CSP-Darknet53
- 0. 引言
- 1. 网络结构图
- 1.1 输入部分
- 1.2 CSP部分结构
- 1.3 输出部分
- 2. 代码实现
- 2.1 代码整体实现
- 2.2 代码各个阶段实现
- 3. 代码测试
- 4. 结论
0. 引言
CSP-Darknet53无论是其作为CV Backbone，还是说它在别的数据集上取得极好的效果。与此同时，它与别的网络的适配能力极强。这些特点都在宣告：CSP-Darknet53的重要性。
关于原理部分的内容请查看这里CV 经典主干网络 (Backbone) 系列: CSPNet
1. 网络结构图
具体网络结构可以参考YOLO V3详解(一)：网络结构介绍中使用的工具来进行操作。具体网址和对应的权重文件下载地址如下：
模型可视化工具：https://lutzroeder.github.io/netron/
cfg文件下载网址：https://github.com/WongKinYiu/CrossStagePartialNetworks
得到的部分网络结构图的如下所示。
1.1 输入部分
1.2 CSP部分结构
1.3 输出部分
2. 代码实现
2.1 代码整体实现
通过代码实现CSP-Darknet53。框架为PyTorch，代码整体框架实现如下所示：
class CsDarkNet53(nn.Module):
def __init__(self, num_classes):
super(CsDarkNet53, self).__init__()
input_channels = 32
# Network
self.stage1 = Conv2dBatchLeaky(3, input_channels, 3, 1, activation='mish')
self.stage2 = Stage2(input_channels)
self.stage3 = Stage3(4*input_channels)
self.stage4 = Stage(4*input_channels, 8)
self.stage5 = Stage(8*input_channels, 8)
self.stage6 = Stage(16*input_channels, 4)
self.conv = Conv2dBatchLeaky(32*input_channels, 32*input_channels, 1, 1, activation='mish')
self.avgpool = nn.AdaptiveAvgPool2d((1,1))
self.fc = nn.Linear(1024, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
stage1 = self.stage1(x)
stage2 = self.stage2(stage1)
stage3 = self.stage3(stage2)
stage4 = self.stage4(stage3)
stage5 = self.stage5(stage4)
stage6 = self.stage6(stage5)
conv = self.conv(stage6)
x = self.avgpool(conv)
x = x.view(-1, 1024)
x = self.fc(x)
return x
2.2 代码各个阶段实现
在代码中，对各个阶段的具体实现如下所示：
class Mish(nn.Module):
def __init__(self):
super(Mish, self).__init__()
def forward(self, x):
return x * torch.tanh(F.softplus(x))
class Conv2dBatchLeaky(nn.Module):
"""
This convenience layer groups a 2D convolution, a batchnorm and a leaky ReLU.
"""
def __init__(self, in_channels, out_channels, kernel_size, stride, activation='leaky', leaky_slope=0.1):
super(Conv2dBatchLeaky, self).__init__()
# Parameters
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.stride = stride
if isinstance(kernel_size, (list, tuple)):
self.padding = [int(k/2) for k in kernel_size]
else:
self.padding = int(kernel_size/2)
self.leaky_slope = leaky_slope
# self.mish = Mish()
# Layer
if activation == "leaky":
self.layers = nn.Sequential(
nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding, bias=False),
nn.BatchNorm2d(self.out_channels),
nn.LeakyReLU(self.leaky_slope, inplace=True)
)
elif activation == "mish":
self.layers = nn.Sequential(
nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding, bias=False),
nn.BatchNorm2d(self.out_channels),
Mish()
)
elif activation == "linear":
self.layers = nn.Sequential(
nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding, bias=False)
)
def __repr__(self):
s = '{name} ({in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}, padding={padding}, negative_slope={leaky_slope})'
return s.format(name=self.__class__.__name__, **self.__dict__)
def forward(self, x):
x = self.layers(x)
return x
class SmallBlock(nn.Module):
def __init__(self, nchannels):
super().__init__()
self.features = nn.Sequential(
Conv2dBatchLeaky(nchannels, nchannels, 1, 1, activation='mish'),
Conv2dBatchLeaky(nchannels, nchannels, 3, 1, activation='mish')
)
# conv_shortcut
'''
参考 https://github.com/bubbliiiing/yolov4-pytorch
shortcut后不接任何conv
'''
# self.active_linear = Conv2dBatchLeaky(nchannels, nchannels, 1, 1, activation='linear')
# self.conv_shortcut = Conv2dBatchLeaky(nchannels, nchannels, 1, 1, activation='mish')
def forward(self, data):
short_cut = data + self.features(data)
# active_linear = self.conv_shortcut(short_cut)
return short_cut
# Stage1 conv [256,256,3]->[256,256,32]
class Stage2(nn.Module):
