[fbsync] Add mask rcnn to instance segmentation docs (#5949)

Summary:
* Add mask rcnn to instance segmentation

* Add docs a use nicolas suggestion

* remov param

* remov docsstring, edit docs

* Add one section level

Reviewed By: YosuaMichael

Differential Revision: D36204384

fbshipit-source-id: 2a9f2f7d524b725ac18f9c9149319e6f1fea6da5

Co-authored-by: Nicolas Hug <[email protected]>

Author: 2 people

docs/source/conf.py

@@ -348,14 +348,14 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
             lines.append("")
 
 
-def generate_weights_table(module, table_name, metrics, include_pattern=None, exclude_pattern=None):
+def generate_weights_table(module, table_name, metrics, include_patterns=None, exclude_patterns=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)]
+    if include_patterns is not None:
+        weights = [w for w in weights if any(p in str(w) for p in include_patterns)]
+    if exclude_patterns is not None:
+        weights = [w for w in weights if all(p not in str(w) for p in exclude_patterns)]
 
     metrics_keys, metrics_names = zip(*metrics)
     column_names = ["Weight"] + list(metrics_names) + ["Params", "Recipe"]
@@ -383,13 +383,19 @@ def generate_weights_table(module, table_name, metrics, include_pattern=None, ex
 
 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")], exclude_pattern="Keypoint"
+    module=M.detection, table_name="detection", metrics=[("box_map", "Box MAP")], exclude_patterns=["Mask", "Keypoint"]
+)
+generate_weights_table(
+    module=M.detection,
+    table_name="instance_segmentation",
+    metrics=[("box_map", "Box MAP"), ("mask_map", "Mask MAP")],
+    include_patterns=["Mask"],
 )
 generate_weights_table(
     module=M.detection,
     table_name="detection_keypoint",
     metrics=[("box_map", "Box MAP"), ("kp_map", "Keypoint MAP")],
-    include_pattern="Keypoint",
+    include_patterns=["Keypoint"],
 )
 generate_weights_table(
     module=M.segmentation, table_name="segmentation", metrics=[("miou", "Mean IoU"), ("pixel_acc", "pixelwise Acc")]

docs/source/models/faster_rcnn.rst

@@ -1,5 +1,5 @@
 Faster R-CNN
-==========
+============
 
 .. currentmodule:: torchvision.models.detection
 

docs/source/models_new.rst

@@ -89,33 +89,54 @@ All models are evaluated on COCO val2017:
 
 
 
+Object Detection, Instance Segmentation and Person Keypoint Detection
+=====================================================================
+
 Object Detection
-================
+----------------
 
 .. currentmodule:: torchvision.models.detection
 
-The following detection models are available, with or without pre-trained
+The following object detection models are available, with or without pre-trained
 weights:
 
 .. toctree::
    :maxdepth: 1
 
    models/faster_rcnn
    models/fcos
-   models/mask_rcnn
    models/retinanet
    models/ssdlite
 
-Table of all available detection weights
-----------------------------------------
+Table of all available Object detection weights
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Box MAPs are reported on COCO
 
 .. include:: generated/detection_table.rst
 
+Instance Segmentation
+---------------------
+
+.. currentmodule:: torchvision.models.detection
+
+The following instance segmentation models are available, with or without pre-trained
+weights:
+
+.. toctree::
+   :maxdepth: 1
+
+   models/mask_rcnn
+
+Table of all available Instance segmentation weights
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Box and Mask MAPs are reported on COCO
+
+.. include:: generated/instance_segmentation_table.rst
 
-Keypoint detection
-==================
+Keypoint Detection
+------------------
 
 .. currentmodule:: torchvision.models.detection
 
@@ -128,7 +149,7 @@ pre-trained weights:
    models/keypoint_rcnn
 
 Table of all available Keypoint detection weights
--------------------------------------------------
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Box and Keypoint MAPs are reported on COCO: