Make v2.utils private. (#7863)

references/segmentation/v2_extras.py

@@ -11,7 +11,7 @@ class PadIfSmaller(v2.Transform):
         self.fill = v2._utils._setup_fill_arg(fill)
 
     def _get_params(self, sample):
-        _, height, width = v2.utils.query_chw(sample)
+        _, height, width = v2._utils.query_chw(sample)
         padding = [0, 0, max(self.size - width, 0), max(self.size - height, 0)]
         needs_padding = any(padding)
         return dict(padding=padding, needs_padding=needs_padding)

test/test_prototype_datasets_builtin.py

@@ -25,7 +25,7 @@ from torchvision.prototype import datasets
 from torchvision.prototype.datapoints import Label
 from torchvision.prototype.datasets.utils import EncodedImage
 from torchvision.prototype.datasets.utils._internal import INFINITE_BUFFER_SIZE
-from torchvision.transforms.v2.utils import is_pure_tensor
+from torchvision.transforms.v2._utils import is_pure_tensor
 
 
 def assert_samples_equal(*args, msg=None, **kwargs):

test/test_prototype_transforms.py

@@ -10,8 +10,8 @@ from prototype_common_utils import make_label
 
 from torchvision.datapoints import BoundingBoxes, BoundingBoxFormat, Image, Mask, Video
 from torchvision.prototype import datapoints, transforms
+from torchvision.transforms.v2._utils import check_type, is_pure_tensor
 from torchvision.transforms.v2.functional import clamp_bounding_boxes, InterpolationMode, pil_to_tensor, to_pil_image
-from torchvision.transforms.v2.utils import check_type, is_pure_tensor
 from transforms_v2_legacy_utils import (
     DEFAULT_EXTRA_DIMS,
     make_bounding_boxes,

test/test_transforms_v2.py

@@ -16,7 +16,7 @@ from torchvision import datapoints
 from torchvision.ops.boxes import box_iou
 from torchvision.transforms.functional import to_pil_image
 from torchvision.transforms.v2 import functional as F
-from torchvision.transforms.v2.utils import check_type, is_pure_tensor, query_chw
+from torchvision.transforms.v2._utils import check_type, is_pure_tensor, query_chw
 from transforms_v2_legacy_utils import (
     make_bounding_boxes,
     make_detection_mask,

test/test_transforms_v2_consistency.py

@@ -19,9 +19,8 @@ from torchvision._utils import sequence_to_str
 
 from torchvision.transforms import functional as legacy_F
 from torchvision.transforms.v2 import functional as prototype_F
-from torchvision.transforms.v2._utils import _get_fill
+from torchvision.transforms.v2._utils import _get_fill, query_size
 from torchvision.transforms.v2.functional import to_pil_image
-from torchvision.transforms.v2.utils import query_size
 from transforms_v2_legacy_utils import (
     ArgsKwargs,
     make_bounding_boxes,

test/test_transforms_v2_functional.py

@@ -13,9 +13,9 @@ from torch.utils._pytree import tree_map
 from torchvision import datapoints
 from torchvision.transforms.functional import _get_perspective_coeffs
 from torchvision.transforms.v2 import functional as F
+from torchvision.transforms.v2._utils import is_pure_tensor
 from torchvision.transforms.v2.functional._geometry import _center_crop_compute_padding
 from torchvision.transforms.v2.functional._meta import clamp_bounding_boxes, convert_bounding_box_format
-from torchvision.transforms.v2.utils import is_pure_tensor
 from transforms_v2_dispatcher_infos import DISPATCHER_INFOS
 from transforms_v2_kernel_infos import KERNEL_INFOS
 from transforms_v2_legacy_utils import (

test/test_transforms_v2_utils.py

@@ -3,12 +3,12 @@ import pytest
 
 import torch
 
-import torchvision.transforms.v2.utils
+import torchvision.transforms.v2._utils
 from common_utils import DEFAULT_SIZE, make_bounding_boxes, make_detection_mask, make_image
 
 from torchvision import datapoints
+from torchvision.transforms.v2._utils import has_all, has_any
 from torchvision.transforms.v2.functional import to_pil_image
-from torchvision.transforms.v2.utils import has_all, has_any
 
