Source code for torchvision.models.quantization.shufflenetv2
from functools import partial
from typing import Any, List, Optional, Union
import torch
import torch.nn as nn
from torch import Tensor
from torchvision.models import shufflenetv2
from ...transforms._presets import ImageClassification
from .._api import register_model, Weights, WeightsEnum
from .._meta import _IMAGENET_CATEGORIES
from .._utils import _ovewrite_named_param, handle_legacy_interface
from ..shufflenetv2 import (
ShuffleNet_V2_X0_5_Weights,
ShuffleNet_V2_X1_0_Weights,
ShuffleNet_V2_X1_5_Weights,
ShuffleNet_V2_X2_0_Weights,
)
from .utils import _fuse_modules, _replace_relu, quantize_model
__all__ = [
"QuantizableShuffleNetV2",
"ShuffleNet_V2_X0_5_QuantizedWeights",
"ShuffleNet_V2_X1_0_QuantizedWeights",
"ShuffleNet_V2_X1_5_QuantizedWeights",
"ShuffleNet_V2_X2_0_QuantizedWeights",
"shufflenet_v2_x0_5",
"shufflenet_v2_x1_0",
"shufflenet_v2_x1_5",
"shufflenet_v2_x2_0",
]
class QuantizableInvertedResidual(shufflenetv2.InvertedResidual):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self.cat = nn.quantized.FloatFunctional()
def forward(self, x: Tensor) -> Tensor:
if self.stride == 1:
x1, x2 = x.chunk(2, dim=1)
out = self.cat.cat([x1, self.branch2(x2)], dim=1)
else:
out = self.cat.cat([self.branch1(x), self.branch2(x)], dim=1)
out = shufflenetv2.channel_shuffle(out, 2)
return out
class QuantizableShuffleNetV2(shufflenetv2.ShuffleNetV2):
# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, inverted_residual=QuantizableInvertedResidual, **kwargs) # type: ignore[misc]
self.quant = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x: Tensor) -> Tensor:
x = self.quant(x)
x = self._forward_impl(x)
x = self.dequant(x)
return x
def fuse_model(self, is_qat: Optional[bool] = None) -> None:
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::
Note that this operation does not change numerics
and the model after modification is in floating point
"""
for name, m in self._modules.items():
if name in ["conv1", "conv5"] and m is not None:
_fuse_modules(m, [["0", "1", "2"]], is_qat, inplace=True)
for m in self.modules():
if type(m) is QuantizableInvertedResidual:
if len(m.branch1._modules.items()) > 0:
_fuse_modules(m.branch1, [["0", "1"], ["2", "3", "4"]], is_qat, inplace=True)
_fuse_modules(
m.branch2,
[["0", "1", "2"], ["3", "4"], ["5", "6", "7"]],
is_qat,
inplace=True,
)
def _shufflenetv2(
stages_repeats: List[int],
stages_out_channels: List[int],
*,
weights: Optional[WeightsEnum],
progress: bool,
quantize: bool,
**kwargs: Any,
) -> QuantizableShuffleNetV2:
if weights is not None:
_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
if "backend" in weights.meta:
_ovewrite_named_param(kwargs, "backend", weights.meta["backend"])
backend = kwargs.pop("backend", "fbgemm")
model = QuantizableShuffleNetV2(stages_repeats, stages_out_channels, **kwargs)
_replace_relu(model)
if quantize:
quantize_model(model, backend)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress))
return model
_COMMON_META = {
"min_size": (1, 1),
"categories": _IMAGENET_CATEGORIES,
"backend": "fbgemm",
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
"_docs": """
These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized
weights listed below.
