expose some prototype transforms utils

test/test_prototype_transforms.py

@@ -23,7 +23,7 @@
 )
 from torchvision.ops.boxes import box_iou
 from torchvision.prototype import features, transforms
-from torchvision.prototype.transforms._utils import _isinstance
+from torchvision.prototype.transforms.utils import check_type
 from torchvision.transforms.functional import InterpolationMode, pil_to_tensor, to_pil_image
 
 BATCH_EXTRA_DIMS = [extra_dims for extra_dims in DEFAULT_EXTRA_DIMS if extra_dims]
@@ -1860,7 +1860,7 @@ def test_permute_dimensions(dims, inverse_dims):
         value_type = type(value)
         transformed_value = transformed_sample[key]
 
-        if _isinstance(value, (features.Image, features.is_simple_tensor, features.Video)):
+        if check_type(value, (features.Image, features.is_simple_tensor, features.Video)):
             if transform.dims.get(value_type) is not None:
                 assert transformed_value.permute(inverse_dims[value_type]).equal(value)
             assert type(transformed_value) == torch.Tensor
@@ -1893,7 +1893,7 @@ def test_transpose_dimensions(dims):
         transformed_value = transformed_sample[key]
 
         transposed_dims = transform.dims.get(value_type)
-        if _isinstance(value, (features.Image, features.is_simple_tensor, features.Video)):
+        if check_type(value, (features.Image, features.is_simple_tensor, features.Video)):
             if transposed_dims is not None:
                 assert transformed_value.transpose(*transposed_dims).equal(value)
             assert type(transformed_value) == torch.Tensor

test/test_prototype_transforms_consistency.py

@@ -26,8 +26,8 @@
 from torchvision._utils import sequence_to_str
 from torchvision.prototype import features, transforms as prototype_transforms
 from torchvision.prototype.transforms import functional as prototype_F
-from torchvision.prototype.transforms._utils import query_spatial_size
 from torchvision.prototype.transforms.functional import to_image_pil
+from torchvision.prototype.transforms.utils import query_spatial_size
 from torchvision.transforms import functional as legacy_F
 
 DEFAULT_MAKE_IMAGES_KWARGS = dict(color_spaces=[features.ColorSpace.RGB], extra_dims=[(4,)])

test/test_prototype_transforms_utils.py

@@ -6,8 +6,8 @@
 from prototype_common_utils import make_bounding_box, make_detection_mask, make_image
 
 from torchvision.prototype import features
-from torchvision.prototype.transforms._utils import has_all, has_any
 from torchvision.prototype.transforms.functional import to_image_pil
+from torchvision.prototype.transforms.utils import has_all, has_any
 
 
 IMAGE = make_image(color_space=features.ColorSpace.RGB)

torchvision/prototype/transforms/__init__.py

@@ -1,6 +1,6 @@
 from torchvision.transforms import AutoAugmentPolicy, InterpolationMode  # usort: skip
 
-from . import functional  # usort: skip
+from . import functional, utils  # usort: skip
 
 from ._transform import Transform  # usort: skip
 from ._presets import StereoMatching  # usort: skip

torchvision/prototype/transforms/_augment.py

@@ -11,7 +11,7 @@
 from torchvision.prototype.transforms import functional as F, InterpolationMode
 
 from ._transform import _RandomApplyTransform
-from ._utils import has_any, query_chw, query_spatial_size
+from .utils import has_any, query_chw, query_spatial_size
 
 
 class RandomErasing(_RandomApplyTransform):

torchvision/prototype/transforms/_auto_augment.py

@@ -10,7 +10,8 @@
 from torchvision.prototype.transforms.functional._meta import get_spatial_size
 from torchvision.transforms import functional_tensor as _FT
 
-from ._utils import _isinstance, _setup_fill_arg
+from ._utils import _setup_fill_arg
+from .utils import check_type
 
 
 class _AutoAugmentBase(Transform):
@@ -38,7 +39,7 @@ def _flatten_and_extract_image_or_video(
 
         image_or_videos = []
         for idx, inpt in enumerate(flat_inputs):
-            if _isinstance(inpt, (features.Image, PIL.Image.Image, features.is_simple_tensor, features.Video)):
+            if check_type(inpt, (features.Image, PIL.Image.Image, features.is_simple_tensor, features.Video)):
                 image_or_videos.append((idx, inpt))
             elif isinstance(inpt, unsupported_types):
                 raise TypeError(f"Inputs of type {type(inpt).__name__} are not supported by {type(self).__name__}()")

torchvision/prototype/transforms/_color.py

@@ -7,7 +7,7 @@
 from torchvision.prototype.transforms import functional as F, Transform
 
 from ._transform import _RandomApplyTransform
-from ._utils import query_chw
+from .utils import query_chw
 
 
 class ColorJitter(Transform):

torchvision/prototype/transforms/_deprecated.py

@@ -11,7 +11,7 @@
 from typing_extensions import Literal
 
 from ._transform import _RandomApplyTransform
-from ._utils import query_chw
+from .utils import query_chw
 
 
 class ToTensor(Transform):

torchvision/prototype/transforms/_geometry.py

@@ -21,11 +21,8 @@
     _setup_fill_arg,
     _setup_float_or_seq,
     _setup_size,
-    has_all,
-    has_any,
-    query_bounding_box,
-    query_spatial_size,
 )
+from .utils import has_all, has_any, query_bounding_box, query_spatial_size
 
 
 class RandomHorizontalFlip(_RandomApplyTransform):

torchvision/prototype/transforms/_misc.py

@@ -7,7 +7,8 @@
 from torchvision.prototype import features
 from torchvision.prototype.transforms import functional as F, Transform
 
-from ._utils import _get_defaultdict, _setup_float_or_seq, _setup_size, has_any, query_bounding_box
+from ._utils import _get_defaultdict, _setup_float_or_seq, _setup_size
+from .utils import has_any, query_bounding_box
 
 
 class Identity(Transform):

torchvision/prototype/transforms/_transform.py

@@ -5,7 +5,7 @@
 import torch
 from torch import nn
 from torch.utils._pytree import tree_flatten, tree_unflatten
-from torchvision.prototype.transforms._utils import _isinstance
+from torchvision.prototype.transforms.utils import check_type
 from torchvision.utils import _log_api_usage_once
 
 
@@ -36,8 +36,7 @@ def forward(self, *inputs: Any) -> Any:
         params = self._get_params(flat_inputs)
 
         flat_outputs = [
-            self._transform(inpt, params) if _isinstance(inpt, self._transformed_types) else inpt
-            for inpt in flat_inputs
+            self._transform(inpt, params) if check_type(inpt, self._transformed_types) else inpt for inpt in flat_inputs
         ]
 
         return tree_unflatten(flat_outputs, spec)
@@ -80,8 +79,7 @@ def forward(self, *inputs: Any) -> Any:
         params = self._get_params(flat_inputs)
 
         flat_outputs = [
-            self._transform(inpt, params) if _isinstance(inpt, self._transformed_types) else inpt
-            for inpt in flat_inputs
+            self._transform(inpt, params) if check_type(inpt, self._transformed_types) else inpt for inpt in flat_inputs
         ]
 
         return tree_unflatten(flat_outputs, spec)

torchvision/prototype/transforms/_utils.py

@@ -1,15 +1,11 @@
 import functools
 import numbers
 from collections import defaultdict
-from typing import Any, Callable, Dict, List, Sequence, Tuple, Type, TypeVar, Union
+from typing import Any, Dict, Sequence, Type, TypeVar, Union
 
-import PIL.Image
-
-from torchvision._utils import sequence_to_str
 from torchvision.prototype import features
 from torchvision.prototype.features._feature import FillType, FillTypeJIT
 
-from torchvision.prototype.transforms.functional._meta import get_dimensions, get_spatial_size
 from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size  # noqa: F401
 
 from typing_extensions import Literal
@@ -100,65 +96,3 @@ def _check_padding_arg(padding: Union[int, Sequence[int]]) -> None:
 def _check_padding_mode_arg(padding_mode: Literal["constant", "edge", "reflect", "symmetric"]) -> None:
     if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
         raise ValueError("Padding mode should be either constant, edge, reflect or symmetric")
-
-
-def query_bounding_box(flat_inputs: List[Any]) -> features.BoundingBox:
-    bounding_boxes = [inpt for inpt in flat_inputs if isinstance(inpt, features.BoundingBox)]
-    if not bounding_boxes:
-        raise TypeError("No bounding box was found in the sample")
-    elif len(bounding_boxes) > 1:
-        raise ValueError("Found multiple bounding boxes in the sample")
-    return bounding_boxes.pop()
-
-
-def query_chw(flat_inputs: List[Any]) -> Tuple[int, int, int]:
-    chws = {
-        tuple(get_dimensions(inpt))
-        for inpt in flat_inputs
-        if isinstance(inpt, (features.Image, PIL.Image.Image, features.Video)) or features.is_simple_tensor(inpt)
-    }
-    if not chws:
-        raise TypeError("No image or video was found in the sample")
-    elif len(chws) > 1:
-        raise ValueError(f"Found multiple CxHxW dimensions in the sample: {sequence_to_str(sorted(chws))}")
-    c, h, w = chws.pop()
-    return c, h, w
-
-
-def query_spatial_size(flat_inputs: List[Any]) -> Tuple[int, int]:
-    sizes = {
-        tuple(get_spatial_size(inpt))
-        for inpt in flat_inputs
-        if isinstance(inpt, (features.Image, PIL.Image.Image, features.Video, features.Mask, features.BoundingBox))
-        or features.is_simple_tensor(inpt)
-    }
-    if not sizes:
-        raise TypeError("No image, video, mask or bounding box was found in the sample")
-    elif len(sizes) > 1:
-        raise ValueError(f"Found multiple HxW dimensions in the sample: {sequence_to_str(sorted(sizes))}")
-    h, w = sizes.pop()
-    return h, w
-
-
-def _isinstance(obj: Any, types_or_checks: Tuple[Union[Type, Callable[[Any], bool]], ...]) -> bool:
-    for type_or_check in types_or_checks:
-        if isinstance(obj, type_or_check) if isinstance(type_or_check, type) else type_or_check(obj):
-            return True
-    return False
-
-
-def has_any(flat_inputs: List[Any], *types_or_checks: Union[Type, Callable[[Any], bool]]) -> bool:
-    for inpt in flat_inputs:
-        if _isinstance(inpt, types_or_checks):
-            return True
-    return False
-
-
-def has_all(flat_inputs: List[Any], *types_or_checks: Union[Type, Callable[[Any], bool]]) -> bool:
-    for type_or_check in types_or_checks:
-        for inpt in flat_inputs:
-            if isinstance(inpt, type_or_check) if isinstance(type_or_check, type) else type_or_check(inpt):
-                break
-        else:
-            return False
-    return True

torchvision/prototype/transforms/utils.py

@@ -0,0 +1,69 @@
+from typing import Any, Callable, List, Tuple, Type, Union
+
+import PIL.Image
+
+from torchvision._utils import sequence_to_str
+from torchvision.prototype import features
+from torchvision.prototype.transforms.functional import get_dimensions, get_spatial_size
+
+
+def query_bounding_box(flat_inputs: List[Any]) -> features.BoundingBox:
+    bounding_boxes = [inpt for inpt in flat_inputs if isinstance(inpt, features.BoundingBox)]
+    if not bounding_boxes:
+        raise TypeError("No bounding box was found in the sample")
+    elif len(bounding_boxes) > 1:
+        raise ValueError("Found multiple bounding boxes in the sample")
+    return bounding_boxes.pop()
+
+
+def query_chw(flat_inputs: List[Any]) -> Tuple[int, int, int]:
+    chws = {
+        tuple(get_dimensions(inpt))
+        for inpt in flat_inputs
+        if isinstance(inpt, (features.Image, PIL.Image.Image, features.Video)) or features.is_simple_tensor(inpt)
+    }
+    if not chws:
+        raise TypeError("No image or video was found in the sample")
+    elif len(chws) > 1:
+        raise ValueError(f"Found multiple CxHxW dimensions in the sample: {sequence_to_str(sorted(chws))}")
+    c, h, w = chws.pop()
+    return c, h, w
+
+
+def query_spatial_size(flat_inputs: List[Any]) -> Tuple[int, int]:
+    sizes = {
+        tuple(get_spatial_size(inpt))
+        for inpt in flat_inputs
+        if isinstance(inpt, (features.Image, PIL.Image.Image, features.Video, features.Mask, features.BoundingBox))
+        or features.is_simple_tensor(inpt)
+    }
+    if not sizes:
+        raise TypeError("No image, video, mask or bounding box was found in the sample")
+    elif len(sizes) > 1:
+        raise ValueError(f"Found multiple HxW dimensions in the sample: {sequence_to_str(sorted(sizes))}")
+    h, w = sizes.pop()
+    return h, w
+
+
+def check_type(obj: Any, types_or_checks: Tuple[Union[Type, Callable[[Any], bool]], ...]) -> bool:
+    for type_or_check in types_or_checks:
+        if isinstance(obj, type_or_check) if isinstance(type_or_check, type) else type_or_check(obj):
+            return True
+    return False
+
+
+def has_any(flat_inputs: List[Any], *types_or_checks: Union[Type, Callable[[Any], bool]]) -> bool:
+    for inpt in flat_inputs:
+        if check_type(inpt, types_or_checks):
+            return True
+    return False
+
+
+def has_all(flat_inputs: List[Any], *types_or_checks: Union[Type, Callable[[Any], bool]]) -> bool:
+    for type_or_check in types_or_checks:
+        for inpt in flat_inputs:
+            if isinstance(inpt, type_or_check) if isinstance(type_or_check, type) else type_or_check(inpt):
+                break
+        else:
+            return False
+    return True