目标检测模型YOLOv3之提取特征
图像分类的章节中，我们已经讲解过了通过卷积神经网络提取图像特征。通过连续使用多层卷积和池化等操作，能得到语义含义更加丰富的特征图。在检测问题中，也使用卷积神经网络逐层提取图像特征，通过最终的输出特征图来表征物体位置和类别等信息。
YOLOv3算法使用的骨干网络是Darknet53。Darknet53网络的具体结构如下图所示，在ImageNet图像分类任务上取得了很好的成绩。在检测任务中，将图中C0后面的平均池化、全连接层和Softmax去掉，保留从输入到C0部分的网络结构，作为检测模型的基础网络结构，也称为骨干网络。YOLOv3模型会在骨干网络的基础上，再添加检测相关的网络模块。
下面的程序是Darknet53骨干网络的实现代码。
名词解释：特征图的步幅(stride)
在提取特征的过程中通常会使用步幅大于1的卷积或者池化，导致后面的特征图尺寸越来越小，特征图的步幅等于输入图片尺寸除以特征图尺寸。例如：C0的尺寸是20×20，原图尺寸是640×640，则C0的步幅是 640 20 = 32 \frac{640}{20}=32 20640=32。同理，C1的步幅是16，C2的步幅是8。
##基于paddlepaddle
import paddle
import paddle.nn.functional as F
import numpy as np
class ConvBNLayer(paddle.nn.Layer):
def __init__(self, ch_in, ch_out,
kernel_size=3, stride=1, groups=1,
padding=0, act="leaky"):
super(ConvBNLayer, self).__init__()
self.conv = paddle.nn.Conv2D(
in_channels=ch_in,
out_channels=ch_out,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.Normal(0., 0.02)),
bias_attr=False)
self.batch_norm = paddle.nn.BatchNorm2D(
num_features=ch_out,
weight_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.Normal(0., 0.02),
regularizer=paddle.regularizer.L2Decay(0.)),
bias_attr=paddle.ParamAttr(
initializer=paddle.nn.initializer.Constant(0.0),
regularizer=paddle.regularizer.L2Decay(0.)))
self.act = act
def forward(self, inputs):
out = self.conv(inputs)
out = self.batch_norm(out)
if self.act == 'leaky':
out = F.leaky_relu(x=out, negative_slope=0.1)
return out
class DownSample(paddle.nn.Layer):
# 下采样，图片尺寸减半，具体实现方式是使用stirde=2的卷积
def __init__(self,
ch_in,
ch_out,
kernel_size=3,
stride=2,
padding=1):
super(DownSample, self).__init__()
self.conv_bn_layer = ConvBNLayer(
ch_in=ch_in,
ch_out=ch_out,
kernel_size=kernel_size,
stride=stride,
padding=padding)
self.ch_out = ch_out
def forward(self, inputs):
out = self.conv_bn_layer(inputs)
return out
class BasicBlock(paddle.nn.Layer):
"""
基本残差块的定义，输入x经过两层卷积，然后接第二层卷积的输出和输入x相加
"""
def __init__(self, ch_in, ch_out):
super(BasicBlock, self).__init__()
self.conv1 = ConvBNLayer(
ch_in=ch_in,
ch_out=ch_out,
kernel_size=1,
stride=1,
padding=0
)
self.conv2 = ConvBNLayer(
ch_in=ch_out,
ch_out=ch_out*2,
kernel_size=3,
stride=1,
padding=1
)
def forward(self, inputs):
conv1 = self.conv1(inputs)
conv2 = self.conv2(conv1)
out = paddle.add(x=inputs, y=conv2)
return out
class LayerWarp(paddle.nn.Layer):
"""
添加多层残差块，组成Darknet53网络的一个层级
"""
def __init__(self, ch_in, ch_out, count, is_test=True):
super(LayerWarp,self).__init__()
self.basicblock0 = BasicBlock(ch_in,
ch_out)
self.res_out_list = []
for i in range(1, count):
res_out = self.add_sublayer("basic_block_%d" % (i), # 使用add_sublayer添加子层
BasicBlock(ch_out*2,
ch_out))
self.res_out_list.append(res_out)
def forward(self,inputs):
y = self.basicblock0(inputs)
for basic_block_i in self.res_out_list:
y = basic_block_i(y)
return y
# DarkNet 每组残差块的个数，来自DarkNet的网络结构图
DarkNet_cfg = {53: ([1, 2, 8, 8, 4])}
class DarkNet53_conv_body(paddle.nn.Layer):
def __init__(self):
super(DarkNet53_conv_body, self).__init__()
self.stages = DarkNet_cfg[53]
self.stages = self.stages[0:5]
# 第一层卷积
self.conv0 = ConvBNLayer(
ch_in=3,
ch_out=32,
kernel_size=3,
stride=1,
padding=1)
# 下采样，使用stride=2的卷积来实现
self.downsample0 = DownSample(
ch_in=32,
ch_out=32 * 2)
# 添加各个层级的实现
self.darknet53_conv_block_list = []
self.downsample_list = []
