Link established to public test set

DUDE_loader.py

@@ -14,15 +14,14 @@
 # limitations under the License.
 """DUDE dataset loader"""
 
-import os
 import copy
 import json
+import os
 from typing import List, Literal
-import pdf2image
-from tqdm import tqdm
 
 import datasets
-
+import pdf2image
+from tqdm import tqdm
 
 _CITATION = """
 @inproceedings{dude2023icdar,
@@ -43,16 +42,19 @@ _HOMEPAGE = "https://rrc.cvc.uab.es/?ch=23"
 
 _LICENSE = "CC BY 4.0"
 
-_SPLITS = ["train", "val"]
+_SPLITS = ["train", "val", "test"]
 
 _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/7680589/files/DUDE_gt_release-candidate.json?download=1", #_trainval
+    "binaries": "https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_train-val-test_binaries.tar.gz",
+    "annotations": "https://zenodo.org/record/7680617/files/2023-03-09_DUDE_gt_release-candidate_PUBLIC.json?download=1"
+    # "blind": "/home/jordy/code/DUchallenge/DUDEeval/gt/2023-03-07_DUDE_gt_release-candidate_NOTSHARABLE.json",
 }
 
-SKIP_DOC_IDS = ["ef03364aa27a0987c9870472e312aceb", "5c5a5880e6a73b4be2315d506ab0b15b"]
+SKIP_DOC_IDS = [
+    "nan",
+    "ef03364aa27a0987c9870472e312aceb",
+    "5c5a5880e6a73b4be2315d506ab0b15b",
+]
 
 
 def parse_bbox(bbox):
@@ -79,14 +81,15 @@ def batched_conversion(pdf_file):
     info = pdf2image.pdfinfo_from_path(pdf_file, userpw=None, poppler_path=None)
     maxPages = info["Pages"]
 
-    logger.info(f"{pdf_file} has {str(maxPages)} pages")
-
     images = []
 
     for page in range(1, maxPages + 1, 10):
         images.extend(
             pdf2image.convert_from_path(
-                pdf_file, dpi=200, first_page=page, last_page=min(page + 10 - 1, maxPages)
+                pdf_file,
+                dpi=200,
+                first_page=page,
+                last_page=min(page + 10 - 1, maxPages),
             )
         )
     return images
@@ -139,7 +142,7 @@ def builder_configs(version):
 class DUDE(datasets.GeneratorBasedBuilder):
     """DUDE dataset."""
 
-    VERSION = datasets.Version("0.0.1")
+    VERSION = datasets.Version("1.0.7")
 
     BUILDER_CONFIGS = builder_configs(VERSION)
 
@@ -188,14 +191,17 @@ 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 "blind" in _URLS and os.path.exists(_URLS[f"blind"]):
+            annotations = json.load(open(_URLS[f"blind"], "r"))
+        else:
+            annotations = json.load(open(_URLS[f"annotations"], "r"))
 
         if self.config.data_dir:  # when unpacked 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)
+            # binaries_archive = dl_manager.iter_archive(binaries_path)
 
         splits = []
         for split in _SPLITS:
@@ -213,7 +219,9 @@ class DUDE(datasets.GeneratorBasedBuilder):
 
     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")
+            extracted_path = os.path.join(
+                binary_extraction_path, "PDF", split, docid + ".pdf"
+            )
             return extracted_path
 
         def retrieve_OCR(docid, ocr_engine="Amazon", format="original"):
@@ -222,14 +230,27 @@ class DUDE(datasets.GeneratorBasedBuilder):
             )
             return extracted_path
 
-        annotations = [x for x in annotations if x["data_split"] == split]
+        split_condition = (
+            lambda x, split: bool(x["data_split"] == split)
+            if split in ["train", "val"]
+            else bool(split in x["data_split"])
+        )  # test, test2; only relevant for blind set
+        annotations = [x for x in annotations if split_condition(x, split)]
 
         for i, a in enumerate(annotations):
             if a["docId"] in SKIP_DOC_IDS:
                 continue
             a = dict(a)
             a["data_split"] = split
-            a["answers_page_bounding_boxes"] = parse_bbox(a["answers_page_bounding_boxes"])
+            if not "answers" in a.keys():  # test set has no ground truth provided
+                a["answers"] = None
+                a["answers_variants"] = None
+                a["answer_type"] = None
+                a["answers_page_bounding_boxes"] = None
+            else:
+                a["answers_page_bounding_boxes"] = parse_bbox(
+                    a.get("answers_page_bounding_boxes", [])
+                )
             docpath = retrieve_doc(a["docId"])
             ocrpath = retrieve_OCR(a["docId"])
             if self.config.binary_mode: