Tag htype

deeplake/api/tests/test_api.py

@@ -1211,6 +1211,7 @@ def test_htypes_list():
         "point_cloud",
         "polygon",
         "segment_mask",
+        "tag",
         "text",
         "video",
     ]

deeplake/api/tests/test_tag.py

@@ -0,0 +1,15 @@
+import numpy as np
+import deeplake
+import pytest
+
+
+@pytest.mark.parametrize("chunk_compression", [None, "lz4"])
+def test_tag(memory_ds, chunk_compression):
+    with memory_ds as ds:
+        ds.create_tensor("abc", htype="tag", chunk_compression=chunk_compression)
+        ds.abc.append("a")
+        ds.abc.append(["a", "b"])
+        ds.abc.extend(["a", ["b", "c"]])
+
+    np.testing.assert_array_equal(ds.abc.shapes(), np.array([[1], [2], [1], [2]]))
+    assert ds.abc.data()["value"] == [["a"], ["a", "b"], ["a"], ["b", "c"]]

deeplake/core/chunk/base_chunk.py

@@ -91,7 +91,7 @@ def __init__(
             else (len(tensor_meta.max_shape) if tensor_meta.max_shape else None)
         )
         self.is_text_like = (
-            self.htype in {"json", "list", "text"} or self.tensor_meta.is_link
+            self.htype in {"json", "list", "text", "tag"} or self.tensor_meta.is_link
         )
 
         self.compression = compression
@@ -328,7 +328,7 @@ def serialize_sample(
                 incoming_sample = incoming_sample.path
             if incoming_sample is None:
                 htype = "text" if self.tensor_meta.is_link else self.htype
-                empty_mapping = {"text": "", "list": [], "json": {}}
+                empty_mapping = {"text": "", "list": [], "json": {}, "tag": []}
                 incoming_sample = empty_mapping[htype]
 
             if isinstance(incoming_sample, Sample):

deeplake/core/chunk_engine.py

@@ -261,7 +261,7 @@ def is_data_cachable(self):
             tensor_meta = self.tensor_meta
             return (
                 self.chunk_class == UncompressedChunk
-                and tensor_meta.htype not in ["text", "json", "list", "polygon"]
+                and tensor_meta.htype not in ["text", "json", "list", "polygon", "tag"]
                 and tensor_meta.max_shape
                 and (tensor_meta.max_shape == tensor_meta.min_shape)
                 and (np.prod(tensor_meta.max_shape) < 20)
@@ -746,6 +746,10 @@ def _sanitize_samples(
                 p if isinstance(p, Polygons) else Polygons(p, dtype=tensor_meta.dtype)
                 for p in samples
             ]
+        elif tensor_meta.htype == "tag":
+            samples = [
+                sample if isinstance(sample, list) else [sample] for sample in samples
+            ]
         return samples, verified_samples
 
     def _convert_class_labels(self, samples):
@@ -3055,7 +3059,7 @@ def get_empty_sample(self):
             raise ValueError("This tensor has no samples, cannot get empty sample.")
         htype = self.tensor_meta.htype
         dtype = self.tensor_meta.dtype
-        if htype in ("text", "json", "list"):
+        if htype in ("text", "json", "list", "tag"):
             return get_empty_text_like_sample(htype)
         ndim = len(self.tensor_meta.max_shape)
         if self.is_sequence:
@@ -3066,7 +3070,7 @@ def get_empty_sample(self):
     @property
     def is_text_like(self):
         return (
-            self.tensor_meta.htype in {"text", "json", "list"}
+            self.tensor_meta.htype in {"text", "json", "list", "tag"}
             or self.tensor_meta.is_link
         )
 

deeplake/core/dataset/dataset.py

@@ -858,7 +858,7 @@ def _create_tensor(
             "nifti",
         ):
             self._create_sample_info_tensor(name)
-        if create_shape_tensor and htype not in ("text", "json"):
+        if create_shape_tensor and htype not in ("text", "json", "tag"):
             self._create_sample_shape_tensor(name, htype=htype)
         if create_id_tensor:
             self._create_sample_id_tensor(name)

deeplake/core/meta/tensor_meta.py

@@ -361,7 +361,7 @@ def _validate_required_htype_overwrites(htype: str, htype_overwrite: dict):
         )
 
     if htype_overwrite["dtype"] is not None:
-        if htype in ("json", "list"):
+        if htype in ("json", "list", "tag"):
             validate_json_schema(htype_overwrite["dtype"])
         else:
             _raise_if_condition(
@@ -385,7 +385,7 @@ def _format_values(htype: str, htype_overwrite: dict):
 
     dtype = htype_overwrite["dtype"]
     if dtype is not None:
-        if htype in ("json", "list", "intrinsics"):
+        if htype in ("json", "list", "tag", "intrinsics"):
             if getattr(dtype, "__module__", None) == "typing":
                 htype_overwrite["dtype"] = str(dtype)
         else:

deeplake/core/sample.py

@@ -147,7 +147,7 @@ def buffer(self):
 
     @property
     def is_text_like(self):
-        return self.htype in {"text", "list", "json"}
+        return self.htype in {"text", "list", "json", "tag"}
 
     @property
     def dtype(self):

deeplake/core/serialize.py

@@ -452,7 +452,7 @@ def check_sample_shape(shape, num_dims):
 
 
 def text_to_bytes(sample, dtype, htype):
-    if htype in ("json", "list"):
+    if htype in ("json", "list", "tag"):
         if isinstance(sample, np.ndarray):
             if htype == "list":
                 sample = list(sample) if sample.dtype == object else sample.tolist()
@@ -463,7 +463,7 @@ def text_to_bytes(sample, dtype, htype):
                     sample = list(sample)
         validate_json_object(sample, dtype)
         byts = json.dumps(sample, cls=HubJsonEncoder).encode()
-        shape = (len(sample),) if htype == "list" else (1,)
+        shape = (len(sample),) if htype in ("list", "tag") else (1,)
     else:  # htype == "text":
         if isinstance(sample, np.ndarray) and sample.size == 1:
             sample = str(sample.reshape(()))
@@ -480,7 +480,7 @@ def bytes_to_text(buffer, htype):
         arr = np.empty(1, dtype=object)
         arr[0] = json.loads(bytes.decode(buffer), cls=HubJsonDecoder)
         return arr
-    elif htype == "list":
+    elif htype in ("list", "tag"):
         lst = json.loads(bytes.decode(buffer), cls=HubJsonDecoder)
         arr = np.empty(len(lst), dtype=object)
         arr[:] = lst

deeplake/core/tensor.py

@@ -570,7 +570,7 @@ def ndim(self) -> int:
     @property
     def dtype(self) -> Optional[np.dtype]:
         """Dtype of the tensor."""
-        if self.base_htype in ("json", "list"):
+        if self.base_htype in ("json", "list", "tag"):
             return np.dtype(str)
         if self.meta.dtype:
             return np.dtype(self.meta.typestr or self.meta.dtype)
@@ -977,7 +977,7 @@ def data(self, aslist: bool = False, fetch_chunks: bool = False) -> Any:
             return {"value": self.text(fetch_chunks=fetch_chunks)}
         if htype == "json":
             return {"value": self.dict(fetch_chunks=fetch_chunks)}
-        if htype == "list":
+        if htype in ("list", "tag"):
             return {"value": self.list(fetch_chunks=fetch_chunks)}
         if self.htype == "video":
             data = {}
@@ -1441,9 +1441,9 @@ def dict(self, fetch_chunks: bool = False):
         return self._extract_value("json", fetch_chunks=fetch_chunks)
 
     def list(self, fetch_chunks: bool = False):
-        """Return list data. Only applicable for tensors with 'list' base htype."""
-        if self.base_htype != "list":
-            raise Exception("Only supported for list tensors.")
+        """Return list data. Only applicable for tensors with 'list' or 'tag' base htype."""
+        if self.base_htype not in ("list", "tag"):
+            raise Exception("Only supported for list and tag tensors.")
 