for i, stage in enumerate(self.stages):
conv_block = self.add_sublayer(
"stage_%d" % (i),
LayerWarp(32*(2**(i+1)),
32*(2**i),
stage))
self.darknet53_conv_block_list.append(conv_block)
# 两个层级之间使用DownSample将尺寸减半
for i in range(len(self.stages) - 1):
downsample = self.add_sublayer(
"stage_%d_downsample" % i,
DownSample(ch_in=32*(2**(i+1)),
ch_out=32*(2**(i+2))))
self.downsample_list.append(downsample)
def forward(self,inputs):
out = self.conv0(inputs)
#print("conv1:",out.numpy())
out = self.downsample0(out)
#print("dy:",out.numpy())
blocks = []
for i, conv_block_i in enumerate(self.darknet53_conv_block_list): #依次将各个层级作用在输入上面
out = conv_block_i(out)
blocks.append(out)
if i < len(self.stages) - 1:
out = self.downsample_list[i](out)
return blocks[-1:-4:-1] # 将C0, C1, C2作为返回值
torch实现
class BN_Conv2d_Leaky(nn.Module):
"""
BN_CONV_LeakyRELU
"""
def __init__(self, in_channels: object, out_channels: object, kernel_size: object, stride: object, padding: object,
dilation=1, groups=1, bias=False) -> object:
super(BN_Conv2d_Leaky, self).__init__()
self.seq = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias),
nn.BatchNorm2d(out_channels)
)
def forward(self, x):
return F.leaky_relu(self.seq(x))
class Dark_block(nn.Module):
"""block for darknet"""
def __init__(self, channels, is_se=False, inner_channels=None):
super(Dark_block, self).__init__()
self.is_se = is_se
if inner_channels is None:
inner_channels = channels // 2
self.conv1 = BN_Conv2d_Leaky(channels, inner_channels, 1, 1, 0)
self.conv2 = nn.Conv2d(inner_channels, channels, 3, 1, 1)
self.bn = nn.BatchNorm2d(channels)
if self.is_se:
self.se = SE(channels, 16)
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
out = self.bn(out)
if self.is_se:
coefficient = self.se(out)
out *= coefficient
out += x
return F.leaky_relu(out)
class DarkNet(nn.Module):
def __init__(self, layers: object, num_classes, is_se=False) -> object:
super(DarkNet, self).__init__()
self.is_se = is_se
filters = [64, 128, 256, 512, 1024]
self.conv1 = BN_Conv2d(3, 32, 3, 1, 1)
self.redu1 = BN_Conv2d(32, 64, 3, 2, 1)
self.conv2 = self.__make_layers(filters[0], layers[0])
self.redu2 = BN_Conv2d(filters[0], filters[1], 3, 2, 1)
self.conv3 = self.__make_layers(filters[1], layers[1])
self.redu3 = BN_Conv2d(filters[1], filters[2], 3, 2, 1)
self.conv4 = self.__make_layers(filters[2], layers[2])
self.redu4 = BN_Conv2d(filters[2], filters[3], 3, 2, 1)
self.conv5 = self.__make_layers(filters[3], layers[3])
self.redu5 = BN_Conv2d(filters[3], filters[4], 3, 2, 1)
self.conv6 = self.__make_layers(filters[4], layers[4])
self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(filters[4], num_classes)
def __make_layers(self, num_filter, num_layers):
layers = []
for _ in range(num_layers):
layers.append(Dark_block(num_filter, self.is_se))
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv1(x)
out = self.redu1(out)
out = self.conv2(out)
out = self.redu2(out)
out = self.conv3(out)
out = self.redu3(out)
out = self.conv4(out)
out = self.redu4(out)
out = self.conv5(out)
out = self.redu5(out)
out = self.conv6(out)
out = self.global_pool(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return F.softmax(out)
def darknet_53(num_classes=1000):
return DarkNet([1, 2, 8, 8, 4], num_classes)