# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from collections import OrderedDict
from typing import Callable, Sequence, Type, Union
import torch
import torch.nn as nn
from torch.hub import load_state_dict_from_url
from monai.networks.layers.factories import Conv, Dropout, Norm, Pool
class _DenseLayer(nn.Module):
def __init__(
self, spatial_dims: int, in_channels: int, growth_rate: int, bn_size: int, dropout_prob: float
) -> None:
"""
Args:
spatial_dims: number of spatial dimensions of the input image.
in_channels: number of the input channel.
growth_rate: how many filters to add each layer (k in paper).
bn_size: multiplicative factor for number of bottle neck layers.
(i.e. bn_size * k features in the bottleneck layer)
dropout_prob: dropout rate after each dense layer.
"""
super(_DenseLayer, self).__init__()
out_channels = bn_size * growth_rate
conv_type: Callable = Conv[Conv.CONV, spatial_dims]
norm_type: Callable = Norm[Norm.BATCH, spatial_dims]
dropout_type: Callable = Dropout[Dropout.DROPOUT, spatial_dims]
self.layers = nn.Sequential()
self.layers.add_module("norm1", norm_type(in_channels))
self.layers.add_module("relu1", nn.ReLU(inplace=True))
self.layers.add_module("conv1", conv_type(in_channels, out_channels, kernel_size=1, bias=False))
self.layers.add_module("norm2", norm_type(out_channels))
self.layers.add_module("relu2", nn.ReLU(inplace=True))
self.layers.add_module("conv2", conv_type(out_channels, growth_rate, kernel_size=3, padding=1, bias=False))
if dropout_prob > 0:
self.layers.add_module("dropout", dropout_type(dropout_prob))
def forward(self, x: torch.Tensor) -> torch.Tensor:
new_features = self.layers(x)
return torch.cat([x, new_features], 1)
class _DenseBlock(nn.Sequential):
def __init__(
self, spatial_dims: int, layers: int, in_channels: int, bn_size: int, growth_rate: int, dropout_prob: float
) -> None:
"""
Args:
spatial_dims: number of spatial dimensions of the input image.
layers: number of layers in the block.
in_channels: number of the input channel.
bn_size: multiplicative factor for number of bottle neck layers.
(i.e. bn_size * k features in the bottleneck layer)
growth_rate: how many filters to add each layer (k in paper).
dropout_prob: dropout rate after each dense layer.
"""
super(_DenseBlock, self).__init__()
for i in range(layers):
layer = _DenseLayer(spatial_dims, in_channels, growth_rate, bn_size, dropout_prob)
in_channels += growth_rate
self.add_module("denselayer%d" % (i + 1), layer)
class _Transition(nn.Sequential):
def __init__(self, spatial_dims: int, in_channels: int, out_channels: int) -> None:
"""
Args:
spatial_dims: number of spatial dimensions of the input image.
in_channels: number of the input channel.
out_channels: number of the output classes.
"""
super(_Transition, self).__init__()
conv_type: Callable = Conv[Conv.CONV, spatial_dims]
norm_type: Callable = Norm[Norm.BATCH, spatial_dims]
pool_type: Callable = Pool[Pool.AVG, spatial_dims]
self.add_module("norm", norm_type(in_channels))
self.add_module("relu", nn.ReLU(inplace=True))
self.add_module("conv", conv_type(in_channels, out_channels, kernel_size=1, bias=False))
self.add_module("pool", pool_type(kernel_size=2, stride=2))
[docs]class DenseNet(nn.Module):
"""
Densenet based on: `Densely Connected Convolutional Networks <https://arxiv.org/pdf/1608.06993.pdf>`_.
Adapted from `PyTorch Hub 2D version
<https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py>`_.
Args:
spatial_dims: number of spatial dimensions of the input image.
in_channels: number of the input channel.
out_channels: number of the output classes.
init_features: number of filters in the first convolution layer.
growth_rate: how many filters to add each layer (k in paper).
block_config: how many layers in each pooling block.
bn_size: multiplicative factor for number of bottle neck layers.
(i.e. bn_size * k features in the bottleneck layer)
dropout_prob: dropout rate after each dense layer.
