- 定义网络结构
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
"""
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):
super(DenseNet, self).__init__()
# 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)
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)
def forward(self, x):
features = self.features(x)
out = F.relu(features, inplace=True)
out = F.avg_pool2d(out, kernel_size=7).view(features.size(0), -1)
out = self.classifier(out)
return out
- 使用网络结构定义模型：
net = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24))
- 载入模型参数
net.load_state_dict(torch.load('/home/wei.fan/.torch/models/densenet161-17b70270.pth'))
4.训练模型
num_ftrs = model_conv.classifier.in_features
net.classifier = nn.Linear(num_ftrs, 100) #调整最后一层的尺寸
net =net.cuda()
criterion = nn.CrossEntropyLoss()
net = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
net =train_net() #训练模型的函数，自定义
torch.save(net.state_dict(), 'net_params.pkl') #只保存模型参数