         if self.ndim == 1:
             return list(self.numpy(fetch_chunks=fetch_chunks))

deeplake/enterprise/test_pytorch.py

@@ -402,6 +402,35 @@ def test_string_tensors(local_auth_ds):
         np.testing.assert_array_equal(batch["strings"], f"string{idx}")
 
 
+@pytest.mark.slow
+@requires_torch
+@requires_libdeeplake
+@pytest.mark.flaky
+def test_tag_tensors(local_auth_ds):
+    with local_auth_ds as ds:
+        ds.create_tensor("tags", htype="tag")
+        ds.tags.extend(
+            [
+                f"tag{idx}" if idx % 2 == 0 else [f"tag{idx}", f"tag{idx}"]
+                for idx in range(5)
+            ]
+        )
+
+    ptds = ds.pytorch(batch_size=1)
+    for idx, batch in enumerate(ptds):
+        if idx % 2 == 0:
+            np.testing.assert_array_equal(batch["tags"], [[f"tag{idx}"]])
+        else:
+            np.testing.assert_array_equal(batch["tags"], [[f"tag{idx}", f"tag{idx}"]])
+
+    ptds2 = ds.pytorch(batch_size=None)
+    for idx, batch in enumerate(ptds2):
+        if idx % 2 == 0:
+            np.testing.assert_array_equal(batch["tags"], [f"tag{idx}"])
+        else:
+            np.testing.assert_array_equal(batch["tags"], [f"tag{idx}", f"tag{idx}"])
+
+
 @pytest.mark.xfail(raises=NotImplementedError, strict=True)
 def test_pytorch_large():
     raise NotImplementedError

deeplake/htype.py

@@ -19,6 +19,7 @@ class htype:
     IMAGE_RGB = "image.rgb"
     IMAGE_GRAY = "image.gray"
     CLASS_LABEL = "class_label"
+    TAG = "tag"
     BBOX = "bbox"
     BBOX_3D = "bbox.3d"
     VIDEO = "video"
@@ -95,6 +96,7 @@ class htype:
     },
     htype.LIST: {"dtype": "List"},
     htype.TEXT: {"dtype": "str"},
+    htype.TAG: {"dtype": "List"},
     htype.DICOM: {"sample_compression": "dcm"},
     htype.NIFTI: {},
     htype.POINT_CLOUD: {"dtype": "float32"},
@@ -135,6 +137,11 @@ def CLASS_LABEL(shape, dtype):
                 constraints.ndim_error("class_label", len(shape))
             )
 
+    @staticmethod
+    def TAG(shape, dtype):
+        if dtype.name != "str":
+            raise IncompatibleHtypeError(constraints.dtype_error("tag", dtype))
+
     @staticmethod
     def BBOX(shape, dtype):
         if len(shape) not in (2, 3):
@@ -185,6 +192,7 @@ def POINT(shape, dtype):
 HTYPE_CONSTRAINTS: Dict[str, Callable] = {
     htype.IMAGE: constraints.IMAGE,
     htype.CLASS_LABEL: constraints.CLASS_LABEL,
+    htype.TAG: constraints.TAG,
     htype.BBOX: constraints.BBOX,
     htype.BBOX_3D: constraints.BBOX_3D,
     htype.EMBEDDING: constraints.EMBEDDING,
@@ -211,6 +219,7 @@ def POINT(shape, dtype):
     htype.IMAGE_GRAY: _image_compressions,
     htype.VIDEO: VIDEO_COMPRESSIONS[:],
     htype.AUDIO: AUDIO_COMPRESSIONS[:],
+    htype.TAG: BYTE_COMPRESSIONS[:],
     htype.TEXT: BYTE_COMPRESSIONS[:],
     htype.LIST: BYTE_COMPRESSIONS[:],
     htype.JSON: BYTE_COMPRESSIONS[:],

deeplake/integrations/pytorch/common.py

@@ -22,7 +22,10 @@
         )
 
