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from functools import partial |
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from typing import Any, cast, Dict, List, Optional, Union |
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import torch |
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import torch.nn as nn |
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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__ = [ |
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"VGG", |
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"VGG11_Weights", |
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"VGG11_BN_Weights", |
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"VGG13_Weights", |
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"VGG13_BN_Weights", |
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"VGG16_Weights", |
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"VGG16_BN_Weights", |
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"VGG19_Weights", |
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"VGG19_BN_Weights", |
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"vgg11", |
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"vgg11_bn", |
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"vgg13", |
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"vgg13_bn", |
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"vgg16", |
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"vgg16_bn", |
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"vgg19", |
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"vgg19_bn", |
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] |
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class VGG(nn.Module): |
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def __init__( |
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self, features: nn.Module, num_classes: int = 1000, init_weights: bool = True, dropout: float = 0.5 |
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) -> None: |
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super().__init__() |
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_log_api_usage_once(self) |
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self.features = features |
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self.avgpool = nn.AdaptiveAvgPool2d((7, 7)) |
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self.classifier = nn.Sequential( |
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nn.Linear(512 * 7 * 7, 4096), |
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nn.ReLU(True), |
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nn.Dropout(p=dropout), |
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nn.Linear(4096, 4096), |
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nn.ReLU(True), |
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nn.Dropout(p=dropout), |
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nn.Linear(4096, num_classes), |
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) |
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if init_weights: |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") |
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if m.bias is not None: |
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nn.init.constant_(m.bias, 0) |
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elif isinstance(m, nn.BatchNorm2d): |
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nn.init.constant_(m.weight, 1) |
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nn.init.constant_(m.bias, 0) |
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elif isinstance(m, nn.Linear): |
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nn.init.normal_(m.weight, 0, 0.01) |
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nn.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.avgpool(x) |
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x = torch.flatten(x, 1) |
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x = self.classifier(x) |
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return x |
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def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) -> nn.Sequential: |
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layers: List[nn.Module] = [] |
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in_channels = 3 |
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for v in cfg: |
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if v == "M": |
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layers += [nn.MaxPool2d(kernel_size=2, stride=2)] |
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else: |
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v = cast(int, v) |
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conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) |
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if batch_norm: |
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layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] |
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else: |
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layers += [conv2d, nn.ReLU(inplace=True)] |
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in_channels = v |
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return nn.Sequential(*layers) |
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cfgs: Dict[str, List[Union[str, int]]] = { |
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"A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"], |
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"B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"], |
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"D": [64, 64, "M", 128, 128, "M", 256, 256, 256, "M", 512, 512, 512, "M", 512, 512, 512, "M"], |
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"E": [64, 64, "M", 128, 128, "M", 256, 256, 256, 256, "M", 512, 512, 512, 512, "M", 512, 512, 512, 512, "M"], |
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} |
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def _vgg(cfg: str, batch_norm: bool, weights: Optional[WeightsEnum], progress: bool, **kwargs: Any) -> VGG: |
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if weights is not None: |
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kwargs["init_weights"] = False |
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if weights.meta["categories"] is not None: |
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_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) |
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model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **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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"min_size": (32, 32), |
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"categories": _IMAGENET_CATEGORIES, |
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"recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg", |
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"_docs": """These weights were trained from scratch by using a simplified training recipe.""", |
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} |
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class VGG11_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/vgg11-8a719046.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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"num_params": 132863336, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 69.020, |
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"acc@5": 88.628, |
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} |
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}, |
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"_ops": 7.609, |
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"_file_size": 506.84, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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class VGG11_BN_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/vgg11_bn-6002323d.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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"num_params": 132868840, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 70.370, |
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"acc@5": 89.810, |
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} |
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}, |
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"_ops": 7.609, |
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"_file_size": 506.881, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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class VGG13_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/vgg13-19584684.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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"num_params": 133047848, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 69.928, |
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"acc@5": 89.246, |
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} |
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}, |
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"_ops": 11.308, |
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"_file_size": 507.545, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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class VGG13_BN_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/vgg13_bn-abd245e5.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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"num_params": 133053736, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 71.586, |
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"acc@5": 90.374, |
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} |
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}, |
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"_ops": 11.308, |
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"_file_size": 507.59, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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class VGG16_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/vgg16-397923af.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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"num_params": 138357544, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 71.592, |
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"acc@5": 90.382, |
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} |
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}, |
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"_ops": 15.47, |
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"_file_size": 527.796, |
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}, |
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) |
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IMAGENET1K_FEATURES = Weights( |
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url="https://download.pytorch.org/models/vgg16_features-amdegroot-88682ab5.pth", |
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transforms=partial( |
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ImageClassification, |
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crop_size=224, |
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mean=(0.48235, 0.45882, 0.40784), |
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std=(1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0), |
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), |
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meta={ |
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**_COMMON_META, |
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"num_params": 138357544, |
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"categories": None, |
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"recipe": "https://github.com/amdegroot/ssd.pytorch#training-ssd", |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": float("nan"), |
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"acc@5": float("nan"), |
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} |
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}, |
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"_ops": 15.47, |
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"_file_size": 527.802, |
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"_docs": """ |
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These weights can't be used for classification because they are missing values in the `classifier` |
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module. Only the `features` module has valid values and can be used for feature extraction. The weights |
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were trained using the original input standardization method as described in the paper. |
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""", |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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class VGG16_BN_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/vgg16_bn-6c64b313.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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"num_params": 138365992, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 73.360, |
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"acc@5": 91.516, |
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} |
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}, |
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"_ops": 15.47, |
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"_file_size": 527.866, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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class VGG19_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/vgg19-dcbb9e9d.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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"num_params": 143667240, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 72.376, |
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"acc@5": 90.876, |
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} |
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}, |
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"_ops": 19.632, |
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"_file_size": 548.051, |
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}, |
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) |
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DEFAULT = IMAGENET1K_V1 |
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|
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class VGG19_BN_Weights(WeightsEnum): |
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IMAGENET1K_V1 = Weights( |
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url="https://download.pytorch.org/models/vgg19_bn-c79401a0.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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"num_params": 143678248, |
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"_metrics": { |
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"ImageNet-1K": { |
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"acc@1": 74.218, |
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"acc@5": 91.842, |
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} |
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}, |
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"_ops": 19.632, |
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"_file_size": 548.143, |
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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", VGG11_Weights.IMAGENET1K_V1)) |
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def vgg11(*, weights: Optional[VGG11_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: |
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"""VGG-11 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__. |
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Args: |
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weights (:class:`~torchvision.models.VGG11_Weights`, optional): The |
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pretrained weights to use. See |
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:class:`~torchvision.models.VGG11_Weights` below for |
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more details, and possible values. By default, no pre-trained |
|
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.vgg.VGG`` |
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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/vgg.py>`_ |
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for more details about this class. |
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|
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.. autoclass:: torchvision.models.VGG11_Weights |
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:members: |
