网站建设协议书,东北网站建设,如何注册WordPress账户,蚌埠网络推广公司常规卷积操作
对于一张55像素、三通道#xff08;shape为553#xff09;#xff0c;经过33卷积核的卷积层#xff08;假设输出通道数为4#xff0c;则卷积核shape为3334#xff0c;最终输出4个Feature Map#xff0c;如果有same padding则尺寸与输入层相同#xff08;…常规卷积操作
对于一张5×5像素、三通道shape为5×5×3经过3×3卷积核的卷积层假设输出通道数为4则卷积核shape为3×3×3×4最终输出4个Feature Map如果有same padding则尺寸与输入层相同5×5如果没有则为尺寸变为3×3 深度可分离卷积
逐通道卷积Depthwise Convolution
Depthwise Convolution的一个卷积核负责一个通道一个通道只被一个卷积核卷积。
一张5×5像素、三通道彩色输入图片shape为5×5×3Depthwise Convolution首先经过第一次卷积运算DW完全是在二维平面内进行。卷积核的数量与上一层的通道数相同通道和卷积核一一对应。所以一个三通道的图像经过运算后生成了3个Feature map(如果有same padding则尺寸与输入层相同为5×5)如下图所示。 Depthwise Convolution完成后的Feature map数量与输入层的通道数相同无法扩展Feature map。而且这种运算对输入层的每个通道独立进行卷积运算没有有效的利用不同通道在相同空间位置上的feature信息。因此需要Pointwise Convolution来将这些Feature map进行组合生成新的Feature map
逐点卷积Pointwise Convolution
Pointwise Convolution的运算与常规卷积运算非常相似它的卷积核的尺寸为 1×1×MM为上一层的通道数。所以这里的卷积运算会将上一步的map在深度方向上进行加权组合生成新的Feature map。有几个卷积核就有几个输出Feature map
经过Pointwise Convolution之后同样输出了4张Feature map与常规卷积的输出维度相同
YOLOV5s中Conv、BottleNeck、C3的代码如下:
原始common.py配置
class Conv(nn.Module):# Standard convolution 通用卷积模块,包括1卷积1BN1激活,激活默认SiLU,可用变量指定,不激活时用nn.Identity()占位,直接返回输入def __init__(self, c1, c2, k1, s1, pNone, g1, actTrue): # ch_in, ch_out, kernel, stride, padding, groupssuper(Conv, self).__init__()self.conv nn.Conv2d(c1, c2, k, s, autopad(k, p), groupsg, biasFalse)self.bn nn.BatchNorm2d(c2)self.act nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())def forward(self, x):return self.act(self.bn(self.conv(x)))def fuseforward(self, x):return self.act(self.conv(x))class Bottleneck(nn.Module):# Standard bottleneck 残差块def __init__(self, c1, c2, shortcutTrue, g1, e0.5): # ch_in, ch_out, shortcut, groups, expansionsuper(Bottleneck, self).__init__()c_ int(c2 * e) # hidden channelsself.cv1 Conv(c1, c_, 1, 1)self.cv2 Conv(c_, c2, 3, 1, gg)self.add shortcut and c1 c2def forward(self, x): # 如果shortcut并且输入输出通道相同则跳层相加return x self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))class C3(nn.Module): # CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n1, shortcutTrue, g1, e0.5): # ch_in, ch_out, number, shortcut, groups, expansionsuper(C3, self).__init__()c_ int(c2 * e) # hidden channelsself.cv1 Conv(c1, c_, 1, 1)self.cv2 Conv(c1, c_, 1, 1)self.cv3 Conv(2 * c_, c2, 1) # actFReLU(c2)self.m nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e1.0) for _ in range(n)]) # n个残差组件(Bottleneck)# self.m nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])def forward(self, x):return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim1))
1.轻量化C3模块
在models/common.py文件中按以下思路修改代码 轻量化C3的改进思路是将原C3模块中使用的普通卷积全部替换为深度可分离卷积其余结构不变改进后的DP_Conv、DP_BottleNeck、DP_C3的代码如下
