Upload tempofunk-s.py

tempofunk-s.py

@@ -0,0 +1,96 @@
+import datasets
+import json
+import numpy
+
+_FEATURES = datasets.Features(
+    {
+        "id": datasets.Value("string"),
+        "prompt": datasets.Array3D(shape=(1, 77, 768), dtype="float32"),
+        "video": datasets.Sequence(feature=datasets.Array3D(shape=(4, 64, 64), dtype="float64")),
+        "description": datasets.Value("string"),
+        "videourl": datasets.Value("string"),
+        "categories": datasets.Value("string"),
+        "duration": datasets.Value("float"),
+        "full_metadata": datasets.Value("string"),
+    }
+)
+
+class FunkLoaderStream(datasets.GeneratorBasedBuilder):
+    """TempoFunk Dataset"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="TempoFunk Dataset",
+            features=_FEATURES,
+            homepage="None",
+            citation="None",
+            license="None"
+        )
+
+    def _split_generators(self, dl_manager):
+
+        print("id_list available at:", dl_manager.download("data/id_list.json"))
+
+        _ID_LIST = json.loads(open(dl_manager.download("data/id_list.json"), 'r').read())
+        
+        _SHARD_LENGTH = 20
+
+        _SPLITS = [_ID_LIST[i:i + _SHARD_LENGTH] for i in range(0, len(_ID_LIST), _SHARD_LENGTH)]
+
+        print("avail splits: ", _SPLITS)
+        
+
+        l=[]
+
+        _split_count = 0
+
+        for split in _SPLITS:
+
+            _list = []
+
+            for video_id in split:
+                _list.append({
+                    "frames": dl_manager.download(f"data/videos/{video_id}.npy"),
+                    "prompt": dl_manager.download(f"data/prompts/{video_id}.npy"),
+                    "metadata": dl_manager.download(f"data/metadata/{video_id}.json"),
+                })
+
+            l.append(
+                datasets.SplitGenerator(
+                    name=f"split_{_split_count}",
+                    gen_kwargs={
+                        "chunk_container": _list,
+                    },)
+            )
+
+            _split_count = _split_count + 1
+
+        print("Total Splits: ", _split_count)
+        
+        return l
+    
+    def _generate_examples(self, chunk_container):
+        """Generate images and labels for splits."""
+        for video_entry in chunk_container:
+            frames_binary = video_entry['frames']
+            prompt_binary = video_entry['prompt']
+            metadata = json.loads(open(video_entry['metadata'], 'r').read())
+
+            txt_embed = numpy.load(prompt_binary)
+            vid_embed = numpy.load(frames_binary)
+
+            # txt_embed = torch.load(open(prompt_binary, 'rb')).cpu().detach().numpy()
+            # vid_embed = torch.load(open(frames_binary, 'rb'))
+
+            print(vid_embed.shape)
+
+            yield metadata['id'], {
+                "id": metadata['id'],
+                "prompt": txt_embed,
+                "video": vid_embed,
+                "description": metadata['description'],
+                "videourl": metadata['videourl'],
+                "categories": metadata['categories'],
+                "duration": metadata['duration'],
+                "full_metadata": metadata
+            }