Multiple configs

Signed-off-by: Jiri Podivin <[email protected]>

plantorgans.py

@@ -31,15 +31,15 @@ _PANOPTIC_METADATA_URLS = {
 class PlantOrgansConfig(datasets.BuilderConfig):
     """Builder Config for PlantOrgans"""
  
-    def __init__(self, data_url, metadata_urls, splits, **kwargs):
+    def __init__(self, data_urls, metadata_urls, splits, **kwargs):
         """BuilderConfig for PlantOrgans.
         Args:
-          data_url: `string`, url to download the zip file from.
+          data_urls: list of `string`s, urls to download the zip files 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.data_urls = data_urls
         self.metadata_urls = metadata_urls
         self.splits = splits
 
@@ -51,23 +51,36 @@ class PlantOrgans(datasets.GeneratorBasedBuilder):
         PlantOrgansConfig(
             name="semantic_segmentation_full",
             description="This configuration contains segmentation masks.",
-            data_url=_BASE_URL,
+            data_urls=_BASE_URL,
             metadata_urls=_SEMANTIC_METADATA_URLS,
             splits=['train', 'test'],
         ),
+        PlantOrgansConfig(
+            name="instance_segmentation_full",
+            description="This configuration contains segmentation masks.",
+            data_urls=_BASE_URL,
+            metadata_urls=_PANOPTIC_METADATA_URLS,
+            splits=['train', 'test'],
+        ),
     ]
 
     def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
+        features=datasets.Features(
                 {
                     "image": datasets.Image(),
                     "mask": datasets.Image(),
                     "image_name": datasets.Value(dtype="string"),
-                    "class": datasets.Value(dtype="string")
-                }
-            ),
+                    "class": datasets.ClassLabel(
+                        names=['Fruit', 'Leaf', 'Flower', 'Stem']),
+                })
+        if self.config.name == 'instance_segmentation_full':
+            features['score'] = datasets.Value(dtype="double")
+        else:
+            features['class'] = datasets.ClassLabel(
+                    names=['Fruit', 'Leaf', 'Flower', 'Stem'])
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
             supervised_keys=("image", "mask"),
             homepage=_HOMEPAGE,
             citation=_CITATION,
@@ -82,18 +95,28 @@ class PlantOrgans(datasets.GeneratorBasedBuilder):
       
         train_paths = []
         test_paths = []
-        
+
         for p in train_archives_paths:
             train_paths.extend(glob.glob(str(p)+'/sourcedata/labeled/**.jpg'))
         for p in test_archives_paths:
             test_paths.extend(glob.glob(str(p)+'/sourcedata/labeled/**.jpg'))
-        split_metadata_paths = dl_manager.download(_SEMANTIC_METADATA_URLS)
-
-        mask_archives_paths = dl_manager.download_and_extract(_SEMANTIC_MASKS_URLS)
+        
+        if self.config.name == 'instance_segmentation_full':
+            metadata_urls = _PANOPTIC_METADATA_URLS
+            mask_urls = _MASKS_URLS
+            mask_glob = '/_masks/**.png'
+        else:
+            metadata_urls = _SEMANTIC_METADATA_URLS
+            mask_urls = _SEMANTIC_MASKS_URLS
+            mask_glob = '/semantic_masks/**.png'
+
+        split_metadata_paths = dl_manager.download(metadata_urls)
+
+        mask_archives_paths = dl_manager.download_and_extract(mask_urls)
             
         mask_paths = []
         for p in mask_archives_paths:
-            mask_paths.extend(glob.glob(str(p)+'/semantic_masks/**.png'))
+            mask_paths.extend(glob.glob(str(p)+mask_glob))
 
         return [
             datasets.SplitGenerator(
@@ -147,5 +170,8 @@ class PlantOrgans(datasets.GeneratorBasedBuilder):
                 'image_name': Path(r['image_path']).parts[-1],
                 'class': r['class']
             }
-
+            if self.config.name == 'instance_segmentation_full':
+                example['score'] = r['score']
+            else:
+                example['class'] = r['class']
             yield i, example