class DP_Conv(nn.Module):def __init__(self, c1, c2, k1, s1, pNone, g1, actTrue): # ch_in, ch_out, kernel, stride, padding, groupssuper(DP_Conv, self).__init__()self.conv1 nn.Conv2d(c1, c1, kernel_size3, stride1, padding1, groupsc1)self.conv2 nn.Conv2d(c1, c2, kernel_size1, strides)self.bn nn.BatchNorm2d(c2)self.act nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())def forward(self, x):return self.act(self.bn(self.conv2(self.conv1(x))))def fuseforward(self, x):return self.act(self.conv2(self.conv1(x)))class DP_Bottleneck(nn.Module):def __init__(self, c1, c2, shortcutTrue, g1, e0.5): # ch_in, ch_out, shortcut, groups, expansionsuper(DP_Bottleneck, self).__init__()c_ int(c2 * e) # hidden channelsself.cv1 DP_Conv(c1, c_, 1)self.cv2 DP_Conv(c_, c2, 1)self.add shortcut and c1 c2def forward(self, x): # 如果shortcut并且输入输出通道相同则跳层相加return x self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))class DP_C3(nn.Module):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n1, shortcutTrue, g1, e0.5): # ch_in, ch_out, number, shortcut, groups, expansionsuper(DP_C3, self).__init__()c_ int(c2 * e) # hidden channelsself.cv1 DP_Conv(c1, c_, 1)self.cv2 DP_Conv(c1, c_, 1)self.cv3 DP_Conv(2 * c_, c2, 1) # actFReLU(c2)self.m nn.Sequential(*[DP_Bottleneck(c_, c_, shortcut, g, e1.0) for _ in range(n)]) # n个残差组件(Bottleneck)# self.m nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])def forward(self, x):return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim1))
2.添加DP_C3.yaml文件 添加至/models/文件中
# parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple# anchors
anchors:- [10,13, 16,30, 33,23] # P3/8- [30,61, 62,45, 59,119] # P4/16- [116,90, 156,198, 373,326] # P5/32# YOLOv5 backbone
backbone:# [from, number, module, args][[-1, 1, DP_Conv, [64, 6, 2, 2]], # 0-P1/2[-1, 1, DP_Conv, [128, 3, 2]], # 1-P2/4[-1, 3, DP_C3, [128]],[-1, 1, DP_Conv, [256, 3, 2]], # 3-P3/8[-1, 9, DP_C3, [256]],[-1, 1, DP_Conv, [512, 3, 2]], # 5-P4/16[-1, 9, DP_C3, [512]],[-1, 1, DP_Conv, [1024, 3, 2]], # 7-P5/32[-1, 3, DP_C3, [1024]],[-1, 1, SPPF, [1024, 5]], # 9]# YOLOv5 head
head:[[-1, 1, DP_Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[[-1, 6], 1, Concat, [1]], # cat backbone P4 # PANet是add, yolov5是concat[-1, 3, C3, [512, False]], # 13[-1, 1, DP_Conv, [256, 1,1]],[-1, 1, nn.Upsample, [None, 2, nearest]],[[-1, 4], 1, Concat, [1]], # cat backbone P3[-1, 3, C3, [256, False]], # 17 (P3/8-small)[-1, 1, DP_Conv, [256, 3,2]],[[-1, 14], 1, Concat, [1]], # cat head P4[-1, 3, C3, [512, False]], # 20 (P4/16-medium)[-1, 1, DP_Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]], # cat head P5[-1, 3, C3, [1024, False]], # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 必须在最后一层, 原代码很多默认了Detect是最后, 并没有全改]
3.yolo.py配置 找到 models/yolo.py 文件中 parse_model() 类 for i, (f, n, m, args) in enumerate(d[backbone] d[head]):在列表中添加DP_Conv、DP_BottleNeck、DP_C3这样可以获得我们要传入的参数。 if m in {Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x,Attention, CondConv, DP_Conv, DP_BottleNeck, DP_C3}:c1, c2 ch[f], args[0]if c2 ! no: # if not outputc2 make_divisible(c2 * gw, 8)args [c1, c2, *args[1:]]if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x, DP_C3}:args.insert(2, n) # number of repeatsn 1elif m is nn.BatchNorm2d:args [ch[f]]elif m is Concat:c2 sum(ch[x] for x in f)# TODO: channel, gw, gd4.训练模型
python train.py --cfg DP_C3.yaml