 
 IMAGE = make_image(DEFAULT_SIZE, color_space="RGB")
@@ -37,15 +37,15 @@ MASK = make_detection_mask(DEFAULT_SIZE)
         ((IMAGE, BOUNDING_BOX, MASK), (lambda obj: isinstance(obj, datapoints.Image),), True),
         ((IMAGE, BOUNDING_BOX, MASK), (lambda _: False,), False),
         ((IMAGE, BOUNDING_BOX, MASK), (lambda _: True,), True),
-        ((IMAGE,), (datapoints.Image, PIL.Image.Image, torchvision.transforms.v2.utils.is_pure_tensor), True),
+        ((IMAGE,), (datapoints.Image, PIL.Image.Image, torchvision.transforms.v2._utils.is_pure_tensor), True),
         (
             (torch.Tensor(IMAGE),),
-            (datapoints.Image, PIL.Image.Image, torchvision.transforms.v2.utils.is_pure_tensor),
+            (datapoints.Image, PIL.Image.Image, torchvision.transforms.v2._utils.is_pure_tensor),
             True,
         ),
         (
             (to_pil_image(IMAGE),),
-            (datapoints.Image, PIL.Image.Image, torchvision.transforms.v2.utils.is_pure_tensor),
+            (datapoints.Image, PIL.Image.Image, torchvision.transforms.v2._utils.is_pure_tensor),
             True,
         ),
     ],

torchvision/prototype/transforms/_augment.py

@@ -7,9 +7,9 @@ from torchvision import datapoints
 from torchvision.ops import masks_to_boxes
 from torchvision.prototype import datapoints as proto_datapoints
 from torchvision.transforms.v2 import functional as F, InterpolationMode, Transform
+from torchvision.transforms.v2._utils import is_pure_tensor
 
 from torchvision.transforms.v2.functional._geometry import _check_interpolation
-from torchvision.transforms.v2.utils import is_pure_tensor
 
 
 class SimpleCopyPaste(Transform):

torchvision/prototype/transforms/_geometry.py

@@ -6,8 +6,16 @@ import torch
 from torchvision import datapoints
 from torchvision.prototype.datapoints import Label, OneHotLabel
 from torchvision.transforms.v2 import functional as F, Transform
-from torchvision.transforms.v2._utils import _FillType, _get_fill, _setup_fill_arg, _setup_size
-from torchvision.transforms.v2.utils import get_bounding_boxes, has_any, is_pure_tensor, query_size
+from torchvision.transforms.v2._utils import (
+    _FillType,
+    _get_fill,
+    _setup_fill_arg,
+    _setup_size,
+    get_bounding_boxes,
+    has_any,
+    is_pure_tensor,
+    query_size,
+)
 
 
 class FixedSizeCrop(Transform):

torchvision/prototype/transforms/_misc.py

@@ -8,7 +8,7 @@ import torch
 from torchvision import datapoints
 from torchvision.transforms.v2 import Transform
 
-from torchvision.transforms.v2.utils import is_pure_tensor
+from torchvision.transforms.v2._utils import is_pure_tensor
 
 
 T = TypeVar("T")

torchvision/transforms/v2/__init__.py

 from torchvision.transforms import AutoAugmentPolicy, InterpolationMode  # usort: skip
 
-from . import functional, utils  # usort: skip
+from . import functional  # usort: skip
 
 from ._transform import Transform  # usort: skip
 

torchvision/transforms/v2/_augment.py

@@ -11,8 +11,7 @@ from torchvision import datapoints, transforms as _transforms
 from torchvision.transforms.v2 import functional as F
 
 from ._transform import _RandomApplyTransform, Transform
-from ._utils import _parse_labels_getter
-from .utils import has_any, is_pure_tensor, query_chw, query_size
+from ._utils import _parse_labels_getter, has_any, is_pure_tensor, query_chw, query_size
 
 
 class RandomErasing(_RandomApplyTransform):

torchvision/transforms/v2/_auto_augment.py

@@ -12,8 +12,7 @@ from torchvision.transforms.v2.functional._geometry import _check_interpolation
 from torchvision.transforms.v2.functional._meta import get_size
 from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
 
-from ._utils import _get_fill, _setup_fill_arg
-from .utils import check_type, is_pure_tensor
+from ._utils import _get_fill, _setup_fill_arg, check_type, is_pure_tensor
 
 
 ImageOrVideo = Union[torch.Tensor, PIL.Image.Image, datapoints.Image, datapoints.Video]

torchvision/transforms/v2/_color.py

@@ -6,7 +6,7 @@ from torchvision import transforms as _transforms
 from torchvision.transforms.v2 import functional as F, Transform
 
 from ._transform import _RandomApplyTransform
-from .utils import query_chw
+from ._utils import query_chw
 
 
 class Grayscale(Transform):

torchvision/transforms/v2/_geometry.py

@@ -23,8 +23,12 @@ from ._utils import (
     _setup_fill_arg,
     _setup_float_or_seq,
     _setup_size,
+    get_bounding_boxes,
+    has_all,
+    has_any,
+    is_pure_tensor,
+    query_size,
 )
-from .utils import get_bounding_boxes, has_all, has_any, is_pure_tensor, query_size
 
 
 class RandomHorizontalFlip(_RandomApplyTransform):

torchvision/transforms/v2/_misc.py

@@ -9,8 +9,7 @@ from torch.utils._pytree import tree_flatten, tree_unflatten
 from torchvision import datapoints, transforms as _transforms
 from torchvision.transforms.v2 import functional as F, Transform
 
