densenet.py 16.8 KB
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import re
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from collections import OrderedDict
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from functools import partial
from typing import Any, List, Optional, Tuple
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import torch
import torch.nn as nn
import torch.nn.functional as F
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import torch.utils.checkpoint as cp
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from torch import Tensor
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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__ = [
    "DenseNet",
    "DenseNet121_Weights",
    "DenseNet161_Weights",
    "DenseNet169_Weights",
    "DenseNet201_Weights",
    "densenet121",
    "densenet161",
    "densenet169",
    "densenet201",
]
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class _DenseLayer(nn.Module):
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    def __init__(
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        self, num_input_features: int, growth_rate: int, bn_size: int, drop_rate: float, memory_efficient: bool = False
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    ) -> None:
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        super().__init__()
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        self.norm1 = nn.BatchNorm2d(num_input_features)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)

        self.norm2 = nn.BatchNorm2d(bn_size * growth_rate)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)

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        self.drop_rate = float(drop_rate)
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        self.memory_efficient = memory_efficient

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    def bn_function(self, inputs: List[Tensor]) -> Tensor:
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        concated_features = torch.cat(inputs, 1)
        bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features)))  # noqa: T484
        return bottleneck_output

    # todo: rewrite when torchscript supports any
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    def any_requires_grad(self, input: List[Tensor]) -> bool:
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        for tensor in input:
            if tensor.requires_grad:
                return True
        return False

    @torch.jit.unused  # noqa: T484
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    def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor:
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        def closure(*inputs):
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            return self.bn_function(inputs)
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        return cp.checkpoint(closure, *input)
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    @torch.jit._overload_method  # noqa: F811
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    def forward(self, input: List[Tensor]) -> Tensor:  # noqa: F811
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        pass

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    @torch.jit._overload_method  # noqa: F811
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    def forward(self, input: Tensor) -> Tensor:  # noqa: F811
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        pass

    # torchscript does not yet support *args, so we overload method
    # allowing it to take either a List[Tensor] or single Tensor
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    def forward(self, input: Tensor) -> Tensor:  # noqa: F811
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        if isinstance(input, Tensor):
            prev_features = [input]
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        else:
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            prev_features = input

        if self.memory_efficient and self.any_requires_grad(prev_features):
            if torch.jit.is_scripting():
                raise Exception("Memory Efficient not supported in JIT")

            bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
        else:
            bottleneck_output = self.bn_function(prev_features)

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        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
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        if self.drop_rate > 0:
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            new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
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        return new_features
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class _DenseBlock(nn.ModuleDict):
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    _version = 2

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    def __init__(
        self,
        num_layers: int,
        num_input_features: int,
        bn_size: int,
        growth_rate: int,
        drop_rate: float,
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        memory_efficient: bool = False,
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    ) -> None:
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        super().__init__()
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        for i in range(num_layers):
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            layer = _DenseLayer(
                num_input_features + i * growth_rate,
                growth_rate=growth_rate,
                bn_size=bn_size,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient,
            )
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            self.add_module("denselayer%d" % (i + 1), layer)
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    def forward(self, init_features: Tensor) -> Tensor:
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        features = [init_features]
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        for name, layer in self.items():
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            new_features = layer(features)
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            features.append(new_features)
        return torch.cat(features, 1)

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class _Transition(nn.Sequential):
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    def __init__(self, num_input_features: int, num_output_features: int) -> None:
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        super().__init__()
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        self.norm = nn.BatchNorm2d(num_input_features)
        self.relu = nn.ReLU(inplace=True)
        self.conv = nn.Conv2d(num_input_features, num_output_features, kernel_size=1, stride=1, bias=False)
        self.pool = nn.AvgPool2d(kernel_size=2, stride=2)
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class DenseNet(nn.Module):
    r"""Densenet-BC model class, based on
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    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.
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    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
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        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
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          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_.
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    """

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    def __init__(
        self,
        growth_rate: int = 32,
        block_config: Tuple[int, int, int, int] = (6, 12, 24, 16),
        num_init_features: int = 64,
        bn_size: int = 4,
        drop_rate: float = 0,
        num_classes: int = 1000,
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        memory_efficient: bool = False,
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    ) -> None:
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        super().__init__()
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        _log_api_usage_once(self)
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        # First convolution
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        self.features = nn.Sequential(
            OrderedDict(
                [
                    ("conv0", nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
                    ("norm0", nn.BatchNorm2d(num_init_features)),
                    ("relu0", nn.ReLU(inplace=True)),
                    ("pool0", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
                ]
            )
        )
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        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
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            block = _DenseBlock(
                num_layers=num_layers,
                num_input_features=num_features,
                bn_size=bn_size,
                growth_rate=growth_rate,
                drop_rate=drop_rate,
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                memory_efficient=memory_efficient,
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            )
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            self.features.add_module("denseblock%d" % (i + 1), block)
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            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
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                trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)
                self.features.add_module("transition%d" % (i + 1), trans)
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                num_features = num_features // 2

        # Final batch norm
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        self.features.add_module("norm5", nn.BatchNorm2d(num_features))
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        # Linear layer
        self.classifier = nn.Linear(num_features, num_classes)

