DenseNet网络代码详解
这个代码是pytorch官方实现的代码，自己做了些备注，主要是方便自己以后学习和使用。
下图自己根据代码画的densenet169的网络结构图，输入图片的尺寸跟官方有所不同，而且对过度层的平均池化也做了一些更改，将ceil_mode=False改为了ceil_mode=Ture
下图是densebock1的一小部分示意图
下图是过度层的模块图
import re
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as cp
from collections import OrderedDict
from utils import load_state_dict_from_url
from torch import Tensor
from torch.jit.annotations import List
__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
# 对应模型预训练下载的地址
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',
'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth',
}
# step1:实现non-linear transformation:bn-relu-conv1x1-bn-relu-conv3x3
# feature维度变化: l*K->bn_size*k->k
# 传入参数有: num_input_features 输入特征数channel
# growth_rate 输出特征数channel
# bn_size bottleneck结构需要先把k*l个通道变成4k个通道，用1x1conv变成k个通道，整体看来是一个降维过程
# drop_rate 进行drop_out时的比例
class _DenseLayer(nn.Module):
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, memory_efficient=False):
super(_DenseLayer, self).__init__()
self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
self.add_module('relu1', nn.ReLU(inplace=True)),
# 1x1conv降维到4k
self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
growth_rate, kernel_size=1, stride=1,
bias=False)),
self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
self.add_module('relu2', nn.ReLU(inplace=True)),
self.add_module('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):
# type: (List[Tensor]) -> Tensor
concated_features = torch.cat(inputs, 1)
bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features))) # noqa: T484
return bottleneck_output
# 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): # 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
# step3:根据non-linear transformation创建_DenseBlock
# 输入参数有：num_layers DenseBlock里有多少个提取特征的“层”
# 接下来这几个参数都是non-linear transformation需要的
# num_input_features k0+k(l-1)
# bn_size 默认为4
# growth_rate k
# drop_rate
class _DenseBlock(nn.ModuleDict):
"""DenseBlock"""
_version = 2
def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, memory_efficient=False):
super(_DenseBlock, self).__init__()
# 遍历num_layers，创建“层”
for i in range(num_layers):
# i=0,输入为k0，输出为k
# i=1,输入为k0+k,输出为k
# i=2,输入为k0+2k,输出为k
# i=3,输入为k0+3k,输出为k
# ...
layer = _DenseLayer(
num_input_features + i * growth_rate,
growth_rate=growth_rate,
bn_size=bn_size,
drop_rate=drop_rate,
memory_efficient=memory_efficient,
)
# 把“层”添加到序列模型中
self.add_module('denselayer%d' % (i + 1), layer)
def forward(self, init_features):
features = [init_features]
for name, layer in self.items():
new_features = layer(features)
features.append(new_features)
return torch.cat(features, 1)
# step2:实现连接两个DenseBlock的_Transition
# 输入参数有：num_input_feature 上一个DenseBlock的输出，(l-1)*k+k0
# num_output_features 下一个DenseBlock的输入
class _Transition(nn.Sequential): #继承Sequential类
"""Transition layer between two adjacent DenseBlock"""
def __init__(self, num_input_features, num_output_features):
super(_Transition, self).__init__()
self.add_module('norm', nn.BatchNorm2d(num_input_features))
self.add_module('relu', nn.ReLU(inplace=True))
# 两个方向的降维度：channel降为num_output_features设定值
# featuremap降为一半AvgPool2d
self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
kernel_size=1, stride=1, bias=False))
self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2, ceil_mode=True))
# step4:根据DenseBlock和transition创建DenseNet
# 输入参数有：growth_rate 每个denseblock输出维度k
# block_config 存放每个denseblock中有多少"层"(num_layers)
# num_init_features 初始卷积层输出channel数
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=32, block_config=(6, 12, 24, 16),
num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, memory_efficient=False):
super(DenseNet, self).__init__()
# 初始卷积层，这个是独立于DenseBlock的
# 7X7conv -> BN+Relu -> maxpool
# 输出channel为num_init_features，featuremap需要计算（(n+2p-f)/s+1）
# 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
# 根据block_config中的数据创建DenseBlock
# 例如这里会创建四个DenseBlock，以第一个为例
num_features = num_init_features
for i, num_layers in enumerate(block_config):
# num_layers为6，会创建6个“层”每层输入都是之前层输出的concat
# i=0,输入为num_features，输出为k
# i=1,输入为num_features+k,输出为k
# i=2,输入为num_features+2k,输出为k
# i=3,输入为num_features+3k,输出为k
# ...
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
)
# 把创建好的_DenseBlock接到最开始创建的序列模型后边
self.features.add_module('denseblock%d' % (i + 1), block)
num_features = num_features + num_layers * growth_rate
# 如果不是最后一个DenseBlock，就需要创建transition连接
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 定义分类器
self.classifier = nn.Linear(num_features, num_classes)
# Official init from torch repo. 初始化权重
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)
#前向传播
def forward(self, x):
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, model_url, progress):
# '.'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 = 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) + res.group(2)
state_dict[new_key] = state_dict[key]
del state_dict[key]
model.load_state_dict(state_dict)
def _densenet(arch, growth_rate, block_config, num_init_features, pretrained, progress,
**kwargs):
model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
if pretrained:
_load_state_dict(model, model_urls[arch], progress)
return model
def densenet121(pretrained=False, progress=True, **kwargs):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" `_
"""
return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress,
**kwargs)
def densenet161(pretrained=False, progress=True, **kwargs):
r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" `_
"""
return _densenet('densenet161', 48, (6, 12, 36, 24), 96, pretrained, progress,
**kwargs)
def densenet169(pretrained=False, progress=True, **kwargs):
r"""Densenet-169 model from
`"Densely Connected Convolutional Networks" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" `_
"""
return _densenet('densenet169', 32, (6, 12, 32, 32), 64, pretrained, progress,
**kwargs)
def densenet201(pretrained=False, progress=True, **kwargs):
r"""Densenet-201 model from
`"Densely Connected Convolutional Networks" `_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr
memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
but slower. Default: *False*. See `"paper" `_
"""
return _densenet('densenet201', 32, (6, 12, 48, 32), 64, pretrained, progress,
**kwargs)
if __name__ == '__main__':
# 'DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161'
# Example
net = densenet169()
net_weights = net.state_dict()
print(net)