Update artwork_for_sdxl.py

artwork_for_sdxl.py

@@ -0,0 +1,88 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""ImageNet-Sketch data set for evaluating model's ability in learning (out-of-domain) semantics at ImageNet scale"""
+
+import os
+
+import pandas as pd
+import datasets
+from datasets.tasks import ImageClassification
+
+# from .classes import IMAGENET2012_CLASSES
+
+
+_HOMEPAGE = "https://huggingface.co/datasets/AIPI540/test2/tree/main"
+
+_CITATION = """\
+@inproceedings{wang2019learning,
+        title={Learning Robust Global Representations by Penalizing Local Predictive Power},
+        author={Wang, Haohan and Ge, Songwei and Lipton, Zachary and Xing, Eric P},
+        booktitle={Advances in Neural Information Processing Systems},
+        pages={10506--10518},
+        year={2019}
+}
+"""
+
+_DESCRIPTION = """\
+Artwork Images, to predict the year of the artwork created.
+"""
+
+_URL = "https://huggingface.co/datasets/AIPI540/Art2/resolve/main/final_art_data.parquet"
+
+class Artwork(datasets.GeneratorBasedBuilder):
+    """Artwork Images - a dataset of centuries of Images classes"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "label": datasets.features.ClassLabel(names=classes),
+                    "image_data": datasets.Value("binary"),
+                }
+            ),
+            supervised_keys=("label","image_data"),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            task_templates=[ImageClassification(image_column="image_data", label_column="label")],
+        )
+
+    def _split_generators(self, dl_manager):
+        data_files = dl_manager.download_and_extract(_URL)
+        df = pd.read_parquet(data_files, engine='pyarrow')
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "files": df,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, files):
+        cnt=0
+        for path in files.itertuples():
+            print(cnt)
+            cnt+=1
+            print(path)
+            print(path.label)
+            print(type(path.label))
+            print(path.image_data)
+            print(type(path.image_data))
+            yield {
+              "label": classes[(path.label)],
+              "image_data": path.image_data,
+            }