def __init__(self, nchannels):
super().__init__()
# stage2 32
self.conv1 = Conv2dBatchLeaky(nchannels, 2*nchannels, 3, 2, activation='mish')
self.split0 = Conv2dBatchLeaky(2*nchannels, 2*nchannels, 1, 1, activation='mish')
self.split1 = Conv2dBatchLeaky(2*nchannels, 2*nchannels, 1, 1, activation='mish')
self.conv2 = Conv2dBatchLeaky(2*nchannels, nchannels, 1, 1, activation='mish')
self.conv3 = Conv2dBatchLeaky(nchannels, 2*nchannels, 3, 1, activation='mish')
self.conv4 = Conv2dBatchLeaky(2*nchannels, 2*nchannels, 1, 1, activation='mish')
def forward(self, data):
conv1 = self.conv1(data)
split0 = self.split0(conv1)
split1 = self.split1(conv1)
conv2 = self.conv2(split1)
conv3 = self.conv3(conv2)
shortcut = split1 + conv3
conv4 = self.conv4(shortcut)
route = torch.cat([split0, conv4], dim=1)
return route
class Stage3(nn.Module):
def __init__(self, nchannels):
super().__init__()
# stage3 128
self.conv1 = Conv2dBatchLeaky(nchannels, int(nchannels/2), 1, 1, activation='mish')
self.conv2 = Conv2dBatchLeaky(int(nchannels/2), nchannels, 3, 2, activation='mish')
self.split0 = Conv2dBatchLeaky(nchannels, int(nchannels/2), 1, 1, activation='mish')
self.split1 = Conv2dBatchLeaky(nchannels, int(nchannels/2), 1, 1, activation='mish')
self.block1 = SmallBlock(int(nchannels/2))
self.block2 = SmallBlock(int(nchannels/2))
self.conv3 = Conv2dBatchLeaky(int(nchannels/2), int(nchannels/2), 1, 1, activation='mish')
def forward(self, data):
conv1 = self.conv1(data)
conv2 = self.conv2(conv1)
split0 = self.split0(conv2)
split1 = self.split1(conv2)
block1 = self.block1(split1)
block2 = self.block2(block1)
conv3 = self.conv3(block2)
route = torch.cat([split0, conv3], dim=1)
return route
# Stage4 Stage5 Stage6
class Stage(nn.Module):
def __init__(self, nchannels, nblocks):
super().__init__()
# stage4 : 128
# stage5 : 256
# stage6 : 512
self.conv1 = Conv2dBatchLeaky(nchannels, nchannels, 1, 1, activation='mish')
self.conv2 = Conv2dBatchLeaky(nchannels, 2*nchannels, 3, 2, activation='mish')
self.split0 = Conv2dBatchLeaky(2*nchannels, nchannels, 1, 1, activation='mish')
self.split1 = Conv2dBatchLeaky(2*nchannels, nchannels, 1, 1, activation='mish')
blocks = []
for i in range(nblocks):
blocks.append(SmallBlock(nchannels))
self.blocks = nn.Sequential(*blocks)
self.conv4 = Conv2dBatchLeaky(nchannels, nchannels, 1, 1, activation='mish')
def forward(self,data):
conv1 = self.conv1(data)
conv2 = self.conv2(conv1)
split0 = self.split0(conv2)
split1 = self.split1(conv2)
blocks = self.blocks(split1)
conv4 = self.conv4(blocks)
route = torch.cat([split0, conv4], dim=1)
return route
3. 代码测试
下面使用一个小例子来对代码进行测试。
if __name__ == "__main__":
use_cuda = torch.cuda.is_available()
if use_cuda:
device = torch.device("cuda")
cudnn.benchmark = True
else:
device = torch.device("cpu")
darknet = CsDarkNet53(num_classes=10)
darknet = darknet.cuda()
with torch.no_grad():
darknet.eval()
data = torch.rand(1, 3, 256, 256)
data = data.cuda()
try:
#print(darknet)
summary(darknet,(3,256,256))
print(darknet(data))
except Exception as e:
print(e)
代码的输出如下所示：
Total params: 26,627,434
Trainable params: 26,627,434
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.75
Forward/backward pass size (MB): 553.51
Params size (MB): 101.58
Estimated Total Size (MB): 655.83
----------------------------------------------------------------
tensor([[ 0.1690, 0.0798, 0.1836, 0.2414, 0.3855, 0.2437, -0.1422, -0.1855,
0.1758, -0.2452]], device='cuda:0')
注意：输出中存在框架结构内容，这里没有将其写在博客中
4. 结论
CSP-Darknet53的代码结构结合着对应的代码实现一起看，可以有效帮助大家理解关于原理部分的内容。希望可以帮助到大家！！！
另外，关于代码中存在的一些小的部分可能会在后面进行介绍。
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