vgg.py

from functools import partial
from typing import Union, List, Dict, Any, Optional, cast

import torch
import torch.nn as nn

from ..transforms._presets import ImageClassification, InterpolationMode
from ..utils import _log_api_usage_once
from ._api import WeightsEnum, Weights
from ._meta import _IMAGENET_CATEGORIES
from ._utils import handle_legacy_interface, _ovewrite_named_param


__all__ = [
    "VGG",
    "VGG11_Weights",
    "VGG11_BN_Weights",
    "VGG13_Weights",
    "VGG13_BN_Weights",
    "VGG16_Weights",
    "VGG16_BN_Weights",
    "VGG19_Weights",
    "VGG19_BN_Weights",
    "vgg11",
    "vgg11_bn",
    "vgg13",
    "vgg13_bn",
    "vgg16",
    "vgg16_bn",
    "vgg19",
    "vgg19_bn",
]


class VGG(nn.Module):
    def __init__(
        self, features: nn.Module, num_classes: int = 1000, init_weights: bool = True, dropout: float = 0.5
    ) -> None:
        super().__init__()
        _log_api_usage_once(self)
        self.features = features
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.ReLU(True),
            nn.Dropout(p=dropout),
            nn.Linear(4096, 4096),
            nn.ReLU(True),
            nn.Dropout(p=dropout),
            nn.Linear(4096, num_classes),
        )
        if init_weights:
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                    if m.bias is not None:
                        nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.BatchNorm2d):
                    nn.init.constant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.Linear):
                    nn.init.normal_(m.weight, 0, 0.01)
                    nn.init.constant_(m.bias, 0)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x


def make_layers(cfg: List[Union[str, int]], batch_norm: bool = False) -> nn.Sequential:
    layers: List[nn.Module] = []
    in_channels = 3
    for v in cfg:
        if v == "M":
            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
        else:
            v = cast(int, v)
            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
            if batch_norm:
                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
            else:
                layers += [conv2d, nn.ReLU(inplace=True)]
            in_channels = v
    return nn.Sequential(*layers)


cfgs: Dict[str, List[Union[str, int]]] = {
    "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
    "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
    "D": [64, 64, "M", 128, 128, "M", 256, 256, 256, "M", 512, 512, 512, "M", 512, 512, 512, "M"],
    "E": [64, 64, "M", 128, 128, "M", 256, 256, 256, 256, "M", 512, 512, 512, 512, "M", 512, 512, 512, 512, "M"],
}


def _vgg(cfg: str, batch_norm: bool, weights: Optional[WeightsEnum], progress: bool, **kwargs: Any) -> VGG:
    if weights is not None:
        kwargs["init_weights"] = False
        if weights.meta["categories"] is not None:
            _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
    model = VGG(make_layers(cfgs[cfg], batch_norm=batch_norm), **kwargs)
    if weights is not None:
        model.load_state_dict(weights.get_state_dict(progress=progress))
    return model


_COMMON_META = {
    "task": "image_classification",
    "architecture": "VGG",
    "publication_year": 2014,
    "size": (224, 224),
    "min_size": (32, 32),
    "categories": _IMAGENET_CATEGORIES,
    "interpolation": InterpolationMode.BILINEAR,
    "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#alexnet-and-vgg",
}


class VGG11_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/vgg11-8a719046.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 132863336,
            "acc@1": 69.020,
            "acc@5": 88.628,
        },
    )
    DEFAULT = IMAGENET1K_V1


class VGG11_BN_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/vgg11_bn-6002323d.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 132868840,
            "acc@1": 70.370,
            "acc@5": 89.810,
        },
    )
    DEFAULT = IMAGENET1K_V1


class VGG13_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/vgg13-19584684.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 133047848,
            "acc@1": 69.928,
            "acc@5": 89.246,
        },
    )
    DEFAULT = IMAGENET1K_V1


class VGG13_BN_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/vgg13_bn-abd245e5.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 133053736,
            "acc@1": 71.586,
            "acc@5": 90.374,
        },
    )
    DEFAULT = IMAGENET1K_V1


class VGG16_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/vgg16-397923af.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 138357544,
            "acc@1": 71.592,
            "acc@5": 90.382,
        },
    )
    # We port the features of a VGG16 backbone trained by amdegroot because unlike the one on TorchVision, it uses the
    # same input standardization method as the paper. Only the `features` weights have proper values, those on the
    # `classifier` module are filled with nans.
    IMAGENET1K_FEATURES = Weights(
        url="https://download.pytorch.org/models/vgg16_features-amdegroot-88682ab5.pth",
        transforms=partial(
            ImageClassification,
            crop_size=224,
            mean=(0.48235, 0.45882, 0.40784),
            std=(1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0),
        ),
        meta={
            **_COMMON_META,
            "num_params": 138357544,
            "categories": None,
            "recipe": "https://github.com/amdegroot/ssd.pytorch#training-ssd",
            "acc@1": float("nan"),
            "acc@5": float("nan"),
        },
    )
    DEFAULT = IMAGENET1K_V1