     if isinstance(elem, np.ndarray) and elem.size > 0 and isinstance(elem[0], str):
-        batch = [it[0] for it in batch]
+        if elem.dtype == object:
+            return [it.tolist() for it in batch]
+        else:
+            batch = [it[0] for it in batch]
     elif isinstance(elem, (tuple, list)) and len(elem) > 0 and isinstance(elem[0], str):
         batch = [it[0] for it in batch]
     elif isinstance(elem, Polygons):
@@ -52,7 +55,10 @@
     if isinstance(data, IterableOrderedDict):
         return IterableOrderedDict((k, convert_fn(v)) for k, v in data.items())
     if isinstance(data, np.ndarray) and data.size > 0 and isinstance(data[0], str):
-        data = data[0]
+        if data.dtype == object:
+            return data.tolist()
+        else:
+            data = data[0]
     elif isinstance(data, Polygons):
         data = data.numpy()
 
@@ -85,6 +91,7 @@
     jpeg_png_compressed_tensors = []
     json_tensors = []
     list_tensors = []
+    tag_tensors = []
     supported_image_compressions = {"png", "jpeg"}
     for tensor_name in tensors:
         tensor = dataset._get_tensor_from_root(tensor_name)
@@ -101,13 +108,17 @@
             json_tensors.append(tensor_name)
         elif meta.htype == "list":
             list_tensors.append(tensor_name)
+        elif meta.htype == "tag":
+            tag_tensors.append(tensor_name)
 
     if verbose and (json_tensors or list_tensors):
         json_list_tensors = set(json_tensors + list_tensors)
         warnings.warn(
             f"The following tensors have json or list htype: {json_list_tensors}. Collation of these tensors will fail by default. Ensure that these tensors are either transformed by specifying a transform or a custom collate_fn is specified to handle them."
         )
 
+    list_tensors += tag_tensors
+
     return jpeg_png_compressed_tensors, json_tensors, list_tensors
 
 

deeplake/integrations/tests/test_pytorch.py

@@ -495,6 +495,33 @@ def test_string_tensors(local_ds):
         np.testing.assert_array_equal(batch["strings"], f"string{idx}")
 
 
+@requires_torch
+@pytest.mark.flaky
+def test_tag_tensors(local_ds):
+    with local_ds:
+        local_ds.create_tensor("tags", htype="tag")
+        local_ds.tags.extend(
+            [
+                f"tag{idx}" if idx % 2 == 0 else [f"tag{idx}", f"tag{idx}"]
+                for idx in range(5)
+            ]
+        )
+
+    ptds = local_ds.pytorch(batch_size=1)
+    for idx, batch in enumerate(ptds):
+        if idx % 2 == 0:
+            np.testing.assert_array_equal(batch["tags"], [[f"tag{idx}"]])
+        else:
+            np.testing.assert_array_equal(batch["tags"], [[f"tag{idx}", f"tag{idx}"]])
+
+    ptds2 = local_ds.pytorch(batch_size=None)
+    for idx, batch in enumerate(ptds2):
+        if idx % 2 == 0:
+            np.testing.assert_array_equal(batch["tags"], [f"tag{idx}"])
+        else:
+            np.testing.assert_array_equal(batch["tags"], [f"tag{idx}", f"tag{idx}"])
+
+
 @pytest.mark.slow
 @requires_torch
 @pytest.mark.flaky

deeplake/util/casting.py

@@ -79,7 +79,7 @@ def get_empty_text_like_sample(htype: str):
         return ""
     elif htype == "json":
         return {}
-    elif htype == "list":
+    elif htype == "list" or htype == "tag":
         return []
     else:
         raise ValueError(

deeplake/util/htype.py

@@ -27,6 +27,8 @@
         raise ValueError(
             "Can't create a linked tensor with a generic htype, you need to specify htype, for example link[image]"
         )
+    if is_sequence and htype == HTYPE.TAG:
+        raise ValueError("Htype sequence[tag] is not supported")
     return is_sequence, is_link, htype