wintercoming6 commited on
Commit
46f2eeb
1 Parent(s): d3b2b0d

Update artwork_for_sdxl.py

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Files changed (1) hide show
  1. artwork_for_sdxl.py +10 -10
artwork_for_sdxl.py CHANGED
@@ -39,7 +39,7 @@ _DESCRIPTION = """\
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  Artwork Images, to predict the year of the artwork created.
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  """
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- _URL = "https://huggingface.co/datasets/AIPI540/Art2/resolve/main/final_art_data.parquet"
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  class Artwork(datasets.GeneratorBasedBuilder):
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  """Artwork Images - a dataset of centuries of Images classes"""
@@ -49,20 +49,20 @@ class Artwork(datasets.GeneratorBasedBuilder):
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  description=_DESCRIPTION,
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  features=datasets.Features(
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  {
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- "label": datasets.features.ClassLabel(names=classes),
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- "image_data": datasets.Value("binary"),
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  }
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  ),
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  supervised_keys=("label","image_data"),
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  homepage=_HOMEPAGE,
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  citation=_CITATION,
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- task_templates=[ImageClassification(image_column="image_data", label_column="label")],
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  )
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  def _split_generators(self, dl_manager):
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  data_files = dl_manager.download_and_extract(_URL)
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- df = pd.read_parquet(data_files, engine='pyarrow')
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-
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  return [
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  datasets.SplitGenerator(
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  name=datasets.Split.TRAIN,
@@ -78,11 +78,11 @@ class Artwork(datasets.GeneratorBasedBuilder):
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  print(cnt)
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  cnt+=1
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  print(path)
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- print(path.label)
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- print(type(path.label))
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  print(path.image_data)
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  print(type(path.image_data))
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  yield {
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- "label": classes[(path.label)],
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- "image_data": path.image_data,
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  }
 
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  Artwork Images, to predict the year of the artwork created.
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  """
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+ _URL = "https://huggingface.co/datasets/wintercoming6/artwork_for_sdxl/resolve/main/metadata.jsonl"
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  class Artwork(datasets.GeneratorBasedBuilder):
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  """Artwork Images - a dataset of centuries of Images classes"""
 
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  description=_DESCRIPTION,
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  features=datasets.Features(
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  {
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+ "prompt": str,
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+ "image_data": Image,
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  }
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  ),
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  supervised_keys=("label","image_data"),
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  homepage=_HOMEPAGE,
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  citation=_CITATION,
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+ task_templates=[ImageClassification(image_column="image_data", label_column="prompt")],
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  )
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  def _split_generators(self, dl_manager):
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  data_files = dl_manager.download_and_extract(_URL)
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+ df = pd.read_json(data_files, lines=True)
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+
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  return [
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  datasets.SplitGenerator(
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  name=datasets.Split.TRAIN,
 
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  print(cnt)
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  cnt+=1
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  print(path)
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+ print(path.prompt)
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+ print(type(path.prompt))
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  print(path.image_data)
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  print(type(path.image_data))
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  yield {
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+ "prompt": path.prompt,
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+ "image_data": Image.open(path.image_data),
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  }