[fbsync] added revamped quantized mobilenetv2 docs (#6004)

Summary:
* added quantized mobilenetv2 docs

* remove quotes

Reviewed By: datumbox

Differential Revision: D36413357

fbshipit-source-id: 43f52f52c7404ac241604452089ee124cb729356

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

Author: NicolasHug

docs/source/models/mobilenetv2_quant.rst

@@ -0,0 +1,24 @@
+Quantized MobileNet V2
+======================
+
+.. currentmodule:: torchvision.models.quantization
+
+The Quantized MobileNet V2 model is based on the `MobileNetV2: Inverted Residuals and Linear
+Bottlenecks <https://arxiv.org/abs/1801.04381>`__ paper.
+
+
+Model builders
+--------------
+
+The following model builders can be used to instantiate a quantized MobileNetV2
+model, with or without pre-trained weights. All the model builders internally
+rely on the ``torchvision.models.quantization.mobilenetv2.QuantizableMobileNetV2``
+base class. Please refer to the `source code
+<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/mobilenetv2.py>`_
+for more details about this class.
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+
+    mobilenet_v2

docs/source/models_new.rst

@@ -76,6 +76,7 @@ pre-trained weights:
    :maxdepth: 1
 
    models/googlenet_quant
+   models/mobilenetv2_quant
 
 
 Table of all available quantized classification weights

torchvision/models/quantization/mobilenetv2.py

@@ -101,18 +101,30 @@ def mobilenet_v2(
 ) -> QuantizableMobileNetV2:
     """
     Constructs a MobileNetV2 architecture from
-    `"MobileNetV2: Inverted Residuals and Linear Bottlenecks"
+    `MobileNetV2: Inverted Residuals and Linear Bottlenecks
     <https://arxiv.org/abs/1801.04381>`_.
 
     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
 
     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, returns a quantized model, else returns a float model
+        weights (:class:`~torchvision.models.quantization.MobileNet_V2_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V2_Weights`, optional): The
+            pretrained weights for the model. See
+            :class:`~torchvision.models.quantization.MobileNet_V2_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, returns a quantized version of the model. Default is False.
+        **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableMobileNetV2``
+            base class. Please refer to the `source code
+            <https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/mobilenetv2.py>`_
+            for more details about this class.
+    .. autoclass:: torchvision.models.quantization.MobileNet_V2_QuantizedWeights
+        :members:
+    .. autoclass:: torchvision.models.MobileNet_V2_Weights
+        :members:
+        :noindex:
     """
     weights = (MobileNet_V2_QuantizedWeights if quantize else MobileNet_V2_Weights).verify(weights)