-from ._utils import _parse_labels_getter, _setup_float_or_seq, _setup_size
-from .utils import get_bounding_boxes, has_any, is_pure_tensor
+from ._utils import _parse_labels_getter, _setup_float_or_seq, _setup_size, get_bounding_boxes, has_any, is_pure_tensor
 
 
 # TODO: do we want/need to expose this?

torchvision/transforms/v2/_transform.py

@@ -8,7 +8,7 @@ import torch
 from torch import nn
 from torch.utils._pytree import tree_flatten, tree_unflatten
 from torchvision import datapoints
-from torchvision.transforms.v2.utils import check_type, has_any, is_pure_tensor
+from torchvision.transforms.v2._utils import check_type, has_any, is_pure_tensor
 from torchvision.utils import _log_api_usage_once
 
 from .functional._utils import _get_kernel

torchvision/transforms/v2/_type_conversion.py

@@ -7,7 +7,7 @@ import torch
 from torchvision import datapoints
 from torchvision.transforms.v2 import functional as F, Transform
 
-from torchvision.transforms.v2.utils import is_pure_tensor
+from torchvision.transforms.v2._utils import is_pure_tensor
 
 
 class PILToTensor(Transform):

torchvision/transforms/v2/_utils.py

+from __future__ import annotations
+
 import collections.abc
 import numbers
 from contextlib import suppress
-from typing import Any, Callable, Dict, Literal, Optional, Sequence, Type, Union
 
+from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Tuple, Type, Union
+
+import PIL.Image
 import torch
 
+from torchvision import datapoints
+
+from torchvision._utils import sequence_to_str
+
 from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size  # noqa: F401
+from torchvision.transforms.v2.functional import get_dimensions, get_size, is_pure_tensor
 from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
 
 
@@ -138,3 +147,73 @@ def _parse_labels_getter(
         return lambda _: None
     else:
         raise ValueError(f"labels_getter should either be 'default', a callable, or None, but got {labels_getter}.")
+
+
+def get_bounding_boxes(flat_inputs: List[Any]) -> datapoints.BoundingBoxes:
+    # This assumes there is only one bbox per sample as per the general convention
+    try:
+        return next(inpt for inpt in flat_inputs if isinstance(inpt, datapoints.BoundingBoxes))
+    except StopIteration:
+        raise ValueError("No bounding boxes were found in the sample")
+
+
+def query_chw(flat_inputs: List[Any]) -> Tuple[int, int, int]:
+    chws = {
+        tuple(get_dimensions(inpt))
+        for inpt in flat_inputs
+        if check_type(inpt, (is_pure_tensor, datapoints.Image, PIL.Image.Image, datapoints.Video))
+    }
+    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_size(flat_inputs: List[Any]) -> Tuple[int, int]:
+    sizes = {
+        tuple(get_size(inpt))
+        for inpt in flat_inputs
+        if check_type(
+            inpt,
+            (
+                is_pure_tensor,
+                datapoints.Image,
+                PIL.Image.Image,
+                datapoints.Video,
+                datapoints.Mask,
+                datapoints.BoundingBoxes,
+            ),
+        )
+    }
+    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

torchvision/transforms/v2/utils.py

-from __future__ import annotations
-
-from typing import Any, Callable, List, Tuple, Type, Union
-
-import PIL.Image
-from torchvision import datapoints
-
-from torchvision._utils import sequence_to_str
-from torchvision.transforms.v2.functional import get_dimensions, get_size, is_pure_tensor
-
-
-def get_bounding_boxes(flat_inputs: List[Any]) -> datapoints.BoundingBoxes:
-    # This assumes there is only one bbox per sample as per the general convention
-    try:
-        return next(inpt for inpt in flat_inputs if isinstance(inpt, datapoints.BoundingBoxes))
-    except StopIteration:
-        raise ValueError("No bounding boxes were found in the sample")
-
-
-def query_chw(flat_inputs: List[Any]) -> Tuple[int, int, int]:
-    chws = {
-        tuple(get_dimensions(inpt))
-        for inpt in flat_inputs
-        if check_type(inpt, (is_pure_tensor, datapoints.Image, PIL.Image.Image, datapoints.Video))
-    }
-    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_size(flat_inputs: List[Any]) -> Tuple[int, int]:
-    sizes = {
-        tuple(get_size(inpt))
-        for inpt in flat_inputs
-        if check_type(
-            inpt,
-            (
-                is_pure_tensor,
-                datapoints.Image,
-                PIL.Image.Image,
-                datapoints.Video,
-                datapoints.Mask,
-                datapoints.BoundingBoxes,
-            ),
-        )
-    }
-    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