Start doc revamp for detection models

docs/source/conf.py

@@ -22,6 +22,7 @@
 
 import os
 import textwrap
+from copy import copy
 from pathlib import Path
 
 import pytorch_sphinx_theme
@@ -330,7 +331,7 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
             # the `meta` dict contains another embedded `metrics` dict. To
             # simplify the table generation below, we create the
             # `meta_with_metrics` dict, where the metrics dict has been "flattened"
-            meta = field.meta
+            meta = copy(field.meta)
             metrics = meta.pop("metrics", {})
             meta_with_metrics = dict(meta, **metrics)
 
@@ -346,17 +347,18 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
             lines.append("")
 
 
-def generate_classification_table():
-
-    weight_enums = [getattr(M, name) for name in dir(M) if name.endswith("_Weights")]
+def generate_weights_table(module, table_name, metrics):
+    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]
 
-    column_names = ("**Weight**", "**Acc@1**", "**Acc@5**", "**Params**", "**Recipe**")
+    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
+
     content = [
         (
             f":class:`{w} <{type(w).__name__}>`",
-            w.meta["metrics"]["acc@1"],
-            w.meta["metrics"]["acc@5"],
+            *(w.meta["metrics"][metric] for metric in metrics_keys),
             f"{w.meta['num_params']/1e6:.1f}M",
             f"`link <{w.meta['recipe']}>`__",
         )
@@ -366,13 +368,14 @@ def generate_classification_table():
 
     generated_dir = Path("generated")
     generated_dir.mkdir(exist_ok=True)
-    with open(generated_dir / "classification_table.rst", "w+") as table_file:
+    with open(generated_dir / f"{table_name}_table.rst", "w+") as table_file:
         table_file.write(".. table::\n")
-        table_file.write("    :widths: 100 10 10 20 10\n\n")
+        table_file.write(f"    :widths: 100 {'20 ' * len(metrics_names)} 20 10\n\n")
         table_file.write(f"{textwrap.indent(table, ' ' * 4)}\n\n")
 
 
-generate_classification_table()
+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")])
 
 
 def setup(app):

docs/source/models/retinanet.rst

@@ -0,0 +1,23 @@
+RetinaNet
+=========
+
+.. currentmodule:: torchvision.models.detection
+
+The RetinaNet model is based on the `Focal Loss for Dense Object Detection
+<https://arxiv.org/abs/1708.02002>`__ paper.
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a RetinaNet model, with or
+without pre-trained weights. All the model buidlers internally rely on the
+``torchvision.models.detection.retinanet.RetinaNet`` base class. Please refer to the `source code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/detection/retinanet.py>`_ for
+more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    retinanet_resnet50_fpn
+    retinanet_resnet50_fpn_v2

docs/source/models_new.rst

@@ -59,4 +59,19 @@ Accuracies are reported on ImageNet
 Object Detection, Instance Segmentation and Person Keypoint Detection
 =====================================================================
 
-TODO: Something similar to classification models: list of models + table of weights
+.. currentmodule:: torchvision.models.detection
+
+The following detection models are available, with or without pre-trained
+weights:
+
+.. toctree::
+   :maxdepth: 1
+
+   models/retinanet
+
+Table of all available detection weights
+----------------------------------------
+
+Box MAPs are reported on COCO
+
+.. include:: generated/detection_table.rst

torchvision/models/detection/retinanet.py

@@ -727,7 +727,7 @@ def retinanet_resnet50_fpn(
     """
     Constructs a RetinaNet model with a ResNet-50-FPN backbone.
 
-    Reference: `"Focal Loss for Dense Object Detection" <https://arxiv.org/abs/1708.02002>`_.
+    Reference: `Focal Loss for Dense Object Detection <https://arxiv.org/abs/1708.02002>`_.
 
     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.
@@ -763,13 +763,21 @@ def retinanet_resnet50_fpn(
         >>> predictions = model(x)
 
     Args:
-        weights (RetinaNet_ResNet50_FPN_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.detection.RetinaNet_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. Default is True.
         num_classes (int, optional): number of output classes of the model (including the background)
-        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.RetinaNet_ResNet50_FPN_Weights
+        :members:
     """
     weights = RetinaNet_ResNet50_FPN_Weights.verify(weights)
     weights_backbone = ResNet50_Weights.verify(weights_backbone)
@@ -811,19 +819,27 @@ def retinanet_resnet50_fpn_v2(
     """
     Constructs an improved RetinaNet model with a ResNet-50-FPN backbone.
 
-    Reference: `"Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection"
+    Reference: `Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
     <https://arxiv.org/abs/1912.02424>`_.
 
     :func:`~torchvision.models.detection.retinanet_resnet50_fpn` for more details.
 
     Args:
-        weights (RetinaNet_ResNet50_FPN_V2_Weights, optional): The pretrained weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
+        weights (:class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_V2_Weights`, optional): The
+            pretrained weights to use. See
+            :class:`~torchvision.models.detection.RetinaNet_ResNet50_FPN_V2_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. Default is True.
         num_classes (int, optional): number of output classes of the model (including the background)
-        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.RetinaNet_ResNet50_FPN_V2_Weights
+        :members:
     """
     weights = RetinaNet_ResNet50_FPN_V2_Weights.verify(weights)
     weights_backbone = ResNet50_Weights.verify(weights_backbone)