loadingscript (#5)

- Adding loading script (47645d8102e494e346512cc8e577b8da28bd6736)
- Basic setup for pulling (35ec7f3402f68e6afeb22bde2006f5b1955b80b0)
- Label files (90f7b96c4f74470d4cd86bc06adca8c62b75080a)

.gitattributes

@@ -56,3 +56,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Repo specific
 labels.csv filter=lfs diff=lfs merge=lfs -text
 labels_raw.json filter=lfs diff=lfs merge=lfs -text
+labels_test.csv filter=lfs diff=lfs merge=lfs -text
+labels_train.csv filter=lfs diff=lfs merge=lfs -text
+labels_raw filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1 @@
+test*

labels.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:27866cd0317906ce4f17b5a25609d62d7dee21dffe3183de3cfc8ec0b8ec8105
-size 3595889996
+oid sha256:a49b0dfbc6791a093ca4f5ea494bf9ce7c0b38ec7dc3dd528f3cb215c2ccc941
+size 3989805491

labels_raw.json → labels_raw.json.tar.gz

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:168e4571e61a489453f8262108557b8fe3d07b4e2b341fbef5340181b1ff663d
-size 3993693859
+oid sha256:9b73aa96821ec151fd2da7ef66ee93c1218d2052e536f30c56eb62b7f266f31e
+size 471437296

labels_raw.tar.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fdfccc23b41dc268ab608d08662b9f90115fcb600423dad13dd87956063de5db
+size 424277514

labels_test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f3fb37cbaa31b31acb6dd7c37b2fd95b21d2d1ff977133b7f355f294ff7b9443
+size 1170590470

labels_train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:718bddbe59318b16d777d0f3f674ac06e1d97b0468b4e9ae152291dd3e8d73cf
+size 2820756387

plantorgans.py

@@ -0,0 +1,100 @@
+import datasets
+import os
+import json
+
+_DESCRIPTION = """Photos of various plants with their major, above ground organs labeled. Includes labels for stem, leafs, fruits and flowers."""
+
+_HOMEPAGE = "https://huggingface.co/datasets/jpodivin/plantorgans"
+
+_CITATION = """"""
+
+_LICENSE = "MIT"
+
+_NAMES = [
+    'Leaf',
+    'Stem',
+    'Flower',
+    'Fruit',
+]
+
+_BASE_URL = "https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/"
+_TRAIN_URLS = [_BASE_URL + f"sourcedata_labeled.tar.{i:02}" for i in range(0, 8)]
+_TEST_URLS = [_BASE_URL + f"sourcedata_labeled.tar.{i:02}" for i in range(8, 12)]
+_METADATA_URLS = {
+    'train': 'https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/labels_train.csv',
+    'test': 'https://huggingface.co/datasets/jpodivin/plantorgans/resolve/main/labels_test.csv'
+}
+
+
+class PlantOrgansConfig(datasets.BuilderConfig):
+    """Builder Config for PlantOrgans"""
+ 
+    def __init__(self, data_url, metadata_urls, splits, **kwargs):
+        """BuilderConfig for PlantOrgans.
+        Args:
+          data_url: `string`, url to download the zip file from.
+          metadata_urls: dictionary with keys 'train' and 'validation' containing the archive metadata URLs
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.data_url = data_url
+        self.metadata_urls = metadata_urls
+        self.splits = splits
+
+
+class PlantOrgans(datasets.GeneratorBasedBuilder):
+    """Plantorgans dataset
+    """
+    BUILDER_CONFIGS = [
+        PlantOrgansConfig(
+            name="semantic_segmentation_full",
+            description="This configuration contains segmentation masks.",
+            data_url=_BASE_URL,
+            metadata_urls=_METADATA_URLS,
+            splits=['train', 'test'],
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+        description=_DESCRIPTION,
+        features=datasets.Features(
+            {
+                "image": datasets.Image(),
+                "annotation": datasets.ClassLabel(names=_NAMES),
+            }
+        ),
+        supervised_keys=("image", "annotation"),
+        homepage=_HOMEPAGE,
+        citation=_CITATION,
+        license=_LICENSE,
+    )
+
+
+    def _split_generators(self, dl_manager):
+        train_archive_path = dl_manager.download_and_extract(_TRAIN_URLS)
+        test_archive_path = dl_manager.download_and_extract(_TEST_URLS)
+
+        split_metadata_paths = dl_manager.download(_METADATA_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(os.path.join(train_archive_path, 'sourcedata/labeled')),
+                    "metadata_path": split_metadata_paths["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "images": dl_manager.iter_archive(os.path.join(test_archive_path, 'sourcedata/labeled')),
+                    "metadata_path": split_metadata_paths["test"],
+                },
+            ),
+        ]
+    def _generate_examples(self, images, metadata_path):
+        
+        with open(metadata_path, 'w', encoding='utf-8') as fp:
+            metadata = json.load(fp)
+            images = metadata['image']
+            annotations = metadata['annotations']