梅州建网站,南通网站开发招聘,大姚县建设工程招标网站,wordpress加代码广告#YOLO# #目标检测# #计算机视觉#
一、本文介绍
作为入门性篇章#xff0c;这里介绍了iRMB注意力在YOLOv8中的使用。包含iRMB原理分析#xff0c;iRMB的代码、iRMB的使用方法、以及添加以后的yaml文件及运行记录。
二、iRMB原理分析
iRMB官方论文地址#xff1a;文章
iR…#YOLO# #目标检测# #计算机视觉#
一、本文介绍
作为入门性篇章这里介绍了iRMB注意力在YOLOv8中的使用。包含iRMB原理分析iRMB的代码、iRMB的使用方法、以及添加以后的yaml文件及运行记录。
二、iRMB原理分析
iRMB官方论文地址文章
iRMB官方代码地址代码
iRMB注意力机制iRMB是一种混合网络模块结合了卷积神经网络CNN的轻量级特性和 Transformer 模型的动态处理能力。 三、相关代码
iRMB注意力的代码如下
from timm.models._efficientnet_blocks import SqueezeExcite
from functools import partial
inplace Trueclass LayerNorm2d(nn.Module):def __init__(self, normalized_shape, eps1e-6, elementwise_affineTrue):super().__init__()self.norm nn.LayerNorm(normalized_shape, eps, elementwise_affine)def forward(self, x):x rearrange(x, b c h w - b h w c).contiguous()x self.norm(x)x rearrange(x, b h w c - b c h w).contiguous()return xdef get_norm(norm_layerin_1d):eps 1e-6norm_dict {none: nn.Identity,in_1d: partial(nn.InstanceNorm1d, epseps),in_2d: partial(nn.InstanceNorm2d, epseps),in_3d: partial(nn.InstanceNorm3d, epseps),bn_1d: partial(nn.BatchNorm1d, epseps),bn_2d: partial(nn.BatchNorm2d, epseps),# bn_2d: partial(nn.SyncBatchNorm, epseps),bn_3d: partial(nn.BatchNorm3d, epseps),gn: partial(nn.GroupNorm, epseps),ln_1d: partial(nn.LayerNorm, epseps),ln_2d: partial(LayerNorm2d, epseps),}return norm_dict[norm_layer]def get_act(act_layerrelu):act_dict {none: nn.Identity,relu: nn.ReLU,relu6: nn.ReLU6,silu: nn.SiLU}return act_dict[act_layer]class ConvNormAct(nn.Module):def __init__(self, dim_in, dim_out, kernel_size, stride1, dilation1, groups1, biasFalse,skipFalse, norm_layerbn_2d, act_layerrelu, inplaceTrue, drop_path_rate0.):super(ConvNormAct, self).__init__()self.has_skip skip and dim_in dim_outpadding math.ceil((kernel_size - stride) / 2)self.conv nn.Conv2d(dim_in, dim_out, kernel_size, stride, padding, dilation, groups, bias)self.norm get_norm(norm_layer)(dim_out)self.act get_act(act_layer)(inplaceinplace)self.drop_path DropPath(drop_path_rate) if drop_path_rate else nn.Identity()def forward(self, x):shortcut xx self.conv(x)x self.norm(x)x self.act(x)if self.has_skip:x self.drop_path(x) shortcutreturn xclass iRMB(nn.Module):def __init__(self, dim_in, dim_out, norm_inTrue, has_skipTrue, exp_ratio1.0, norm_layerbn_2d,act_layerrelu, v_projTrue, dw_ks3, stride1, dilation1, se_ratio0.0, dim_head8, window_size7,attn_sTrue, qkv_biasFalse, attn_drop0., drop0., drop_path0., v_groupFalse, attn_preFalse):super().__init__()self.norm get_norm(norm_layer)(dim_in) if norm_in else nn.Identity()dim_mid int(dim_in * exp_ratio)self.has_skip (dim_in dim_out and stride 1) and has_skipself.attn_s attn_sif self.attn_s:assert dim_in % dim_head 0, dim should be divisible by num_headsself.dim_head dim_headself.window_size window_sizeself.num_head dim_in // dim_headself.scale self.dim_head ** -0.5self.attn_pre attn_preself.qk ConvNormAct(dim_in, int(dim_in * 2), kernel_size1, biasqkv_bias, norm_layernone,act_layernone)self.v ConvNormAct(dim_in, dim_mid, kernel_size1, groupsself.num_head if v_group else 1, biasqkv_bias,norm_layernone, act_layeract_layer, inplaceinplace)self.attn_drop nn.Dropout(attn_drop)else:if v_proj:self.v ConvNormAct(dim_in, dim_mid, kernel_size1, biasqkv_bias, norm_layernone,act_layeract_layer, inplaceinplace)else:self.v nn.Identity()self.conv_local ConvNormAct(dim_mid, dim_mid, kernel_sizedw_ks, stridestride, dilationdilation,groupsdim_mid, norm_layerbn_2d, act_layersilu, inplaceinplace)self.se SqueezeExcite(dim_mid, rd_ratiose_ratio,act_layerget_act(act_layer)) if se_ratio 0.0 else nn.Identity()self.proj_drop nn.Dropout(drop)self.proj ConvNormAct(dim_mid, dim_out, kernel_size1, norm_layernone, act_layernone, inplaceinplace)self.drop_path DropPath(drop_path) if drop_path else nn.Identity()def forward(self, x):shortcut xx self.norm(x)B, C, H, W x.shapeif self.attn_s:# paddingif self.window_size 0:window_size_W, window_size_H W, Helse:window_size_W, window_size_H self.window_size, self.window_sizepad_l, pad_t 0, 0pad_r (window_size_W - W % window_size_W) % window_size_Wpad_b (window_size_H - H % window_size_H) % window_size_Hx F.pad(x, (pad_l, pad_r, pad_t, pad_b, 0, 0,))n1, n2 (H pad_b) // window_size_H, (W pad_r) // window_size_Wx rearrange(x, b c (h1 n1) (w1 n2) - (b n1 n2) c h1 w1, n1n1, n2n2).contiguous()# attentionb, c, h, w x.shapeqk self.qk(x)qk rearrange(qk, b (qk heads dim_head) h w - qk b heads (h w) dim_head, qk2, headsself.num_head,dim_headself.dim_head).contiguous()q, k qk[0], qk[1]attn_spa (q k.transpose(-2, -1)) * self.scaleattn_spa attn_spa.softmax(dim-1)attn_spa self.attn_drop(attn_spa)if self.attn_pre:x rearrange(x, b (heads dim_head) h w - b heads (h w) dim_head, headsself.num_head).contiguous()x_spa attn_spa xx_spa rearrange(x_spa, b heads (h w) dim_head - b (heads dim_head) h w, headsself.num_head, hh,ww).contiguous()x_spa self.v(x_spa)else:v self.v(x)v rearrange(v, b (heads dim_head) h w - b heads (h w) dim_head, headsself.num_head).contiguous()x_spa attn_spa vx_spa rearrange(x_spa, b heads (h w) dim_head - b (heads dim_head) h w, headsself.num_head, hh,ww).contiguous()# unpaddingx rearrange(x_spa, (b n1 n2) c h1 w1 - b c (h1 n1) (w1 n2), n1n1, n2n2).contiguous()if pad_r 0 or pad_b 0:x x[:, :, :H, :W].contiguous()else:x self.v(x)x x self.se(self.conv_local(x)) if self.has_skip else self.se(self.conv_local(x))x self.proj_drop(x)x self.proj(x)x (shortcut self.drop_path(x)) if self.has_skip else xreturn x
四、YOLOv8中iRMB使用方法
1.YOLOv8中添加iRMB模块
首先在ultralytics/nn/modules/conv.py最后添加模块的代码。
2.在conv.py的开头__all__ 内添加iRMB模块的类别名
3.在同级文件夹下的__init__.py内添加iRMB的相关内容分别是from .conv import iRMB 以及在__all__内添加iRMB
4.在ultralytics/nn/tasks.py进行iRMB注意力机制的注册以及在YOLOv8的yaml配置文件中添加iRMB即可。
首先打开task.py文件按住CtrlF输入parse_model进行搜索。找到parse_model函数。找以下注册代码将iRMB添加进去即可 elif m in {iRMB}:args [ch[f], ch[f]]
然后就是新建一个名为YOLOv8_iRMB.yaml的配置文件路径:ultralytics/cfg/models/v8/YOLOv8_iRMB.yaml其中参数中nc由自己的数据集决定。本文测试采用的coco8数据集有80个类别。
# Ultralytics YOLO , AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. modelyolov8n.yaml will call CPAM-yolov8.yaml with scale n# [depth, width, max_channels]n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPss: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPsm: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPsl: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs# YOLOv8.0n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, iRMB, [1024,1024]]- [-1, 1, SPPF, [1024, 5]] # 9# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 3, C2f, [512]] # 12- [-1, 1, nn.Upsample, [None, 2, nearest]]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 3, C2f, [256]] # 15 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 13], 1, Concat, [1]] # cat head P4- [-1, 3, C2f, [512]] # 18 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 10], 1, Concat, [1]] # cat head P5- [-1, 3, C2f, [1024]] # 21 (P5/32-large)- [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)在根目录新建一个train.py文件内容如下
from ultralytics import YOLO# 加载一个模型
model YOLO(ultralytics/cfg/models/v8/YOLOv8_iRMB.yaml) # 从YAML建立一个新模型
# 训练模型
results model.train(dataultralytics/cfg/datasets/coco8.yaml, epochs1,imgsz640,optimizerSGD)
训练输出
五、总结
以上就是iRMB的原理及使用方式但具体iRMB注意力机制的具体位置放哪里效果更好。需要根据不同的数据集做相应的实验验证。希望本文能够帮助你入门YOLO中注意力机制的使用。
