From 46a47bce861e2b1ff5321da2cb30111e0d0673bf Mon Sep 17 00:00:00 2001
From: Philip Meier <github.pmeier@posteo.de>
Date: Wed, 24 May 2023 15:32:57 +0200
Subject: [PATCH 1/8] add PermuteChannels transform

---
 test/test_transforms_v2.py                    |  1 +
 torchvision/datapoints/_datapoint.py          |  3 ++
 torchvision/datapoints/_image.py              |  4 ++
 torchvision/datapoints/_video.py              |  4 ++
 torchvision/transforms/v2/__init__.py         |  1 +
 torchvision/transforms/v2/_color.py           | 34 +++++++-------
 .../transforms/v2/functional/__init__.py      |  4 ++
 .../transforms/v2/functional/_color.py        | 45 ++++++++++++++++++-
 8 files changed, 78 insertions(+), 18 deletions(-)

diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py
index 02e3e1e569a..e4d6b8754ba 100644
--- a/test/test_transforms_v2.py
+++ b/test/test_transforms_v2.py
@@ -126,6 +126,7 @@ class TestSmoke:
             (transforms.RandomEqualize(p=1.0), None),
             (transforms.RandomGrayscale(p=1.0), None),
             (transforms.RandomInvert(p=1.0), None),
+            (transforms.PermuteChannels(), None),
             (transforms.RandomPhotometricDistort(p=1.0), None),
             (transforms.RandomPosterize(bits=4, p=1.0), None),
             (transforms.RandomSolarize(threshold=0.5, p=1.0), None),
diff --git a/torchvision/datapoints/_datapoint.py b/torchvision/datapoints/_datapoint.py
index fe489d13ea0..c2fb90b202a 100644
--- a/torchvision/datapoints/_datapoint.py
+++ b/torchvision/datapoints/_datapoint.py
@@ -254,6 +254,9 @@ def invert(self) -> Datapoint:
     def gaussian_blur(self, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Datapoint:
         return self
 
+    def permute_channels(self, permutation: List[int]) -> Datapoint:
+        return self
+
 
 _InputType = Union[torch.Tensor, PIL.Image.Image, Datapoint]
 _InputTypeJIT = torch.Tensor
diff --git a/torchvision/datapoints/_image.py b/torchvision/datapoints/_image.py
index e47a6c10fc3..59aa35bcd6f 100644
--- a/torchvision/datapoints/_image.py
+++ b/torchvision/datapoints/_image.py
@@ -253,6 +253,10 @@ def normalize(self, mean: List[float], std: List[float], inplace: bool = False)
         output = self._F.normalize_image_tensor(self.as_subclass(torch.Tensor), mean=mean, std=std, inplace=inplace)
         return Image.wrap_like(self, output)
 
+    def permute_channels(self, permutation: List[int]) -> Image:
+        output = self._F.permute_channels_image_tensor(self.as_subclass(torch.Tensor), permutation=permutation)
+        return Image.wrap_like(self, output)
+
 
 _ImageType = Union[torch.Tensor, PIL.Image.Image, Image]
 _ImageTypeJIT = torch.Tensor
diff --git a/torchvision/datapoints/_video.py b/torchvision/datapoints/_video.py
index a6fbe2bd473..62afff52aac 100644
--- a/torchvision/datapoints/_video.py
+++ b/torchvision/datapoints/_video.py
@@ -243,6 +243,10 @@ def normalize(self, mean: List[float], std: List[float], inplace: bool = False)
         output = self._F.normalize_video(self.as_subclass(torch.Tensor), mean=mean, std=std, inplace=inplace)
         return Video.wrap_like(self, output)
 
+    def permute_channels(self, permutation: List[int]) -> Video:
+        output = self._F.permute_channels_image_tensor(self.as_subclass(torch.Tensor), permutation=permutation)
+        return Video.wrap_like(self, output)
+
 
 _VideoType = Union[torch.Tensor, Video]
 _VideoTypeJIT = torch.Tensor
diff --git a/torchvision/transforms/v2/__init__.py b/torchvision/transforms/v2/__init__.py
index 6573446a33a..47cf0e47df7 100644
--- a/torchvision/transforms/v2/__init__.py
+++ b/torchvision/transforms/v2/__init__.py
@@ -9,6 +9,7 @@
 from ._color import (
     ColorJitter,
     Grayscale,
+    PermuteChannels,
     RandomAdjustSharpness,
     RandomAutocontrast,
     RandomEqualize,
diff --git a/torchvision/transforms/v2/_color.py b/torchvision/transforms/v2/_color.py
index 7dd8eeae236..1d08d251a48 100644
--- a/torchvision/transforms/v2/_color.py
+++ b/torchvision/transforms/v2/_color.py
@@ -177,7 +177,22 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         return output
 
