Source code for torchvision.models.densenet
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)
@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" <https://arxiv.org/pdf/1608.06993.pdf>`_.
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" <https://arxiv.org/pdf/1707.06990.pdf>`_.
"""
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.""",
}
[docs]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
[docs]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
[docs]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
[docs]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
[docs]@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 <https://arxiv.org/abs/1608.06993>`_.
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
<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
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)
[docs]@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 <https://arxiv.org/abs/1608.06993>`_.
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
<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
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)
[docs]@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 <https://arxiv.org/abs/1608.06993>`_.
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
<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
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)
[docs]@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 <https://arxiv.org/abs/1608.06993>`_.
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
<https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py>`_
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)
