Dataloader supporting trainval - remaining memory issue

DUDE_loader.py

@@ -17,7 +17,6 @@
 import os
 import copy
 import json
-from pathlib import Path
 from typing import List
 import pdf2image
 from tqdm import tqdm
@@ -29,7 +28,7 @@ import datasets
 _CITATION = """
 @inproceedings{dude2023icdar,
     title={ICDAR 2023 Challenge on Document UnderstanDing of Everything (DUDE)},
-    author={Van Landeghem, Jordy  et . al.},
+    author={Van Landeghem, Jordy et . al.},
     booktitle={Proceedings of the ICDAR},
     year={2023}
 }
@@ -38,26 +37,53 @@ _CITATION = """
 _DESCRIPTION = """\
 DUDE requires models to reason and understand about document layouts in multi-page images/PDFs to answer questions about them.
 Specifically, models need to incorporate a new modality of layout present in the images/PDFs and reason
-over it to answer DUDE questions. DUDE Contains X questions and Y and ...
-"""
+over it to answer DUDE questions. 
+"""  # DUDE Contains X questions and Y and ...
 
 _HOMEPAGE = "https://rrc.cvc.uab.es/?ch=23"
 
 _LICENSE = "CC BY 4.0"
 
-_SPLITS = ["sample"]  # ["train", "val", "test"]
+_SPLITS = ["train", "val"]
+
+_URLS = {
+    # "binaries": "https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_binaries.tar.gz", #
+    # "annotations": "https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_dataset-sample_gt.json" #"
+    "binaries": "https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_train-val-test_binaries.tar.gz",  # DUDE_train-val-test_binaries.tar.gz
+    "annotations": "https://zenodo.org/record/7600505/files/DUDE_gt_release-candidate_trainval_exabs.json?download=1",
+}
+
+#'0017b64bd017f06db47e56a6a113e22e'
+SKIP_DOC_IDS = ["ef03364aa27a0987c9870472e312aceb", "5c5a5880e6a73b4be2315d506ab0b15b"]
+
+
+def parse_bbox(bbox):
+
+    if bbox in [[], [[]]]:
+        return None
+
+    answers_page_bounding_boxes = []
+
+    if isinstance(bbox[0], list):
+        bbox = bbox[0]
 
-_URLS = {}
-for split in _SPLITS:
-    _URLS[
-        f"{split}_annotations"
-    ] = f"https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_{split}_dataset.json"
-    _URLS[
-        f"{split}_pdfs"
-    ] = f"https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_{split}_pdfs.tar.gz"
-    _URLS[
-        f"{split}_OCR"
-    ] = f"https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_{split}_OCR.tar.gz"
+    keys = ["left", "top", "width", "height", "page"]
+
+    for page_bb in bbox:
+        if len(page_bb) == 0:
+            continue
+        page_bb = {key: page_bb[key] for key in keys}
+        """
+        if page_bb.get("label"):
+            del page_bb["label"]
+        if page_bb.get("error"):
+            del page_bb["error"]
+        if page_bb.get("multipage_box"):
+            del page_bb["multipage_box"]
+        #assert all(key in page_bb for key in keys)
+        """
+        answers_page_bounding_boxes.append(page_bb)
+    return answers_page_bounding_boxes
 
 
 def batched_conversion(pdf_file):
@@ -133,21 +159,27 @@ class DUDE(datasets.GeneratorBasedBuilder):
         self, dl_manager: datasets.DownloadManager
     ) -> List[datasets.SplitGenerator]:
 
+        annotations = json.load(open(_URLS[f"annotations"], "r"))
+        # binaries_archive = dl_manager.iter_archive(binaries_path)
+
+        if self.config.data_dir: #unpacked it to a custom directory
+            binary_extraction_path = self.config.data_dir
+        else:
+            binaries_path = dl_manager.download(_URLS['binaries'])
+            binary_extraction_path = dl_manager.extract(binaries_path)
+            binary_extraction_path += "/home/jordy/Downloads/" + _URLS["binaries"].split("/")[
+                -1
+            ].replace(
+                ".tar.gz", ""
+            )  # weird unpacking behaviour
+
         splits = []
-        for split in _SPLITS:
-            annotations = {}
-            if f"{split}_annotations" in _URLS:  # blind test set
-                annotations = json.load(open(_URLS[f"{split}_annotations"], "r"))
-            pdfs_archive_path = dl_manager.download(_URLS[f"{split}_pdfs"])
-            pdfs_archive = dl_manager.iter_archive(pdfs_archive_path)
-            OCR_archive_path = dl_manager.download(_URLS[f"{split}_OCR"])
-            OCR_archive = dl_manager.iter_archive(OCR_archive_path)
+        for split in _SPLITS:  # split archive
             splits.append(
                 datasets.SplitGenerator(
                     name=split,
                     gen_kwargs={
-                        "pdfs_archive": pdfs_archive,
-                        "OCR_archive": OCR_archive,
+                        "binary_extraction_path": binary_extraction_path,
                         "annotations": annotations,
                         "split": split,
                     },
@@ -155,41 +187,47 @@ class DUDE(datasets.GeneratorBasedBuilder):
             )
         return splits
 
