labels=eval_labels, zero_division=0.0 - ) - print(report) - - -if __name__ == "__main__": - main() diff --git a/scripts/test_rnn_voting.py b/scripts/test_rnn_voting.py deleted file mode 100644 index ac9d20e..0000000 --- a/scripts/test_rnn_voting.py +++ /dev/null @@ -1,107 +0,0 @@ -"""Tester un RNN""" - -import argparse -import json -import logging -from pathlib import Path - -import torch -from sklearn_crfsuite import metrics -from torch.utils.data import DataLoader -from tokenizers import Tokenizer - -from alexi.segment import ( - load, - load_rnn_data, - pad_collate_fn_predict, - RNN, - RNNCRF, - retokenize, -) - -LOGGER = logging.getLogger(Path(__file__).stem) - - -def make_argparse(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "-m", "--model", help="Fichier modele", default="rnn.pt", type=Path - ) - parser.add_argument( - "--device", default="cuda:0", help="Device pour rouler la prediction" - ) - parser.add_argument( - "-t", "--tokenize", action="store_true", help="Tokeniser les mots" - ) - parser.add_argument("csvs", nargs="+", help="Fichiers CSV de test", type=Path) - return parser - - -def main(): - parser = make_argparse() - args = parser.parse_args() - logging.basicConfig(level=logging.INFO) - - multi_predictions = [] - for fold in args.model.parent.glob(f"{args.model.stem}_fold*.pt"): - LOGGER.info("Evaluating with %s", fold) - with open(fold.with_suffix(".json"), "rt") as infh: - config = json.load(infh) - id2label = config["id2label"] - feat2id = config["feat2id"] - words = load(args.csvs) - if args.tokenize: - tokenizer = Tokenizer.from_pretrained("camembert-base") - words = retokenize(words, tokenizer, drop=True) - all_data = load_rnn_data( - words, feat2id, id2label, config["featdims"], config["labels"] - ) - ordering, sorted_test_data = zip( - *sorted(enumerate(all_data), reverse=True, key=lambda x: len(x[1][0])) - ) - test_loader = DataLoader( - sorted_test_data, - batch_size=32, - collate_fn=pad_collate_fn_predict, - ) - device = torch.device(args.device) - if "crf" in args.model.name: - model = RNNCRF(**config) - else: - model = RNN(**config) - model.load_state_dict(torch.load(fold)) - model.eval() - model.to(device) - fold_predictions = [] - lengths = [len(tokens) for tokens, _ in sorted_test_data] - for batch in test_loader: - if "crf" in args.model.name: - _logits, labels, _mask = model(*(t.to(device) for t in batch)) - fold_predictions.extend(torch.LongTensor(x) for x in labels) - else: - out = model(*(t.to(device) for t in batch)) - out = out.transpose(1, -1).argmax(-1).cpu() - for length, row in zip(lengths, out): - fold_predictions.append(row[:length]) - del lengths[: len(out)] - multi_predictions.append(fold_predictions) - predictions = [] - for page in zip(*multi_predictions): - assert all(len(y) == len(page[0]) for y in page) - tpage = torch.stack(page) - values, indices = torch.mode(tpage, 0) - predictions.append(values) - y_pred = [[id2label[x] for x in page] for page in predictions] - y_true = [[id2label[x] for x in page] for _, page in sorted_test_data] - eval_labels = sorted( - ["O", *(c for c in id2label if c.startswith("B-"))], - key=lambda name: (name[1:], name[0]), - ) - report = metrics.flat_classification_report( - y_true, y_pred, labels=eval_labels, zero_division=0.0 - ) - print(report) - - -if __name__ == "__main__": - main() diff --git a/scripts/train_crf.py b/scripts/train_crf.py index fad07a5..a0b4a81 100644 --- a/scripts/train_crf.py +++ b/scripts/train_crf.py @@ -20,9 +20,7 @@ page2labels, split_pages, filter_tab, - retokenize, ) -from tokenizers import Tokenizer LOGGER = logging.getLogger("train-crf") @@ -35,7 +33,9 @@ def make_argparse(): parser.add_argument( "--niter", default=200, type=int, help="Nombre d'iterations d'entrainement" ) - parser.add_argument("--features", default="text+layout+structure", help="Extracteur de traits") + parser.add_argument( + "--features", default="text+layout+structure", help="Extracteur de traits" + ) parser.add_argument("--labels", default="literal", help="Transformateur de classes") parser.add_argument("-n", default=2, type=int, help="Largeur du contexte de traits") parser.add_argument( @@ -60,9 +60,6 @@ def make_argparse(): ) parser.add_argument("-o", "--outfile", help="Fichier destination pour modele") parser.add_argument("-s", "--scores", help="Fichier destination pour évaluations") - parser.add_argument( - "-t", "--tokenize", action="store_true", help="Tokeniser les mots" - ) return parser @@ -144,9 +141,6 @@ def main(): args = parser.parse_args() logging.basicConfig(level=logging.INFO) data = filter_tab(load(args.csvs)) - if args.tokenize: - tokenizer = Tokenizer.from_pretrained("camembert-base") - data = retokenize(data, tokenizer) pages = list(split_pages(data)) X = [page2features(s, args.features, args.n) for s in pages] y = [page2labels(s, args.labels) for s in pages] diff --git a/scripts/train_layoutlm.py b/scripts/train_layoutlm.py deleted file mode 100644 index 3a48d37..0000000 --- a/scripts/train_layoutlm.py +++ /dev/null @@ -1,632 +0,0 @@ -import argparse -import csv -import logging -import os -from collections import Counter -from pathlib import Path -from typing import Optional, Union, Tuple - -import numpy as np -import torch -from sklearn_crfsuite import metrics -from torch.nn import CrossEntropyLoss -from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler -from tqdm import tqdm -from transformers import ( - LayoutLMForTokenClassification, - LayoutLMTokenizer, -) -from transformers.modeling_outputs import TokenClassifierOutput -from transformers.optimization import Adafactor - -logger = logging.getLogger(__name__) -logging.basicConfig(level=logging.INFO) -torch.manual_seed(1234) -np.random.seed(1234) - - -class FunsdDataset(Dataset): - def __init__(self, args, tokenizer, labels, pad_token_label_id, mode): - logger.info("Creating features from dataset file at %s", args.data_dir) - examples = read_examples_from_file(args.data_dir, mode) - features = convert_examples_to_features( - examples, - labels, - args.max_seq_length, - tokenizer, - cls_token_at_end=bool(args.model_type in ["xlnet"]), - # xlnet has a cls token at the end - cls_token=tokenizer.cls_token, - cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0, - sep_token=tokenizer.sep_token, - sep_token_extra=bool(args.model_type in ["roberta"]), - # roberta uses an extra separator b/w pairs of - # sentences, - # cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 - pad_on_left=bool(args.model_type in ["xlnet"]), - # pad on the left for xlnet - pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0], - pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0, - pad_token_label_id=pad_token_label_id, - ) - - self.features = features - # Convert to Tensors and build dataset - self.all_input_ids = torch.tensor( - [f.input_ids for f in features], dtype=torch.long - ) - self.all_input_mask = torch.tensor( - [f.input_mask for f in features], dtype=torch.long - ) - self.all_segment_ids = torch.tensor( - [f.segment_ids for f in features], dtype=torch.long - ) - self.all_label_ids = torch.tensor( - [f.label_ids for f in features], dtype=torch.long - ) - self.all_bboxes = torch.tensor([f.boxes for f in features], dtype=torch.long) - - def __len__(self): - return len(self.features) - - def __getitem__(self, index): - return ( - self.all_input_ids[index], - self.all_input_mask[index], - self.all_segment_ids[index], - self.all_label_ids[index], - self.all_bboxes[index], - ) - - -class InputExample(object): - """A single training/test example for token classification.""" - - def __init__(self, guid, words, labels, boxes, actual_bboxes, file_name, page_size): - """Constructs a InputExample. - - Args: - guid: Unique id for the example. - words: list. The words of the sequence. - labels: (Optional) list. The labels for each word of the - sequence. This should be specified for train and dev - examples, but not for test examples. - """ - self.guid = guid - self.words = words - self.labels = labels - self.boxes = boxes - self.actual_bboxes = actual_bboxes - self.file_name = file_name - self.page_size = page_size - - -class InputFeatures(object): - """A single set of features of data.""" - - def __init__( - self, - input_ids, - input_mask, - segment_ids, - label_ids, - boxes, - actual_bboxes, - file_name, - page_size, - ): - assert ( - 0 <= all(boxes) <= 1000 - ), "Error with input bbox ({}): the coordinate value is not between 0 and 1000".format( - boxes - ) - self.input_ids = input_ids - self.input_mask = input_mask - self.segment_ids = segment_ids - self.label_ids = label_ids - self.boxes = boxes - self.actual_bboxes = actual_bboxes - self.file_name = file_name - self.page_size = page_size - - -def read_examples_from_file(data_dir, mode): - file_path = os.path.join(data_dir, "{}.txt".format(mode)) - box_file_path = os.path.join(data_dir, "{}_box.txt".format(mode)) - image_file_path = os.path.join(data_dir, "{}_image.txt".format(mode)) - guid_index = 1 - examples = [] - with open(file_path, encoding="utf-8") as f, open( - box_file_path, encoding="utf-8" - ) as fb, open(image_file_path, encoding="utf-8") as fi: - words = [] - boxes = [] - actual_bboxes = [] - file_name = None - page_size = None - labels = [] - for line, bline, iline in zip(f, fb, fi): - if line.startswith("-DOCSTART-") or line == "" or line == "\n": - if words: - examples.append( - InputExample( - guid="{}-{}".format(mode, guid_index), - words=words, - labels=labels, - boxes=boxes, - actual_bboxes=actual_bboxes, - file_name=file_name, - page_size=page_size, - ) - ) - guid_index += 1 - words = [] - boxes = [] - actual_bboxes = [] - file_name = None - page_size = None - labels = [] - else: - splits = line.split("\t") - bsplits = bline.split("\t") - isplits = iline.split("\t") - assert len(splits) == 2 - assert len(bsplits) == 2 - assert len(isplits) == 4 - assert splits[0] == bsplits[0] - words.append(splits[0]) - if len(splits) > 1: - labels.append(splits[-1].replace("\n", "")) - box = bsplits[-1].replace("\n", "") - box = [int(b) for b in box.split()] - boxes.append(box) - actual_bbox = [int(b) for b in isplits[1].split()] - actual_bboxes.append(actual_bbox) - page_size = [int(i) for i in isplits[2].split()] - file_name = isplits[3].strip() - else: - # Examples could have no label for mode = "test" - labels.append("O") - if words: - examples.append( - InputExample( - guid=("%s-%d" % (mode, guid_index)), - words=words, - labels=labels, - boxes=boxes, - actual_bboxes=actual_bboxes, - file_name=file_name, - page_size=page_size, - ) - ) - return examples - - -def convert_examples_to_features( - examples, - label_list, - max_seq_length, - tokenizer, - cls_token_at_end=False, - cls_token="[CLS]", - cls_token_segment_id=1, - sep_token="[SEP]", - sep_token_extra=False, - pad_on_left=False, - pad_token=0, - cls_token_box=[0, 0, 0, 0], - sep_token_box=[1000, 1000, 1000, 1000], - pad_token_box=[0, 0, 0, 0], - pad_token_segment_id=0, - pad_token_label_id=-1, - sequence_a_segment_id=0, - mask_padding_with_zero=True, -): - """Loads a data file into a list of `InputBatch`s - `cls_token_at_end` define the location of the CLS token: - - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP] - - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS] - `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet) - """ - - label_map = {label: i for i, label in enumerate(label_list)} - - features = [] - for ex_index, example in enumerate(examples): - file_name = example.file_name - page_size = example.page_size - width, height = page_size - if ex_index % 10000 == 0: - logger.info("Writing example %d of %d", ex_index, len(examples)) - - tokens = [] - token_boxes = [] - actual_bboxes = [] - label_ids = [] - for word, label, box, actual_bbox in zip( - example.words, example.labels, example.boxes, example.actual_bboxes - ): - word_tokens = tokenizer.tokenize(word) - tokens.extend(word_tokens) - token_boxes.extend([box] * len(word_tokens)) - actual_bboxes.extend([actual_bbox] * len(word_tokens)) - # Use the real label id for the first token of the word, and padding ids for the remaining tokens - label_ids.extend( - [label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1) - ) - - # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa. - special_tokens_count = 3 if sep_token_extra else 2 - if len(tokens) > max_seq_length - special_tokens_count: - tokens = tokens[: (max_seq_length - special_tokens_count)] - token_boxes = token_boxes[: (max_seq_length - special_tokens_count)] - actual_bboxes = actual_bboxes[: (max_seq_length - special_tokens_count)] - label_ids = label_ids[: (max_seq_length - special_tokens_count)] - - # The convention in BERT is: - # (a) For sequence pairs: - # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] - # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1 - # (b) For single sequences: - # tokens: [CLS] the dog is hairy . [SEP] - # type_ids: 0 0 0 0 0 0 0 - # - # Where "type_ids" are used to indicate whether this is the first - # sequence or the second sequence. The embedding vectors for `type=0` and - # `type=1` were learned during pre-training and are added to the wordpiece - # embedding vector (and position vector). This is not *strictly* necessary - # since the [SEP] token unambiguously separates the sequences, but it makes - # it easier for the model to learn the concept of sequences. - # - # For classification tasks, the first vector (corresponding to [CLS]) is - # used as as the "sentence vector". Note that this only makes sense because - # the entire model is fine-tuned. - tokens += [sep_token] - token_boxes += [sep_token_box] - actual_bboxes += [[0, 0, width, height]] - label_ids += [pad_token_label_id] - if sep_token_extra: - # roberta uses an extra separator b/w pairs of sentences - tokens += [sep_token] - token_boxes += [sep_token_box] - actual_bboxes += [[0, 0, width, height]] - label_ids += [pad_token_label_id] - segment_ids = [sequence_a_segment_id] * len(tokens) - - if cls_token_at_end: - tokens += [cls_token] - token_boxes += [cls_token_box] - actual_bboxes += [[0, 0, width, height]] - label_ids += [pad_token_label_id] - segment_ids += [cls_token_segment_id] - else: - tokens = [cls_token] + tokens - token_boxes = [cls_token_box] + token_boxes - actual_bboxes = [[0, 0, width, height]] + actual_bboxes - label_ids = [pad_token_label_id] + label_ids - segment_ids = [cls_token_segment_id] + segment_ids - - input_ids = tokenizer.convert_tokens_to_ids(tokens) - - # The mask has 1 for real tokens and 0 for padding tokens. Only real - # tokens are attended to. - input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids) - - # Zero-pad up to the sequence length. - padding_length = max_seq_length - len(input_ids) - if pad_on_left: - input_ids = ([pad_token] * padding_length) + input_ids - input_mask = ( - [0 if mask_padding_with_zero else 1] * padding_length - ) + input_mask - segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids - label_ids = ([pad_token_label_id] * padding_length) + label_ids - token_boxes = ([pad_token_box] * padding_length) + token_boxes - else: - input_ids += [pad_token] * padding_length - input_mask += [0 if mask_padding_with_zero else 1] * padding_length - segment_ids += [pad_token_segment_id] * padding_length - label_ids += [pad_token_label_id] * padding_length - token_boxes += [pad_token_box] * padding_length - - assert len(input_ids) == max_seq_length - assert len(input_mask) == max_seq_length - assert len(segment_ids) == max_seq_length - assert len(label_ids) == max_seq_length - assert len(token_boxes) == max_seq_length - - if False: # ex_index < 5: - logger.info("*** Example ***") - logger.info("guid: %s", example.guid) - logger.info("tokens: %s", " ".join([str(x) for x in tokens])) - logger.info("input_ids: %s", " ".join([str(x) for x in input_ids])) - logger.info("input_mask: %s", " ".join([str(x) for x in input_mask])) - logger.info("segment_ids: %s", " ".join([str(x) for x in segment_ids])) - logger.info("label_ids: %s", " ".join([str(x) for x in label_ids])) - logger.info("boxes: %s", " ".join([str(x) for x in token_boxes])) - logger.info("actual_bboxes: %s", " ".join([str(x) for x in actual_bboxes])) - - features.append( - InputFeatures( - input_ids=input_ids, - input_mask=input_mask, - segment_ids=segment_ids, - label_ids=label_ids, - boxes=token_boxes, - actual_bboxes=actual_bboxes, - file_name=file_name, - page_size=page_size, - ) - ) - return features - - -class MyLayoutLMForTokenClassification(LayoutLMForTokenClassification): - def __init__(self, config, label_weights): - super().