 
-# TODO: This class seems to be untested
+class PermuteChannels(Transform):
+    _transformed_types = (
+        datapoints.Image,
+        PIL.Image.Image,
+        is_simple_tensor,
+        datapoints.Video,
+    )
+
+    def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+        num_channels, *_ = query_chw(flat_inputs)
+        return dict(permutation=torch.randperm(num_channels).tolist())
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        return F.permute_channels(inpt, params["permutation"])
+
+
 class RandomPhotometricDistort(Transform):
     """[BETA] Randomly distorts the image or video as used in `SSD: Single Shot
     MultiBox Detector <https://arxiv.org/abs/1512.02325>`_.
@@ -241,21 +256,6 @@ def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
         params["channel_permutation"] = torch.randperm(num_channels) if torch.rand(1) < self.p else None
         return params
 
-    def _permute_channels(
-        self, inpt: Union[datapoints._ImageType, datapoints._VideoType], permutation: torch.Tensor
-    ) -> Union[datapoints._ImageType, datapoints._VideoType]:
-        orig_inpt = inpt
-        if isinstance(orig_inpt, PIL.Image.Image):
-            inpt = F.pil_to_tensor(inpt)
-
-        # TODO: Find a better fix than as_subclass???
-        output = inpt[..., permutation, :, :].as_subclass(type(inpt))
-
-        if isinstance(orig_inpt, PIL.Image.Image):
-            output = F.to_image_pil(output)
-
-        return output
-
     def _transform(
         self, inpt: Union[datapoints._ImageType, datapoints._VideoType], params: Dict[str, Any]
     ) -> Union[datapoints._ImageType, datapoints._VideoType]:
@@ -270,7 +270,7 @@ def _transform(
         if params["contrast_factor"] is not None and not params["contrast_before"]:
             inpt = F.adjust_contrast(inpt, contrast_factor=params["contrast_factor"])
         if params["channel_permutation"] is not None:
-            inpt = self._permute_channels(inpt, permutation=params["channel_permutation"])
+            inpt = F.permute_channels(inpt, permutation=params["channel_permutation"])
         return inpt
 
 
diff --git a/torchvision/transforms/v2/functional/__init__.py b/torchvision/transforms/v2/functional/__init__.py
index ffb34c87748..91516972f15 100644
--- a/torchvision/transforms/v2/functional/__init__.py
+++ b/torchvision/transforms/v2/functional/__init__.py
@@ -65,6 +65,10 @@
     invert_image_pil,
     invert_image_tensor,
     invert_video,
+    permute_channels,
+    permute_channels_image_pil,
+    permute_channels_image_tensor,
+    permute_channels_video,
     posterize,
     posterize_image_pil,
     posterize_image_tensor,
diff --git a/torchvision/transforms/v2/functional/_color.py b/torchvision/transforms/v2/functional/_color.py
index 4ba7e5b36b3..e1b08efa3e9 100644
--- a/torchvision/transforms/v2/functional/_color.py
+++ b/torchvision/transforms/v2/functional/_color.py
@@ -1,4 +1,4 @@
-from typing import Union
+from typing import List, Union
 
 import PIL.Image
 import torch
@@ -10,6 +10,7 @@
 from torchvision.utils import _log_api_usage_once
 
 from ._meta import _num_value_bits, convert_dtype_image_tensor
+from ._type_conversion import pil_to_tensor, to_image_pil
 from ._utils import is_simple_tensor
 
