Document Keypoint RCNN separately

docs/source/conf.py

@@ -348,10 +348,15 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
             lines.append("")
 
 
-def generate_weights_table(module, table_name, metrics):
+def generate_weights_table(module, table_name, metrics, include_pattern=None, exclude_pattern=None):
     weight_enums = [getattr(module, name) for name in dir(module) if name.endswith("_Weights")]
     weights = [w for weight_enum in weight_enums for w in weight_enum]
 
+    if include_pattern is not None:
+        weights = [w for w in weights if include_pattern in str(w)]
+    if exclude_pattern is not None:
+        weights = [w for w in weights if exclude_pattern not in str(w)]
+
     metrics_keys, metrics_names = zip(*metrics)
     column_names = ["Weight"] + list(metrics_names) + ["Params", "Recipe"]
     column_names = [f"**{name}**" for name in column_names]  # Add bold
@@ -377,7 +382,15 @@ def generate_weights_table(module, table_name, metrics):
 
 
 generate_weights_table(module=M, table_name="classification", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")])
-generate_weights_table(module=M.detection, table_name="detection", metrics=[("box_map", "Box MAP")])
+generate_weights_table(
+    module=M.detection, table_name="detection", metrics=[("box_map", "Box MAP")], exclude_pattern="Keypoint"
+)
+generate_weights_table(
+    module=M.detection,
+    table_name="detection_keypoint",
+    metrics=[("box_map", "Box MAP"), ("kp_map", "Keypoint MAP")],
+    include_pattern="Keypoint",
+)
 generate_weights_table(
     module=M.segmentation, table_name="segmentation", metrics=[("miou", "Mean IoU"), ("pixel_acc", "pixelwise Acc")]
 )

docs/source/models/keypoint_rcnn.rst

@@ -0,0 +1,24 @@
+Keypoint R-CNN
+==============
+
+.. currentmodule:: torchvision.models.detection
+
+The Keypoint R-CNN model is based on the `Mask R-CNN
+<https://arxiv.org/abs/1703.06870>`__ paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a Keypoint R-CNN model,
+with or without pre-trained weights. All the model builders internally rely on
+the ``torchvision.models.detection.KeypointRCNN`` base class. Please refer to the `source
+code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/detection/keypoint_rcnn.py>`__
+for more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    keypointrcnn_resnet50_fpn

docs/source/models_new.rst

@@ -89,8 +89,8 @@ All models are evaluated on COCO val2017:
 
 
 
-Object Detection, Instance Segmentation and Person Keypoint Detection
-=====================================================================
+Object Detection
+================
 
 .. currentmodule:: torchvision.models.detection
 
@@ -114,6 +114,27 @@ Box MAPs are reported on COCO
 .. include:: generated/detection_table.rst
 
 
+Keypoint detection
+==================
+
+.. currentmodule:: torchvision.models.detection
+
+The following keypoint detection models are available, with or without
+pre-trained weights:
+
+.. toctree::
+   :maxdepth: 1
+
+   models/keypoint_rcnn
+
+Table of all available Keypoint detection weights
+-------------------------------------------------
+
+Box and Keypoint MAPs are reported on COCO:
+
+.. include:: generated/detection_keypoint_table.rst
+
+
 Video Classification
 ====================
 

torchvision/models/detection/keypoint_rcnn.py

@@ -366,7 +366,7 @@ def keypointrcnn_resnet50_fpn(
     """
     Constructs a Keypoint R-CNN model with a ResNet-50-FPN backbone.
 
-    Reference: `"Mask R-CNN" <https://arxiv.org/abs/1703.06870>`_.
+    Reference: `Mask R-CNN <https://arxiv.org/abs/1703.06870>`__.
 
     The input to the model is expected to be a list of tensors, each of shape ``[C, H, W]``, one for each
     image, and should be in ``0-1`` range. Different images can have different sizes.
@@ -410,14 +410,22 @@ def keypointrcnn_resnet50_fpn(
         >>> torch.onnx.export(model, x, "keypoint_rcnn.onnx", opset_version = 11)
 
     Args:
-        weights (KeypointRCNN_ResNet50_FPN_Weights, optional): The pretrained weights for the model
+        weights (:class:`~torchvision.models.detection.KeypointRCNN_ResNet50_FPN_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.detection.KeypointRCNN_ResNet50_FPN_Weights`
+            below for more details, and possible values. By default, no
+            pre-trained weights are used.
         progress (bool): If True, displays a progress bar of the download to stderr
         num_classes (int, optional): number of output classes of the model (including the background)
         num_keypoints (int, optional): number of keypoints
-        weights_backbone (ResNet50_Weights, optional): The pretrained weights for the backbone
+        weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The
+            pretrained weights for the backbone.
         trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from final block.
             Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
             passed (the default) this value is set to 3.
+
+    .. autoclass:: torchvision.models.detection.KeypointRCNN_ResNet50_FPN_Weights
+        :members:
     """
     weights = KeypointRCNN_ResNet50_FPN_Weights.verify(weights)
     weights_backbone = ResNet50_Weights.verify(weights_backbone)