__init__(config) - self.label_weights = label_weights - - # Just copy the code ... not very robust - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - bbox: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - token_type_ids: Optional[torch.LongTensor] = None, - position_ids: Optional[torch.LongTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - labels: Optional[torch.LongTensor] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - ) -> Union[Tuple, TokenClassifierOutput]: - return_dict = ( - return_dict if return_dict is not None else self.config.use_return_dict - ) - - outputs = self.layoutlm( - input_ids=input_ids, - bbox=bbox, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - sequence_output = outputs[0] - - sequence_output = self.dropout(sequence_output) - logits = self.classifier(sequence_output) - - loss = None - if labels is not None: - loss_fct = CrossEntropyLoss(weight=self.label_weights) - - loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) - - if not return_dict: - output = (logits,) + outputs[2:] - return ((loss,) + output) if loss is not None else output - - return TokenClassifierOutput( - loss=loss, - logits=logits, - hidden_states=outputs.hidden_states, - attentions=outputs.attentions, - ) - - -parser = argparse.ArgumentParser(description=__doc__) -parser.add_argument("-d", "--data-dir", default="data", type=Path) -parser.add_argument( - "-m", "--model-name-or-path", default="microsoft/layoutlm-base-uncased" -) -parser.add_argument("--max-seq-length", default=512, type=int) -parser.add_argument("-t", "--model-type", default="layoutlm") -parser.add_argument("-n", "--nepoch", default=3, type=int) -parser.add_argument("-s", "--scores", default="layoutlmscores.csv", type=Path) -args = parser.parse_args() -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased") -# Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later -pad_token_label_id = CrossEntropyLoss().ignore_index - -scores = {"test_macro_f1": []} -folds = sorted(args.data_dir.iterdir()) -for folddir in folds: - if not folddir.is_dir(): - continue - if not folddir.name.startswith("fold"): - continue - fold = int(folddir.name[4:]) - logger.info("Training fold %d", fold) - - def get_labels(path): - with open(path, "r") as f: - labels = f.read().splitlines() - if "O" not in labels: - labels = ["O"] + labels - return labels - - def count_labels(path): - with open(path, "rt") as infh: - return Counter( - spam.split(maxsplit=2)[1] for spam in infh if not spam.isspace() - ) - - labels = get_labels(folddir / "labels.txt") - # FIXME: This is really redundant - label_counts = count_labels(folddir / "train.txt") - num_labels = len(labels) - # FIXME: This is so dumb! It does nothing a list wouldn't do! - label_map = {i: label for i, label in enumerate(labels)} - - # We created our own dataset following the FUNSD conventions (see make_funsd_data.py) - args.data_dir = folddir - train_dataset = FunsdDataset( - args, tokenizer, labels, pad_token_label_id, mode="train" - ) - train_sampler = RandomSampler(train_dataset) - train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=2) - - eval_dataset = FunsdDataset( - args, tokenizer, labels, pad_token_label_id, mode="test" - ) - eval_sampler = SequentialSampler(eval_dataset) - eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=2) - - model = MyLayoutLMForTokenClassification.from_pretrained( - "microsoft/layoutlm-base-uncased", - num_labels=num_labels, - label_weights=torch.FloatTensor([1.0 / label_counts[x] for x in labels]).to(device), - ) - model.to(device) - - # optimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None) - optimizer = Adafactor( - model.parameters(), - lr=5e-5, - relative_step=False, - scale_parameter=False, - warmup_init=False, - ) - - global_step = 0 - t_total = len(train_dataloader) * args.nepoch # total number of training steps - - # put the model in training mode - model.train() - for _epoch in range(args.nepoch): - for batch in tqdm(train_dataloader, desc="Training"): - input_ids = batch[0].to(device) - bbox = batch[4].to(device) - attention_mask = batch[1].to(device) - token_type_ids = batch[2].to(device) - labels = batch[3].to(device) - - # forward pass - outputs = model( - input_ids=input_ids, - bbox=bbox, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - labels=labels, - ) - loss = outputs.loss - - # print loss every 50 steps - if global_step % 50 == 0: - logger.info(f"Loss after {global_step} steps: {loss.item()}") - - # backward pass to get the gradients - loss.backward() - - # print("Gradients on classification head:") - # print(model.classifier.weight.grad[6,:].sum()) - - # update - optimizer.step() - optimizer.zero_grad() - global_step += 1 - - eval_loss = 0.0 - nb_eval_steps = 0 - preds = None - out_label_ids = None - - # put model in evaluation mode - model.eval() - for batch in tqdm(eval_dataloader, desc="Evaluating"): - with torch.no_grad(): - input_ids = batch[0].to(device) - bbox = batch[4].to(device) - attention_mask = batch[1].to(device) - token_type_ids = batch[2].to(device) - labels = batch[3].to(device) - - # forward pass - outputs = model( - input_ids=input_ids, - bbox=bbox, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - labels=labels, - ) - # get the loss and logits - tmp_eval_loss = outputs.loss - logits = outputs.logits - - eval_loss += tmp_eval_loss.item() - nb_eval_steps += 1 - - # compute the predictions - if preds is None: - preds = logits.detach().cpu().numpy() - out_label_ids = labels.detach().cpu().numpy() - else: - preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) - out_label_ids = np.append( - out_label_ids, labels.detach().cpu().numpy(), axis=0 - ) - - # compute average evaluation loss - eval_loss = eval_loss / nb_eval_steps - preds = np.argmax(preds, axis=2) - - y_true = [[] for _ in range(out_label_ids.shape[0])] - y_pred = [[] for _ in range(out_label_ids.shape[0])] - - for i in range(out_label_ids.shape[0]): - for j in range(out_label_ids.shape[1]): - if out_label_ids[i, j] != pad_token_label_id: - y_true[i].append(label_map[out_label_ids[i][j]]) - y_pred[i].append(label_map[preds[i][j]]) - eval_labels = [ - "B-Article", - "B-Chapitre", - "B-Section", - "B-SousSection", - "B-Titre", - ] - - macro_f1 = metrics.flat_f1_score( - y_true, y_pred, labels=eval_labels, average="macro", zero_division=0.0 - ) - scores["test_macro_f1"].append(macro_f1) - logger.info("fold %d ALL %f", fold, macro_f1) - for name in eval_labels: - label_f1 = metrics.flat_f1_score( - y_true, y_pred, labels=[name], average="micro", zero_division=0.0 - ) - scores.setdefault(name, []).append(label_f1) - logger.info("fold %d %s %f", fold, name, label_f1) - -with open(args.scores, "wt") as outfh: - fieldnames = [ - "Label", - "Average", - *range(1, len(scores["test_macro_f1"]) + 1), - ] - writer = csv.DictWriter(outfh, fieldnames=fieldnames) - writer.writeheader() - - def makerow(name, scores): - row = {"Label": name, "Average": np.mean(scores)} - for idx, score in enumerate(scores): - row[idx + 1] = score - return row - - row = makerow("ALL", scores["test_macro_f1"]) - writer.writerow(row) - logger.info("average ALL %f", row["Average"]) - for name in eval_labels: - row = makerow(name, scores[name]) - writer.writerow(row) - logger.info("average %s %f", row["Label"], row["Average"]) - -model.save_pretrained("layoutlm-alexi") diff --git a/scripts/train_rnn.py b/scripts/train_rnn.py deleted file mode 100644 index 29338c4..0000000 --- a/scripts/train_rnn.py +++ /dev/null @@ -1,320 +0,0 @@ -"""Entrainer un LSTM pour segmentation/identification""" - -import argparse -import csv -import json -import logging -from pathlib import Path -from typing import Dict - -import numpy as np -import torch -import torch.nn as nn -import torch.optim as optim -from poutyne import EarlyStopping, ExponentialLR, Model, ModelCheckpoint, set_seeds -from sklearn.model_selection import KFold -from sklearn_crfsuite import metrics -from torch.utils.data import DataLoader, Subset - -from alexi.segment import make_rnn_data, pad_collate_fn, pad_collate_fn_predict, RNN -from tokenizers import Tokenizer - - -def make_argparse(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "csvs", nargs="+", help="Fichiers CSV d'entrainement", type=Path - ) - parser.add_argument( - "--device", default="cuda:0", help="Device pour rouler l'entrainement" - ) - parser.add_argument( - "--nepoch", default=45, type=int, help="Nombre maximal d'epochs d'entrainement" - ) - parser.add_argument("--batch-size", default=32, type=int, help="Taille du batch") - parser.add_argument( - "--word-dim", default=32, type=int, help="Dimension des embeddings des mots" - ) - parser.add_argument( - "--feat-dim", default=8, type=int, help="Dimension des embeddings des traits" - ) - parser.add_argument( - "--min-feat", default=15, type=int, help="Nombre minimal d'instances d'un trait" - ) - parser.add_argument( - "--lr", default=0.01, type=float, help="Facteur d'apprentissage" - ) - parser.add_argument( - "--gamma", default=0.99, type=float, help="Coefficient de reduction du LR" - ) - parser.add_argument( - "--bscale", default=1.0, type=float, help="Facteur applique aux debuts de bloc" - ) - parser.add_argument( - "--hidden-size", default=80, type=int, help="Largeur de la couche cachee" - ) - parser.add_argument("--early-stopping", action="store_true", help="Arret anticipe") - parser.add_argument( - "--min-epochs", type=int, default=40, help="Nombre minimal d'epochs" - ) - parser.add_argument( - "--patience", default=10, type=int, help="Patience pour arret anticipe" - ) - parser.add_argument("--seed", default=1381, type=int, help="Graine aléatoire") - parser.add_argument("--labels", default="literal", help="Transformateur de classes") - parser.add_argument( - "--min-count", - default=10, - type=int, - help="Seuil d'évaluation pour chaque classification", - ) - parser.add_argument( - "-x", - "--cross-validation-folds", - default=1, - type=int, - help="Faire la validation croisée pour évaluer le modèle si plus que 1.", - ) - parser.add_argument( - "-o", - "--outfile", - help="Fichier destination pour modele", - type=Path, - default="rnn.pt", - ) - parser.add_argument( - "-s", - "--scores", - help="Fichier destination pour évaluations", - ) - parser.add_argument( - "-t", "--tokenize", action="store_true", help="Tokeniser les mots" - ) - return parser - - -class MyEarlyStopping(EarlyStopping): - def __init__(self, *, min_epochs=0, **kwargs): - super().