 
@@ -670,3 +671,45 @@ def invert(inpt: datapoints._InputTypeJIT) -> datapoints._InputTypeJIT:
             f"Input can either be a plain tensor, any TorchVision datapoint, or a PIL image, "
             f"but got {type(inpt)} instead."
         )
+
+
+def permute_channels_image_tensor(image: torch.Tensor, permutation: List[int]) -> torch.Tensor:
+    shape = image.shape
+    num_channels, height, width = shape[-3:]
+
+    if len(permutation) != num_channels:
+        raise ValueError(
+            f"Length of permutation does not match number of channels: " f"{len(permutation)} != {num_channels}"
+        )
+
+    if image.numel() == 0:
+        return image
+
+    image = image.reshape(-1, num_channels, height, width)
+    image = image[:, permutation, :, :]
+    return image.reshape(shape)
+
+
+def permute_channels_image_pil(image: PIL.Image.Image, permutation: List[int]) -> PIL.Image:
+    return to_image_pil(permute_channels_image_tensor(pil_to_tensor(image), permutation=permutation))
+
+
+def permute_channels_video(video: torch.Tensor, permutation: List[int]) -> torch.Tensor:
+    return permute_channels_image_tensor(video, permutation=permutation)
+
+
+def permute_channels(inpt: datapoints._InputTypeJIT, permutation: List[int]) -> datapoints._InputTypeJIT:
+    if not torch.jit.is_scripting():
+        _log_api_usage_once(permute_channels)
+
+    if torch.jit.is_scripting() or is_simple_tensor(inpt):
+        return permute_channels_image_tensor(inpt, permutation=permutation)
+    elif isinstance(inpt, datapoints._datapoint.Datapoint):
+        return inpt.permute_channels(permutation=permutation)
+    elif isinstance(inpt, PIL.Image.Image):
+        return permute_channels_image_pil(inpt, permutation=permutation)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, any TorchVision datapoint, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )

From e4be568959c86623d9e46ae9968e336fbecce6a9 Mon Sep 17 00:00:00 2001
From: Philip Meier <github.pmeier@posteo.de>
Date: Mon, 7 Aug 2023 13:00:59 +0200
Subject: [PATCH 2/8] cleanup

---
 torchvision/transforms/v2/functional/_color.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/torchvision/transforms/v2/functional/_color.py b/torchvision/transforms/v2/functional/_color.py
index bd94c8339f2..01fcf6913ac 100644
--- a/torchvision/transforms/v2/functional/_color.py
+++ b/torchvision/transforms/v2/functional/_color.py
@@ -647,7 +647,7 @@ def invert_video(video: torch.Tensor) -> torch.Tensor:
 
 def permute_channels(inpt: datapoints._InputTypeJIT, permutation: List[int]) -> datapoints._InputTypeJIT:
     if torch.jit.is_scripting():
-        return invert_image_tensor(inpt, permutation=permutation)
+        return permute_channels_image_tensor(inpt, permutation=permutation)
 
     _log_api_usage_once(permute_channels)
 

From 8b94f6d69b07ee8dac126de5c981502e1ac04545 Mon Sep 17 00:00:00 2001
From: Philip Meier <github.pmeier@posteo.de>
Date: Mon, 7 Aug 2023 13:15:54 +0200
Subject: [PATCH 3/8] add tests

---
 test/test_transforms_v2_refactored.py | 57 +++++++++++++++++++++++++++
 1 file changed, 57 insertions(+)

diff --git a/test/test_transforms_v2_refactored.py b/test/test_transforms_v2_refactored.py
index c910882f9fd..ff67e8a7e9f 100644
--- a/test/test_transforms_v2_refactored.py
+++ b/test/test_transforms_v2_refactored.py
@@ -1,6 +1,7 @@
 import contextlib
 import decimal
 import inspect
+import itertools
 import math
 import re
 from unittest import mock
@@ -2280,3 +2281,59 @@ def resize_my_datapoint():
         _register_kernel_internal(F.resize, MyDatapoint, datapoint_wrapper=False)(resize_my_datapoint)
 
         assert _get_kernel(F.resize, MyDatapoint) is resize_my_datapoint
+
+
+class TestPermuteChannels:
+    _CORRECTNESS_PERMUTATIONS = list(itertools.permutations(range(3)))
+    _DEFAULT_PERMUTATION = _CORRECTNESS_PERMUTATIONS[-1]
+
+    @pytest.mark.parametrize(
+        ("kernel", "make_input"),
+        [
+            (F.permute_channels_image_tensor, make_image_tensor),
+            # FIXME
+            # check_kernel does not support PIL kernel, but it should
+            (F.permute_channels_image_tensor, make_image),
+            (F.permute_channels_video, make_video),
+        ],
+    )
+    @pytest.mark.parametrize("dtype", [torch.float32, torch.uint8])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_kernel(self, kernel, make_input, dtype, device):
+        check_kernel(kernel, make_input(dtype=dtype, device=device), permutation=self._DEFAULT_PERMUTATION)
+
+    @pytest.mark.parametrize(
+        ("kernel", "make_input"),
+        [
+            (F.permute_channels_image_tensor, make_image_tensor),
+            (F.permute_channels_image_pil, make_image_pil),
+            (F.permute_channels_image_tensor, make_image),
+            (F.permute_channels_video, make_video),
+        ],
+    )
+    def test_dispatcher(self, kernel, make_input):
+        check_dispatcher(F.permute_channels, kernel, make_input(), permutation=self._DEFAULT_PERMUTATION)
+
+    @pytest.mark.parametrize(
+        ("kernel", "input_type"),
+        [
+            (F.adjust_brightness_image_tensor, torch.Tensor),
+            (F.adjust_brightness_image_pil, PIL.Image.Image),
+            (F.adjust_brightness_image_tensor, datapoints.Image),
+            (F.adjust_brightness_video, datapoints.Video),
+        ],
+    )
+    def test_dispatcher_signature(self, kernel, input_type):
+        check_dispatcher_kernel_signature_match(F.adjust_brightness, kernel=kernel, input_type=input_type)
+
+    def reference_image_correctness(self, image, permutation):
+        return datapoints.Image(image.numpy()[permutation, ...])
+
+    @pytest.mark.parametrize("permutation", _CORRECTNESS_PERMUTATIONS)
+    def test_image_correctness(self, permutation):
+        image = make_image()
+
+        actual = F.permute_channels(image, permutation=permutation)
+        expected = self.reference_image_correctness(image, permutation=permutation)
+
+        torch.testing.assert_close(actual, expected)

From fdbb4adc96eba14f0cd2c731f7605b67bfb884d4 Mon Sep 17 00:00:00 2001
From: Philip Meier <github.pmeier@posteo.de>
Date: Mon, 7 Aug 2023 14:11:12 +0200
Subject: [PATCH 4/8] fix tests

---
 test/test_transforms_v2_refactored.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/test/test_transforms_v2_refactored.py b/test/test_transforms_v2_refactored.py
index ff67e8a7e9f..10b7dd91581 100644
--- a/test/test_transforms_v2_refactored.py
+++ b/test/test_transforms_v2_refactored.py
@@ -2317,14 +2317,14 @@ def test_dispatcher(self, kernel, make_input):
     @pytest.mark.parametrize(
         ("kernel", "input_type"),
         [
-            (F.adjust_brightness_image_tensor, torch.Tensor),
-            (F.adjust_brightness_image_pil, PIL.Image.Image),
-            (F.adjust_brightness_image_tensor, datapoints.Image),
-            (F.adjust_brightness_video, datapoints.Video),
+            (F.permute_channels_image_tensor, torch.Tensor),
+            (F.permute_channels_image_pil, PIL.Image.Image),
+            (F.permute_channels_image_tensor, datapoints.Image),
+            (F.permute_channels_video, datapoints.Video),
         ],
     )
     def test_dispatcher_signature(self, kernel, input_type):
-        check_dispatcher_kernel_signature_match(F.adjust_brightness, kernel=kernel, input_type=input_type)
+        check_dispatcher_kernel_signature_match(F.permute_channels, kernel=kernel, input_type=input_type)
 
     def reference_image_correctness(self, image, permutation):
         return datapoints.Image(image.numpy()[permutation, ...])

From 95f7d4680ed03546cae96d0f1ed2653ef88ea214 Mon Sep 17 00:00:00 2001
From: Philip Meier <github.pmeier@posteo.de>
Date: Mon, 7 Aug 2023 14:13:37 +0200
Subject: [PATCH 5/8] fix tests

---
 torchvision/transforms/v2/functional/_color.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/torchvision/transforms/v2/functional/_color.py b/torchvision/transforms/v2/functional/_color.py
index 01fcf6913ac..ad530600708 100644
--- a/torchvision/transforms/v2/functional/_color.py
+++ b/torchvision/transforms/v2/functional/_color.py
@@ -645,6 +645,7 @@ def invert_video(video: torch.Tensor) -> torch.Tensor:
     return invert_image_tensor(video)
 
 
+@_register_explicit_noop(datapoints.BoundingBoxes, datapoints.Mask)
 def permute_channels(inpt: datapoints._InputTypeJIT, permutation: List[int]) -> datapoints._InputTypeJIT:
     if torch.jit.is_scripting():
         return permute_channels_image_tensor(inpt, permutation=permutation)

From a5708e4a88028195992b6e473d209a75d446fa65 Mon Sep 17 00:00:00 2001
From: Philip Meier <github.pmeier@posteo.de>
Date: Wed, 9 Aug 2023 09:29:52 +0200
Subject: [PATCH 6/8] address comments

