Densely Connected Convolutional Networks
PDF: https://arxiv.org/pdf/1608.06993.pdf
PyTorch代码: https://github.com/shanglianlm0525/PyTorch-Networks
PyTorch代码:
import torch
import torch.nn as nn
import torchvision
print("PyTorch Version: ",torch.__version__)
print("Torchvision Version: ",torchvision.__version__)
__all__ = ['DenseNet121', 'DenseNet169','DenseNet201','DenseNet264']
def Conv1(in_planes, places, stride=2):
return nn.Sequential(
nn.Conv2d(in_channels=in_planes,out_channels=places,kernel_size=7,stride=stride,padding=3, bias=False),
nn.BatchNorm2d(places),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
class _TransitionLayer(nn.Module):
def __init__(self, inplace, plance):
super(_TransitionLayer, self).__init__()
self.transition_layer = nn.Sequential(
nn.BatchNorm2d(inplace),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=inplace,out_channels=plance,kernel_size=1,stride=1,padding=0,bias=False),
nn.AvgPool2d(kernel_size=2,stride=2),
)
def forward(self, x):
return self.transition_layer(x)
class _DenseLayer(nn.Module):
def __init__(self, inplace, growth_rate, bn_size, drop_rate=0):
super(_DenseLayer, self).__init__()
self.drop_rate = drop_rate
self.dense_layer = nn.Sequential(
nn.BatchNorm2d(inplace),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=inplace, out_channels=bn_size * growth_rate, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(bn_size * growth_rate),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=bn_size * growth_rate, out_channels=growth_rate, kernel_size=3, stride=1, padding=1, bias=False),
)
self.dropout = nn.Dropout(p=self.drop_rate)
def forward(self, x):
y = self.dense_layer(x)
if self.drop_rate > 0:
y = self.dropout(y)
return torch.cat([x, y], 1)
class DenseBlock(nn.Module):
def __init__(self, num_layers, inplances, growth_rate, bn_size , drop_rate=0):
super(DenseBlock, self).__init__()
layers = []
for i in range(num_layers):
layers.append(_DenseLayer(inplances + i * growth_rate, growth_rate, bn_size, drop_rate))
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(x)
class DenseNet(nn.Module):
def __init__(self, init_channels=64, growth_rate=32, blocks=[6, 12, 24, 16],num_classes=1000):
super(DenseNet, self).__init__()
bn_size = 4
drop_rate = 0
self.conv1 = Conv1(in_planes=3, places=init_channels)
blocks*4
num_features = init_channels
self.layer1 = DenseBlock(num_layers=blocks[0], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate)
num_features = num_features + blocks[0] * growth_rate
self.transition1 = _TransitionLayer(inplace=num_features, plance=num_features // 2)
num_features = num_features // 2
self.layer2 = DenseBlock(num_layers=blocks[1], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate)
num_features = num_features + blocks[1] * growth_rate
self.transition2 = _TransitionLayer(inplace=num_features, plance=num_features // 2)
num_features = num_features // 2
self.layer3 = DenseBlock(num_layers=blocks[2], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate)
num_features = num_features + blocks[2] * growth_rate
self.transition3 = _TransitionLayer(inplace=num_features, plance=num_features // 2)
num_features = num_features // 2
self.layer4 = DenseBlock(num_layers=blocks[3], inplances=num_features, growth_rate=growth_rate, bn_size=bn_size, drop_rate=drop_rate)
num_features = num_features + blocks[3] * growth_rate
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(num_features, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.layer1(x)
x = self.transition1(x)
x = self.layer2(x)
x = self.transition2(x)
x = self.layer3(x)
x = self.transition3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def DenseNet121():
return DenseNet(init_channels=64, growth_rate=32, blocks=[6, 12, 24, 16])
def DenseNet169():
return DenseNet(init_channels=64, growth_rate=32, blocks=[6, 12, 32, 32])
def DenseNet201():
return DenseNet(init_channels=64, growth_rate=32, blocks=[6, 12, 48, 32])
def DenseNet264():
return DenseNet(init_channels=64, growth_rate=32, blocks=[6, 12, 64, 48])
if __name__=='__main__':
# model = torchvision.models.densenet121()
model = DenseNet121()
print(model)
input = torch.randn(1, 3, 224, 224)
out = model(input)
print(out.shape)