""",
}
[docs]class ShuffleNet_V2_X0_5_QuantizedWeights(WeightsEnum):
IMAGENET1K_FBGEMM_V1 = Weights(
url="https://download.pytorch.org/models/quantized/shufflenetv2_x0.5_fbgemm-00845098.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 1366792,
"unquantized": ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1,
"_metrics": {
"ImageNet-1K": {
"acc@1": 57.972,
"acc@5": 79.780,
}
},
"_ops": 0.04,
"_file_size": 1.501,
},
)
DEFAULT = IMAGENET1K_FBGEMM_V1
[docs]class ShuffleNet_V2_X1_0_QuantizedWeights(WeightsEnum):
IMAGENET1K_FBGEMM_V1 = Weights(
url="https://download.pytorch.org/models/quantized/shufflenetv2_x1_fbgemm-db332c57.pth",
transforms=partial(ImageClassification, crop_size=224),
meta={
**_COMMON_META,
"num_params": 2278604,
"unquantized": ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1,
"_metrics": {
"ImageNet-1K": {
"acc@1": 68.360,
"acc@5": 87.582,
}
},
"_ops": 0.145,
"_file_size": 2.334,
},
)
DEFAULT = IMAGENET1K_FBGEMM_V1
[docs]class ShuffleNet_V2_X1_5_QuantizedWeights(WeightsEnum):
IMAGENET1K_FBGEMM_V1 = Weights(
url="https://download.pytorch.org/models/quantized/shufflenetv2_x1_5_fbgemm-d7401f05.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"recipe": "https://github.com/pytorch/vision/pull/5906",
"num_params": 3503624,
"unquantized": ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1,
"_metrics": {
"ImageNet-1K": {
"acc@1": 72.052,
"acc@5": 90.700,
}
},
"_ops": 0.296,
"_file_size": 3.672,
},
)
DEFAULT = IMAGENET1K_FBGEMM_V1
[docs]class ShuffleNet_V2_X2_0_QuantizedWeights(WeightsEnum):
IMAGENET1K_FBGEMM_V1 = Weights(
url="https://download.pytorch.org/models/quantized/shufflenetv2_x2_0_fbgemm-5cac526c.pth",
transforms=partial(ImageClassification, crop_size=224, resize_size=232),
meta={
**_COMMON_META,
"recipe": "https://github.com/pytorch/vision/pull/5906",
"num_params": 7393996,
"unquantized": ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1,
"_metrics": {
"ImageNet-1K": {
"acc@1": 75.354,
"acc@5": 92.488,
}
},
"_ops": 0.583,
"_file_size": 7.467,
},
)
DEFAULT = IMAGENET1K_FBGEMM_V1
[docs]@register_model(name="quantized_shufflenet_v2_x0_5")
@handle_legacy_interface(
weights=(
"pretrained",
lambda kwargs: ShuffleNet_V2_X0_5_QuantizedWeights.IMAGENET1K_FBGEMM_V1
if kwargs.get("quantize", False)
else ShuffleNet_V2_X0_5_Weights.IMAGENET1K_V1,
)
)
def shufflenet_v2_x0_5(
*,
weights: Optional[Union[ShuffleNet_V2_X0_5_QuantizedWeights, ShuffleNet_V2_X0_5_Weights]] = None,
progress: bool = True,
quantize: bool = False,
**kwargs: Any,
) -> 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>`__.
.. note::
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 (: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(
[4, 8, 4], [24, 48, 96, 192, 1024], weights=weights, progress=progress, quantize=quantize, **kwargs
)
[docs]@register_model(name="quantized_shufflenet_v2_x1_0")
@handle_legacy_interface(
weights=(
"pretrained",
lambda kwargs: ShuffleNet_V2_X1_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1
if kwargs.get("quantize", False)
else ShuffleNet_V2_X1_0_Weights.IMAGENET1K_V1,
)
)
def shufflenet_v2_x1_0(
*,
weights: Optional[Union[ShuffleNet_V2_X1_0_QuantizedWeights, ShuffleNet_V2_X1_0_Weights]] = None,
progress: bool = True,
quantize: bool = False,
**kwargs: Any,
) -> 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>`__.
.. note::
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 (: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(
[4, 8, 4], [24, 116, 232, 464, 1024], weights=weights, progress=progress, quantize=quantize, **kwargs
)
[docs]@register_model(name="quantized_shufflenet_v2_x1_5")
@handle_legacy_interface(
weights=(
"pretrained",
lambda kwargs: ShuffleNet_V2_X1_5_QuantizedWeights.IMAGENET1K_FBGEMM_V1
if kwargs.get("quantize", False)
else ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1,
)
)
def shufflenet_v2_x1_5(
*,
weights: Optional[Union[ShuffleNet_V2_X1_5_QuantizedWeights, ShuffleNet_V2_X1_5_Weights]] = None,
progress: bool = True,
quantize: bool = False,
**kwargs: Any,
) -> 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>`__.
.. note::
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 (: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(
[4, 8, 4], [24, 176, 352, 704, 1024], weights=weights, progress=progress, quantize=quantize, **kwargs
)
[docs]@register_model(name="quantized_shufflenet_v2_x2_0")
@handle_legacy_interface(
weights=(
"pretrained",
lambda kwargs: ShuffleNet_V2_X2_0_QuantizedWeights.IMAGENET1K_FBGEMM_V1
if kwargs.get("quantize", False)
else ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1,
)
)
def shufflenet_v2_x2_0(
*,
weights: Optional[Union[ShuffleNet_V2_X2_0_QuantizedWeights, ShuffleNet_V2_X2_0_Weights]] = None,
progress: bool = True,
quantize: bool = False,
**kwargs: Any,
) -> 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>`__.
.. note::
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 (: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(
[4, 8, 4], [24, 244, 488, 976, 2048], weights=weights, progress=progress, quantize=quantize, **kwargs
)