"""
def __init__(
self,
spatial_dims: int,
in_channels: int,
out_channels: int,
init_features: int = 64,
growth_rate: int = 32,
block_config: Sequence[int] = (6, 12, 24, 16),
bn_size: int = 4,
dropout_prob: float = 0.0,
) -> None:
super(DenseNet, self).__init__()
conv_type: Type[Union[nn.Conv1d, nn.Conv2d, nn.Conv3d]] = Conv[Conv.CONV, spatial_dims]
norm_type: Type[Union[nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d]] = Norm[Norm.BATCH, spatial_dims]
pool_type: Type[Union[nn.MaxPool1d, nn.MaxPool2d, nn.MaxPool3d]] = Pool[Pool.MAX, spatial_dims]
avg_pool_type: Type[Union[nn.AdaptiveAvgPool1d, nn.AdaptiveAvgPool2d, nn.AdaptiveAvgPool3d]] = Pool[
Pool.ADAPTIVEAVG, spatial_dims
]
self.features = nn.Sequential(
OrderedDict(
[
("conv0", conv_type(in_channels, init_features, kernel_size=7, stride=2, padding=3, bias=False)),
("norm0", norm_type(init_features)),
("relu0", nn.ReLU(inplace=True)),
("pool0", pool_type(kernel_size=3, stride=2, padding=1)),
]
)
)
in_channels = init_features
for i, num_layers in enumerate(block_config):
block = _DenseBlock(
spatial_dims=spatial_dims,
layers=num_layers,
in_channels=in_channels,
bn_size=bn_size,
growth_rate=growth_rate,
dropout_prob=dropout_prob,
)
self.features.add_module(f"denseblock{i + 1}", block)
in_channels += num_layers * growth_rate
if i == len(block_config) - 1:
self.features.add_module("norm5", norm_type(in_channels))
else:
_out_channels = in_channels // 2
trans = _Transition(spatial_dims, in_channels=in_channels, out_channels=_out_channels)
self.features.add_module(f"transition{i + 1}", trans)
in_channels = _out_channels
# pooling and classification
self.class_layers = nn.Sequential(
OrderedDict(
[
("relu", nn.ReLU(inplace=True)),
("pool", avg_pool_type(1)),
("flatten", nn.Flatten(1)),
("out", nn.Linear(in_channels, out_channels)),
]
)
)
for m in self.modules():
if isinstance(m, conv_type):
nn.init.kaiming_normal_(torch.as_tensor(m.weight))
elif isinstance(m, norm_type):
nn.init.constant_(torch.as_tensor(m.weight), 1)
nn.init.constant_(torch.as_tensor(m.bias), 0)
elif isinstance(m, nn.Linear):
nn.init.constant_(torch.as_tensor(m.bias), 0)
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.features(x)
x = self.class_layers(x)
return x
def _load_state_dict(model, arch, progress):
"""
This function is used to load pretrained models.
Adapted from `PyTorch Hub 2D version
<https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py>`_
"""
model_urls = {
"densenet121": "https://download.pytorch.org/models/densenet121-a639ec97.pth",
"densenet169": "https://download.pytorch.org/models/densenet169-b2777c0a.pth",
"densenet201": "https://download.pytorch.org/models/densenet201-c1103571.pth",
}
if arch in model_urls:
model_url = model_urls[arch]
else:
raise ValueError(
"only 'densenet121', 'densenet169' and 'densenet201' are supported to load pretrained weights."
)
pattern = re.compile(
r"^(.*denselayer\d+)(\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
)
state_dict = load_state_dict_from_url(model_url, progress=progress)
for key in list(state_dict.keys()):
res = pattern.match(key)
if res:
new_key = res.group(1) + ".layers" + res.group(2) + res.group(3)
state_dict[new_key] = state_dict[key]
del state_dict[key]
model_dict = model.state_dict()
state_dict = {
k: v for k, v in state_dict.items() if (k in model_dict) and (model_dict[k].shape == state_dict[k].shape)
}
model_dict.update(state_dict)
model.load_state_dict(model_dict)
class DenseNet121(DenseNet):
def __init__(
self,
init_features: int = 64,
growth_rate: int = 32,
block_config: Sequence[int] = (6, 12, 24, 16),
pretrained: bool = False,
progress: bool = True,
**kwargs,
) -> None:
super(DenseNet121, self).__init__(
init_features=init_features,
growth_rate=growth_rate,
block_config=block_config,
**kwargs,
)
if pretrained:
# it only worked when `spatial_dims` is 2
_load_state_dict(self, "densenet121", progress)
class DenseNet169(DenseNet):
def __init__(
self,
init_features: int = 64,
growth_rate: int = 32,
block_config: Sequence[int] = (6, 12, 32, 32),
pretrained: bool = False,
progress: bool = True,
**kwargs,
) -> None:
super(DenseNet169, self).__init__(
init_features=init_features,
growth_rate=growth_rate,
block_config=block_config,
**kwargs,
)
if pretrained:
# it only worked when `spatial_dims` is 2
_load_state_dict(self, "densenet169", progress)
class DenseNet201(DenseNet):
def __init__(
self,
init_features: int = 64,
growth_rate: int = 32,
block_config: Sequence[int] = (6, 12, 48, 32),
pretrained: bool = False,
progress: bool = True,
**kwargs,
) -> None:
super(DenseNet201, self).__init__(
init_features=init_features,
growth_rate=growth_rate,
block_config=block_config,
**kwargs,
)
if pretrained:
# it only worked when `spatial_dims` is 2
_load_state_dict(self, "densenet201", progress)
class DenseNet264(DenseNet):
def __init__(
self,
init_features: int = 64,
growth_rate: int = 32,
block_config: Sequence[int] = (6, 12, 48, 32),
pretrained: bool = False,
progress: bool = True,
**kwargs,
) -> None:
super(DenseNet264, self).__init__(
init_features=init_features,
growth_rate=growth_rate,
block_config=block_config,
**kwargs,
)
if pretrained:
print("Currently PyTorch Hub does not provide densenet264 pretrained models.")
Densenet = densenet = DenseNet
Densenet121 = densenet121 = DenseNet121
Densenet169 = densenet169 = DenseNet169
Densenet201 = densenet201 = DenseNet201
Densenet264 = densenet264 = DenseNet264