__init__(**kwargs) - self.min_epochs = min_epochs - - def on_epoch_end(self, epoch_number: int, logs: Dict): - super().on_epoch_end(epoch_number, logs) - # Un-stop (having updated best result anyway) - if epoch_number < self.min_epochs: - self.stopped_epoch = 0 - self.model.stop_training = False - - -def run_cv(args, all_data, featdims, feat2id, label_counts, id2label): - kf = KFold( - n_splits=args.cross_validation_folds, shuffle=True, random_state=args.seed - ) - scores = {"test_macro_f1": []} - if args.labels == "iobonly": - eval_labels = sorted(label_counts.keys()) - else: - eval_labels = sorted( - x for x in label_counts if x[0] == "B" and label_counts[x] >= args.min_count - ) - # NOTE: This is cheating slightly since label_counts comes from - # the entire dataset - label_weights = [ - (args.bscale if x[0] == "B" else 1.0) / label_counts[x] for x in id2label - ] - veclen = len(all_data[0][0][0][1]) - device = torch.device(args.device) - - for fold, (train_idx, dev_idx) in enumerate(kf.split(all_data)): - train_data = Subset(all_data, train_idx) - train_loader = DataLoader( - train_data, - batch_size=args.batch_size, - shuffle=True, - collate_fn=pad_collate_fn, - ) - dev_data = Subset(all_data, dev_idx) - dev_loader = DataLoader( - dev_data, batch_size=args.batch_size, collate_fn=pad_collate_fn - ) - config = { - "feat2id": feat2id, - "id2label": id2label, - "featdims": featdims, - "veclen": veclen, - "label_weights": label_weights, # Unused here but included to match RNNCRF - "hidden_size": args.hidden_size, - "labels": args.labels, - } - my_network = RNN(**config) - optimizer = optim.Adam(my_network.parameters(), lr=args.lr) - loss_function = nn.CrossEntropyLoss(weight=torch.FloatTensor(label_weights)) - model = Model( - my_network, - optimizer, - loss_function, - batch_metrics=["accuracy", "f1"], - device=device, - ) - foldfile = args.outfile.with_stem(args.outfile.stem + f"_fold{fold+1}") - with open(foldfile.with_suffix(".json"), "wt", encoding="utf-8") as outfh: - json.dump(config, outfh, ensure_ascii=False, indent=2) - callbacks = [ExponentialLR(gamma=args.gamma)] - if args.early_stopping: - callbacks.append( - ModelCheckpoint( - monitor="val_fscore_macro", - filename=str(foldfile), - mode="max", - save_best_only=True, - restore_best=True, - keep_only_last_best=True, - verbose=True, - ) - ) - callbacks.append( - MyEarlyStopping( - monitor="val_fscore_macro", - mode="max", - patience=args.patience, - min_epochs=args.min_epochs, - verbose=True, - ) - ) - model.fit_generator( - train_loader, - dev_loader, - epochs=args.nepoch, - callbacks=callbacks, - ) - ordering, sorted_test_data = zip( - *sorted(enumerate(dev_data), reverse=True, key=lambda x: len(x[1][0])) - ) - test_loader = DataLoader( - sorted_test_data, - batch_size=args.batch_size, # FIXME: Actually not relevant here - collate_fn=pad_collate_fn_predict, - ) - out = model.predict_generator(test_loader, concatenate_returns=False) - predictions = [] - lengths = [len(tokens) for tokens, _ in sorted_test_data] - for batch in out: - # numpy.transpose != torch.transpose because Reasons - batch = batch.transpose((0, 2, 1)).argmax(-1) - for length, row in zip(lengths, batch): - predictions.append(row[:length]) - del lengths[: len(batch)] - y_pred = [[id2label[x] for x in page] for page in predictions] - y_true = [[id2label[x] for x in page] for _, page in sorted_test_data] - macro_f1 = metrics.flat_f1_score( - y_true, y_pred, labels=eval_labels, average="macro", zero_division=0.0 - ) - scores["test_macro_f1"].append(macro_f1) - print("fold", fold + 1, "ALL", macro_f1) - for name in eval_labels: - label_f1 = metrics.flat_f1_score( - y_true, y_pred, labels=[name], average="micro", zero_division=0.0 - ) - scores.setdefault(name, []).append(label_f1) - print("fold", fold + 1, name, label_f1) - if not args.early_stopping: - torch.save(my_network.state_dict(), foldfile) - - with open(args.scores, "wt") as outfh: - fieldnames = [ - "Label", - "Average", - *range(1, len(scores["test_macro_f1"]) + 1), - ] - writer = csv.DictWriter(outfh, fieldnames=fieldnames) - writer.writeheader() - - def makerow(name, scores): - row = {"Label": name, "Average": np.mean(scores)} - for idx, score in enumerate(scores): - row[idx + 1] = score - return row - - row = makerow("ALL", scores["test_macro_f1"]) - writer.writerow(row) - print("average", "ALL", row["Average"]) - for name in eval_labels: - row = makerow(name, scores[name]) - writer.writerow(row) - print("average", row["Label"], row["Average"]) - - -def run_training(args, train_data, featdims, feat2id, label_counts, id2label): - train_loader = DataLoader( - train_data, - batch_size=args.batch_size, - shuffle=True, - collate_fn=pad_collate_fn, - ) - veclen = len(train_data[0][0][0][1]) - label_weights = [ - (args.bscale if x[0] == "B" else 1.0) / label_counts[x] for x in id2label - ] - device = torch.device(args.device) - config = { - "feat2id": feat2id, - "id2label": id2label, - "featdims": featdims, - "veclen": veclen, - "label_weights": label_weights, # Unused here but included to match RNNCRF - "hidden_size": args.hidden_size, - "labels": args.labels, - } - with open(args.outfile.with_suffix(".json"), "wt", encoding="utf-8") as outfh: - json.dump(config, outfh, ensure_ascii=False, indent=2) - my_network = RNN(**config) - optimizer = optim.Adam(my_network.parameters(), lr=args.lr) - loss_function = nn.CrossEntropyLoss(weight=torch.FloatTensor(label_weights)) - model = Model( - my_network, - optimizer, - loss_function, - device=device, - ) - model.fit_generator( - train_loader, - epochs=args.nepoch, - callbacks=[ - ExponentialLR(gamma=args.gamma), - ], - ) - torch.save(my_network.state_dict(), args.outfile) - - -def main(): - parser = make_argparse() - args = parser.parse_args() - logging.basicConfig(level=logging.INFO) - set_seeds(args.seed) - if args.scores is None: - args.scores = args.outfile.with_suffix(".csv") - tokenizer = None - if args.tokenize: - tokenizer = Tokenizer.from_pretrained("camembert-base") - - all_data, featdims, feat2id, label_counts, id2label = make_rnn_data( - args.csvs, - labels=args.labels, - tokenizer=tokenizer, - min_count=args.min_feat, - word_dim=args.word_dim, - feat_dim=args.feat_dim, - ) - - print("Vocabulary