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python">from functools import partial
from typing import Any, Callable, List, Optionalimport torch
from torch import nn, Tensorfrom ..ops.misc import Conv2dNormActivation
from ..transforms._presets import ImageClassification
from ..utils import _log_api_usage_once
from ._api import register_model, Weights, WeightsEnum
from ._meta import _IMAGENET_CATEGORIES
from ._utils import _make_divisible, _ovewrite_named_param, handle_legacy_interface__all__ = ["MobileNetV2", "MobileNet_V2_Weights", "mobilenet_v2"]# necessary for backwards compatibility
class InvertedResidual(nn.Module):def __init__(self, inp: int, oup: int, stride: int, expand_ratio: int, norm_layer: Optional[Callable[..., nn.Module]] = None) -> None:super().__init__()self.stride = strideif stride not in [1, 2]:raise ValueError(f"stride should be 1 or 2 instead of {stride}")if norm_layer is None:norm_layer = nn.BatchNorm2dhidden_dim = int(round(inp * expand_ratio))self.use_res_connect = self.stride == 1 and inp == ouplayers: List[nn.Module] = []if expand_ratio != 1:# pwlayers.append(Conv2dNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6))layers.extend([# dwConv2dNormActivation(hidden_dim,hidden_dim,stride=stride,groups=hidden_dim,norm_layer=norm_layer,activation_layer=nn.ReLU6,),# pw-linearnn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),norm_layer(oup),])self.conv = nn.Sequential(*layers)self.out_channels = oupself._is_cn = stride > 1def forward(self, x: Tensor) -> Tensor:if self.use_res_connect:return x + self.conv(x)else:return self.conv(x)class MobileNetV2(nn.Module):def __init__(self,num_classes: int = 1000,width_mult: float = 1.0,inverted_residual_setting: Optional[List[List[int]]] = None,round_nearest: int = 8,block: Optional[Callable[..., nn.Module]] = None,norm_layer: Optional[Callable[..., nn.Module]] = None,dropout: float = 0.2,) -> None:"""MobileNet V2 main classArgs:num_classes (int): Number of classeswidth_mult (float): Width multiplier - adjusts number of channels in each layer by this amountinverted_residual_setting: Network structureround_nearest (int): Round the number of channels in each layer to be a multiple of this numberSet to 1 to turn off roundingblock: Module specifying inverted residual building block for mobilenetnorm_layer: Module specifying the normalization layer to usedropout (float): The droupout probability"""super().__init__()_log_api_usage_once(self)if block is None:block = InvertedResidualif norm_layer is None:norm_layer = nn.BatchNorm2dinput_channel = 32last_channel = 1280if inverted_residual_setting is None:inverted_residual_setting = [# t, c, n, s[1, 16, 1, 1],[6, 24, 2, 2],[6, 32, 3, 2],[6, 64, 4, 2],[6, 96, 3, 1],[6, 160, 3, 2],[6, 320, 1, 1],]# only check the first element, assuming user knows t,c,n,s are requiredif len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:raise ValueError(f"inverted_residual_setting should be non-empty or a 4-element list, got {inverted_residual_setting}")# building first layerinput_channel = _make_divisible(input_channel * width_mult, round_nearest)self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)features: List[nn.Module] = [Conv2dNormActivation(3, input_channel, stride=2, norm_layer=norm_layer, activation_layer=nn.ReLU6)]# building inverted residual blocksfor t, c, n, s in inverted_residual_setting:output_channel = _make_divisible(c * width_mult, round_nearest)for i in range(n):stride = s if i == 0 else 1features.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer))input_channel = output_channel# building last several layersfeatures.append(Conv2dNormActivation(input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6))# make it nn.Sequentialself.features = nn.Sequential(*features)# building classifierself.classifier = nn.Sequential(nn.Dropout(p=dropout),nn.Linear(self.last_channel, num_classes),)# weight initializationfor m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode="fan_out")if m.bias is not None:nn.init.zeros_(m.bias)elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):nn.init.ones_(m.weight)nn.init.zeros_(m.bias)elif isinstance(m, nn.Linear):nn.init.normal_(m.weight, 0, 0.01)nn.init.zeros_(m.bias)def _forward_impl(self, x: Tensor) -> Tensor:# This exists since TorchScript doesn't support inheritance, so the superclass method# (this one) needs to have a name other than `forward` that can be accessed in a subclassx = self.features(x)# Cannot use "squeeze" as batch-size can be 1x = nn.functional.adaptive_avg_pool2d(x, (1, 1))x = torch.flatten(x, 1)x = self.classifier(x)return xdef forward(self, x: Tensor) -> Tensor:return self._forward_impl(x)_COMMON_META = {"num_params": 3504872,"min_size": (1, 1),"categories": _IMAGENET_CATEGORIES,
}class MobileNet_V2_Weights(WeightsEnum):IMAGENET1K_V1 = Weights(url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",transforms=partial(ImageClassification, crop_size=224),meta={**_COMMON_META,"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2","_metrics": {"ImageNet-1K": {"acc@1": 71.878,"acc@5": 90.286,}},"_ops": 0.301,"_file_size": 13.555,"_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",},)IMAGENET1K_V2 = Weights(url="https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth",transforms=partial(ImageClassification, crop_size=224, resize_size=232),meta={**_COMMON_META,"recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning","_metrics": {"ImageNet-1K": {"acc@1": 72.154,"acc@5": 90.822,}},"_ops": 0.301,"_file_size": 13.598,"_docs": """These weights improve upon the results of the original paper by using a modified version of TorchVision's`new training recipe<https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.""",},)DEFAULT = IMAGENET1K_V2@register_model()
@handle_legacy_interface(weights=("pretrained", MobileNet_V2_Weights.IMAGENET1K_V1))
def mobilenet_v2(*, weights: Optional[MobileNet_V2_Weights] = None, progress: bool = True, **kwargs: Any
) -> MobileNetV2:"""MobileNetV2 architecture from the `MobileNetV2: Inverted Residuals and LinearBottlenecks <https://arxiv.org/abs/1801.04381>`_ paper.Args:weights (:class:`~torchvision.models.MobileNet_V2_Weights`, optional): Thepretrained weights to use. See:class:`~torchvision.models.MobileNet_V2_Weights` below formore details, and possible values. By default, no pre-trainedweights are used.progress (bool, optional): If True, displays a progress bar of thedownload to stderr. Default is True.**kwargs: parameters passed to the ``torchvision.models.mobilenetv2.MobileNetV2``base class. Please refer to the `source code<https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv2.py>`_for more details about this class... autoclass:: torchvision.models.MobileNet_V2_Weights:members:"""weights = MobileNet_V2_Weights.verify(weights)if weights is not None:_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))model = MobileNetV2(**kwargs)if weights is not None:model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))return model