import re from collections import OrderedDict from functools import partial from typing import Any, List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F import torch.utils.checkpoint as cp from torch import Tensor from ..transforms._presets import ImageClassification from ..utils import _log_api_usage_once from ._api import register_model, Weights, WeightsEnum from ._meta import _IMAGENET_CATEGORIES from ._utils import _ovewrite_named_param, handle_legacy_interface __all__ = [ "DenseNet", "DenseNet121_Weights", "DenseNet161_Weights", "DenseNet169_Weights", "DenseNet201_Weights", "densenet121", "densenet161", "densenet169", "densenet201", ] class _DenseLayer(nn.Module): def __init__( self, num_input_features: int, growth_rate: int, bn_size: int, drop_rate: float, memory_efficient: bool = False ) -> None: super().__init__() 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) self.drop_rate = float(drop_rate) self.memory_efficient = memory_efficient def bn_function(self, inputs: List[Tensor]) -> Tensor: 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 def any_requires_grad(self, input: List[Tensor]) -> bool: for tensor in input: if tensor.requires_grad: return True return False @torch.jit.unused # noqa: T484 def call_checkpoint_bottleneck(self, input: List[Tensor]) -> Tensor: def closure(*inputs): return self.bn_function(inputs) return cp.checkpoint(closure, *input, use_reentrant=False) @torch.jit._overload_method # noqa: F811 def forward(self, input: List[Tensor]) -> Tensor: # noqa: F811 pass @torch.jit._overload_method # noqa: F811 def forward(self, input: Tensor) -> Tensor: # noqa: F811 pass # torchscript does not yet support *args, so we overload method # allowing it to take either a List[Tensor] or single Tensor def forward(self, input: Tensor) -> Tensor: # noqa: F811 if isinstance(input, Tensor): prev_features = [input] else: 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) new_features = self.conv2(self.relu2(self.norm2(bottleneck_output))) if self.drop_rate > 0: new_features = F.dropout(new_features, p=self.drop_rate, training=self.training) return new_features class _DenseBlock(nn.ModuleDict): _version = 2 def __init__( self, num_layers: int, num_input_features: int, bn_size: int, growth_rate: int, drop_rate: float, memory_efficient: bool = False, ) -> None: super().__init__() for i in range(num_layers): layer = _DenseLayer( num_input_features + i * growth_rate, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate, memory_efficient=memory_efficient, ) self.add_module("denselayer%d" % (i + 1), layer) def forward(self, init_features: Tensor) -> Tensor: features = [init_features] for name, layer in self.items(): new_features = layer(features) features.append(new_features) return torch.cat(features, 1) class _Transition(nn.Sequential): def __init__(self, num_input_features: int, num_output_features: int) -> None: super().__init__() 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) class DenseNet(nn.Module): r"""Densenet-BC model class, based on `"Densely Connected Convolutional Networks" `_. 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 memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient, but slower. Default: *False*. See `"paper" `_. """ 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, memory_efficient: bool = False, ) -> None: super().__init__() _log_api_usage_once(self) # First convolution 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)), ] ) ) # Each denseblock num_features = num_init_features for i, num_layers in enumerate(block_config): block = _DenseBlock( num_layers=num_layers, num_input_features=num_features, bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate, memory_efficient=memory_efficient, ) self.features.add_module("denseblock%d" % (i + 1), block) num_features = num_features + num_layers * growth_rate if i != len(block_config) - 1: trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2) self.features.add_module("transition%d" % (i + 1), trans) num_features = num_features // 2 # Final batch norm self.features.add_module("norm5", nn.BatchNorm2d(num_features)) # 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) def forward(self, x: Tensor) -> Tensor: features = self.features(x) out = F.relu(features, inplace=True) out = F.adaptive_avg_pool2d(out, (1, 1)) out = torch.flatten(out, 1) out = self.classifier(out) return out def _load_state_dict(model: nn.Module, weights: WeightsEnum, progress: bool) -> None: # '.'s are no longer allowed in module names, but previous _DenseLayer # 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( r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$" ) state_dict = weights.get_state_dict(progress=progress, check_hash=True) 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) def _densenet( growth_rate: int, block_config: Tuple[int, int, int, int], num_init_features: int, weights: Optional[WeightsEnum], progress: bool, **kwargs: Any, ) -> DenseNet: if weights is not None: _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"])) model = DenseNet(growth_rate, block_config, num_init_features, **kwargs) if weights is not None: _load_state_dict(model=model, weights=weights, progress=progress) return model _COMMON_META = { "min_size": (29, 29), "categories": _IMAGENET_CATEGORIES, "recipe": "https://github.com/pytorch/vision/pull/116", "_docs": """These weights are ported from LuaTorch.""", } 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, "_metrics": { "ImageNet-1K": { "acc@1": 74.434, "acc@5": 91.972, } }, "_ops": 2.834, "_file_size": 30.845, }, ) 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, "_metrics": { "ImageNet-1K": { "acc@1": 77.138, "acc@5": 93.560, } }, "_ops": 7.728, "_file_size": 110.369, }, ) 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, "_metrics": { "ImageNet-1K": { "acc@1": 75.600, "acc@5": 92.806, } }, "_ops": 3.36, "_file_size": 54.708, }, ) 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, "_metrics": { "ImageNet-1K": { "acc@1": 76.896, "acc@5": 93.370, } }, "_ops": 4.291, "_file_size": 77.373, }, ) DEFAULT = IMAGENET1K_V1 @register_model() @handle_legacy_interface(weights=("pretrained", DenseNet121_Weights.IMAGENET1K_V1)) def densenet121(*, weights: Optional[DenseNet121_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: r"""Densenet-121 model from `Densely Connected Convolutional Networks `_. Args: 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 `_ for more details about this class. .. autoclass:: torchvision.models.DenseNet121_Weights :members: """ weights = DenseNet121_Weights.verify(weights) return _densenet(32, (6, 12, 24, 16), 64, weights, progress, **kwargs) @register_model() @handle_legacy_interface(weights=("pretrained", DenseNet161_Weights.IMAGENET1K_V1)) def densenet161(*, weights: Optional[DenseNet161_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: r"""Densenet-161 model from `Densely Connected Convolutional Networks `_. Args: 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 `_ for more details about this class. .. autoclass:: torchvision.models.DenseNet161_Weights :members: """ weights = DenseNet161_Weights.verify(weights) return _densenet(48, (6, 12, 36, 24), 96, weights, progress, **kwargs) @register_model() @handle_legacy_interface(weights=("pretrained", DenseNet169_Weights.IMAGENET1K_V1)) def densenet169(*, weights: Optional[DenseNet169_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: r"""Densenet-169 model from `Densely Connected Convolutional Networks `_. Args: 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 `_ for more details about this class. .. autoclass:: torchvision.models.DenseNet169_Weights :members: """ weights = DenseNet169_Weights.verify(weights) return _densenet(32, (6, 12, 32, 32), 64, weights, progress, **kwargs) @register_model() @handle_legacy_interface(weights=("pretrained", DenseNet201_Weights.IMAGENET1K_V1)) def densenet201(*, weights: Optional[DenseNet201_Weights] = None, progress: bool = True, **kwargs: Any) -> DenseNet: r"""Densenet-201 model from `Densely Connected Convolutional Networks `_. Args: 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 `_ for more details about this class. .. autoclass:: torchvision.models.DenseNet201_Weights :members: """ weights = DenseNet201_Weights.verify(weights) return _densenet(32, (6, 12, 48, 32), 64, weights, progress, **kwargs)