Add Docs for QuantizedGoogleNet (#5975)

* Add Docs for QuantizedGoogleNet

* ufmt

* Use explicit _QuantizedWeights or _Weights suffix

docs/source/conf.py

@@ -315,7 +315,7 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
       used within the autoclass directive.
     """
 
-    if obj.__name__.endswith("_Weights"):
+    if obj.__name__.endswith(("_Weights", "_QuantizedWeights")):
         lines[:] = [
             "The model builder above accepts the following values as the ``weights`` parameter.",
             f"``{obj.__name__}.DEFAULT`` is equivalent to ``{obj.DEFAULT}``.",
@@ -349,7 +349,8 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
 
 
 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_endswith = "_QuantizedWeights" if module.__name__.split(".")[-1] == "quantization" else "_Weights"
+    weight_enums = [getattr(module, name) for name in dir(module) if name.endswith(weights_endswith)]
     weights = [w for weight_enum in weight_enums for w in weight_enum]
 
     if include_patterns is not None:
@@ -382,6 +383,9 @@ def generate_weights_table(module, table_name, metrics, include_patterns=None, e
 
 
 generate_weights_table(module=M, table_name="classification", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")])
+generate_weights_table(
+    module=M.quantization, table_name="classification_quant", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")]
+)
 generate_weights_table(
     module=M.detection, table_name="detection", metrics=[("box_map", "Box MAP")], exclude_patterns=["Mask", "Keypoint"]
 )

docs/source/models/googlenet_quant.rst

+Quantized GoogLeNet
+===================
+
+.. currentmodule:: torchvision.models.quantization
+
+The Quantized GoogleNet model is based on the `Going Deeper with Convolutions <https://arxiv.org/abs/1409.4842>`__
+paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instanciate a quantized GoogLeNet
+model, with or without pre-trained weights. All the model builders internally
+rely on the ``torchvision.models.quantization.googlenet.QuantizableGoogLeNet``
+base class. Please refer to the `source code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/googlenet.py>`_
+for more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    googlenet

docs/source/models_new.rst

@@ -65,6 +65,27 @@ Accuracies are reported on ImageNet
 
 .. include:: generated/classification_table.rst
 
+Quantized models
+----------------
+
+.. currentmodule:: torchvision.models.quantization
+
+The following quantized classification models are available, with or without
+pre-trained weights:
+
+.. toctree::
+   :maxdepth: 1
+
+   models/googlenet_quant
+
+
+Table of all available quantized classification weights
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Accuracies are reported on ImageNet
+
+.. include:: generated/classification_quant_table.rst
+
 Semantic Segmentation
 =====================
 

torchvision/models/googlenet.py

@@ -295,8 +295,9 @@ class GoogLeNet_Weights(WeightsEnum):
 
 @handle_legacy_interface(weights=("pretrained", GoogLeNet_Weights.IMAGENET1K_V1))
 def googlenet(*, weights: Optional[GoogLeNet_Weights] = None, progress: bool = True, **kwargs: Any) -> GoogLeNet:
-    r"""GoogLeNet (Inception v1) model architecture from
+    """GoogLeNet (Inception v1) model architecture from
     `Going Deeper with Convolutions <http://arxiv.org/abs/1409.4842>`_.
+
     The required minimum input size of the model is 15x15.
 
     Args:

torchvision/models/quantization/googlenet.py

@@ -141,18 +141,34 @@ def googlenet(
     quantize: bool = False,
     **kwargs: Any,
 ) -> QuantizableGoogLeNet:
-    r"""GoogLeNet (Inception v1) model architecture from
-    `"Going Deeper with Convolutions" <http://arxiv.org/abs/1409.4842>`_.
+    """GoogLeNet (Inception v1) model architecture from `Going Deeper with Convolutions <http://arxiv.org/abs/1409.4842>`__.
 
-    Note that quantize = True returns a quantized model with 8 bit
+    Note that ``quantize = True`` returns a quantized model with 8 bit
     weights. Quantized models only support inference and run on CPUs.
     GPU inference is not yet supported
 
+    The required minimum input size of the model is 15x15.
+
     Args:
-        weights (GoogLeNet_QuantizedWeights or GoogLeNet_Weights, optional): The pretrained
-            weights for the model
-        progress (bool): If True, displays a progress bar of the download to stderr
-        quantize (bool): If True, return a quantized version of the model
+        weights (:class:`~torchvision.models.quantization.GoogLeNet_QuantizedWeights` or :class:`~torchvision.models.GoogLeNet_Weights`, optional): The
+            pretrained weights for the model. See
+            :class:`~torchvision.models.quantization.GoogLeNet_QuantizedWeights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool, optional): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        quantize (bool, optional): If True, return a quantized version of the model. Default is False.
+        **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableGoogLeNet``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization.googlenet.py>`_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.quantization.GoogLeNet_QuantizedWeights
+        :members:
+
+    .. autoclass:: torchvision.models.GoogLeNet_Weights
+        :members:
+        :noindex:
     """
     weights = (GoogLeNet_QuantizedWeights if quantize else GoogLeNet_Weights).verify(weights)