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274 lines
11 KiB
274 lines
11 KiB
5 months ago
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import warnings
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from functools import partial
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from typing import Any, List, Optional, Union
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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from torchvision.models import inception as inception_module
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from torchvision.models.inception import Inception_V3_Weights, InceptionOutputs
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from ...transforms._presets import ImageClassification
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from .._api import register_model, Weights, WeightsEnum
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from .._meta import _IMAGENET_CATEGORIES
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from .._utils import _ovewrite_named_param, handle_legacy_interface
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from .utils import _fuse_modules, _replace_relu, quantize_model
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__all__ = [
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"QuantizableInception3",
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"Inception_V3_QuantizedWeights",
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"inception_v3",
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]
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class QuantizableBasicConv2d(inception_module.BasicConv2d):
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, **kwargs)
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self.relu = nn.ReLU()
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def forward(self, x: Tensor) -> Tensor:
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x = self.conv(x)
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x = self.bn(x)
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x = self.relu(x)
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return x
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def fuse_model(self, is_qat: Optional[bool] = None) -> None:
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_fuse_modules(self, ["conv", "bn", "relu"], is_qat, inplace=True)
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class QuantizableInceptionA(inception_module.InceptionA):
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# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
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self.myop = nn.quantized.FloatFunctional()
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def forward(self, x: Tensor) -> Tensor:
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outputs = self._forward(x)
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return self.myop.cat(outputs, 1)
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class QuantizableInceptionB(inception_module.InceptionB):
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# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
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self.myop = nn.quantized.FloatFunctional()
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def forward(self, x: Tensor) -> Tensor:
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outputs = self._forward(x)
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return self.myop.cat(outputs, 1)
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class QuantizableInceptionC(inception_module.InceptionC):
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# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
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self.myop = nn.quantized.FloatFunctional()
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def forward(self, x: Tensor) -> Tensor:
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outputs = self._forward(x)
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return self.myop.cat(outputs, 1)
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class QuantizableInceptionD(inception_module.InceptionD):
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# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
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self.myop = nn.quantized.FloatFunctional()
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def forward(self, x: Tensor) -> Tensor:
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outputs = self._forward(x)
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return self.myop.cat(outputs, 1)
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class QuantizableInceptionE(inception_module.InceptionE):
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# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
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self.myop1 = nn.quantized.FloatFunctional()
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self.myop2 = nn.quantized.FloatFunctional()
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self.myop3 = nn.quantized.FloatFunctional()
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def _forward(self, x: Tensor) -> List[Tensor]:
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branch1x1 = self.branch1x1(x)
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branch3x3 = self.branch3x3_1(x)
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branch3x3 = [self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3)]
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branch3x3 = self.myop1.cat(branch3x3, 1)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = [
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self.branch3x3dbl_3a(branch3x3dbl),
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self.branch3x3dbl_3b(branch3x3dbl),
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]
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branch3x3dbl = self.myop2.cat(branch3x3dbl, 1)
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branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
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branch_pool = self.branch_pool(branch_pool)
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outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
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return outputs
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def forward(self, x: Tensor) -> Tensor:
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outputs = self._forward(x)
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return self.myop3.cat(outputs, 1)
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class QuantizableInceptionAux(inception_module.InceptionAux):
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# TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs) # type: ignore[misc]
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class QuantizableInception3(inception_module.Inception3):
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def __init__(self, *args: Any, **kwargs: Any) -> None:
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super().__init__( # type: ignore[misc]
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*args,
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inception_blocks=[
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QuantizableBasicConv2d,
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QuantizableInceptionA,
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QuantizableInceptionB,
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QuantizableInceptionC,
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QuantizableInceptionD,
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QuantizableInceptionE,
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QuantizableInceptionAux,
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],
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**kwargs,
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)
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self.quant = torch.ao.quantization.QuantStub()
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self.dequant = torch.ao.quantization.DeQuantStub()
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def forward(self, x: Tensor) -> InceptionOutputs:
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x = self._transform_input(x)
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x = self.quant(x)
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x, aux = self._forward(x)
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x = self.dequant(x)
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aux_defined = self.training and self.aux_logits
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if torch.jit.is_scripting():
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if not aux_defined:
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warnings.warn("Scripted QuantizableInception3 always returns QuantizableInception3 Tuple")
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return InceptionOutputs(x, aux)
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else:
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return self.eager_outputs(x, aux)
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def fuse_model(self, is_qat: Optional[bool] = None) -> None:
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r"""Fuse conv/bn/relu modules in inception model
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Fuse conv+bn+relu/ conv+relu/conv+bn modules to prepare for quantization.
