Bunch of doc edits (#7906)

docs/source/transforms.rst

@@ -33,20 +33,33 @@ tasks (image classification, detection, segmentation, video classification).
     from torchvision import tv_tensors
 
     img = torch.randint(0, 256, size=(3, H, W), dtype=torch.uint8)
-    bboxes = torch.randint(0, H // 2, size=(3, 4))
-    bboxes[:, 2:] += bboxes[:, :2]
-    bboxes = tv_tensors.BoundingBoxes(bboxes, format="XYXY", canvas_size=(H, W))
+    boxes = torch.randint(0, H // 2, size=(3, 4))
+    boxes[:, 2:] += boxes[:, :2]
+    boxes = tv_tensors.BoundingBoxes(boxes, format="XYXY", canvas_size=(H, W))
 
     # The same transforms can be used!
-    img, bboxes = transforms(img, bboxes)
+    img, boxes = transforms(img, boxes)
     # And you can pass arbitrary input structures
-    output_dict = transforms({"image": img, "bboxes": bboxes})
+    output_dict = transforms({"image": img, "boxes": boxes})
 
 Transforms are typically passed as the ``transform`` or ``transforms`` argument
 to the :ref:`Datasets <datasets>`.
 
-.. TODO: Reader guide, i.e. what to read depending on what you're looking for
-.. TODO: add link to getting started guide here.
+Start here
+----------
+
+Whether you're new to Torchvision transforms, or you're already experienced with
+them, we encourage you to start with
+:ref:`sphx_glr_auto_examples_transforms_plot_transforms_getting_started.py` in
+order to learn more about what can be done with the new v2 transforms.
+
+Then, browse the sections in below this page for general information and
+performance tips. The available transforms and functionals are listed in the
+:ref:`API reference <v2_api_ref>`.
+
+More information and tutorials can also be found in our :ref:`example gallery
+<gallery>`, e.g. :ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py`
+or :ref:`sphx_glr_auto_examples_transforms_plot_custom_transforms.py`.
 
 .. _conventions:
 
@@ -98,25 +111,21 @@ advantages compared to the v1 ones (in ``torchvision.transforms``):
 
 - They can transform images **but also** bounding boxes, masks, or videos. This
   provides support for tasks beyond image classification: detection, segmentation,
-  video classification, etc.
+  video classification, etc. See
+  :ref:`sphx_glr_auto_examples_transforms_plot_transforms_getting_started.py`
+  and :ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py`.
 - They support more transforms like :class:`~torchvision.transforms.v2.CutMix`
-  and :class:`~torchvision.transforms.v2.MixUp`.
+  and :class:`~torchvision.transforms.v2.MixUp`. See
+  :ref:`sphx_glr_auto_examples_transforms_plot_cutmix_mixup.py`.
 - They're :ref:`faster <transforms_perf>`.
 - They support arbitrary input structures (dicts, lists, tuples, etc.).
 - Future improvements and features will be added to the v2 transforms only.
 
-.. TODO: Add link to e2e example for first bullet point.
-
 These transforms are **fully backward compatible** with the v1 ones, so if
 you're already using tranforms from ``torchvision.transforms``, all you need to
 do to is to update the import to ``torchvision.transforms.v2``. In terms of
 output, there might be negligible differences due to implementation differences.
 
-To learn more about the v2 transforms, check out
-:ref:`sphx_glr_auto_examples_transforms_plot_transforms_getting_started.py`.
-
-.. TODO: make sure link is still good!!
-
 .. note::
 
     The v2 transforms are still BETA, but at this point we do not expect
@@ -184,7 +193,7 @@ This is very much like the :mod:`torch.nn` package which defines both classes
 and functional equivalents in :mod:`torch.nn.functional`.
 
 The functionals support PIL images, pure tensors, or :ref:`TVTensors
-<tv_tensors>`, e.g. both ``resize(image_tensor)`` and ``resize(bboxes)`` are
+<tv_tensors>`, e.g. both ``resize(image_tensor)`` and ``resize(boxes)`` are
 valid.
 
 .. note::
@@ -248,6 +257,8 @@ be derived from ``torch.nn.Module``.
 
 See also: :ref:`sphx_glr_auto_examples_others_plot_scripted_tensor_transforms.py`.
 
+.. _v2_api_ref:
+
 V2 API reference - Recommended
 ------------------------------
 

docs/source/tv_tensors.rst

@@ -7,9 +7,13 @@ TVTensors
 
 TVTensors are :class:`torch.Tensor` subclasses which the v2 :ref:`transforms
 <transforms>` use under the hood to dispatch their inputs to the appropriate
-lower-level kernels. Most users do not need to manipulate TVTensors directly and
-can simply rely on dataset wrapping - see e.g.
-:ref:`sphx_glr_auto_examples_transforms_plot_transforms_e2e.py`.
+lower-level kernels. Most users do not need to manipulate TVTensors directly.
+
+Refer to
+:ref:`sphx_glr_auto_examples_transforms_plot_transforms_getting_started.py` for
+an introduction to TVTensors, or
+:ref:`sphx_glr_auto_examples_transforms_plot_tv_tensors.py` for more advanced
+info.
 
 .. autosummary::
     :toctree: generated/

gallery/README.rst

+.. _gallery:
+
 Examples and tutorials
 ======================

gallery/transforms/plot_transforms_e2e.py

@@ -166,3 +166,16 @@ for imgs, targets in data_loader:
     print(f"{[type(target) for target in targets] = }")
     for name, loss_val in loss_dict.items():
         print(f"{name:<20}{loss_val:.3f}")
+
+# %%
+# Training References
+# -------------------
+#
+# From there, you can check out the `torchvision references
+# <https://github.com/pytorch/vision/tree/main/references>`_ where you'll find
+# the actual training scripts we use to train our models.
+#
+# **Disclaimer** The code in our references is more complex than what you'll
+# need for your own use-cases: this is because we're supporting different
+# backends (PIL, tensors, TVTensors) and different transforms namespaces (v1 and
+# v2). So don't be afraid to simplify and only keep what you need.

gallery/transforms/plot_transforms_getting_started.py

@@ -217,6 +217,8 @@ print(f"{out_target['this_is_ignored']}")
 #     can still be transformed by some transforms like
 #     :class:`~torchvision.transforms.v2.SanitizeBoundingBoxes`!).
 #
+# .. _transforms_datasets_intercompatibility:
+#
 # Transforms and Datasets intercompatibility
 # ------------------------------------------
 #