102 lines
3.8 KiB
Python
102 lines
3.8 KiB
Python
import math
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from collections import OrderedDict
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import torch.nn as nn
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# ---------------------------------------------------------------------#
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# 残差结构
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# 利用一个1x1卷积下降通道数，然后利用一个3x3卷积提取特征并且上升通道数
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# 最后接上一个残差边
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# ---------------------------------------------------------------------#
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class BasicBlock(nn.Module):
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def __init__(self, inplanes, planes):
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super(BasicBlock, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes[0], kernel_size=1, stride=1, padding=0, bias=False) # 从大通道转化小通道。又从小通道转为大通道。
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self.bn1 = nn.BatchNorm2d(planes[0])
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self.relu1 = nn.LeakyReLU(0.1)
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self.conv2 = nn.Conv2d(planes[0], planes[1], kernel_size=3, stride=1, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes[1])
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self.relu2 = nn.LeakyReLU(0.1)
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu1(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu2(out)
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out += residual
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return out
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class DarkNet(nn.Module):
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def __init__(self, layers):
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super(DarkNet, self).__init__()
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self.inplanes = 32 # 第一次卷积，输出通道为32
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# 416,416,3 -> 416,416,32
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self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(self.inplanes)
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self.relu1 = nn.LeakyReLU(0.1)
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# 416,416,32 -> 208,208,64
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self.layer1 = self._make_layer([32, 64], layers[0]) # layers 中保存的是程序块重复的次数
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# 208,208,64 -> 104,104,128
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self.layer2 = self._make_layer([64, 128], layers[1])
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# 104,104,128 -> 52,52,256
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self.layer3 = self._make_layer([128, 256], layers[2])
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# 52,52,256 -> 26,26,512
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self.layer4 = self._make_layer([256, 512], layers[3])
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# 26,26,512 -> 13,13,1024
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self.layer5 = self._make_layer([512, 1024], layers[4])
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self.layers_out_filters = [64, 128, 256, 512, 1024]
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# 进行权值初始化
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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# ---------------------------------------------------------------------#
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# 在每一个layer里面，首先利用一个步长为2的3x3卷积进行下采样
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# 然后进行残差结构的堆叠
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# ---------------------------------------------------------------------#
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def _make_layer(self, planes, blocks):
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layers = []
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# 下采样，步长为2，卷积核大小为3 # 进入_make_layer先创建一层网络，用于降采样，然后再是多个重复的block
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layers.append(("ds_conv", nn.Conv2d(self.inplanes, planes[1], kernel_size=3, stride=2, padding=1, bias=False)))
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layers.append(("ds_bn", nn.BatchNorm2d(planes[1])))
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layers.append(("ds_relu", nn.LeakyReLU(0.1)))
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# 加入残差结构
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self.inplanes = planes[1] # 保存这一层的输出通道，也是下一层的输入通道
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for i in range(0, blocks):
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layers.append(("residual_{}".format(i), BasicBlock(self.inplanes, planes)))
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return nn.Sequential(OrderedDict(layers))
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu1(x)
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x = self.layer1(x)
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x = self.layer2(x)
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out3 = self.layer3(x)
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out4 = self.layer4(out3)
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out5 = self.layer5(out4)
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return out3, out4, out5
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def darknet53():
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model = DarkNet([1, 2, 8, 8, 4])
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return model
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