Make dataset streamable (#1)

- Make dataset streamable (96b39edc32669adf984fc97a8967b2c95296808f)

illustrated_ads.py

@@ -14,10 +14,10 @@
 """Dataset of illustrated and non illustrated 19th Century newspaper ads."""
 
 import ast
+import os
 import pandas as pd
 import datasets
 from PIL import Image
-from pathlib import Path
 
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
@@ -46,6 +46,19 @@ _LICENSE = "Public Domain"
 
 _URLS = "https://zenodo.org/record/5838410/files/images.zip?download=1"
 
+_DTYPES = {
+ "page_seq_num": "int64",
+ "edition_seq_num": "int64",
+ "batch": "string",
+ "lccn": "string",
+ "score": "float64",
+ "place_of_publication": "string",
+ "name": "string",
+ "publisher": "string",
+ "url": "string",
+ "page_url": "string",
+}
+
 
 # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
 class IllustratedAds(datasets.GeneratorBasedBuilder):
@@ -94,48 +107,43 @@ class IllustratedAds(datasets.GeneratorBasedBuilder):
  )
 
  def _split_generators(self, dl_manager):
- data_dir = dl_manager.download_and_extract(_URLS)
- return [
- datasets.SplitGenerator(
- name=datasets.Split.TRAIN,
- gen_kwargs={
- "data_dir": Path(data_dir),
- },
- ),
- ]
-
- def _generate_examples(self, data_dir):
- dtypes = {
- "page_seq_num": "int64",
- "edition_seq_num": "int64",
- "batch": "string",
- "lccn": "string",
- "score": "float64",
- "place_of_publication": "string",
- "name": "string",
- "publisher": "string",
- "url": "string",
- "page_url": "string",
- }
+ images = dl_manager.download_and_extract(_URLS)
+ annotations = dl_manager.download(
+ [
+ "https://zenodo.org/record/5838410/files/ads.csv?download=1",
+ "https://zenodo.org/record/5838410/files/sample.csv?download=1"
+ ]
+ )
  df_labels = pd.read_csv(
- "https://zenodo.org/record/5838410/files/ads.csv?download=1", index_col=0
+ annotations[0], index_col=0
  )
  df_metadata = pd.read_csv(
- "https://zenodo.org/record/5838410/files/sample.csv?download=1",
+ annotations[1],
  index_col=0,
- dtype=dtypes,
+ dtype=_DTYPES,
  )
  df_metadata["file"] = df_metadata.filepath.str.replace("/", "_")
  df_metadata = df_metadata.set_index("file", drop=True)
  df = df_labels.join(df_metadata)
  df = df.reset_index()
- data = df.to_dict(orient="records")
- for id_, row in enumerate(data):
+ annotations = df.to_dict(orient="records")
+ return [
+ datasets.SplitGenerator(
+ name=datasets.Split.TRAIN,
+ gen_kwargs={
+ "images": images,
+ "annotations": annotations,
+ },
+ ),
+ ]
+
+ def _generate_examples(self, images, annotations):
+ for id_, row in enumerate(annotations):
  box = ast.literal_eval(row["box"])
  row["box"] = box
  row.pop("filepath")
  ocr = " ".join(ast.literal_eval(row["ocr"]))
  row["ocr"] = ocr
  image = row["file"]
- row["image"] = Image.open(Path(data_dir / image))
+ row["image"] = os.path.join(images, image)
  yield id_, row