-    def _generate_examples(self, pdfs_archive, OCR_archive, annotations, split):
-        def retrieve_doc(pdfs_archive, docid):
-            for file_path, file_obj in pdfs_archive:
-                path, ext = file_path.split(".")
-                md5 = path.split("/")[-1]
+    def _generate_examples(self, binary_extraction_path, annotations, split):
+        def retrieve_doc(docid):
+            extracted_path = os.path.join(binary_extraction_path, "PDF", split, docid + ".pdf")
+            with open(extracted_path, "rb") as f:
+                return f.read()
 
-                if md5 == docid:
-                    # images = pdf2image.convert_from_bytes(file_obj.read())
-                    return file_obj.read()  # binary
-
-        def retrieve_OCR(OCR_archive, docid):
-            for file_path, file_obj in OCR_archive:
-                # /DUDE_sample_OCR/OCR/Amazon Textract/md5_{original,due}.json
-                path, ext = file_path.split(".")
-                filename = path.split("/")[-1]
-                md5 = filename.split("_")[0]
+        def retrieve_OCR(docid, ocr_engine="Amazon", format="original"):
+            extracted_path = os.path.join(
+                binary_extraction_path, "OCR", ocr_engine, docid + f"_{format}.json"
+            )
 
-                if md5 == docid and "original" in filename:
-                    return json.loads(file_obj.read())  # binary
+            with open(extracted_path, "rb") as f:
+                return f.read()
 
         question = self.info.features["question"]
         answers = self.info.features["answers"]
 
         extensions = {"pdf", "PDF"}
 
+        annotations = [x for x in annotations if x["data_split"] == split]
+
         for i, a in enumerate(annotations):
             a["data_split"] = split
-            a["document"] = retrieve_doc(pdfs_archive, a["docId"])
-            a["OCR"] = retrieve_OCR(OCR_archive, a["docId"])
+            if a["docId"] in SKIP_DOC_IDS:
+                continue
+            a["document"] = retrieve_doc(a["docId"])
+            a["OCR"] = retrieve_OCR(a["docId"])
+
+            a["answers_page_bounding_boxes"] = parse_bbox(a["answers_page_bounding_boxes"])
+            yield i, a
 
+            """
             # FIXES for faulty generation
-            #a.pop("answers_page_bounding_boxes")  # fix later            
-            if a["answers_page_bounding_boxes"] in [ [], [[]] ]:
+            # a.pop("answers_page_bounding_boxes")  # fix later
+            if a["answers_page_bounding_boxes"] in [[], [[]]]:
                 a["answers_page_bounding_boxes"] = None
             else:
-                if isinstance(a['answers_page_bounding_boxes'][0], list):
-                    a["answers_page_bounding_boxes"] = a['answers_page_bounding_boxes'][0]                    
-            yield i, a
+                if isinstance(a["answers_page_bounding_boxes"][0], list):
+                    a["answers_page_bounding_boxes"] = a["answers_page_bounding_boxes"][0]
+            # if i == 2303:
+            try:
+            except Exception as e:
+                print(f"Something wrong in {split}-{i} {e}")
+            """

README.md

@@ -1,3 +1,4 @@
 ---
 license: cc-by-4.0
 ---
+

data/DUDE_dataset-sample_gt.json

@@ -1,13 +1,24 @@
-[{'questionId': '0017b64bd017f06db47e56a6a113e22e_bb55e0af451429f2dcae69e6d0713616',
-  'question': 'What is the first and last name of the indvidual in list # 539?',
-  'answers': ['Ajay Dev Goud'],
-  'answers_page_bounding_boxes': [[{'left': 353,
-     'top': 409,
-     'width': 198,
-     'height': 26,
-     'page': 8}]],
-  'answers_variants': [],
-  'answer_type': 'extractive',
-  'docId': '0017b64bd017f06db47e56a6a113e22e',
-  'data_split': 'train'}
+[
+  {
+        "questionId": "0017b64bd017f06db47e56a6a113e22e_bb55e0af451429f2dcae69e6d0713616",
+        "question": "What is the first and last name of the indvidual in list # 539?",
+        "answers": [
+            "Ajay Dev Goud"
+        ],
+        "answers_page_bounding_boxes": [
+            [
+                {
+                    "left": 353,
+                    "top": 409,
+                    "width": 198,
+                    "height": 26,
+                    "page": 8
+                }
+            ]
+        ],
+        "answers_variants": [],
+        "answer_type": "extractive",
+        "docId": "0017b64bd017f06db47e56a6a113e22e",
+        "data_split": "train"
+    }
 ]

test_loader.py

@@ -19,10 +19,10 @@ from datasets import load_dataset
 
 from codetiming import Timer
 
-for binding in ["dict_PDF", "dict_annotations (new)"]:
+for binding in ["dict_annotations (new)"]: #"dict_PDF", 
     with Timer(name=f"{binding}", text=binding + " Elapsed time: {:.4f} seconds"):
         if binding == "dict_annotations (new)":
-            ds = load_dataset("../DUDE_loader/DUDE_loader.py")
+            ds = load_dataset("../DUDE_loader/DUDE_loader.py", data_dir="/home/jordy/Downloads/DUDE_train-val-test_binaries", writer_batch_size=10) #ignore_verifications=True,
         else:
             ds = load_dataset("jordyvl/DUDE_loader", revision='db20bbf751b14e14e8143170bc201948ef5ac83c')