[prototype] Remove _FT aliases from functional

torchvision/prototype/transforms/functional/_augment.py

@@ -4,10 +4,17 @@
 
 import torch
 from torchvision.prototype import features
-from torchvision.transforms import functional_tensor as _FT
 from torchvision.transforms.functional import pil_to_tensor, to_pil_image
 
-erase_image_tensor = _FT.erase
+
+def erase_image_tensor(
+    image: torch.Tensor, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False
+) -> torch.Tensor:
+    if not inplace:
+        image = image.clone()
+
+    image[..., i : i + h, j : j + w] = v
+    return image
 
 
 @torch.jit.unused

torchvision/prototype/transforms/functional/_color.py

@@ -1,15 +1,16 @@
 import torch
 from torch.nn.functional import conv2d
 from torchvision.prototype import features
-from torchvision.transforms import functional_pil as _FP, functional_tensor as _FT
+from torchvision.transforms import functional_pil as _FP
+from torchvision.transforms.functional_tensor import _max_value
 
 from ._meta import _num_value_bits, _rgb_to_gray, convert_dtype_image_tensor
 
 
 def _blend(image1: torch.Tensor, image2: torch.Tensor, ratio: float) -> torch.Tensor:
     ratio = float(ratio)
     fp = image1.is_floating_point()
-    bound = _FT._max_value(image1.dtype)
+    bound = _max_value(image1.dtype)
     output = image1.mul(ratio).add_(image2, alpha=(1.0 - ratio)).clamp_(0, bound)
     return output if fp else output.to(image1.dtype)
 
@@ -18,10 +19,12 @@ def adjust_brightness_image_tensor(image: torch.Tensor, brightness_factor: float
     if brightness_factor < 0:
         raise ValueError(f"brightness_factor ({brightness_factor}) is not non-negative.")
 
-    _FT._assert_channels(image, [1, 3])
+    c = image.shape[-3]
+    if c not in [1, 3]:
+        raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}")
 
     fp = image.is_floating_point()
-    bound = _FT._max_value(image.dtype)
+    bound = _max_value(image.dtype)
     output = image.mul(brightness_factor).clamp_(0, bound)
     return output if fp else output.to(image.dtype)
 
@@ -48,7 +51,7 @@ def adjust_saturation_image_tensor(image: torch.Tensor, saturation_factor: float
 
     c = image.shape[-3]
     if c not in [1, 3]:
-        raise TypeError(f"Input image tensor permitted channel values are {[1, 3]}, but found {c}")
+        raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}")
 
     if c == 1:  # Match PIL behaviour
         return image
@@ -82,7 +85,7 @@ def adjust_contrast_image_tensor(image: torch.Tensor, contrast_factor: float) ->
 
     c = image.shape[-3]
     if c not in [1, 3]:
-        raise TypeError(f"Input image tensor permitted channel values are {[1, 3]}, but found {c}")
+        raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}")
     fp = image.is_floating_point()
     if c == 3:
         grayscale_image = _rgb_to_gray(image, cast=False)
@@ -121,7 +124,7 @@ def adjust_sharpness_image_tensor(image: torch.Tensor, sharpness_factor: float)
     if image.numel() == 0 or height <= 2 or width <= 2:
         return image
 
-    bound = _FT._max_value(image.dtype)
+    bound = _max_value(image.dtype)
     fp = image.is_floating_point()
     shape = image.shape
 
@@ -248,7 +251,7 @@ def adjust_hue_image_tensor(image: torch.Tensor, hue_factor: float) -> torch.Ten
 
     c = image.shape[-3]
     if c not in [1, 3]:
-        raise TypeError(f"Input image tensor permitted channel values are {[1, 3]}, but found {c}")
+        raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}")
 
     if c == 1:  # Match PIL behaviour
         return image
@@ -350,7 +353,7 @@ def posterize(inpt: features.InputTypeJIT, bits: int) -> features.InputTypeJIT:
 
 
 def solarize_image_tensor(image: torch.Tensor, threshold: float) -> torch.Tensor:
-    if threshold > _FT._max_value(image.dtype):
+    if threshold > _max_value(image.dtype):
         raise TypeError(f"Threshold should be less or equal the maximum value of the dtype, but got {threshold}")
 
     return torch.where(image >= threshold, invert_image_tensor(image), image)
@@ -375,13 +378,13 @@ def solarize(inpt: features.InputTypeJIT, threshold: float) -> features.InputTyp
 def autocontrast_image_tensor(image: torch.Tensor) -> torch.Tensor:
     c = image.shape[-3]
     if c not in [1, 3]:
-        raise TypeError(f"Input image tensor permitted channel values are {[1, 3]}, but found {c}")
+        raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}")
 
     if image.numel() == 0:
         # exit earlier on empty images
         return image
 
