512 lines
19 KiB
Python
512 lines
19 KiB
Python
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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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# Weights ported from https://github.com/amdegroot/ssd.pytorch/
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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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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
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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.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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.. 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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Args:
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weights (:class:`~torchvision.models.VGG11_BN_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.VGG11_BN_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.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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.. 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)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model()
|
||
|
|
@handle_legacy_interface(weights=("pretrained", VGG13_Weights.IMAGENET1K_V1))
|
||
|
|
def vgg13(*, weights: Optional[VGG13_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
|
||
|
|
"""VGG-13 from `Very Deep Convolutional Networks for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`__.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
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.
|
||
|
|
|
||
|
|
.. autoclass:: torchvision.models.VGG13_Weights
|
||
|
|
:members:
|
||
|
|
"""
|
||
|
|
weights = VGG13_Weights.verify(weights)
|
||
|
|
|
||
|
|
return _vgg("B", False, weights, progress, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model()
|
||
|
|
@handle_legacy_interface(weights=("pretrained", VGG13_BN_Weights.IMAGENET1K_V1))
|
||
|
|
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>`__.
|
||
|
|
|
||
|
|
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
|
||
|
|
:members:
|
||
|
|
"""
|
||
|
|
weights = VGG13_BN_Weights.verify(weights)
|
||
|
|
|
||
|
|
return _vgg("B", True, weights, progress, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@register_model()
|
||
|
|
@handle_legacy_interface(weights=("pretrained", VGG16_Weights.IMAGENET1K_V1))
|
||
|
|
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)
|
||
|
|
|
||
|
|
return _vgg("D", False, weights, progress, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
@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>`_
|
||
|
|
for more details about this class.
|
||
|
|
|
||
|
|
.. autoclass:: torchvision.models.VGG19_BN_Weights
|
||
|
|
:members:
|
||
|
|
"""
|
||
|
|
weights = VGG19_BN_Weights.verify(weights)
|
||
|
|
|
||
|
|
return _vgg("E", True, weights, progress, **kwargs)
|