size:") - for feat, vals in feat2id.items(): - print(f"\t{feat}: {len(vals)}") - - if args.cross_validation_folds == 1: - run_training(args, all_data, featdims, feat2id, label_counts, id2label) - else: - run_cv(args, all_data, featdims, feat2id, label_counts, id2label) - - -if __name__ == "__main__": - main() diff --git a/scripts/train_rnn_crf.py b/scripts/train_rnn_crf.py deleted file mode 100644 index fd42ab1..0000000 --- a/scripts/train_rnn_crf.py +++ /dev/null @@ -1,368 +0,0 @@ -"""Entrainer un LSTM pour segmentation/identification""" - -import argparse -import csv -import json -import logging -import math -from pathlib import Path - -import numpy as np -import torch -import torch.optim as optim -from poutyne import ( - EarlyStopping, - ExponentialLR, - Model, - ModelCheckpoint, - set_seeds, - Accuracy, - F1, -) -from sklearn.model_selection import KFold -from sklearn_crfsuite import metrics -from torch.utils.data import DataLoader, Subset - -from alexi.segment import make_rnn_data, pad_collate_fn, pad_collate_fn_predict, RNNCRF -from tokenizers import Tokenizer - - -def make_argparse(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument( - "csvs", nargs="+", help="Fichiers CSV d'entrainement", type=Path - ) - parser.add_argument( - "--device", default="cuda:0", help="Device pour rouler l'entrainement" - ) - parser.add_argument( - "--nepoch", default=45, type=int, help="Nombre maximal d'epochs d'entrainement" - ) - parser.add_argument("--batch-size", default=32, type=int, help="Taille du batch") - parser.add_argument( - "--word-dim", default=32, type=int, help="Dimension des embeddings des mots" - ) - parser.add_argument( - "--feat-dim", default=8, type=int, help="Dimension des embeddings des traits" - ) - parser.add_argument( - "--min-feat", default=15, type=int, help="Nombre minimal d'instances d'un trait" - ) - parser.add_argument( - "--lr", default=0.01, type=float, help="Facteur d'apprentissage" - ) - parser.add_argument( - "--gamma", default=0.99, type=float, help="Coefficient de reduction du LR" - ) - parser.add_argument( - "--bscale", default=1.0, type=float, help="Facteur applique aux debuts de bloc" - ) - parser.add_argument( - "--hidden-size", default=80, type=int, help="Largeur de la couche cachee" - ) - parser.add_argument("--early-stopping", action="store_true", help="Arret anticipe") - parser.add_argument( - "--patience", default=10, type=int, help="Patience pour arret anticipe" - ) - parser.add_argument("--seed", default=1381, type=int, help="Graine aléatoire") - parser.add_argument("--labels", default="literal", help="Transformateur de classes") - parser.add_argument( - "--min-count", - default=10, - type=int, - help="Seuil d'évaluation pour chaque classification", - ) - parser.add_argument( - "-x", - "--cross-validation-folds", - default=1, - type=int, - help="Faire la validation croisée pour évaluer le modèle si plus que 1.", - ) - parser.add_argument( - "-o", - "--outfile", - help="Fichier destination pour modele", - type=Path, - default="rnn_crf.pt", - ) - parser.add_argument( - "-s", - "--scores", - help="Fichier destination pour évaluations", - ) - parser.add_argument( - "-t", "--tokenize", action="store_true", help="Tokeniser les mots" - ) - parser.add_argument( - "-i", - "--init-model", - help="Fichier modele pour initialisation", - default="rnn.pt", - type=Path, - ) - parser.add_argument( - "--freeze", action="store_true", help="Geler les parametres initialisees" - ) - parser.add_argument( - "--constrain", action="store_true", help="Interdire les transitions impossibles" - ) - return parser - - -class MyCRFLoss: - def __init__(self, crf): - self.crf = crf - - def __call__(self, returns, y_true): - logits, _labels, mask = returns - # CRF requires masked labels to be valid indices - y_true[~mask] = 0 - return -self.crf(logits, y_true, mask) - - -def CRFMetric(cls, *args, **kwargs): - metric = cls(*args, **kwargs) - - def crf_forward(y_pred, y_true): - _score, preds, mask = y_pred - # For some unknown reason poutyne expects logits/probabilities and not labels here - n_labels = y_true.max() + 1 - fake_logits = torch.zeros(y_true.shape + (n_labels,), device=y_true.device) - for idx, labels in enumerate(preds): - fake_logits[idx, range(len(labels)), labels] = 1.0 - # Have to set masked labels back to the ignore index - y_true[~mask] = metric.ignore_index - # Also the shape is weird because pytorch is weird - return cls.forward(metric, fake_logits.transpose(1, -1), y_true) - - metric.forward = crf_forward - return metric - - -def freeze_rnn(my_network): - for layer in my_network.embedding_layers.values(): - layer.requires_grad_(False) - my_network.lstm_layer.requires_grad_(False) - my_network.output_layer.requires_grad_(False) - - -def run_cv(args, all_data, featdims, feat2id, label_counts, label_weights, id2label): - kf = KFold( - n_splits=args.cross_validation_folds, shuffle=True, random_state=args.seed - ) - scores = {"test_macro_f1": []} - if args.labels == "iobonly": - eval_labels = sorted(label_counts.keys()) - else: - eval_labels = sorted( - x for x in label_counts if x[0] == "B" and label_counts[x] >= args.min_count - ) - veclen = len(all_data[0][0][0][1]) - device = torch.device(args.device) - - for fold, (train_idx, dev_idx) in enumerate(kf.split(all_data)): - train_data = Subset(all_data, train_idx) - train_loader = DataLoader( - train_data, - batch_size=args.batch_size, - shuffle=True, - collate_fn=pad_collate_fn, - ) - dev_data = Subset(all_data, dev_idx) - dev_loader = DataLoader( - dev_data, batch_size=args.batch_size, collate_fn=pad_collate_fn - ) - config = { - "feat2id": feat2id, - "id2label": id2label, - "featdims": featdims, - "veclen": veclen, - "label_weights": label_weights, - "hidden_size": args.hidden_size, - "labels": args.labels, - "constrain": args.constrain, - } - foldfile = args.outfile.with_stem(args.outfile.stem + f"_fold{fold+1}") - with open(foldfile.with_suffix(".json"), "wt", encoding="utf-8") as outfh: - json.dump(config, outfh, ensure_ascii=False, indent=2) - my_network = RNNCRF(**config) - if args.init_model: - fold_init = args.init_model.with_stem( - args.init_model.stem + f"_fold{fold+1}" - ) - state_dict = torch.load(fold_init) - my_network.load_state_dict(state_dict, strict=False) - if args.freeze: - freeze_rnn(my_network) - optimizer = optim.Adam(my_network.parameters(), lr=args.lr) - loss_function = MyCRFLoss(my_network.crf_layer) - model = Model( - my_network, - optimizer, - loss_function, - batch_metrics=[CRFMetric(Accuracy), CRFMetric(F1)], - device=device, - ) - callbacks = [ExponentialLR(gamma=args.gamma)] - if args.early_stopping: - callbacks.append( - ModelCheckpoint( - monitor="val_fscore_macro", - filename=str(foldfile), - mode="max", - save_best_only=True, - restore_best=True, - keep_only_last_best=True, - verbose=True, - ) - ) - callbacks.append( - EarlyStopping( - monitor="val_fscore_macro", - mode="max", - patience=args.patience, - verbose=True, - ) - ) - model.fit_generator( - train_loader, - dev_loader, - epochs=args.nepoch, - callbacks=callbacks, - ) - ordering, sorted_test_data = zip( - *sorted(enumerate(dev_data), reverse=True, key=lambda x: len(x[1][0])) - ) - test_loader = DataLoader( - sorted_test_data, - batch_size=args.batch_size, # FIXME: Actually not relevant here - collate_fn=pad_collate_fn_predict, - ) - out = model.predict_generator(test_loader, concatenate_returns=False) - predictions = [] - for batch in out: - _logits, tags, _mask = batch - predictions.extend(tags) - y_pred = [[id2label[x] for x in page] for page in predictions] - y_true = [[id2label[x] for x in page] for _, page in sorted_test_data] - macro_f1 = metrics.flat_f1_score( - y_true, y_pred, labels=eval_labels, average="macro", zero_division=0.0 - ) - scores["test_macro_f1"].append(macro_f1) - print("fold", fold + 1, "ALL", macro_f1) - for name in eval_labels: - label_f1 = metrics.flat_f1_score( - y_true, y_pred, labels=[name], average="micro", zero_division=0.0 - ) - scores.setdefault(name, []).append(label_f1) - print("fold", fold + 1, name, label_f1) - if not args.early_stopping: - torch.save(my_network.state_dict(), foldfile) - - with open(args.scores, "wt") as outfh: - fieldnames = [ - "Label", - "Average", - *range(1, len(scores["test_macro_f1"]) + 1), - ] - writer = csv.DictWriter(outfh, fieldnames=fieldnames) - writer.writeheader() - - def makerow(name, scores): - row = {"Label": name, "Average": np.mean(scores)} - for idx, score in enumerate(scores): - row[idx + 1] = score - return row - - row = makerow("ALL", scores["test_macro_f1"]) - writer.writerow(row) - print("average", "ALL", row["Average"]) - for name in eval_labels: - row = makerow(name, scores[name]) - writer.writerow(row) - print("average", row["Label"], row["Average"]) - - -def run_training(args, train_data, featdims, feat2id, label_weights, id2label): - train_loader = DataLoader( - train_data, - batch_size=args.batch_size, - shuffle=True, - collate_fn=pad_collate_fn, - ) - veclen = len(train_data[0][0][0][1]) - device = torch.device(args.device) - config = { - "feat2id": feat2id, - "id2label": id2label, - "featdims": featdims, - "veclen": veclen, - "label_weights": label_weights, - "hidden_size": args.hidden_size, - "labels": args.labels, - "constrain": args.constrain, - } - with open(args.outfile.with_suffix(".json"), "wt", encoding="utf-8") as outfh: - json.dump(config, outfh, ensure_ascii=False, indent=2) - my_network = RNNCRF(**config) - if args.init_model: - state_dict = torch.load(args.init_model) - my_network.load_state_dict(state_dict, strict=False) - if args.freeze: - freeze_rnn(my_network) - optimizer = optim.Adam(my_network.parameters(), lr=args.lr) - loss_function = MyCRFLoss(my_network.crf_layer) - model = Model( - my_network, - optimizer, - loss_function, - device=device, - ) - model.fit_generator( - train_loader, - epochs=args.nepoch, - callbacks=[ - ExponentialLR(gamma=args.gamma), - ], - ) - torch.save(my_network.state_dict(), args.outfile) - - -def main(): - parser = make_argparse() - args = parser.parse_args() - logging.basicConfig(level=logging.INFO) - set_seeds(args.seed) - if args.scores is None: - args.scores = args.outfile.with_suffix(".csv") - tokenizer = None - if args.tokenize: - tokenizer = Tokenizer.from_pretrained("camembert-base") - - all_data, featdims, feat2id, label_counts, id2label = make_rnn_data( - args.csvs, - labels=args.labels, - tokenizer=tokenizer, - min_count=args.min_feat, - word_dim=args.word_dim, - feat_dim=args.feat_dim, - ) - # Note that weights must be greater than 1.0 for training to work - label_weights = [ - (args.bscale if x[0] == "B" else 1.0) * math.exp(1.0 / label_counts[x]) - for x in id2label - ] - - print("Vocabulary size:") - for feat, vals in feat2id.items(): - print(f"\t{feat}: {len(vals)}") - - if args.cross_validation_folds == 1: - run_training(args, all_data, featdims, feat2id, label_weights, id2label) - else: - run_cv(args, all_data, featdims, feat2id, label_counts, label_weights, id2label) - - -if __name__ == "__main__": - main()