from functools import partialfrom typing import Any, Callable, List, Optional, Type, Unionimport torchimport torch.nn as nnfrom torch import Tensorfrom ..transforms._presets import ImageClassificationfrom ..utils import _log_api_usage_oncefrom ._api import register_model, Weights, WeightsEnumfrom ._meta import _IMAGENET_CATEGORIESfrom ._utils import _ovewrite_named_param, handle_legacy_interface__all__ = [ "ResNet", "ResNet18_Weights", "ResNet34_Weights", "ResNet50_Weights", "ResNet101_Weights", "ResNet152_Weights", "ResNeXt50_32X4D_Weights", "ResNeXt101_32X8D_Weights", "ResNeXt101_64X4D_Weights", "Wide_ResNet50_2_Weights", "Wide_ResNet101_2_Weights", "resnet18", "resnet34", "resnet50", "resnet101", "resnet152", "resnext50_32x4d", "resnext101_32x8d", "resnext101_64x4d", "wide_resnet50_2", "wide_resnet101_2",]def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d: """3x3 convolution with padding""" return nn.Conv2d( in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation, )def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)class BasicBlock(nn.Module): expansion: int = 1 def __init__( self, inplanes: int, planes: int, stride: int = 1, downsample: Optional[nn.Module] = None, groups: int = 1, base_width: int = 64, dilation: int = 1, norm_layer: Optional[Callable[..., nn.Module]] = None, ) -> None: super().__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError("BasicBlock only supports groups=1 and base_width=64") if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.stride = stride def forward(self, x: Tensor) -> Tensor: identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return outclass Bottleneck(nn.Module): # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) # while original implementation places the stride at the first 1x1 convolution(self.conv1) # according to "Deep residual learning for image recognition" https://arxiv.org/abs/1512.03385. # This variant is also known as ResNet V1.5 and improves accuracy according to # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. expansion: int = 4 def __init__( self, inplanes: int, planes: int, stride: int = 1, downsample: Optional[nn.Module] = None, groups: int = 1, base_width: int = 64, dilation: int = 1, norm_layer: Optional[Callable[..., nn.Module]] = None, ) -> None: super().__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d width = int(planes * (base_width / 64.0)) * groups # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x: Tensor) -> Tensor: identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) out = self.relu(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu(out) return outclass ResNet(nn.Module): def __init__( self, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int], num_classes: int = 1000, zero_init_residual: bool = False, groups: int = 1, width_per_group: int = 64, replace_stride_with_dilation: Optional[List[bool]] = None, norm_layer: Optional[Callable[..., nn.Module]] = None, ) -> None: super().__init__() _log_api_usage_once(self) if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1 if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError( "replace_stride_with_dilation should be None " f"or a 3-element tuple, got {replace_stride_with_dilation}" ) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.bn1 = norm_layer(self.inplanes) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.fc = nn.Linear(512 * block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck) and m.bn3.weight is not None: nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type] elif isinstance(m, BasicBlock) and m.bn2.weight is not None: nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type] def _make_layer( self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int, stride: int = 1, dilate: bool = False, ) -> nn.Sequential: norm_layer = self._norm_layer downsample = None previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion), ) layers = [] layers.append( block( self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer ) ) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append( block( self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer, ) ) return nn.Sequential(*layers) def _forward_impl(self, x: Tensor) -> Tensor: # See note [TorchScript super()] x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.fc(x) return x def forward(self, x: Tensor) -> Tensor: return self._forward_impl(x)def _resnet( block: Type[Union[BasicBlock, Bottleneck]], layers: List[int], weights: Optional[WeightsEnum], progress: bool, **kwargs: Any,) -> ResNet: if weights is not None: _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) model = ResNet(block, layers, **kwargs) if weights is not None: model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) return model_COMMON_META = { "min_size": (1, 1), "categories": _IMAGENET_CATEGORIES,}[docs]class ResNet18_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/resnet18-f37072fd.pth", transforms=partial(ImageClassification, crop_size=224), meta={ **_COMMON_META, "num_params": 11689512, "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", "_metrics": { "ImageNet-1K": { "acc@1": 69.758, "acc@5": 89.078, } }, "_ops": 1.814, "_file_size": 44.661, "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", }, ) DEFAULT = IMAGENET1K_V1

[docs]class ResNet34_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/resnet34-b627a593.pth", transforms=partial(ImageClassification, crop_size=224), meta={ **_COMMON_META, "num_params": 21797672, "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", "_metrics": { "ImageNet-1K": { "acc@1": 73.314, "acc@5": 91.420, } }, "_ops": 3.664, "_file_size": 83.275, "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", }, ) DEFAULT = IMAGENET1K_V1

[docs]class ResNet50_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/resnet50-0676ba61.pth", transforms=partial(ImageClassification, crop_size=224), meta={ **_COMMON_META, "num_params": 25557032, "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", "_metrics": { "ImageNet-1K": { "acc@1": 76.130, "acc@5": 92.862, } }, "_ops": 4.089, "_file_size": 97.781, "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", }, ) IMAGENET1K_V2 = Weights( url="https://download.pytorch.org/models/resnet50-11ad3fa6.pth", transforms=partial(ImageClassification, crop_size=224, resize_size=232), meta={ **_COMMON_META, "num_params": 25557032, "recipe": "https://github.com/pytorch/vision/issues/3995#issuecomment-1013906621", "_metrics": { "ImageNet-1K": { "acc@1": 80.858, "acc@5": 95.434, } }, "_ops": 4.089, "_file_size": 97.79, "_docs": """ These weights improve upon the results of the original paper by using TorchVision's `new training recipe <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_. """, }, ) DEFAULT = IMAGENET1K_V2

[docs]class ResNet101_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/resnet101-63fe2227.pth", transforms=partial(ImageClassification, crop_size=224), meta={ **_COMMON_META, "num_params": 44549160, "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", "_metrics": { "ImageNet-1K": { "acc@1": 77.374, "acc@5": 93.546, } }, "_ops": 7.801, "_file_size": 170.511, "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", }, ) IMAGENET1K_V2 = Weights( url="https://download.pytorch.org/models/resnet101-cd907fc2.pth", transforms=partial(ImageClassification, crop_size=224, resize_size=232), meta={ **_COMMON_META, "num_params": 44549160, "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", "_metrics": { "ImageNet-1K": { "acc@1": 81.886, "acc@5": 95.780, } }, "_ops": 7.801, "_file_size": 170.53, "_docs": """ These weights improve upon the results of the original paper by using TorchVision's `new training recipe <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_. """, }, ) DEFAULT = IMAGENET1K_V2

[docs]class ResNet152_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/resnet152-394f9c45.pth", transforms=partial(ImageClassification, crop_size=224), meta={ **_COMMON_META, "num_params": 60192808, "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnet", "_metrics": { "ImageNet-1K": { "acc@1": 78.312, "acc@5": 94.046, } }, "_ops": 11.514, "_file_size": 230.434, "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", }, ) IMAGENET1K_V2 = Weights( url="https://download.pytorch.org/models/resnet152-f82ba261.pth", transforms=partial(ImageClassification, crop_size=224, resize_size=232), meta={ **_COMMON_META, "num_params": 60192808, "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", "_metrics": { "ImageNet-1K": { "acc@1": 82.284, "acc@5": 96.002, } }, "_ops": 11.514, "_file_size": 230.474, "_docs": """ These weights improve upon the results of the original paper by using TorchVision's `new training recipe <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_. """, }, ) DEFAULT = IMAGENET1K_V2

See AlsoResNet — Understand and Implement from scratchUnderstanding and visualizing ResNetsIntroduction to ResNetsThe Annotated ResNet-50[docs]class ResNeXt50_32X4D_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth", transforms=partial(ImageClassification, crop_size=224), meta={ **_COMMON_META, "num_params": 25028904, "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext", "_metrics": { "ImageNet-1K": { "acc@1": 77.618, "acc@5": 93.698, } }, "_ops": 4.23, "_file_size": 95.789, "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", }, ) IMAGENET1K_V2 = Weights( url="https://download.pytorch.org/models/resnext50_32x4d-1a0047aa.pth", transforms=partial(ImageClassification, crop_size=224, resize_size=232), meta={ **_COMMON_META, "num_params": 25028904, "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", "_metrics": { "ImageNet-1K": { "acc@1": 81.198, "acc@5": 95.340, } }, "_ops": 4.23, "_file_size": 95.833, "_docs": """ These weights improve upon the results of the original paper by using TorchVision's `new training recipe <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_. """, }, ) DEFAULT = IMAGENET1K_V2

