include uda subset

epic_kitchens_100.py

@@ -63,6 +63,7 @@ _URL_BASE = "https://raw.githubusercontent.com/epic-kitchens/epic-kitchens-100-a
 _VARIANTS = [
     "action_recognition", # This split is used by four challenges: Action Recognition, Weakly supervised action recognition, Action detection, Action anticipation
     "multi_instance_retrieval",
+    "unsupervised_domain_adaptation",
 ]
 class EpicKitchens100(datasets.GeneratorBasedBuilder):
     """Epic Kitchens"""
@@ -110,36 +111,59 @@ class EpicKitchens100(datasets.GeneratorBasedBuilder):
             "multi_instance_retrieval": {
                 "train": os.path.join(_URL_BASE, "retrieval_annotations/EPIC_100_retrieval_train.csv"),
                 "test": os.path.join(_URL_BASE, "retrieval_annotations/EPIC_100_retrieval_test.csv")
+            },
+            "unsupervised_domain_adaptation": {
+                "source_train": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_source_train.csv"),
+                "target_train": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_target_train_timestamps.csv"),
+                "source_test": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_source_test_timestamps.csv"),
+                "target_test": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_target_test_timestamps.csv"),
+                "source_val": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_source_val.csv"),
+                "target_val": os.path.join(_URL_BASE, "UDA_annotations/EPIC_100_uda_target_val.csv"),
             }
         }
+        # Download data for all splits once for all since they are tiny csv files
         files_path = dl_manager.download_and_extract(urls)
-        splits = [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "annotations": files_path[self.config.name]["train"],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "annotations": files_path[self.config.name]["test"],
-                    "split": "test",
-                },
-            ),
-        ]
-        if self.config.name == "action_recognition":
-            splits.append(
+
+        if self.config.name ==  "unsupervised_domain_adaptation":
+            splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split(n_),
+                    gen_kwargs={
+                        "annotations": files_path[self.config.name][n_],
+                        "split": n_,
+                    },
+                )
+                for n_ in ["source_train", "target_train", "source_test", "target_test", "source_val", "target_val"]
+            ]
+            return splits
+        else:
+            splits = [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "annotations": files_path[self.config.name]["train"],
+                        "split": "train",
+                    },
+                ),
                 datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
+                    name=datasets.Split.TEST,
                     gen_kwargs={
-                        "annotations": files_path[self.config.name]["validation"],
-                        "split": "validation",
+                        "annotations": files_path[self.config.name]["test"],
+                        "split": "test",
                     },
                 ),
-            )
-        return splits
+            ]
+            if self.config.name == "action_recognition":
+                splits.append(
+                    datasets.SplitGenerator(
+                        name=datasets.Split.VALIDATION,
+                        gen_kwargs={
+                            "annotations": files_path[self.config.name]["validation"],
+                            "split": "validation",
+                        },
+                    ),
+                )
+            return splits
 
     def _generate_examples(self, annotations, split):
         """This function returns the examples."""
@@ -148,7 +172,8 @@ class EpicKitchens100(datasets.GeneratorBasedBuilder):
             next(csv_reader) # Skip header
             for idx, row in enumerate(csv_reader):
                 narration_id, participant_id, video_id, narration_timestamp, start_timestamp, stop_timestamp = row[:6]
-                if split != "test":
+                if (self.config.name in ["action_recognition", "multi_instance_retrieval"] and split in ["train", "validation"]) or \
+                    (self.config.name == "unsupervised_domain_adaptation" and split in ["source_train", "source_val", "target_val"]):
                     # The reason why it's jumping from 5 to 8 is that we are skipping `start_frame` and `stop_frame`
                     # since we are not exposing the frames, but just the videos
                     narration, verb, verb_class, noun, noun_class, all_nouns, all_noun_classes = row[8:15]