---
 docs/source/transforms.rst                     |  1 +
 test/test_transforms_v2.py                     |  2 +-
 test/test_transforms_v2_refactored.py          | 17 +++++++++--------
 torchvision/transforms/v2/__init__.py          |  2 +-
 torchvision/transforms/v2/_color.py            |  7 ++++++-
 torchvision/transforms/v2/functional/_color.py | 15 +++++++++++++++
 6 files changed, 33 insertions(+), 11 deletions(-)

diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst
index a1858c6b514..0df46c92530 100644
--- a/docs/source/transforms.rst
+++ b/docs/source/transforms.rst
@@ -155,6 +155,7 @@ Color
 
     ColorJitter
     v2.ColorJitter
+    v2.RandomChannelPermutation
     v2.RandomPhotometricDistort
     Grayscale
     v2.Grayscale
diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py
index ef2cc2a1270..5f4a9b62898 100644
--- a/test/test_transforms_v2.py
+++ b/test/test_transforms_v2.py
@@ -124,7 +124,7 @@ class TestSmoke:
             (transforms.RandomEqualize(p=1.0), None),
             (transforms.RandomGrayscale(p=1.0), None),
             (transforms.RandomInvert(p=1.0), None),
-            (transforms.RandomPermuteChannels(), None),
+            (transforms.RandomChannelPermutation(), None),
             (transforms.RandomPhotometricDistort(p=1.0), None),
             (transforms.RandomPosterize(bits=4, p=1.0), None),
             (transforms.RandomSolarize(threshold=0.5, p=1.0), None),
diff --git a/test/test_transforms_v2_refactored.py b/test/test_transforms_v2_refactored.py
index 10b7dd91581..fa04d5deb0c 100644
--- a/test/test_transforms_v2_refactored.py
+++ b/test/test_transforms_v2_refactored.py
@@ -1,7 +1,6 @@
 import contextlib
 import decimal
 import inspect
-import itertools
 import math
 import re
 from unittest import mock
@@ -2284,8 +2283,7 @@ def resize_my_datapoint():
 
 
 class TestPermuteChannels:
-    _CORRECTNESS_PERMUTATIONS = list(itertools.permutations(range(3)))
-    _DEFAULT_PERMUTATION = _CORRECTNESS_PERMUTATIONS[-1]
+    _DEFAULT_PERMUTATION = [2, 0, 1]
 
     @pytest.mark.parametrize(
         ("kernel", "make_input"),
@@ -2327,11 +2325,14 @@ def test_dispatcher_signature(self, kernel, input_type):
         check_dispatcher_kernel_signature_match(F.permute_channels, kernel=kernel, input_type=input_type)
 
     def reference_image_correctness(self, image, permutation):
-        return datapoints.Image(image.numpy()[permutation, ...])
-
-    @pytest.mark.parametrize("permutation", _CORRECTNESS_PERMUTATIONS)
-    def test_image_correctness(self, permutation):
-        image = make_image()
+        channel_images = image.split(1, dim=-3)
+        permuted_channel_images = [channel_images[channel_idx] for channel_idx in permutation]
+        return datapoints.Image(torch.concat(permuted_channel_images, dim=-3))
+
+    @pytest.mark.parametrize("permutation", [[2, 0, 1], [1, 2, 0], [2, 0, 1], [0, 1, 2]])
+    @pytest.mark.parametrize("batch_dims", [(), (2,), (2, 1)])
+    def test_image_correctness(self, permutation, batch_dims):
+        image = make_image(batch_dims=batch_dims)
 
         actual = F.permute_channels(image, permutation=permutation)
         expected = self.reference_image_correctness(image, permutation=permutation)
diff --git a/torchvision/transforms/v2/__init__.py b/torchvision/transforms/v2/__init__.py
index 432bdc21577..4451cb7a1a2 100644
--- a/torchvision/transforms/v2/__init__.py
+++ b/torchvision/transforms/v2/__init__.py
@@ -11,10 +11,10 @@
     Grayscale,
     RandomAdjustSharpness,
     RandomAutocontrast,
+    RandomChannelPermutation,
     RandomEqualize,
     RandomGrayscale,
     RandomInvert,
-    RandomPermuteChannels,
     RandomPhotometricDistort,
     RandomPosterize,
     RandomSolarize,
diff --git a/torchvision/transforms/v2/_color.py b/torchvision/transforms/v2/_color.py
index 3bc01c7ba28..1543a43c9eb 100644
--- a/torchvision/transforms/v2/_color.py
+++ b/torchvision/transforms/v2/_color.py
@@ -177,7 +177,12 @@ def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         return output
 