[docs]class ResNeXt101_32X8D_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth", transforms=partial(ImageClassification, crop_size=224), meta={ **_COMMON_META, "num_params": 88791336, "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#resnext", "_metrics": { "ImageNet-1K": { "acc@1": 79.312, "acc@5": 94.526, } }, "_ops": 16.414, "_file_size": 339.586, "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", }, ) IMAGENET1K_V2 = Weights( url="https://download.pytorch.org/models/resnext101_32x8d-110c445d.pth", transforms=partial(ImageClassification, crop_size=224, resize_size=232), meta={ **_COMMON_META, "num_params": 88791336, "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres", "_metrics": { "ImageNet-1K": { "acc@1": 82.834, "acc@5": 96.228, } }, "_ops": 16.414, "_file_size": 339.673, "_docs": """ These weights improve upon the results of the original paper by using TorchVision's `new training recipe <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_. """, }, ) DEFAULT = IMAGENET1K_V2

[docs]class ResNeXt101_64X4D_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/resnext101_64x4d-173b62eb.pth", transforms=partial(ImageClassification, crop_size=224, resize_size=232), meta={ **_COMMON_META, "num_params": 83455272, "recipe": "https://github.com/pytorch/vision/pull/5935", "_metrics": { "ImageNet-1K": { "acc@1": 83.246, "acc@5": 96.454, } }, "_ops": 15.46, "_file_size": 319.318, "_docs": """ These weights were trained from scratch by using TorchVision's `new training recipe <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_. """, }, ) DEFAULT = IMAGENET1K_V1

[docs]class Wide_ResNet50_2_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth", transforms=partial(ImageClassification, crop_size=224), meta={ **_COMMON_META, "num_params": 68883240, "recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439", "_metrics": { "ImageNet-1K": { "acc@1": 78.468, "acc@5": 94.086, } }, "_ops": 11.398, "_file_size": 131.82, "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", }, ) IMAGENET1K_V2 = Weights( url="https://download.pytorch.org/models/wide_resnet50_2-9ba9bcbe.pth", transforms=partial(ImageClassification, crop_size=224, resize_size=232), meta={ **_COMMON_META, "num_params": 68883240, "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-fixres", "_metrics": { "ImageNet-1K": { "acc@1": 81.602, "acc@5": 95.758, } }, "_ops": 11.398, "_file_size": 263.124, "_docs": """ These weights improve upon the results of the original paper by using TorchVision's `new training recipe <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_. """, }, ) DEFAULT = IMAGENET1K_V2

[docs]class Wide_ResNet101_2_Weights(WeightsEnum): IMAGENET1K_V1 = Weights( url="https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth", transforms=partial(ImageClassification, crop_size=224), meta={ **_COMMON_META, "num_params": 126886696, "recipe": "https://github.com/pytorch/vision/pull/912#issue-445437439", "_metrics": { "ImageNet-1K": { "acc@1": 78.848, "acc@5": 94.284, } }, "_ops": 22.753, "_file_size": 242.896, "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", }, ) IMAGENET1K_V2 = Weights( url="https://download.pytorch.org/models/wide_resnet101_2-d733dc28.pth", transforms=partial(ImageClassification, crop_size=224, resize_size=232), meta={ **_COMMON_META, "num_params": 126886696, "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe", "_metrics": { "ImageNet-1K": { "acc@1": 82.510, "acc@5": 96.020, } }, "_ops": 22.753, "_file_size": 484.747, "_docs": """ These weights improve upon the results of the original paper by using TorchVision's `new training recipe <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_. """, }, ) DEFAULT = IMAGENET1K_V2

See AlsoPapers with Code - ResNet[docs]@register_model()@handle_legacy_interface(weights=("pretrained", ResNet18_Weights.IMAGENET1K_V1))def resnet18(*, weights: Optional[ResNet18_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: """ResNet-18 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__. Args: weights (:class:`~torchvision.models.ResNet18_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ResNet18_Weights` 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. **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for more details about this class. .. autoclass:: torchvision.models.ResNet18_Weights :members: """ weights = ResNet18_Weights.verify(weights) return _resnet(BasicBlock, [2, 2, 2, 2], weights, progress, **kwargs)

