Upload fashion.py

fashion.py

@@ -0,0 +1,106 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""SQUAD: The Stanford Question Answering Dataset."""
+
+
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{2016arXiv160605250R,
+       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
+                 Konstantin and {Liang}, Percy},
+        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
+      journal = {arXiv e-prints},
+         year = 2016,
+          eid = {arXiv:1606.05250},
+        pages = {arXiv:1606.05250},
+archivePrefix = {arXiv},
+       eprint = {1606.05250},
+}
+"""
+
+_DESCRIPTION = """\
+Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
+dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
+articles, where the answer to every question is a segment of text, or span, \
+from the corresponding reading passage, or the question might be unanswerable.
+"""
+
+_URL = "https://huggingface.co/datasets/proan/fashion/resolve/main/images.tar.gz"
+
+
+
+
+class Fashion(datasets.GeneratorBasedBuilder):
+    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
+
+
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int64"),
+                    "color": datasets.Value("string"),
+                    "description": datasets.Value("string"),
+                    "shop_id": datasets.Value("string"),
+                    "year": datasets.Value("float64"),
+                    "image": datasets.Image(),  
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/proan/fashion",
+            citation=_CITATION,
+            
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download_and_extract(_URL)
+        image_iters = dl_manager.iter_archive(path)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images": image_iters}),
+        
+        ]
+
+    def _generate_examples(self, images):
+  
+        idx = 0
+        #iterate through images
+        for filepath, image in images:
+            yield idx, {
+                "image": {"path": filepath, "image": image.read()},
+                "id": datasets.Value("int64"),
+                "color": datasets.Value("string"),
+                "description": datasets.Value("string"),
+                "shop_id": datasets.Value("string"),
+                "year": datasets.Value("float64"),
+                "image": datasets.Image(),  
+            }
+            idx +=1
+
+