class VGG16_BN_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/vgg16_bn-6c64b313.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 138365992,
            "acc@1": 73.360,
            "acc@5": 91.516,
        },
    )
    DEFAULT = IMAGENET1K_V1


class VGG19_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/vgg19-dcbb9e9d.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 143667240,
            "acc@1": 72.376,
            "acc@5": 90.876,
        },
    )
    DEFAULT = IMAGENET1K_V1


class VGG19_BN_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/vgg19_bn-c79401a0.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 143678248,
            "acc@1": 74.218,
            "acc@5": 91.842,
        },
    )
    DEFAULT = IMAGENET1K_V1


@handle_legacy_interface(weights=("pretrained", VGG11_Weights.IMAGENET1K_V1))
def vgg11(*, weights: Optional[VGG11_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    r"""VGG 11-layer model (configuration "A") from
    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
    The required minimum input size of the model is 32x32.

    Args:
        weights (VGG11_Weights, optional): The pretrained weights for the model
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    weights = VGG11_Weights.verify(weights)

    return _vgg("A", False, weights, progress, **kwargs)


@handle_legacy_interface(weights=("pretrained", VGG11_BN_Weights.IMAGENET1K_V1))
def vgg11_bn(*, weights: Optional[VGG11_BN_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    r"""VGG 11-layer model (configuration "A") with batch normalization
    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
    The required minimum input size of the model is 32x32.

    Args:
        weights (VGG11_BN_Weights, optional): The pretrained weights for the model
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    weights = VGG11_BN_Weights.verify(weights)

    return _vgg("A", True, weights, progress, **kwargs)


@handle_legacy_interface(weights=("pretrained", VGG13_Weights.IMAGENET1K_V1))
def vgg13(*, weights: Optional[VGG13_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    r"""VGG 13-layer model (configuration "B")
    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
    The required minimum input size of the model is 32x32.

    Args:
        weights (VGG13_Weights, optional): The pretrained weights for the model
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    weights = VGG13_Weights.verify(weights)

    return _vgg("B", False, weights, progress, **kwargs)


@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:
    r"""VGG 13-layer model (configuration "B") with batch normalization
    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
    The required minimum input size of the model is 32x32.

    Args:
        weights (VGG13_BN_Weights, optional): The pretrained weights for the model
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    weights = VGG13_BN_Weights.verify(weights)

    return _vgg("B", True, weights, progress, **kwargs)


@handle_legacy_interface(weights=("pretrained", VGG16_Weights.IMAGENET1K_V1))
def vgg16(*, weights: Optional[VGG16_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    r"""VGG 16-layer model (configuration "D")
    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
    The required minimum input size of the model is 32x32.

    Args:
        weights (VGG16_Weights, optional): The pretrained weights for the model
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    weights = VGG16_Weights.verify(weights)

    return _vgg("D", False, weights, progress, **kwargs)


@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:
    r"""VGG 16-layer model (configuration "D") with batch normalization
    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
    The required minimum input size of the model is 32x32.

    Args:
        weights (VGG16_BN_Weights, optional): The pretrained weights for the model
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    weights = VGG16_BN_Weights.verify(weights)

    return _vgg("D", True, weights, progress, **kwargs)


@handle_legacy_interface(weights=("pretrained", VGG19_Weights.IMAGENET1K_V1))
def vgg19(*, weights: Optional[VGG19_Weights] = None, progress: bool = True, **kwargs: Any) -> VGG:
    r"""VGG 19-layer model (configuration "E")
    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
    The required minimum input size of the model is 32x32.

    Args:
        weights (VGG19_Weights, optional): The pretrained weights for the model
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    weights = VGG19_Weights.verify(weights)

    return _vgg("E", False, weights, progress, **kwargs)


@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:
    r"""VGG 19-layer model (configuration 'E') with batch normalization
    `"Very Deep Convolutional Networks For Large-Scale Image Recognition" <https://arxiv.org/pdf/1409.1556.pdf>`_.
    The required minimum input size of the model is 32x32.

    Args:
        weights (VGG19_BN_Weights, optional): The pretrained weights for the model
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    weights = VGG19_BN_Weights.verify(weights)

    return _vgg("E", True, weights, progress, **kwargs)