[docs]@register_model()@handle_legacy_interface(weights=("pretrained", ResNet34_Weights.IMAGENET1K_V1))def resnet34(*, weights: Optional[ResNet34_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: """ResNet-34 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__. Args: weights (:class:`~torchvision.models.ResNet34_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ResNet34_Weights` 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. **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for more details about this class. .. autoclass:: torchvision.models.ResNet34_Weights :members: """ weights = ResNet34_Weights.verify(weights) return _resnet(BasicBlock, [3, 4, 6, 3], weights, progress, **kwargs)

[docs]@register_model()@handle_legacy_interface(weights=("pretrained", ResNet50_Weights.IMAGENET1K_V1))def resnet50(*, weights: Optional[ResNet50_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: """ResNet-50 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__. .. note:: The bottleneck of TorchVision places the stride for downsampling to the second 3x3 convolution while the original paper places it to the first 1x1 convolution. This variant improves the accuracy and is known as `ResNet V1.5 <https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch>`_. Args: weights (:class:`~torchvision.models.ResNet50_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ResNet50_Weights` 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. **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for more details about this class. .. autoclass:: torchvision.models.ResNet50_Weights :members: """ weights = ResNet50_Weights.verify(weights) return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs)

[docs]@register_model()@handle_legacy_interface(weights=("pretrained", ResNet101_Weights.IMAGENET1K_V1))def resnet101(*, weights: Optional[ResNet101_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: """ResNet-101 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__. .. note:: The bottleneck of TorchVision places the stride for downsampling to the second 3x3 convolution while the original paper places it to the first 1x1 convolution. This variant improves the accuracy and is known as `ResNet V1.5 <https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch>`_. Args: weights (:class:`~torchvision.models.ResNet101_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ResNet101_Weights` 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. **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for more details about this class. .. autoclass:: torchvision.models.ResNet101_Weights :members: """ weights = ResNet101_Weights.verify(weights) return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs)

[docs]@register_model()@handle_legacy_interface(weights=("pretrained", ResNet152_Weights.IMAGENET1K_V1))def resnet152(*, weights: Optional[ResNet152_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: """ResNet-152 from `Deep Residual Learning for Image Recognition <https://arxiv.org/abs/1512.03385>`__. .. note:: The bottleneck of TorchVision places the stride for downsampling to the second 3x3 convolution while the original paper places it to the first 1x1 convolution. This variant improves the accuracy and is known as `ResNet V1.5 <https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch>`_. Args: weights (:class:`~torchvision.models.ResNet152_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ResNet152_Weights` 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. **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for more details about this class. .. autoclass:: torchvision.models.ResNet152_Weights :members: """ weights = ResNet152_Weights.verify(weights) return _resnet(Bottleneck, [3, 8, 36, 3], weights, progress, **kwargs)

[docs]@register_model()@handle_legacy_interface(weights=("pretrained", ResNeXt50_32X4D_Weights.IMAGENET1K_V1))def resnext50_32x4d( *, weights: Optional[ResNeXt50_32X4D_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: """ResNeXt-50 32x4d model from `Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/abs/1611.05431>`_. Args: weights (:class:`~torchvision.models.ResNeXt50_32X4D_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ResNext50_32X4D_Weights` 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. **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for more details about this class. .. autoclass:: torchvision.models.ResNeXt50_32X4D_Weights :members: """ weights = ResNeXt50_32X4D_Weights.verify(weights) _ovewrite_named_param(kwargs, "groups", 32) _ovewrite_named_param(kwargs, "width_per_group", 4) return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs)

[docs]@register_model()@handle_legacy_interface(weights=("pretrained", ResNeXt101_32X8D_Weights.IMAGENET1K_V1))def resnext101_32x8d( *, weights: Optional[ResNeXt101_32X8D_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: """ResNeXt-101 32x8d model from `Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/abs/1611.05431>`_. Args: weights (:class:`~torchvision.models.ResNeXt101_32X8D_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ResNeXt101_32X8D_Weights` 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. **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for more details about this class. .. autoclass:: torchvision.models.ResNeXt101_32X8D_Weights :members: """ weights = ResNeXt101_32X8D_Weights.verify(weights) _ovewrite_named_param(kwargs, "groups", 32) _ovewrite_named_param(kwargs, "width_per_group", 8) return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs)

