Make transforms illutration example use v2 instead of v1 (#7886)

docs/source/conf.py

@@ -84,6 +84,7 @@ def __init__(self, src_dir):
 
     transforms_subsection_order = [
         "plot_transforms_getting_started.py",
+        "plot_transforms_illustrations.py",
         "plot_transforms_e2e.py",
         "plot_cutmix_mixup.py",
         "plot_custom_transforms.py",

gallery/others/plot_scripted_tensor_transforms.py

@@ -62,7 +62,7 @@ def show(imgs):
 # --------------------------
 # Most transforms natively support tensors on top of PIL images (to visualize
 # the effect of the transforms, you may refer to see
-# :ref:`sphx_glr_auto_examples_others_plot_transforms.py`).
+# :ref:`sphx_glr_auto_examples_transforms_plot_transforms_illustrations.py`).
 # Using tensor images, we can run the transforms on GPUs if cuda is available!
 
 import torch.nn as nn

gallery/transforms/helpers.py

@@ -5,7 +5,7 @@
 from torchvision.transforms.v2 import functional as F
 
 
-def plot(imgs):
+def plot(imgs, row_title=None, **imshow_kwargs):
     if not isinstance(imgs[0], list):
         # Make a 2d grid even if there's just 1 row
         imgs = [imgs]
@@ -40,7 +40,11 @@ def plot(imgs):
                 img = draw_segmentation_masks(img, masks.to(torch.bool), colors=["green"] * masks.shape[0], alpha=.65)
 
             ax = axs[row_idx, col_idx]
-            ax.imshow(img.permute(1, 2, 0).numpy())
+            ax.imshow(img.permute(1, 2, 0).numpy(), **imshow_kwargs)
             ax.set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])
 
+    if row_title is not None:
+        for row_idx in range(num_rows):
+            axs[row_idx, 0].set(ylabel=row_title[row_idx])
+
     plt.tight_layout()