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""" |
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weights = VGG11_Weights.verify(weights) |
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return _vgg("A", False, weights, progress, **kwargs) |
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@register_model() |
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@handle_legacy_interface(weights=("pretrained", VGG11_BN_Weights.IMAGENET1K_V1)) |
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def vgg11_bn(*, weights: Optional[VGG11_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: |
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"""VGG-11-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__. |
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|
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Args: |
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weights (:class:`~torchvision.models.VGG11_BN_Weights`, optional): The |
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pretrained weights to use. See |
|
:class:`~torchvision.models.VGG11_BN_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.vgg.VGG`` |
|
base class. Please refer to the `source code |
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_ |
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for more details about this class. |
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|
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.. autoclass:: torchvision.models.VGG11_BN_Weights |
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:members: |
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""" |
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weights = VGG11_BN_Weights.verify(weights) |
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return _vgg("A", True, weights, progress, **kwargs) |
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|
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@register_model() |
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@handle_legacy_interface(weights=("pretrained", VGG13_Weights.IMAGENET1K_V1)) |
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def vgg13(*, weights: Optional[VGG13_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: |
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"""VGG-13 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__. |
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|
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Args: |
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weights (:class:`~torchvision.models.VGG13_Weights`, optional): The |
|
pretrained weights to use. See |
|
:class:`~torchvision.models.VGG13_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.vgg.VGG`` |
|
base class. Please refer to the `source code |
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_ |
|
for more details about this class. |
|
|
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.. autoclass:: torchvision.models.VGG13_Weights |
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:members: |
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""" |
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weights = VGG13_Weights.verify(weights) |
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return _vgg("B", False, weights, progress, **kwargs) |
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|
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@register_model() |
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@handle_legacy_interface(weights=("pretrained", VGG13_BN_Weights.IMAGENET1K_V1)) |
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def vgg13_bn(*, weights: Optional[VGG13_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: |
|
"""VGG-13-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__. |
|
|
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Args: |
|
weights (:class:`~torchvision.models.VGG13_BN_Weights`, optional): The |
|
pretrained weights to use. See |
|
:class:`~torchvision.models.VGG13_BN_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.vgg.VGG`` |
|
base class. Please refer to the `source code |
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_ |
|
for more details about this class. |
|
|
|
.. autoclass:: torchvision.models.VGG13_BN_Weights |
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:members: |
|
""" |
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weights = VGG13_BN_Weights.verify(weights) |
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return _vgg("B", True, weights, progress, **kwargs) |
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|
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@register_model() |
|
@handle_legacy_interface(weights=("pretrained", VGG16_Weights.IMAGENET1K_V1)) |
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def vgg16(*, weights: Optional[VGG16_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: |
|
"""VGG-16 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__. |
|
|
|
Args: |
|
weights (:class:`~torchvision.models.VGG16_Weights`, optional): The |
|
pretrained weights to use. See |
|
:class:`~torchvision.models.VGG16_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.vgg.VGG`` |
|
base class. Please refer to the `source code |
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_ |
|
for more details about this class. |
|
|
|
.. autoclass:: torchvision.models.VGG16_Weights |
|
:members: |
|
""" |
|
weights = VGG16_Weights.verify(weights) |
|
|
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return _vgg("D", False, weights, progress, **kwargs) |
|
|
|
|
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@register_model() |
|
@handle_legacy_interface(weights=("pretrained", VGG16_BN_Weights.IMAGENET1K_V1)) |
|
def vgg16_bn(*, weights: Optional[VGG16_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: |
|
"""VGG-16-BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__. |
|
|
|
Args: |
|
weights (:class:`~torchvision.models.VGG16_BN_Weights`, optional): The |
|
pretrained weights to use. See |
|
:class:`~torchvision.models.VGG16_BN_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.vgg.VGG`` |
|
base class. Please refer to the `source code |
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_ |
|
for more details about this class. |
|
|
|
.. autoclass:: torchvision.models.VGG16_BN_Weights |
|
:members: |
|
""" |
|
weights = VGG16_BN_Weights.verify(weights) |
|
|
|
return _vgg("D", True, weights, progress, **kwargs) |
|
|
|
|
|
@register_model() |
|
@handle_legacy_interface(weights=("pretrained", VGG19_Weights.IMAGENET1K_V1)) |
|
def vgg19(*, weights: Optional[VGG19_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: |
|
"""VGG-19 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__. |
|
|
|
Args: |
|
weights (:class:`~torchvision.models.VGG19_Weights`, optional): The |
|
pretrained weights to use. See |
|
:class:`~torchvision.models.VGG19_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.vgg.VGG`` |
|
base class. Please refer to the `source code |
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_ |
|
for more details about this class. |
|
|
|
.. autoclass:: torchvision.models.VGG19_Weights |
|
:members: |
|
""" |
|
weights = VGG19_Weights.verify(weights) |
|
|
|
return _vgg("E", False, weights, progress, **kwargs) |
|
|
|
|
|
@register_model() |
|
@handle_legacy_interface(weights=("pretrained", VGG19_BN_Weights.IMAGENET1K_V1)) |
|
def vgg19_bn(*, weights: Optional[VGG19_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG: |
|
"""VGG-19_BN from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__. |
|
|
|
Args: |
|
weights (:class:`~torchvision.models.VGG19_BN_Weights`, optional): The |
|
pretrained weights to use. See |
|
:class:`~torchvision.models.VGG19_BN_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.vgg.VGG`` |
|
base class. Please refer to the `source code |
|
<https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py>`_ |
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for more details about this class. |
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.. autoclass:: torchvision.models.VGG19_BN_Weights |
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:members: |
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""" |
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weights = VGG19_BN_Weights.verify(weights) |
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return _vgg("E", True, weights, progress, **kwargs) |
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