[docs]@register_model()@handle_legacy_interface(weights=("pretrained", ResNeXt101_64X4D_Weights.IMAGENET1K_V1))def resnext101_64x4d( *, weights: Optional[ResNeXt101_64X4D_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: """ResNeXt-101 64x4d model from `Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/abs/1611.05431>`_. Args: weights (:class:`~torchvision.models.ResNeXt101_64X4D_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.ResNeXt101_64X4D_Weights` 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. **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for more details about this class. .. autoclass:: torchvision.models.ResNeXt101_64X4D_Weights :members: """ weights = ResNeXt101_64X4D_Weights.verify(weights) _ovewrite_named_param(kwargs, "groups", 64) _ovewrite_named_param(kwargs, "width_per_group", 4) return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs)

[docs]@register_model()@handle_legacy_interface(weights=("pretrained", Wide_ResNet50_2_Weights.IMAGENET1K_V1))def wide_resnet50_2( *, weights: Optional[Wide_ResNet50_2_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: """Wide ResNet-50-2 model from `Wide Residual Networks <https://arxiv.org/abs/1605.07146>`_. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. Args: weights (:class:`~torchvision.models.Wide_ResNet50_2_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.Wide_ResNet50_2_Weights` 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. **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for more details about this class. .. autoclass:: torchvision.models.Wide_ResNet50_2_Weights :members: """ weights = Wide_ResNet50_2_Weights.verify(weights) _ovewrite_named_param(kwargs, "width_per_group", 64 * 2) return _resnet(Bottleneck, [3, 4, 6, 3], weights, progress, **kwargs)

[docs]@register_model()@handle_legacy_interface(weights=("pretrained", Wide_ResNet101_2_Weights.IMAGENET1K_V1))def wide_resnet101_2( *, weights: Optional[Wide_ResNet101_2_Weights] = None, progress: bool = True, **kwargs: Any) -> ResNet: """Wide ResNet-101-2 model from `Wide Residual Networks <https://arxiv.org/abs/1605.07146>`_. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-101 has 2048-512-2048 channels, and in Wide ResNet-101-2 has 2048-1024-2048. Args: weights (:class:`~torchvision.models.Wide_ResNet101_2_Weights`, optional): The pretrained weights to use. See :class:`~torchvision.models.Wide_ResNet101_2_Weights` 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. **kwargs: parameters passed to the ``torchvision.models.resnet.ResNet`` base class. Please refer to the `source code <https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py>`_ for more details about this class. .. autoclass:: torchvision.models.Wide_ResNet101_2_Weights :members: """ weights = Wide_ResNet101_2_Weights.verify(weights) _ovewrite_named_param(kwargs, "width_per_group", 64 * 2) return _resnet(Bottleneck, [3, 4, 23, 3], weights, progress, **kwargs)

# torchvision.models.resnet — Torchvision 0.18 documentation (2024)

Top Articles

The Courier-Journal from Louisville, Kentucky

Site Planning and Design Handbook

Total Dramarama - Series 1: 29. Melter Skelter

Medical Billing and Coding Workshops

Math Launch – UCF STEM

U.S. Department of the Treasury hiring IRS LB&I and TEGE Divisions - Accepting Resumes from Disabled Veterans (10 pt. Preference) in Tampa, Florida, United States | LinkedIn

Custom modifications and transportation for containers or container - general for sale - by dealer - craigslist

Used Trucks for Sale Near Me in Dothan, AL - Autotrader

Latest Posts

How to Watch Today's Milwaukee Bucks vs. Indiana Pacers Playoff Game 3

Television Archive News Search Service

Article information

Author: Golda Nolan II

Last Updated:

Views: 5908

Rating: 4.8 / 5 (78 voted)

Reviews: 93% of readers found this page helpful

Author information

Name: Golda Nolan II

Birthday: 1998-05-14

Address: Suite 369 9754 Roberts Pines, West Benitaburgh, NM 69180-7958

Phone: +522993866487

Job: Sales Executive

Hobby: Worldbuilding, Shopping, Quilting, Cooking, Homebrewing, Leather crafting, Pet

Introduction: My name is Golda Nolan II, I am a thoughtful, clever, cute, jolly, brave, powerful, splendid person who loves writing and wants to share my knowledge and understanding with you.