        # Official init from torch repo.
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.constant_(m.bias, 0)

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    def forward(self, x: Tensor) -> Tensor:
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        features = self.features(x)
        out = F.relu(features, inplace=True)
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        out = F.adaptive_avg_pool2d(out, (1, 1))
        out = torch.flatten(out, 1)
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        out = self.classifier(out)
        return out


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def _load_state_dict(model: nn.Module, weights: WeightsEnum, progress: bool) -> None:
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    # '.'s are no longer allowed in module names, but previous _DenseLayer
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    # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
    # They are also in the checkpoints in model_urls. This pattern is used
    # to find such keys.
    pattern = re.compile(
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        r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
    )
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    state_dict = weights.get_state_dict(progress=progress)
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    for key in list(state_dict.keys()):
        res = pattern.match(key)
        if res:
            new_key = res.group(1) + res.group(2)
            state_dict[new_key] = state_dict[key]
            del state_dict[key]
    model.load_state_dict(state_dict)


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def _densenet(
    growth_rate: int,
    block_config: Tuple[int, int, int, int],
    num_init_features: int,
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    weights: Optional[WeightsEnum],
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    progress: bool,
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    **kwargs: Any,
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) -> DenseNet:
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    if weights is not None:
        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))

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    model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
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    if weights is not None:
        _load_state_dict(model=model, weights=weights, progress=progress)

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    return model


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_COMMON_META = {
    "min_size": (29, 29),
    "categories": _IMAGENET_CATEGORIES,
    "recipe": "https://github.com/pytorch/vision/pull/116",
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    "_docs": """These weights are ported from LuaTorch.""",
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}


class DenseNet121_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 7978856,
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            "_metrics": {
                "ImageNet-1K": {
                    "acc@1": 74.434,
                    "acc@5": 91.972,
                }
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            },
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            "_ops": 2.834,
            "_weight_size": 30.845,
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        },
    )
    DEFAULT = IMAGENET1K_V1


class DenseNet161_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/densenet161-8d451a50.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 28681000,
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            "_metrics": {
                "ImageNet-1K": {
                    "acc@1": 77.138,
                    "acc@5": 93.560,
                }
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            },
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            "_ops": 7.728,
            "_weight_size": 110.369,
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        },
    )
    DEFAULT = IMAGENET1K_V1


class DenseNet169_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/densenet169-b2777c0a.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 14149480,
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            "_metrics": {
                "ImageNet-1K": {
                    "acc@1": 75.600,
                    "acc@5": 92.806,
                }
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            },
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            "_ops": 3.36,
            "_weight_size": 54.708,
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        },
    )
    DEFAULT = IMAGENET1K_V1


class DenseNet201_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/densenet201-c1103571.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "num_params": 20013928,
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            "_metrics": {
                "ImageNet-1K": {
                    "acc@1": 76.896,
                    "acc@5": 93.370,
                }
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            },
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            "_ops": 4.291,
            "_weight_size": 77.373,
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        },
    )
    DEFAULT = IMAGENET1K_V1


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@register_model()
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@handle_legacy_interface(weights=("pretrained", DenseNet121_Weights.IMAGENET1K_V1))
def densenet121(*, weights: Optional[DenseNet121_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
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    r"""Densenet-121 model from
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    `Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_.
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    Args:
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        weights (:class:`~torchvision.models.DenseNet121_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.DenseNet121_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.densenet.DenseNet``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.DenseNet121_Weights
        :members:
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    """
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    weights = DenseNet121_Weights.verify(weights)
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    return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", DenseNet161_Weights.IMAGENET1K_V1))
def densenet161(*, weights: Optional[DenseNet161_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
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    r"""Densenet-161 model from
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    `Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_.
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    Args:
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        weights (:class:`~torchvision.models.DenseNet161_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.DenseNet161_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.densenet.DenseNet``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.DenseNet161_Weights
        :members:
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    """
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    weights = DenseNet161_Weights.verify(weights)

    return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", DenseNet169_Weights.IMAGENET1K_V1))
def densenet169(*, weights: Optional[DenseNet169_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
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    r"""Densenet-169 model from
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    `Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_.
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    Args:
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        weights (:class:`~torchvision.models.DenseNet169_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.DenseNet169_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.densenet.DenseNet``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.DenseNet169_Weights
        :members:
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    """
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    weights = DenseNet169_Weights.verify(weights)
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    return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", DenseNet201_Weights.IMAGENET1K_V1))
def densenet201(*, weights: Optional[DenseNet201_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet:
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    r"""Densenet-201 model from
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    `Densely Connected Convolutional Networks <https://arxiv.org/abs/1608.06993>`_.
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    Args:
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        weights (:class:`~torchvision.models.DenseNet201_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.DenseNet201_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.densenet.DenseNet``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.DenseNet201_Weights
        :members:
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    """
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    weights = DenseNet201_Weights.verify(weights)

    return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)
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# The dictionary below is internal implementation detail and will be removed in v0.15
from ._utils import _ModelURLs

model_urls = _ModelURLs(
    {
        "densenet121": DenseNet121_Weights.IMAGENET1K_V1.url,
        "densenet169": DenseNet169_Weights.IMAGENET1K_V1.url,
        "densenet201": DenseNet201_Weights.IMAGENET1K_V1.url,
        "densenet161": DenseNet161_Weights.IMAGENET1K_V1.url,
    }
)