-    bound = _FT._max_value(image.dtype)
+    bound = _max_value(image.dtype)
     fp = image.is_floating_point()
     float_image = image if fp else image.to(torch.float32)
 

torchvision/prototype/transforms/functional/_geometry.py

@@ -8,7 +8,7 @@
 from torch.nn.functional import grid_sample, interpolate, pad as torch_pad
 
 from torchvision.prototype import features
-from torchvision.transforms import functional_pil as _FP, functional_tensor as _FT
+from torchvision.transforms import functional_pil as _FP
 from torchvision.transforms.functional import (
     _compute_resized_output_size as __compute_resized_output_size,
     _get_perspective_coeffs,
@@ -17,10 +17,15 @@
     pil_to_tensor,
     to_pil_image,
 )
+from torchvision.transforms.functional_tensor import _pad_symmetric
 
 from ._meta import convert_format_bounding_box, get_spatial_size_image_pil
 
-horizontal_flip_image_tensor = _FT.hflip
+
+def horizontal_flip_image_tensor(image: torch.Tensor) -> torch.Tensor:
+    return image.flip(-1)
+
+
 horizontal_flip_image_pil = _FP.hflip
 
 
@@ -58,7 +63,10 @@ def horizontal_flip(inpt: features.InputTypeJIT) -> features.InputTypeJIT:
         return horizontal_flip_image_pil(inpt)
 
 
-vertical_flip_image_tensor = _FT.vflip
+def vertical_flip_image_tensor(image: torch.Tensor) -> torch.Tensor:
+    return image.flip(-2)
+
+
 vertical_flip_image_pil = _FP.vflip
 
 
@@ -975,7 +983,7 @@ def _pad_with_scalar_fill(
         if needs_cast:
             image = image.to(dtype)
     else:  # padding_mode == "symmetric"
-        image = _FT._pad_symmetric(image, torch_padding)
+        image = _pad_symmetric(image, torch_padding)
 
     new_height, new_width = image.shape[-2:]
 

torchvision/prototype/transforms/functional/_meta.py

@@ -4,7 +4,8 @@
 import torch
 from torchvision.prototype import features
 from torchvision.prototype.features import BoundingBoxFormat, ColorSpace
-from torchvision.transforms import functional_pil as _FP, functional_tensor as _FT
+from torchvision.transforms import functional_pil as _FP
+from torchvision.transforms.functional_tensor import _max_value
 
 
 def get_dimensions_image_tensor(image: torch.Tensor) -> List[int]:
@@ -193,7 +194,7 @@ def clamp_bounding_box(
 
 def _strip_alpha(image: torch.Tensor) -> torch.Tensor:
     image, alpha = torch.tensor_split(image, indices=(-1,), dim=-3)
-    if not torch.all(alpha == _FT._max_value(alpha.dtype)):
+    if not torch.all(alpha == _max_value(alpha.dtype)):
         raise RuntimeError(
             "Stripping the alpha channel if it contains values other than the max value is not supported."
         )
@@ -204,7 +205,7 @@ def _add_alpha(image: torch.Tensor, alpha: Optional[torch.Tensor] = None) -> tor
     if alpha is None:
         shape = list(image.shape)
         shape[-3] = 1
-        alpha = torch.full(shape, _FT._max_value(image.dtype), dtype=image.dtype, device=image.device)
+        alpha = torch.full(shape, _max_value(image.dtype), dtype=image.dtype, device=image.device)
     return torch.cat((image, alpha), dim=-3)
 
 
@@ -363,14 +364,14 @@ def convert_dtype_image_tensor(image: torch.Tensor, dtype: torch.dtype = torch.f
         # Instead, we can also multiply by the maximum value plus something close to `1`. See
         # https://github.com/pytorch/vision/pull/2078#issuecomment-613524965 for details.
         eps = 1e-3
-        max_value = float(_FT._max_value(dtype))
+        max_value = float(_max_value(dtype))
         # We need to scale first since the conversion would otherwise turn the input range `[0.0, 1.0]` into the
         # discrete set `{0, 1}`.
         return image.mul(max_value + 1.0 - eps).to(dtype)
     else:
         # int to float
         if float_output:
-            return image.to(dtype).mul_(1.0 / _FT._max_value(image.dtype))
+            return image.to(dtype).mul_(1.0 / _max_value(image.dtype))
 
         # int to int
         num_value_bits_input = _num_value_bits(image.dtype)