 
-class RandomPermuteChannels(Transform):
+class RandomChannelPermutation(Transform):
+    """[BETA] Randomly permute the channels of an image or video
+
+    .. v2betastatus:: RandomChannelPermutation transform
+    """
+
     _transformed_types = (
         datapoints.Image,
         PIL.Image.Image,
diff --git a/torchvision/transforms/v2/functional/_color.py b/torchvision/transforms/v2/functional/_color.py
index ad530600708..777463a8efb 100644
--- a/torchvision/transforms/v2/functional/_color.py
+++ b/torchvision/transforms/v2/functional/_color.py
@@ -647,6 +647,21 @@ def invert_video(video: torch.Tensor) -> torch.Tensor:
 
 @_register_explicit_noop(datapoints.BoundingBoxes, datapoints.Mask)
 def permute_channels(inpt: datapoints._InputTypeJIT, permutation: List[int]) -> datapoints._InputTypeJIT:
+    """Permute the channels of the input according to the given permutation.
+
+    This function supports plain :class:`~torch.Tensor`'s, :class:`PIL.Image.Image`'s, and
+    :class:`torchvision.datapoints.Image` and :class:`torchvision.datapoints.Video`.
+
+    Example:
+        >>> rgb_image = torch.rand(3, 256, 256)
+        >>> bgr_image = F.permutate_channels(rgb_image, permutation=[2, 1, 0])
+
+    Args:
+        permutation (List[int]): Valid permutation of the input channel indices.
+
+    Raises:
+        ValueError: If ``len(permutation)`` doesn't match the number of channels in the input.
+    """
     if torch.jit.is_scripting():
         return permute_channels_image_tensor(inpt, permutation=permutation)
 

From ec8546ab81acffe5c827ddb1fc4e83b699c5621f Mon Sep 17 00:00:00 2001
From: Philip Meier <github.pmeier@posteo.de>
Date: Wed, 9 Aug 2023 09:35:14 +0200
Subject: [PATCH 7/8] remove the .tolist() call in the transform

---
 torchvision/transforms/v2/_color.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/torchvision/transforms/v2/_color.py b/torchvision/transforms/v2/_color.py
index 1543a43c9eb..8315e2f36b4 100644
--- a/torchvision/transforms/v2/_color.py
+++ b/torchvision/transforms/v2/_color.py
@@ -192,7 +192,7 @@ class RandomChannelPermutation(Transform):
 
     def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
         num_channels, *_ = query_chw(flat_inputs)
-        return dict(permutation=torch.randperm(num_channels).tolist())
+        return dict(permutation=torch.randperm(num_channels))
 
     def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
         return F.permute_channels(inpt, params["permutation"])

From 72c3a937b2e9b8b75a7fee6654b7c7db1c09cbdb Mon Sep 17 00:00:00 2001
From: Philip Meier <github.pmeier@posteo.de>
Date: Wed, 9 Aug 2023 10:44:13 +0200
Subject: [PATCH 8/8] improve functional doc

---
 torchvision/transforms/v2/functional/_color.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/torchvision/transforms/v2/functional/_color.py b/torchvision/transforms/v2/functional/_color.py
index 777463a8efb..9b6bf3886fa 100644
--- a/torchvision/transforms/v2/functional/_color.py
+++ b/torchvision/transforms/v2/functional/_color.py
@@ -657,7 +657,13 @@ def permute_channels(inpt: datapoints._InputTypeJIT, permutation: List[int]) ->
         >>> bgr_image = F.permutate_channels(rgb_image, permutation=[2, 1, 0])
 
     Args:
-        permutation (List[int]): Valid permutation of the input channel indices.
+        permutation (List[int]): Valid permutation of the input channel indices. The index of the element determines the
+            channel index in the input and the value determines the channel index in the output. For example,
+            ``permutation=[2, 0 , 1]``
+
+            - takes ``ìnpt[..., 0, :, :]`` and puts it at ``output[..., 2, :, :]``,
+            - takes ``ìnpt[..., 1, :, :]`` and puts it at ``output[..., 0, :, :]``, and
+            - takes ``ìnpt[..., 2, :, :]`` and puts it at ``output[..., 1, :, :]``.
 
     Raises:
         ValueError: If ``len(permutation)`` doesn't match the number of channels in the input.