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YOLO_V1_Model.py
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YOLO_V1_Model.py
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import torch.nn as nn
import torch
class Convention(nn.Module):
def __init__(self,in_channels,out_channels,conv_size,conv_stride,padding):
super(Convention,self).__init__()
self.Conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, conv_size, conv_stride, padding),
nn.LeakyReLU(inplace=True),
nn.BatchNorm2d(out_channels)
)
def forward(self, x):
return self.Conv(x)
def weight_init(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
torch.nn.init.kaiming_normal_(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
class YOLO_V1(nn.Module):
def __init__(self,B=2,Classes_Num=20):
super(YOLO_V1,self).__init__()
self.B = B
self.Classes_Num = Classes_Num
self.Conv_448 = nn.Sequential(
Convention(3, 64, 7, 2, 3),
nn.MaxPool2d(2,2),
)
self.Conv_112 = nn.Sequential(
Convention(64, 192, 3, 1, 1),
nn.MaxPool2d(2, 2),
)
self.Conv_56 = nn.Sequential(
Convention(192, 128, 1, 1, 0),
Convention(128, 256, 3, 1, 1),
Convention(256, 256, 1, 1, 0),
Convention(256, 512, 3, 1, 1),
nn.MaxPool2d(2, 2),
)
self.Conv_28 = nn.Sequential(
Convention(512, 256, 1, 1, 0),
Convention(256, 512, 3, 1, 1),
Convention(512, 256, 1, 1, 0),
Convention(256, 512, 3, 1, 1),
Convention(512, 256, 1, 1, 0),
Convention(256, 512, 3, 1, 1),
Convention(512, 256, 1, 1, 0),
Convention(256, 512, 3, 1, 1),
Convention(512,512,1,1,0),
Convention(512,1024,3,1,1),
nn.MaxPool2d(2, 2),
)
self.Conv_14 = nn.Sequential(
Convention(1024,512,1,1,0),
Convention(512,1024,3,1,1),
Convention(1024, 512, 1, 1, 0),
Convention(512, 1024, 3, 1, 1),
Convention(1024, 1024, 3, 1, 1),
Convention(1024, 1024, 3, 2, 1),
)
self.Conv_7 = nn.Sequential(
Convention(1024,1024,3,1,1),
Convention(1024, 1024, 3, 1, 1),
)
self.Fc = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(7*7*1024,4096),
nn.ReLU(inplace=True),
#nn.Dropout(0.5),
nn.Linear(4096,7 * 7 * (B*5 + Classes_Num)),
nn.Sigmoid()
)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = self.Conv_448(x)
x = self.Conv_112(x)
x = self.Conv_56(x)
x = self.Conv_28(x)
x = self.Conv_14(x)
x = self.Conv_7(x)
# batch_size * channel * height * weight -> batch_size * height * weight * channel
x = x.permute(0, 2, 3, 1)
x = torch.flatten(x, start_dim=1, end_dim=3)
x = self.Fc(x)
x = x.view((-1,7,7,(self.B*5 + self.Classes_Num)))
x = torch.cat((x[:,0:10],self.softmax(x[:,10:])),dim=1)
return x
# 定义权值初始化
def initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
torch.nn.init.kaiming_normal_(m.weight.data)
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
torch.nn.init.normal_(m.weight.data, 0, 0.01)
m.bias.data.zero_()
elif isinstance(m, Convention):
m.weight_init()