http://zh-v2.d2l.ai/chapter_convolutional-modern/densenet.html
有人知道为什么过渡层用平均池化，但不用最大池化吗？
我自己换成了max pooling尝试了一下，发现accuracy没啥变化。
DenseNet121/169/201模型代码，参照DenseNet原文和torchvision源码实现，供参考
import re
import torch
import torchvision
import torch.utils.checkpoint as cp
from torch import nn, Tensor
from typing import List, Tuple
from collections import OrderedDict
from torchvision.models.utils import load_state_dict_from_url
class _DenseLayer(nn.Module):
def __init__(self, in_channels, growth_rate, bn_size,
dropout_rate, momery_efficient: bool):
super(_DenseLayer, self).__init__()
self.momery_efficient = momery_efficient
self.dropout_rate = dropout_rate
# 用于bottleneck前向计算, 标号2表示加载checkpoints的
self.norm1: nn.BatchNorm2d
self.relu1: nn.ReLU
self.conv1: nn.Conv2d
self.norm2: nn.BatchNorm2d
self.relu2: nn.ReLU
self.conv2: Conv2d
# 将bottleneck添加到网络中
self.add_module('norm1', nn.BatchNorm2d(in_channels))
self.add_module('relu1', nn.ReLU(inplace=True))
self.add_module('conv1', nn.Conv2d(in_channels, growth_rate * bn_size,
kernel_size=1, stride=1, bias=False))
self.add_module('norm2', nn.BatchNorm2d(growth_rate * bn_size))
self.add_module('relu2', nn.ReLU(inplace=True))
self.add_module('conv2', nn.Conv2d(growth_rate * bn_size, growth_rate,
kernel_size=3, stride=1, padding=1,
bias=False))
def bottleneck(self, inputs: List[Tensor]):
concated_features = torch.cat(inputs, 1)
bottleneck_ouputs = self.conv1(self.relu1(self.norm1(concated_features)))
return bottleneck_ouputs
@torch.jit.unused
def call_checkpoints_bottleneck(self, inputs: List[Tensor]):
def closure(*inputs):
return self.bottleneck(inputs)
return cp.checkpoint(closure, *inputs)
def forward(self, input: Tensor):
if isinstance(input, Tensor):
prev_features = [input]
else:
prev_features = input
if self.momery_efficient:
bottleneck_ouputs = self.call_checkpoints_bottleneck(prev_features)
else:
bottleneck_ouputs = self.bottleneck(prev_features)
new_features = self.conv2(self.relu2(self.norm2(bottleneck_ouputs)))
if self.dropout_rate > 0:
new_features = F.dropout(new_features, p=self.dropout_rate,
training=self.training)
return new_features
class _DenseBlock(nn.ModuleDict):
def __init__(self, num_layers, in_channels, growth_rate, bn_size,
dropout_rate, momery_efficient):
super(_DenseBlock, self).__init__()
for i in range(num_layers):
layer = _DenseLayer(in_channels + growth_rate * i, growth_rate,
bn_size, dropout_rate, momery_efficient)
self.add_module('denselayer%d' % (i + 1), layer)
def forward(self, x):
# 先把上个denseblock的输入放到一个列表，后面逐渐添加各denselayer输出
features = [x]
# self.items()访问以OrderedDict方式存在当前self._modules中的layers
for name, layer in self.items():
new_features = layer(features)
features.append(new_features)
return features
class _Transition(nn.Module):#
def __init__(self, in_channels, out_channels):
super(_Transition, self).__init__()
self.add_module('norm', nn.BatchNorm2d(in_channels))
self.add_module('relu', nn.ReLU(inplace=True))
# 调整（增加）channels
self.add_module('conv', nn.Conv2d(in_channels, out_channels,
kernel_size=1, stride=1, bias=False))
# 减小feature-maps尺寸
self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
class DenseNet(nn.Module):
def __init__(self, block_config: Tuple[int, int, int, int],
num_classes: int = 1000,
in_channels: int = 64,
growth_rate: int = 32,
bn_size: int = 4,
dropout_rate: float = 0.,
momery_efficient: bool = False):
super(DenseNet, self).__init__()
# 前面初始部分
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv2d(3, in_channels, kernel_size=7, stride=2,
padding=3, bias=False)),
('norm0', nn.BatchNorm2d(in_channels)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
]))
# 密集部分
num_features = in_channels
for i, num_layers in enumerate(block_config):
denseblock = _DenseBlock(num_layers, num_features, growth_rate,
bn_size, dropout_rate, momery_efficient)
self.features.add_module('denseblock%d' % (i + 1), denseblock)
num_features += growth_rate * num_layers
if i < len(block_config) - 1:
trans = _Transition(num_features, num_features // 2)
self.features.add_module('transition%d' % (i + 1), trans)
# transition通道减半，更新
num_features = num_features // 2
# 结尾前batchnorm
self.features.add_module('norm5', nn.BatchNorm2d(num_features))
self.classifier = nn.Linear(num_features, num_classes)
# 初始化参数
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):
out = self.features(x)
out = F.relu(out, inplace=True)
out = F.adaptive_avg_pool2d(out, (1, 1))
out = torch.flatten(out, 1)
out = self.classifier(out)
return out
class Constructor:
def __init__(self, num_classes: int = 1000,
momery_efficient: bool = False,
load: bool = False,
progress: bool = True):
self.num_classes = num_classes
self.momery_efficient = momery_efficient
self.load = load
self.progress = progress
self.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',
}
def _load_state_dict(self, model: nn.Module, model_url: str):
state_dict = load_state_dict_from_url(model_url, progress=self.progress)
pattern = re.compile(
r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
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]
return model.load_state_dict(state_dict)
def _build_model(self, block_config, moder_url=None):
model = DenseNet(block_config=block_config,
num_classes=self.num_classes,
momery_efficient=self.momery_efficient)
if self.load:
model = self._load_state_dict(model, moder_url)
return model
def DenseNet121(self):
return self._build_model((6, 12, 24, 16), self.model_urls['densenet121'])
def DenseNet169(self):
return self._build_model((6, 12, 32, 32), self.model_urls['densenet169'])
def DenseNet201(self):
return self._build_model((6, 12, 48, 32), self.model_urls['densenet201'])
if __name__ == '__main__':
num_classes = 1000
momery_efficient = True
load = True
progress = True
densenet169 = Constructor(num_classes, momery_efficient, load,
progress).DenseNet169()
print(densenet169)个人感觉（基于神经网络的不可解释性）：nn.AvgPool2d(kernel_size=2, stride=2))，这里池化窗口大小为2x2，所以平均池化和最大池化效果差不多。平均池化可以更多的提取到相邻像素的信息。