Merge branch 'main' into turboooo

docs/source/conf.py

@@ -320,7 +320,7 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
       used within the autoclass directive.
     """
 
-    if obj.__name__.endswith(("_Weights", "_QuantizedWeights")):
+    if getattr(obj, ".__name__", "").endswith(("_Weights", "_QuantizedWeights")):
 
         if len(obj) == 0:
             lines[:] = ["There are no available pre-trained weights."]

docs/source/datapoints.rst

@@ -17,3 +17,4 @@ see e.g. :ref:`sphx_glr_auto_examples_plot_transforms_v2_e2e.py`.
     BoundingBoxFormat
     BoundingBoxes
     Mask
+    Datapoint

docs/source/transforms.rst

@@ -155,6 +155,7 @@ Color
 
     ColorJitter
     v2.ColorJitter
+    v2.RandomChannelPermutation
     v2.RandomPhotometricDistort
     Grayscale
     v2.Grayscale
@@ -375,3 +376,14 @@ you can use a functional transform to build transform classes with custom behavi
     to_pil_image
     to_tensor
     vflip
+
+Developer tools
+---------------
+
+.. currentmodule:: torchvision.transforms.v2.functional
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    register_kernel

gallery/plot_custom_datapoints.py

@@ -0,0 +1,125 @@
+"""
+=====================================
+How to write your own Datapoint class
+=====================================
+
+This guide is intended for downstream library maintainers. We explain how to
+write your own datapoint class, and how to make it compatible with the built-in
+Torchvision v2 transforms. Before continuing, make sure you have read
+:ref:`sphx_glr_auto_examples_plot_datapoints.py`.
+"""
+
+# %%
+import torch
+import torchvision
+
+# We are using BETA APIs, so we deactivate the associated warning, thereby acknowledging that
+# some APIs may slightly change in the future
+torchvision.disable_beta_transforms_warning()
+
+from torchvision import datapoints
+from torchvision.transforms import v2
+
+# %%
+# We will create a very simple class that just inherits from the base
+# :class:`~torchvision.datapoints.Datapoint` class. It will be enough to cover
+# what you need to know to implement your more elaborate uses-cases. If you need
+# to create a class that carries meta-data, take a look at how the
+# :class:`~torchvision.datapoints.BoundingBoxes` class is `implemented
+# <https://github.com/pytorch/vision/blob/main/torchvision/datapoints/_bounding_box.py>`_.
+
+
+class MyDatapoint(datapoints.Datapoint):
+    pass
+
+
+my_dp = MyDatapoint([1, 2, 3])
+my_dp
+
+# %%
+# Now that we have defined our custom Datapoint class, we want it to be
+# compatible with the built-in torchvision transforms, and the functional API.
+# For that, we need to implement a kernel which performs the core of the
+# transformation, and then "hook" it to the functional that we want to support
+# via :func:`~torchvision.transforms.v2.functional.register_kernel`.
+#
+# We illustrate this process below: we create a kernel for the "horizontal flip"
+# operation of our MyDatapoint class, and register it to the functional API.
+
+from torchvision.transforms.v2 import functional as F
+
+
+@F.register_kernel(dispatcher="hflip", datapoint_cls=MyDatapoint)
+def hflip_my_datapoint(my_dp, *args, **kwargs):
+    print("Flipping!")
+    out = my_dp.flip(-1)
+    return MyDatapoint.wrap_like(my_dp, out)
+
+
+# %%
+# To understand why ``wrap_like`` is used, see
+# :ref:`datapoint_unwrapping_behaviour`. Ignore the ``*args, **kwargs`` for now,
+# we will explain it below in :ref:`param_forwarding`.
+#
+# .. note::
+#
+#     In our call to ``register_kernel`` above we used a string
+#     ``dispatcher="hflip"`` to refer to the functional we want to hook into. We
+#     could also have used the  functional *itself*, i.e.
+#     ``@register_kernel(dispatcher=F.hflip, ...)``.
+#
+#     The functionals that you can be hooked into are the ones in
+#     ``torchvision.transforms.v2.functional`` and they are documented in
+#     :ref:`functional_transforms`.
+#
+# Now that we have registered our kernel, we can call the functional API on a
+# ``MyDatapoint`` instance:
+
+my_dp = MyDatapoint(torch.rand(3, 256, 256))
+_ = F.hflip(my_dp)
+
+# %%
+# And we can also use the
+# :class:`~torchvision.transforms.v2.RandomHorizontalFlip` transform, since it relies on :func:`~torchvision.transforms.v2.functional.hflip` internally:
+t = v2.RandomHorizontalFlip(p=1)
+_ = t(my_dp)
+
+# %%
+# .. note::
+#
+#     We cannot register a kernel for a transform class, we can only register a
+#     kernel for a **functional**. The reason we can't register a transform
+#     class is because one transform may internally rely on more than one
+#     functional, so in general we can't register a single kernel for a given
+#     class.
+#
+# .. _param_forwarding:
+#
+# Parameter forwarding, and ensuring future compatibility of your kernels
+# -----------------------------------------------------------------------
+#
+# The functional API that you're hooking into is public and therefore
+# **backward** compatible: we guarantee that the parameters of these functionals
+# won't be removed or renamed without a proper deprecation cycle. However, we
+# don't guarantee **forward** compatibility, and we may add new parameters in
+# the future.
+#
+# Imagine that in a future version, Torchvision adds a new ``inplace`` parameter
+# to its :func:`~torchvision.transforms.v2.functional.hflip` functional. If you
+# already defined and registered your own kernel as
+
+def hflip_my_datapoint(my_dp):  # noqa
+    print("Flipping!")
+    out = my_dp.flip(-1)
+    return MyDatapoint.wrap_like(my_dp, out)
+
+
+# %%
+# then calling ``F.hflip(my_dp)`` will **fail**, because ``hflip`` will try to
+# pass the new ``inplace`` parameter to your kernel, but your kernel doesn't
+# accept it.
+#
+# For this reason, we recommend to always define your kernels with
+# ``*args, **kwargs`` in their signature, as done above. This way, your kernel
+# will be able to accept any new parameter that we may add in the future.
+# (Technically, adding `**kwargs` only should be enough).

gallery/plot_custom_transforms.py

@@ -0,0 +1,123 @@
+"""
+===================================
+How to write your own v2 transforms
+===================================
+
+This guide explains how to write transforms that are compatible with the
+torchvision transforms V2 API.
+"""
+
+# %%
+import torch
+import torchvision
+
+# We are using BETA APIs, so we deactivate the associated warning, thereby acknowledging that
+# some APIs may slightly change in the future
+torchvision.disable_beta_transforms_warning()
+
+from torchvision import datapoints
+from torchvision.transforms import v2
+
+
+# %%
+# Just create a ``nn.Module`` and override the ``forward`` method
+# ===============================================================
+#
+# In most cases, this is all you're going to need, as long as you already know
+# the structure of the input that your transform will expect. For example if
+# you're just doing image classification, your transform will typically accept a
+# single image as input, or a ``(img, label)`` input. So you can just hard-code
+# your ``forward`` method to accept just that, e.g.
+#
+# .. code:: python
+#
+#     class MyCustomTransform(torch.nn.Module):
+#         def forward(self, img, label):
+#             # Do some transformations
+#             return new_img, new_label
+#
+# .. note::
+#
+#     This means that if you have a custom transform that is already compatible
+#     with the V1 transforms (those in ``torchvision.transforms``), it will
+#     still work with the V2 transforms without any change!
+#
+# We will illustrate this more completely below with a typical detection case,
+# where our samples are just images, bounding boxes and labels:
+
+class MyCustomTransform(torch.nn.Module):
+    def forward(self, img, bboxes, label):  # we assume inputs are always structured like this
+        print(
+            f"I'm transforming an image of shape {img.shape} "
+            f"with bboxes = {bboxes}\n{label = }"
+        )
+        # Do some transformations. Here, we're just passing though the input
+        return img, bboxes, label
+
+
+transforms = v2.Compose([
+    MyCustomTransform(),
+    v2.RandomResizedCrop((224, 224), antialias=True),
+    v2.RandomHorizontalFlip(p=1),
+    v2.Normalize(mean=[0, 0, 0], std=[1, 1, 1])
+])
+
+H, W = 256, 256
+img = torch.rand(3, H, W)
+bboxes = datapoints.BoundingBoxes(
+    torch.tensor([[0, 10, 10, 20], [50, 50, 70, 70]]),
+    format="XYXY",
+    canvas_size=(H, W)
+)
+label = 3
+
+out_img, out_bboxes, out_label = transforms(img, bboxes, label)
+# %%
+print(f"Output image shape: {out_img.shape}\nout_bboxes = {out_bboxes}\n{out_label = }")
+# %%
+# .. note::
+#     While working with datapoint classes in your code, make sure to
+#     familiarize yourself with this section:
+#     :ref:`datapoint_unwrapping_behaviour`
+#
+# Supporting arbitrary input structures
+# =====================================
+#
+# In the section above, we have assumed that you already know the structure of
+# your inputs and that you're OK with hard-coding this expected structure in
+# your code. If you want your custom transforms to be as flexible as possible,
+# this can be a bit limitting.
+#
+# A key feature of the builtin Torchvision V2 transforms is that they can accept
+# arbitrary input structure and return the same structure as output (with
+# transformed entries). For example, transforms can accept a single image, or a
+# tuple of ``(img, label)``, or an arbitrary nested dictionary as input:
+
+structured_input = {
+    "img": img,
+    "annotations": (bboxes, label),
+    "something_that_will_be_ignored": (1, "hello")
+}
+structured_output = v2.RandomHorizontalFlip(p=1)(structured_input)
+
+assert isinstance(structured_output, dict)
+assert structured_output["something_that_will_be_ignored"] == (1, "hello")
+print(f"The transformed bboxes are:\n{structured_output['annotations'][0]}")
+
+# %%
+# If you want to reproduce this behavior in your own transform, we invite you to
+# look at our `code
+# <https://github.com/pytorch/vision/blob/main/torchvision/transforms/v2/_transform.py>`_
+# and adapt it to your needs.
+#
+# In brief, the core logic is to unpack the input into a flat list using `pytree
+# <https://github.com/pytorch/pytorch/blob/main/torch/utils/_pytree.py>`_, and
+# then transform only the entries that can be transformed (the decision is made
+# based on the **class** of the entries, as all datapoints are
+# tensor-subclasses) plus some custom logic that is out of score here - check the
+# code for details. The (potentially transformed) entries are then repacked and
+# returned, in the same structure as the input.
+#
+# We do not provide public dev-facing tools to achieve that at this time, but if
+# this is something that would be valuable to you, please let us know by opening
+# an issue on our `GitHub repo <https://github.com/pytorch/vision/issues>`_.