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from functools import partial |
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from typing import Any, Optional |
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import torch |
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import torch.nn as nn |
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import torch.nn.init as init |
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from ..transforms._presets import ImageClassification |
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from ..utils import _log_api_usage_once |
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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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__all__ = ["SqueezeNet", "SqueezeNet1_0_Weights", "SqueezeNet1_1_Weights", "squeezenet1_0", "squeezenet1_1"] |
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class Fire(nn.Module): |
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def __init__(self, inplanes: int, squeeze_planes: int, expand1x1_planes: int, expand3x3_planes: int) -> None: |
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super().__init__() |
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self.inplanes = inplanes |
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self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1) |
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self.squeeze_activation = nn.ReLU(inplace=True) |
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self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes, kernel_size=1) |
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self.expand1x1_activation = nn.ReLU(inplace=True) |
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self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes, kernel_size=3, padding=1) |
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self.expand3x3_activation = nn.ReLU(inplace=True) |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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x = self.squeeze_activation(self.squeeze(x)) |
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return torch.cat( |
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[self.expand1x1_activation(self.expand1x1(x)), self.expand3x3_activation(self.expand3x3(x))], 1 |
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) |
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class SqueezeNet(nn.Module): |
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def __init__(self, version: str = "1_0", num_classes: int = 1000, dropout: float = 0.5) -> None: |
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super().__init__() |
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_log_api_usage_once(self) |
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self.num_classes = num_classes |
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if version == "1_0": |
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self.features = nn.Sequential( |
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nn.Conv2d(3, 96, kernel_size=7, stride=2), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(96, 16, 64, 64), |
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Fire(128, 16, 64, 64), |
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Fire(128, 32, 128, 128), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(256, 32, 128, 128), |
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Fire(256, 48, 192, 192), |
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Fire(384, 48, 192, 192), |
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Fire(384, 64, 256, 256), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(512, 64, 256, 256), |
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) |
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elif version == "1_1": |
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self.features = nn.Sequential( |
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nn.Conv2d(3, 64, kernel_size=3, stride=2), |
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nn.ReLU(inplace=True), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(64, 16, 64, 64), |
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Fire(128, 16, 64, 64), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(128, 32, 128, 128), |
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Fire(256, 32, 128, 128), |
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nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), |
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Fire(256, 48, 192, 192), |
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Fire(384, 48, 192, 192), |
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Fire(384, 64, 256, 256), |
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Fire(512, 64, 256, 256), |
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) |
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else: |
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raise ValueError(f"Unsupported SqueezeNet version {version}: 1_0 or 1_1 expected") |
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final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1) |
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self.classifier = nn.Sequential( |
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nn.Dropout(p=dropout), final_conv, nn.ReLU(inplace=True), nn.AdaptiveAvgPool2d((1, 1)) |
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) |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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if m is final_conv: |
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init.normal_(m.weight, mean=0.0, std=0.01) |
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else: |
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init.kaiming_uniform_(m.weight) |
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if m.bias is not None: |
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init.constant_(m.bias, 0) |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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x = self.features(x) |
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x = self.classifier(x) |
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return torch.flatten(x, 1) |
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def _squeezenet( |
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version: str, |
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weights: Optional[WeightsEnum], |
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progress: bool, |
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**kwargs: Any, |
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) -> SqueezeNet: |
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if weights is not None: |
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_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) |
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model = SqueezeNet(version, **kwargs) |
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if weights is not None: |
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model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True)) |
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return model |
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_COMMON_META = { |
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"categories": _IMAGENET_CATEGORIES, |
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"recipe": "https://github.com/pytorch/vision/pull/49#issuecomment-277560717", |
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"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""", |
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} |
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class SqueezeNet1_0_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth", |
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transforms=partial(ImageClassification, crop_size=224), |
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meta={ |
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**_COMMON_META, |
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"min_size": (21, 21), |
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"num_params": 1248424, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 58.092, |
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"acc@5": 80.420, |
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} |
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}, |
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"_ops": 0.819, |
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"_file_size": 4.778, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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class SqueezeNet1_1_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/squeezenet1_1-b8a52dc0.pth", |
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transforms=partial(ImageClassification, crop_size=224), |
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meta={ |
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**_COMMON_META, |
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"min_size": (17, 17), |
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"num_params": 1235496, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 58.178, |
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"acc@5": 80.624, |
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} |
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}, |
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"_ops": 0.349, |
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"_file_size": 4.729, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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@register_model() |
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@handle_legacy_interface(weights=("pretrained", SqueezeNet1_0_Weights.IMAGENET1K_V1)) |
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def squeezenet1_0( |
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*, weights: Optional[SqueezeNet1_0_Weights] = None, progress: bool = True, **kwargs: Any |
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) -> SqueezeNet: |
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"""SqueezeNet model architecture from the `SqueezeNet: AlexNet-level |
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accuracy with 50x fewer parameters and <0.5MB model size |
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<https://arxiv.org/abs/1602.07360>`_ paper. |
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Args: |
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weights (:class:`~torchvision.models.SqueezeNet1_0_Weights`, optional): The |
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pretrained weights to use. See |
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:class:`~torchvision.models.SqueezeNet1_0_Weights` 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 |
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download to stderr. Default is True. |
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**kwargs: parameters passed to the ``torchvision.models.squeezenet.SqueezeNet`` |
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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/squeezenet.py>`_ |
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for more details about this class. |
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.. autoclass:: torchvision.models.SqueezeNet1_0_Weights |
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:members: |
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""" |
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weights = SqueezeNet1_0_Weights.verify(weights) |
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return _squeezenet("1_0", weights, progress, **kwargs) |
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@register_model() |
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@handle_legacy_interface(weights=("pretrained", SqueezeNet1_1_Weights.IMAGENET1K_V1)) |
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def squeezenet1_1( |
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*, weights: Optional[SqueezeNet1_1_Weights] = None, progress: bool = True, **kwargs: Any |
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) -> SqueezeNet: |
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"""SqueezeNet 1.1 model from the `official SqueezeNet repo |
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<https://github.com/DeepScale/SqueezeNet/tree/master/SqueezeNet_v1.1>`_. |
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SqueezeNet 1.1 has 2.4x less computation and slightly fewer parameters |
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than SqueezeNet 1.0, without sacrificing accuracy. |
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Args: |
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weights (:class:`~torchvision.models.SqueezeNet1_1_Weights`, optional): The |
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pretrained weights to use. See |
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:class:`~torchvision.models.SqueezeNet1_1_Weights` 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 |
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download to stderr. Default is True. |
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**kwargs: parameters passed to the ``torchvision.models.squeezenet.SqueezeNet`` |
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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/squeezenet.py>`_ |
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for more details about this class. |
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.. autoclass:: torchvision.models.SqueezeNet1_1_Weights |
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:members: |
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""" |
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weights = SqueezeNet1_1_Weights.verify(weights) |
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return _squeezenet("1_1", weights, progress, **kwargs) |
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