Source code for torchvision.models.quantization.inception
import warnings
from functools import partial
from typing import Any, List, Optional, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torchvision.models import inception as inception_module
from torchvision.models.inception import Inception_V3_Weights, InceptionOutputs
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 .utils import _fuse_modules, _replace_relu, quantize_model
__all__ = [
"QuantizableInception3",
"Inception_V3_QuantizedWeights",
"inception_v3",
]
class QuantizableBasicConv2d(inception_module.BasicConv2d):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self.relu = nn.ReLU()
def forward(self, x: Tensor) -> Tensor:
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
def fuse_model(self, is_qat: Optional[bool] = None) -> None:
_fuse_modules(self, ["conv", "bn", "relu"], is_qat, inplace=True)
class QuantizableInceptionA(inception_module.InceptionA):
# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
self.myop = nn.quantized.FloatFunctional()
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return self.myop.cat(outputs, 1)
class QuantizableInceptionB(inception_module.InceptionB):
# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
self.myop = nn.quantized.FloatFunctional()
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return self.myop.cat(outputs, 1)
class QuantizableInceptionC(inception_module.InceptionC):
# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
self.myop = nn.quantized.FloatFunctional()
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return self.myop.cat(outputs, 1)
class QuantizableInceptionD(inception_module.InceptionD):
# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
self.myop = nn.quantized.FloatFunctional()
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return self.myop.cat(outputs, 1)
class QuantizableInceptionE(inception_module.InceptionE):
# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
self.myop1 = nn.quantized.FloatFunctional()
self.myop2 = nn.quantized.FloatFunctional()
self.myop3 = nn.quantized.FloatFunctional()
def _forward(self, x: Tensor) -> List[Tensor]:
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3)]
branch3x3 = self.myop1.cat(branch3x3, 1)
branch3x3dbl = self.branch3x3dbl_1(x)
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
branch3x3dbl = [
self.branch3x3dbl_3a(branch3x3dbl),
self.branch3x3dbl_3b(branch3x3dbl),
]
branch3x3dbl = self.myop2.cat(branch3x3dbl, 1)
branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
branch_pool = self.branch_pool(branch_pool)
outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
return outputs
def forward(self, x: Tensor) -> Tensor:
outputs = self._forward(x)
return self.myop3.cat(outputs, 1)
class QuantizableInceptionAux(inception_module.InceptionAux):
# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
class QuantizableInception3(inception_module.Inception3):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__( # type: ignore[misc]
*args,
inception_blocks=[
QuantizableBasicConv2d,
QuantizableInceptionA,
QuantizableInceptionB,
QuantizableInceptionC,
QuantizableInceptionD,
QuantizableInceptionE,
QuantizableInceptionAux,
],
**kwargs,
)
self.quant = torch.ao.quantization.QuantStub()
self.dequant = torch.ao.quantization.DeQuantStub()
def forward(self, x: Tensor) -> InceptionOutputs:
x = self._transform_input(x)
x = self.quant(x)
x, aux = self._forward(x)
x = self.dequant(x)
aux_defined = self.training and self.aux_logits
if torch.jit.is_scripting():
if not aux_defined:
warnings.warn("Scripted QuantizableInception3 always returns QuantizableInception3 Tuple")
return InceptionOutputs(x, aux)
else:
return self.eager_outputs(x, aux)
def fuse_model(self, is_qat: Optional[bool] = None) -> None:
r"""Fuse conv/bn/relu modules in inception 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
and the model after modification is in floating point
"""
for m in self.modules():
if type(m) is QuantizableBasicConv2d:
m.fuse_model(is_qat)
[docs]class Inception_V3_QuantizedWeights(WeightsEnum):
IMAGENET1K_FBGEMM_V1 = Weights(
url="https://download.pytorch.org/models/quantized/inception_v3_google_fbgemm-a2837893.pth",
transforms=partial(ImageClassification, crop_size=299, resize_size=342),
meta={
"num_params": 27161264,
"min_size": (75, 75),
"categories": _IMAGENET_CATEGORIES,
"backend": "fbgemm",
"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
"unquantized": Inception_V3_Weights.IMAGENET1K_V1,
"_metrics": {
"ImageNet-1K": {
"acc@1": 77.176,
"acc@5": 93.354,
}
},
"_ops": 5.713,
"_file_size": 23.146,
"_docs": """
These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized
weights listed below.
""",
},
)
DEFAULT = IMAGENET1K_FBGEMM_V1
[docs]@register_model(name="quantized_inception_v3")
@handle_legacy_interface(
weights=(
"pretrained",
lambda kwargs: Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1
if kwargs.get("quantize", False)
else Inception_V3_Weights.IMAGENET1K_V1,
)
)
def inception_v3(
*,
weights: Optional[Union[Inception_V3_QuantizedWeights, Inception_V3_Weights]] = None,
progress: bool = True,
quantize: bool = False,
**kwargs: Any,
) -> QuantizableInception3:
r"""Inception v3 model architecture from
`Rethinking the Inception Architecture for Computer Vision <http://arxiv.org/abs/1512.00567>`__.
.. note::
**Important**: In contrast to the other models the inception_v3 expects tensors with a size of
N x 3 x 299 x 299, so ensure your images are sized accordingly.
.. 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.Inception_V3_QuantizedWeights` or :class:`~torchvision.models.Inception_V3_Weights`, optional): The pretrained
weights for the model. See
:class:`~torchvision.models.quantization.Inception_V3_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.QuantizableInception3``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/inception.py>`_
for more details about this class.
.. autoclass:: torchvision.models.quantization.Inception_V3_QuantizedWeights
:members:
.. autoclass:: torchvision.models.Inception_V3_Weights
:members:
:noindex:
"""
weights = (Inception_V3_QuantizedWeights if quantize else Inception_V3_Weights).verify(weights)
original_aux_logits = kwargs.get("aux_logits", False)
if weights is not None:
if "transform_input" not in kwargs:
_ovewrite_named_param(kwargs, "transform_input", True)
_ovewrite_named_param(kwargs, "aux_logits", True)
_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 = QuantizableInception3(**kwargs)
_replace_relu(model)
if quantize:
quantize_model(model, backend)
if weights is not None:
if quantize and not original_aux_logits:
model.aux_logits = False
model.AuxLogits = None
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
if not quantize and not original_aux_logits:
model.aux_logits = False
model.AuxLogits = None
return model
