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Revamp docs for Quantized ShuffleNetV2 (#6028)

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docs/source/models/shufflenetv2_quant.rst 0 → 100644
View file @ a1232c21
Quantized ShuffleNet V2
=======================
.. currentmodule:: torchvision.models.quantization
The Quantized ShuffleNet V2 model is based on the `ShuffleNet V2: Practical Guidelines for Efficient
CNN Architecture Design <https://arxiv.org/abs/1807.11164>`__ paper.
Model builders
--------------
The following model builders can be used to instantiate a quantized ShuffleNetV2
model, with or without pre-trained weights. All the model builders internally rely
on the ``torchvision.models.quantization.shufflenetv2.QuantizableShuffleNetV2``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/shufflenetv2.py>`_
for more details about this class.
.. autosummary::
:toctree: generated/
:template: function.rst
shufflenet_v2_x0_5
shufflenet_v2_x1_0
shufflenet_v2_x1_5
shufflenet_v2_x2_0
docs/source/models_new.rst
View file @ a1232c21
......@@ -206,6 +206,7 @@ pre-trained weights:
models/mobilenetv2_quant
models/mobilenetv3_quant
models/resnet_quant
models/shufflenetv2_quant
|
......
torchvision/models/quantization/shufflenetv2.py
View file @ a1232c21
......@@ -66,7 +66,10 @@ class QuantizableShuffleNetV2(shufflenetv2.ShuffleNetV2):
r"""Fuse conv/bn/relu modules in shufflenetv2 model
Fuse conv+bn+relu/ conv+relu/conv+bn modules to prepare for quantization.
Model is modified in place. Note that this operation does not change numerics
Model is modified in place.
.. note::
Note that this operation does not change numerics
and the model after modification is in floating point
"""
for name, m in self._modules.items():
......@@ -205,8 +208,8 @@ def shufflenet_v2_x0_5(
) -> QuantizableShuffleNetV2:
"""
Constructs a ShuffleNetV2 with 0.5x output channels, as described in
`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
<https://arxiv.org/abs/1807.11164>`_.
`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
<https://arxiv.org/abs/1807.11164>`__.
.. note::
Note that ``quantize = True`` returns a quantized model with 8 bit
......@@ -214,10 +217,26 @@ def shufflenet_v2_x0_5(
GPU inference is not yet supported.
Args:
weights (ShuffleNet_V2_X0_5_QuantizedWeights or ShuffleNet_V2_X0_5_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.ShuffleNet_V2_X0_5_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X0_5_Weights`, optional): The
pretrained weights for the model. See
:class:`~torchvision.models.quantization.ShuffleNet_V2_X0_5_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.ShuffleNet_V2_X0_5_QuantizedWeights``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/shufflenetv2.py>`_
for more details about this class.
.. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X0_5_QuantizedWeights
:members:
.. autoclass:: torchvision.models.ShuffleNet_V2_X0_5_Weights
:members:
:noindex:
"""
weights = (ShuffleNet_V2_X0_5_QuantizedWeights if quantize else ShuffleNet_V2_X0_5_Weights).verify(weights)
return _shufflenetv2(
......@@ -242,8 +261,8 @@ def shufflenet_v2_x1_0(
) -> QuantizableShuffleNetV2:
"""
Constructs a ShuffleNetV2 with 1.0x output channels, as described in
`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
<https://arxiv.org/abs/1807.11164>`_.
`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
<https://arxiv.org/abs/1807.11164>`__.
.. note::
Note that ``quantize = True`` returns a quantized model with 8 bit
......@@ -251,10 +270,26 @@ def shufflenet_v2_x1_0(
GPU inference is not yet supported.
Args:
weights (ShuffleNet_V2_X1_0_QuantizedWeights or ShuffleNet_V2_X1_0_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.ShuffleNet_V2_X1_0_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X1_0_Weights`, optional): The
pretrained weights for the model. See
:class:`~torchvision.models.quantization.ShuffleNet_V2_X1_0_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.ShuffleNet_V2_X1_0_QuantizedWeights``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/shufflenetv2.py>`_
for more details about this class.
.. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X1_0_QuantizedWeights
:members:
.. autoclass:: torchvision.models.ShuffleNet_V2_X1_0_Weights
:members:
:noindex:
"""
weights = (ShuffleNet_V2_X1_0_QuantizedWeights if quantize else ShuffleNet_V2_X1_0_Weights).verify(weights)
return _shufflenetv2(
......@@ -271,8 +306,8 @@ def shufflenet_v2_x1_5(
) -> QuantizableShuffleNetV2:
"""
Constructs a ShuffleNetV2 with 1.5x output channels, as described in
`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
<https://arxiv.org/abs/1807.11164>`_.
`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
<https://arxiv.org/abs/1807.11164>`__.
.. note::
Note that ``quantize = True`` returns a quantized model with 8 bit
......@@ -280,10 +315,26 @@ def shufflenet_v2_x1_5(
GPU inference is not yet supported.
Args:
weights (ShuffleNet_V2_X1_5_QuantizedWeights or ShuffleNet_V2_X1_5_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.ShuffleNet_V2_X1_5_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X1_5_Weights`, optional): The
pretrained weights for the model. See
:class:`~torchvision.models.quantization.ShuffleNet_V2_X1_5_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.ShuffleNet_V2_X1_5_QuantizedWeights``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/shufflenetv2.py>`_
for more details about this class.
.. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X1_5_QuantizedWeights
:members:
.. autoclass:: torchvision.models.ShuffleNet_V2_X1_5_Weights
:members:
:noindex:
"""
weights = (ShuffleNet_V2_X1_5_QuantizedWeights if quantize else ShuffleNet_V2_X1_5_Weights).verify(weights)
return _shufflenetv2(
......@@ -300,8 +351,8 @@ def shufflenet_v2_x2_0(
) -> QuantizableShuffleNetV2:
"""
Constructs a ShuffleNetV2 with 2.0x output channels, as described in
`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design"
<https://arxiv.org/abs/1807.11164>`_.
`ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
<https://arxiv.org/abs/1807.11164>`__.
.. note::
Note that ``quantize = True`` returns a quantized model with 8 bit
......@@ -309,10 +360,26 @@ def shufflenet_v2_x2_0(
GPU inference is not yet supported.
Args:
weights (ShuffleNet_V2_X2_0_QuantizedWeights or ShuffleNet_V2_X2_0_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.ShuffleNet_V2_X2_0_QuantizedWeights` or :class:`~torchvision.models.ShuffleNet_V2_X2_0_Weights`, optional): The
pretrained weights for the model. See
:class:`~torchvision.models.quantization.ShuffleNet_V2_X2_0_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.ShuffleNet_V2_X2_0_QuantizedWeights``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/shufflenetv2.py>`_
for more details about this class.
.. autoclass:: torchvision.models.quantization.ShuffleNet_V2_X2_0_QuantizedWeights
:members:
.. autoclass:: torchvision.models.ShuffleNet_V2_X2_0_Weights
:members:
:noindex:
"""
weights = (ShuffleNet_V2_X2_0_QuantizedWeights if quantize else ShuffleNet_V2_X2_0_Weights).verify(weights)
return _shufflenetv2(
......
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