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Model is modified in place. Note that this operation does not change numerics
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and the model after modification is in floating point
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"""
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for m in self.modules():
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if type(m) is QuantizableBasicConv2d:
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m.fuse_model(is_qat)
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class Inception_V3_QuantizedWeights(WeightsEnum):
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IMAGENET1K_FBGEMM_V1 = Weights(
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url="https://download.pytorch.org/models/quantized/inception_v3_google_fbgemm-a2837893.pth",
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transforms=partial(ImageClassification, crop_size=299, resize_size=342),
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meta={
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"num_params": 27161264,
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"min_size": (75, 75),
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"categories": _IMAGENET_CATEGORIES,
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"backend": "fbgemm",
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"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#post-training-quantized-models",
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"unquantized": Inception_V3_Weights.IMAGENET1K_V1,
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"_metrics": {
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"ImageNet-1K": {
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"acc@1": 77.176,
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"acc@5": 93.354,
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}
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},
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"_ops": 5.713,
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"_file_size": 23.146,
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"_docs": """
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These weights were produced by doing Post Training Quantization (eager mode) on top of the unquantized
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weights listed below.
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""",
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},
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)
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DEFAULT = IMAGENET1K_FBGEMM_V1
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@register_model(name="quantized_inception_v3")
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@handle_legacy_interface(
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weights=(
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"pretrained",
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lambda kwargs: Inception_V3_QuantizedWeights.IMAGENET1K_FBGEMM_V1
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if kwargs.get("quantize", False)
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else Inception_V3_Weights.IMAGENET1K_V1,
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)
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)
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def inception_v3(
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*,
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weights: Optional[Union[Inception_V3_QuantizedWeights, Inception_V3_Weights]] = None,
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progress: bool = True,
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quantize: bool = False,
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**kwargs: Any,
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) -> QuantizableInception3:
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r"""Inception v3 model architecture from
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`Rethinking the Inception Architecture for Computer Vision <http://arxiv.org/abs/1512.00567>`__.
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.. note::
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**Important**: In contrast to the other models the inception_v3 expects tensors with a size of
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N x 3 x 299 x 299, so ensure your images are sized accordingly.
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.. note::
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Note that ``quantize = True`` returns a quantized model with 8 bit
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weights. Quantized models only support inference and run on CPUs.
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GPU inference is not yet supported.
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Args:
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weights (:class:`~torchvision.models.quantization.Inception_V3_QuantizedWeights` or :class:`~torchvision.models.Inception_V3_Weights`, optional): The pretrained
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weights for the model. See
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:class:`~torchvision.models.quantization.Inception_V3_QuantizedWeights` below for
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more details, and possible values. By default, no pre-trained
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weights are used.
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progress (bool, optional): If True, displays a progress bar of the download to stderr.
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Default is True.
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quantize (bool, optional): If True, return a quantized version of the model.
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Default is False.
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**kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableInception3``
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base class. Please refer to the `source code
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<https://github.com/pytorch/vision/blob/main/torchvision/models/quantization/inception.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.quantization.Inception_V3_QuantizedWeights
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:members:
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.. autoclass:: torchvision.models.Inception_V3_Weights
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:members:
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:noindex:
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"""
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weights = (Inception_V3_QuantizedWeights if quantize else Inception_V3_Weights).verify(weights)
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original_aux_logits = kwargs.get("aux_logits", False)
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if weights is not None:
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if "transform_input" not in kwargs:
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_ovewrite_named_param(kwargs, "transform_input", True)
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_ovewrite_named_param(kwargs, "aux_logits", True)
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_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
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if "backend" in weights.meta:
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_ovewrite_named_param(kwargs, "backend", weights.meta["backend"])
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backend = kwargs.pop("backend", "fbgemm")
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model = QuantizableInception3(**kwargs)
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_replace_relu(model)
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if quantize:
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quantize_model(model, backend)
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if weights is not None:
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if quantize and not original_aux_logits:
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model.aux_logits = False
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model.AuxLogits = None
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model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
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if not quantize and not original_aux_logits:
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model.aux_logits = False
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model.AuxLogits = None
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return model
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