Refactor data

data/set1/train.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7bc049f7336b92c842c080007d5a6da9625d74807bf318d4d8c80e1b950bc512
+size 1459611

data/set2/dev.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7bc049f7336b92c842c080007d5a6da9625d74807bf318d4d8c80e1b950bc512
+size 1459611

data/set2/train.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7bc049f7336b92c842c080007d5a6da9625d74807bf318d4d8c80e1b950bc512
+size 1459611

test.py

@@ -20,7 +20,7 @@ import json
 import os
 
 import datasets
-
+from datasets import DownloadManager
 
 # TODO: Add BibTeX citation
 # Find for instance the citation on arxiv or on the dataset repo/website
@@ -49,13 +49,15 @@ _LICENSE = ""
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLS = {
-    "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
-    "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+    # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+    # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
 }
 
+_BASE_URL = ""
+
 
 # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class NewDataset(datasets.GeneratorBasedBuilder):
+class Test(datasets.GeneratorBasedBuilder):
     """TODO: Short description of my dataset."""
 
     VERSION = datasets.Version("1.1.0")
@@ -73,46 +75,32 @@ class NewDataset(datasets.GeneratorBasedBuilder):
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
-            name="first_domain",
+            name="set_1",
             version=VERSION,
-            description="This part of my dataset covers a first domain",
-        ),
-        datasets.BuilderConfig(
-            name="second_domain",
-            version=VERSION,
-            description="This part of my dataset covers a second domain",
+            description="RGB-D SOD Set 1",
         ),
+        # datasets.BuilderConfig(
+        #     name="set_2",
+        #     version=VERSION,
+        #     description="RGB-D SOD Set 2",
+        # ),
     ]
 
-    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "set_1"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if (
-            self.config.name == "first_domain"
-        ):  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option2": datasets.Value("string"),
-                    "second_domain_answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
             # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
+            features=datasets.Features(
+                {
+                    "depth": datasets.Image(),
+                    "rgb": datasets.Image(),
+                    "gt": datasets.Image(),
+                    # These are the features of your dataset like images, labels ...
+                }
+            ),  # Here we define them above because they are different between the two configurations
             # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
             # specify them. They'll be used if as_supervised=True in builder.as_dataset.
             # supervised_keys=("sentence", "label"),
@@ -124,7 +112,7 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             citation=_CITATION,
         )
 
-    def _split_generators(self, dl_manager):
+    def _split_generators(self, dl_manager: DownloadManager):
         # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
         # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
 
@@ -161,7 +149,7 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
+    def _generate_examples(self, filepath: str, split: str):
         # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         with open(filepath, encoding="utf-8") as f: