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🍁YOLOv8入门+改进专栏🍁
🍁如果再也不能见到你，祝你早安，午安，晚安🍁
【YOLOv8改进系列】：
【YOLOv8】YOLOv8结构解读
YOLOv8改进系列（1）----替换主干网络之EfficientViT
YOLOv8改进系列（2）----替换主干网络之FasterNet
YOLOv8改进系列（3）----替换主干网络之ConvNeXt V2
YOLOv8改进系列（4）----替换C2f之FasterNet中的FasterBlock替换C2f中的Bottleneck
YOLOv8改进系列（5）----替换主干网络之EfficientFormerV2
YOLOv8改进系列（6）----替换主干网络之VanillaNet
目录
💯一、LSKNet介绍
1. 简介
2. LSKNet架构设计
背景知识
研究方法
3. 实验与结果
数据集
实现细节
4. 关键结论
💯二、具体添加方法
第①步：创建LSKNet.py
第②步：修改task.py
(1)引入创建的lsknet文件
(2)修改_predict_once函数
(3)修改parse_model函数
第③步：yolov8.yaml文件修改
第④步：验证是否加入成功
💯一、LSKNet介绍
- 论文题目：《Large Selective Kernel Network for Remote Sensing Object Detection》
- 论文地址：https://arxiv.org/pdf/2303.09030
1. 简介
文章提出了一种名为 Large Selective Kernel Network（LSKNet）的新型网络架构，专门用于遥感图像中的目标检测任务。LSKNet 通过动态调整其大空间感受野，能够更好地模拟遥感场景中不同目标所需的长程上下文信息，从而显著提高了遥感目标检测的性能。
2. LSKNet架构设计
背景知识
遥感目标检测是计算机视觉中的一个重要领域，主要关注在高分辨率的航拍图像中识别和定位感兴趣的目标，如车辆、船只和飞机等。近年来，研究主要集中在改进目标的方向边界框表示上，但往往忽略了遥感场景中独特的先验知识。例如，遥感图像中的小目标可能因缺乏足够的长程上下文信息而被误检，且不同类型的目标所需的上下文范围差异很大。
研究方法
为了解决上述问题，作者提出了 LSKNet，其核心是 Large Kernel Selection（LSK）模块。LSK 模块通过以下机制实现：
-
大核分解：通过将大核分解为一系列深度可分离卷积（depth-wise convolutions），每个卷积核的大小和膨胀率逐渐增加，从而快速扩展感受野。例如，将一个大核分解为两个或三个深度可分离卷积，理论感受野可分别达到 23 和 29。
-
空间选择机制：通过空间选择机制，LSK 模块能够动态地选择不同尺度的大核特征图。具体来说，将不同感受野的特征图进行通道拼接后，通过平均池化和最大池化提取空间关系，再通过卷积层生成空间注意力图。每个空间注意力图通过 Sigmoid 函数生成对应的空间选择掩码，用于加权融合不同大核的特征图。
-
动态调整感受野：LSK 模块能够根据输入动态调整大核的选择，从而为每个目标自适应地调整感受野。这种设计使得网络能够更好地捕捉不同类型目标所需的长程上下文信息。
3. 实验与结果
数据集
作者在三个标准的遥感目标检测数据集上进行了实验：
-
HRSC2016：包含 1061 张高分辨率遥感图像，用于船只检测。
-
DOTA-v1.0：包含 2806 张遥感图像，涵盖 15 个类别，如飞机、桥梁、车辆等。
-
FAIR1M-v1.0：包含 15266 张高分辨率图像，涵盖 5 个类别和 37 个子类别。
实现细节
-
LSKNet 的骨干网络基于流行的结构设计，包含重复的 LSKNet 块，每个块由 Large Kernel Selection 子块和 Feed-forward Network 子块组成。
-
骨干网络首先在 ImageNet-1K 数据集上预训练，然后在目标遥感数据集上进行微调。
-
使用 AdamW 优化器进行训练，初始学习率分别为 0.0004 和 0.0002，训练周期分别为 36 轮和 12 轮。
4. 关键结论
-
性能提升：LSKNet 在 HRSC2016 数据集上达到了 98.46% 的 mAP，在 DOTA-v1.0 数据集上达到了 81.85% 的 mAP，在 FAIR1M-v1.0 数据集上达到了 47.87% 的 mAP，均超过了以往的最佳记录。
-
效率与性能的平衡：通过大核分解，LSKNet 在保持高性能的同时，显著减少了参数量和计算量。例如，LSKNet-T 的参数量仅为 4.3M，FLOPs 为 19.1G，相比 ResNet-18（11.2M 参数和 38.1G FLOPs）大幅减少。
-
上下文建模能力：通过可视化和分析，LSKNet 能够根据目标类型动态调整感受野，为不同类型的目标提供所需的长程上下文信息。例如，桥梁需要较大的上下文信息来区分与道路的相似性，而足球场等类别则需要较少的上下文信息。
💯二、具体添加方法
第①步：创建LSKNet.py
创建完成后，将下面代码直接复制粘贴进去：
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair as to_2tuple
from timm.layers import DropPath, to_2tuple
from functools import partial
import numpy as np
__all__ = 'lsknet_t', 'lsknet_s'
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
self.dwconv = DWConv(hidden_features)
self.act = act_layer()
self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.dwconv(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class LSKblock(nn.Module):
def __init__(self, dim):
super().__init__()
self.conv0 = nn.Conv2d(dim, dim, 5, padding=2, groups=dim)
self.conv_spatial = nn.Conv2d(dim, dim, 7, stride=1, padding=9, groups=dim, dilation=3)
self.conv1 = nn.Conv2d(dim, dim//2, 1)
self.conv2 = nn.Conv2d(dim, dim//2, 1)
self.conv_squeeze = nn.Conv2d(2, 2, 7, padding=3)
self.conv = nn.Conv2d(dim//2, dim, 1)
def forward(self, x):
attn1 = self.conv0(x)
attn2 = self.conv_spatial(attn1)
attn1 = self.conv1(attn1)
attn2 = self.conv2(attn2)
attn = torch.cat([attn1, attn2], dim=1)
avg_attn = torch.mean(attn, dim=1, keepdim=True)
max_attn, _ = torch.max(attn, dim=1, keepdim=True)
agg = torch.cat([avg_attn, max_attn], dim=1)
sig = self.conv_squeeze(agg).sigmoid()
attn = attn1 * sig[:,0,:,:].unsqueeze(1) + attn2 * sig[:,1,:,:].unsqueeze(1)
attn = self.conv(attn)
return x * attn
class Attention(nn.Module):
def __init__(self, d_model):
super().__init__()
self.proj_1 = nn.Conv2d(d_model, d_model, 1)
self.activation = nn.GELU()
self.spatial_gating_unit = LSKblock(d_model)
self.proj_2 = nn.Conv2d(d_model, d_model, 1)
def forward(self, x):
shorcut = x.clone()
x = self.proj_1(x)
x = self.activation(x)
x = self.spatial_gating_unit(x)
x = self.proj_2(x)
x = x + shorcut
return x
class Block(nn.Module):
def __init__(self, dim, mlp_ratio=4., drop=0.,drop_path=0., act_layer=nn.GELU, norm_cfg=None):
super().__init__()
self.norm1 = nn.BatchNorm2d(dim)
self.norm2 = nn.BatchNorm2d(dim)
self.attn = Attention(dim)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
layer_scale_init_value = 1e-2
self.layer_scale_1 = nn.Parameter(
layer_scale_init_value * torch.ones((dim)), requires_grad=True)
self.layer_scale_2 = nn.Parameter(
layer_scale_init_value * torch.ones((dim)), requires_grad=True)
def forward(self, x):
x = x + self.drop_path(self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * self.attn(self.norm1(x)))
x = x + self.drop_path(self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * self.mlp(self.norm2(x)))
return x
class OverlapPatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768, norm_cfg=None):
super().__init__()
patch_size = to_2tuple(patch_size)
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
padding=(patch_size[0] // 2, patch_size[1] // 2))
self.norm = nn.BatchNorm2d(embed_dim)
def forward(self, x):
x = self.proj(x)
_, _, H, W = x.shape
x = self.norm(x)
return x, H, W
class LSKNet(nn.Module):
def __init__(self, img_size=224, in_chans=3, embed_dims=[64, 128, 256, 512],
mlp_ratios=[8, 8, 4, 4], drop_rate=0., drop_path_rate=0., norm_layer=partial(nn.LayerNorm, eps=1e-6),
depths=[3, 4, 6, 3], num_stages=4,
norm_cfg=None):
super().__init__()
self.depths = depths
self.num_stages = num_stages
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
cur = 0
for i in range(num_stages):
patch_embed = OverlapPatchEmbed(img_size=img_size if i == 0 else img_size // (2 ** (i + 1)),
patch_size=7 if i == 0 else 3,
stride=4 if i == 0 else 2,
in_chans=in_chans if i == 0 else embed_dims[i - 1],
embed_dim=embed_dims[i], norm_cfg=norm_cfg)
block = nn.ModuleList([Block(
dim=embed_dims[i], mlp_ratio=mlp_ratios[i], drop=drop_rate, drop_path=dpr[cur + j],norm_cfg=norm_cfg)
for j in range(depths[i])])
norm = norm_layer(embed_dims[i])
cur += depths[i]
setattr(self, f"patch_embed{i + 1}", patch_embed)
setattr(self, f"block{i + 1}", block)
setattr(self, f"norm{i + 1}", norm)
self.channel = [i.size(1) for i in self.forward(torch.randn(1, 3, 640, 640))]
def forward(self, x):
B = x.shape[0]
outs = []
for i in range(self.num_stages):
patch_embed = getattr(self, f"patch_embed{i + 1}")
block = getattr(self, f"block{i + 1}")
norm = getattr(self, f"norm{i + 1}")
x, H, W = patch_embed(x)
for blk in block:
x = blk(x)
x = x.flatten(2).transpose(1, 2)
x = norm(x)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
outs.append(x)
return outs
class DWConv(nn.Module):
def __init__(self, dim=768):
super(DWConv, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, x):
x = self.dwconv(x)
return x
def update_weight(model_dict, weight_dict):
idx, temp_dict = 0, {}
for k, v in weight_dict.items():
if k in model_dict.keys() and np.shape(model_dict[k]) == np.shape(v):
temp_dict[k] = v
idx += 1
model_dict.update(temp_dict)
print(f'loading weights... {idx}/{len(model_dict)} items')
return model_dict
def lsknet_t(weights=''):
model = LSKNet(embed_dims=[32, 64, 160, 256], depths=[3, 3, 5, 2], drop_rate=0.1, drop_path_rate=0.1)
if weights:
model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)['state_dict']))
return model
def lsknet_s(weights=''):
model = LSKNet(embed_dims=[64, 128, 256, 512], depths=[2, 2, 4, 2], drop_rate=0.1, drop_path_rate=0.1)
if weights:
model.load_state_dict(update_weight(model.state_dict(), torch.load(weights)['state_dict']))
return model
if __name__ == '__main__':
model = lsknet_t('lsk_t_backbone-2ef8a593.pth')
inputs = torch.randn((1, 3, 640, 640))
for i in model(inputs):
print(i.size())第②步：修改task.py
(1)引入创建的lsknet文件
from ultralytics.nn.backbone.lsknet import *(2)修改_predict_once函数
def _predict_once(self, x, profile=False, visualize=False, embed=None):
"""
Perform a forward pass through the network.
Args:
x (torch.Tensor): The input tensor to the model.
profile (bool): Print the computation time of each layer if True, defaults to False.
visualize (bool): Save the feature maps of the model if True, defaults to False.
embed (list, optional): A list of feature vectors/embeddings to return.
Returns:
(torch.Tensor): The last output of the model.
"""
y, dt, embeddings = [], [], [] # outputs
for idx, m in enumerate(self.model):
if m.f != -1: # if not from previous layer
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
if profile:
self._profile_one_layer(m, x, dt)
if hasattr(m, 'backbone'):
x = m(x)
for _ in range(5 - len(x)):
x.insert(0, None)
for i_idx, i in enumerate(x):
if i_idx in self.save:
y.append(i)
else:
y.append(None)
# print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x if x_ is not None])}')
x = x[-1]
else:
x = m(x) # run
y.append(x if m.i in self.save else None) # save output
# if type(x) in {list, tuple}:
# if idx == (len(self.model) - 1):
# if type(x[1]) is dict:
# print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x[1]["one2one"]])}')
# else:
# print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x[1]])}')
# else:
# print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x if x_ is not None])}')
# elif type(x) is dict:
# print(f'layer id:{idx:>2} {m.type:>50} output shape:{", ".join([str(x_.size()) for x_ in x["one2one"]])}')
# else:
# if not hasattr(m, 'backbone'):
# print(f'layer id:{idx:>2} {m.type:>50} output shape:{x.size()}')
if visualize:
feature_visualization(x, m.type, m.i, save_dir=visualize)
if embed and m.i in embed:
embeddings.append(nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze(-1).squeeze(-1)) # flatten
if m.i == max(embed):
return torch.unbind(torch.cat(embeddings, 1), dim=0)
return x(3)修改parse_model函数
可以直接把下面的代码粘贴到对应的位置中，后续的改进中，对应的模块就不需要做出改变，有改变处，后续会另有说明
def parse_model(d, ch, verbose=True, warehouse_manager=None): # model_dict, input_channels(3)
"""Parse a YOLO model.yaml dictionary into a PyTorch model."""
import ast
# Args
max_channels = float("inf")
nc, act, scales = (d.get(x) for x in ("nc", "activation", "scales"))
depth, width, kpt_shape = (d.get(x, 1.0) for x in ("depth_multiple", "width_multiple", "kpt_shape"))
if scales:
scale = d.get("scale")
if not scale:
scale = tuple(scales.keys())[0]
LOGGER.warning(f"WARNING ⚠️ no model scale passed. Assuming scale='{scale}'.")
if len(scales[scale]) == 3:
depth, width, max_channels = scales[scale]
elif len(scales[scale]) == 4:
depth, width, max_channels, threshold = scales[scale]
if act:
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
if verbose:
LOGGER.info(f"{colorstr('activation:')} {act}") # print
if verbose:
LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10} {'module':<60}{'arguments':<50}")
ch = [ch]
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
is_backbone = False
for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args
try:
if m == 'node_mode':
m = d[m]
if len(args) > 0:
if args[0] == 'head_channel':
args[0] = int(d[args[0]])
t = m
m = getattr(torch.nn, m[3:]) if 'nn.' in m else globals()[m] # get module
except:
pass
for j, a in enumerate(args):
if isinstance(a, str):
with contextlib.suppress(ValueError):
try:
args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
except:
args[j] = a
n = n_ = max(round(n * depth), 1) if n > 1 else n # depth gain
if m in {
Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus, BottleneckCSP, C1, C2, C2f, ELAN1, AConv, SPPELAN, C2fAttn, C3, C3TR,
C3Ghost, nn.Conv2d, nn.ConvTranspose2d, DWConvTranspose2d, C3x, RepC3, PSA, SCDown, C2fCIB, C2f_Faster, C2f_ODConv,
C2f_Faster_EMA, C2f_DBB, GSConv, GSConvns, VoVGSCSP, VoVGSCSPns, VoVGSCSPC, C2f_CloAtt, C3_CloAtt, SCConv, C2f_SCConv, C3_SCConv, C2f_ScConv, C3_ScConv,
C3_EMSC, C3_EMSCP, C2f_EMSC, C2f_EMSCP, RCSOSA, KWConv, C2f_KW, C3_KW, DySnakeConv, C2f_DySnakeConv, C3_DySnakeConv,
DCNv2, C3_DCNv2, C2f_DCNv2, DCNV3_YOLO, C3_DCNv3, C2f_DCNv3, C3_Faster, C3_Faster_EMA, C3_ODConv,
OREPA, OREPA_LargeConv, RepVGGBlock_OREPA, C3_OREPA, C2f_OREPA, C3_DBB, C3_REPVGGOREPA, C2f_REPVGGOREPA,
C3_DCNv2_Dynamic, C2f_DCNv2_Dynamic, C3_ContextGuided, C2f_ContextGuided, C3_MSBlock, C2f_MSBlock,
C3_DLKA, C2f_DLKA, CSPStage, SPDConv, RepBlock, C3_EMBC, C2f_EMBC, SPPF_LSKA, C3_DAttention, C2f_DAttention,
C3_Parc, C2f_Parc, C3_DWR, C2f_DWR, RFAConv, RFCAConv, RFCBAMConv, C3_RFAConv, C2f_RFAConv,
C3_RFCBAMConv, C2f_RFCBAMConv, C3_RFCAConv, C2f_RFCAConv, C3_FocusedLinearAttention, C2f_FocusedLinearAttention,
C3_AKConv, C2f_AKConv, AKConv, C3_MLCA, C2f_MLCA,
C3_UniRepLKNetBlock, C2f_UniRepLKNetBlock, C3_DRB, C2f_DRB, C3_DWR_DRB, C2f_DWR_DRB, CSP_EDLAN,
C3_AggregatedAtt, C2f_AggregatedAtt, DCNV4_YOLO, C3_DCNv4, C2f_DCNv4, HWD, SEAM,
C3_SWC, C2f_SWC, C3_iRMB, C2f_iRMB, C3_iRMB_Cascaded, C2f_iRMB_Cascaded, C3_iRMB_DRB, C2f_iRMB_DRB, C3_iRMB_SWC, C2f_iRMB_SWC,
C3_VSS, C2f_VSS, C3_LVMB, C2f_LVMB, RepNCSPELAN4, DBBNCSPELAN4, OREPANCSPELAN4, DRBNCSPELAN4, ADown, V7DownSampling,
C3_DynamicConv, C2f_DynamicConv, C3_GhostDynamicConv, C2f_GhostDynamicConv, C3_RVB, C2f_RVB, C3_RVB_SE, C2f_RVB_SE, C3_RVB_EMA, C2f_RVB_EMA, DGCST,
C3_RetBlock, C2f_RetBlock, C3_PKIModule, C2f_PKIModule, RepNCSPELAN4_CAA, C3_FADC, C2f_FADC, C3_PPA, C2f_PPA, SRFD, DRFD, RGCSPELAN,
C3_Faster_CGLU, C2f_Faster_CGLU, C3_Star, C2f_Star, C3_Star_CAA, C2f_Star_CAA, C3_KAN, C2f_KAN, C3_EIEM, C2f_EIEM, C3_DEConv, C2f_DEConv,
C3_SMPCGLU, C2f_SMPCGLU, C3_Heat, C2f_Heat, CSP_PTB, SimpleStem, VisionClueMerge, VSSBlock_YOLO, XSSBlock, GLSA, C2f_WTConv, WTConv2d, FeaturePyramidSharedConv,
C2f_FMB, LDConv, C2f_gConv, C2f_WDBB, C2f_DeepDBB, C2f_AdditiveBlock, C2f_AdditiveBlock_CGLU, CSP_MSCB, C2f_MSMHSA_CGLU, CSP_PMSFA, C2f_MogaBlock,
C2f_SHSA, C2f_SHSA_CGLU, C2f_SMAFB, C2f_SMAFB_CGLU, C2f_IdentityFormer, C2f_RandomMixing, C2f_PoolingFormer, C2f_ConvFormer, C2f_CaFormer,
C2f_IdentityFormerCGLU, C2f_RandomMixingCGLU, C2f_PoolingFormerCGLU, C2f_ConvFormerCGLU, C2f_CaFormerCGLU, CSP_MutilScaleEdgeInformationEnhance, C2f_FFCM,
C2f_SFHF, CSP_FreqSpatial, C2f_MSM, C2f_RAB, C2f_HDRAB, C2f_LFE, CSP_MutilScaleEdgeInformationSelect, C2f_SFA, C2f_CTA, C2f_CAMixer, MANet,
MANet_FasterBlock, MANet_FasterCGLU, MANet_Star, C2f_HFERB, C2f_DTAB, C2f_ETB, C2f_JDPM, C2f_AP, PSConv, C2f_Kat, C2f_Faster_KAN, C2f_Strip, C2f_StripCGLU
}:
if args[0] == 'head_channel':
args[0] = d[args[0]]
c1, c2 = ch[f], args[0]
if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output)
c2 = make_divisible(min(c2, max_channels) * width, 8)
if m is C2fAttn:
args[1] = make_divisible(min(args[1], max_channels // 2) * width, 8) # embed channels
args[2] = int(
max(round(min(args[2], max_channels // 2 // 32)) * width, 1) if args[2] > 1 else args[2]
) # num heads
args = [c1, c2, *args[1:]]
if m in (KWConv, C2f_KW, C3_KW):
args.insert(2, f'layer{i}')
args.insert(2, warehouse_manager)
if m in (DySnakeConv,):
c2 = c2 * 3
if m in (RepNCSPELAN4, DBBNCSPELAN4, OREPANCSPELAN4, DRBNCSPELAN4, RepNCSPELAN4_CAA):
args[2] = make_divisible(min(args[2], max_channels) * width, 8)
args[3] = make_divisible(min(args[3], max_channels) * width, 8)
if m in {
BottleneckCSP, C1, C2, C2f, C2fAttn, C3, C3TR, C3Ghost, C3x, RepC3, C2fCIB, C2f_Faster, C2f_ODConv, C2f_Faster_EMA, C2f_DBB,
VoVGSCSP, VoVGSCSPns, VoVGSCSPC, C2f_CloAtt, C3_CloAtt, C2f_SCConv, C3_SCConv, C2f_ScConv, C3_ScConv,
C3_EMSC, C3_EMSCP, C2f_EMSC, C2f_EMSCP, RCSOSA, C2f_KW, C3_KW, C2f_DySnakeConv, C3_DySnakeConv,
C3_DCNv2, C2f_DCNv2, C3_DCNv3, C2f_DCNv3, C3_Faster, C3_Faster_EMA, C3_ODConv, C3_OREPA, C2f_OREPA, C3_DBB,
C3_REPVGGOREPA, C2f_REPVGGOREPA, C3_DCNv2_Dynamic, C2f_DCNv2_Dynamic, C3_ContextGuided, C2f_ContextGuided,
C3_MSBlock, C2f_MSBlock, C3_DLKA, C2f_DLKA, CSPStage, RepBlock, C3_EMBC, C2f_EMBC, C3_DAttention, C2f_DAttention,
C3_Parc, C2f_Parc, C3_DWR, C2f_DWR, C3_RFAConv, C2f_RFAConv, C3_RFCBAMConv, C2f_RFCBAMConv, C3_RFCAConv, C2f_RFCAConv,
C3_FocusedLinearAttention, C2f_FocusedLinearAttention, C3_AKConv, C2f_AKConv, C3_MLCA, C2f_MLCA,
C3_UniRepLKNetBlock, C2f_UniRepLKNetBlock, C3_DRB, C2f_DRB, C3_DWR_DRB, C2f_DWR_DRB, CSP_EDLAN,
C3_AggregatedAtt, C2f_AggregatedAtt, C3_DCNv4, C2f_DCNv4, C3_SWC, C2f_SWC,
C3_iRMB, C2f_iRMB, C3_iRMB_Cascaded, C2f_iRMB_Cascaded, C3_iRMB_DRB, C2f_iRMB_DRB, C3_iRMB_SWC, C2f_iRMB_SWC,
C3_VSS, C2f_VSS, C3_LVMB, C2f_LVMB, C3_DynamicConv, C2f_DynamicConv, C3_GhostDynamicConv, C2f_GhostDynamicConv,
C3_RVB, C2f_RVB, C3_RVB_SE, C2f_RVB_SE, C3_RVB_EMA, C2f_RVB_EMA, C3_RetBlock, C2f_RetBlock, C3_PKIModule, C2f_PKIModule,
C3_FADC, C2f_FADC, C3_PPA, C2f_PPA, RGCSPELAN, C3_Faster_CGLU, C2f_Faster_CGLU, C3_Star, C2f_Star, C3_Star_CAA, C2f_Star_CAA,
C3_KAN, C2f_KAN, C3_EIEM, C2f_EIEM, C3_DEConv, C2f_DEConv, C3_SMPCGLU, C2f_SMPCGLU, C3_Heat, C2f_Heat, CSP_PTB, XSSBlock, C2f_WTConv,
C2f_FMB, C2f_gConv, C2f_WDBB, C2f_DeepDBB, C2f_AdditiveBlock, C2f_AdditiveBlock_CGLU, CSP_MSCB, C2f_MSMHSA_CGLU, CSP_PMSFA, C2f_MogaBlock,
C2f_SHSA, C2f_SHSA_CGLU, C2f_SMAFB, C2f_SMAFB_CGLU, C2f_IdentityFormer, C2f_RandomMixing, C2f_PoolingFormer, C2f_ConvFormer, C2f_CaFormer,
C2f_IdentityFormerCGLU, C2f_RandomMixingCGLU, C2f_PoolingFormerCGLU, C2f_ConvFormerCGLU, C2f_CaFormerCGLU, CSP_MutilScaleEdgeInformationEnhance, C2f_FFCM,
C2f_SFHF, CSP_FreqSpatial, C2f_MSM, C2f_RAB, C2f_HDRAB, C2f_LFE, CSP_MutilScaleEdgeInformationSelect, C2f_SFA, C2f_CTA, C2f_CAMixer, MANet,
MANet_FasterBlock, MANet_FasterCGLU, MANet_Star, C2f_HFERB, C2f_DTAB, C2f_ETB, C2f_JDPM, C2f_AP, C2f_Kat, C2f_Faster_KAN, C2f_Strip, C2f_StripCGLU
}:
args.insert(2, n) # number of repeats
n = 1
elif m in {AIFI, AIFI_RepBN}:
args = [ch[f], *args]
c2 = args[0]
elif m in (HGStem, HGBlock, Ghost_HGBlock, Rep_HGBlock, Dynamic_HGBlock, EIEStem):
c1, cm, c2 = ch[f], args[0], args[1]
if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output)
c2 = make_divisible(min(c2, max_channels) * width, 8)
cm = make_divisible(min(cm, max_channels) * width, 8)
args = [c1, cm, c2, *args[2:]]
if m in (HGBlock, Ghost_HGBlock, Rep_HGBlock, Dynamic_HGBlock):
args.insert(4, n) # number of repeats
n = 1
elif m is ResNetLayer:
c2 = args[1] if args[3] else args[1] * 4
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum(ch[x] for x in f)
elif m in ((WorldDetect, ImagePoolingAttn) + DETECT_CLASS + V10_DETECT_CLASS + SEGMENT_CLASS + POSE_CLASS + OBB_CLASS):
args.append([ch[x] for x in f])
if m in SEGMENT_CLASS:
args[2] = make_divisible(min(args[2], max_channels) * width, 8)
if m in (Segment_LSCD, Segment_TADDH, Segment_LSCSBD, Segment_LSDECD, Segment_RSCD):
args[3] = make_divisible(min(args[3], max_channels) * width, 8)
if m in (Detect_LSCD, Detect_TADDH, Detect_LSCSBD, Detect_LSDECD, Detect_RSCD, v10Detect_LSCD, v10Detect_TADDH, v10Detect_RSCD, v10Detect_LSDECD):
args[1] = make_divisible(min(args[1], max_channels) * width, 8)
if m in (Pose_LSCD, Pose_TADDH, Pose_LSCSBD, Pose_LSDECD, Pose_RSCD, OBB_LSCD, OBB_TADDH, OBB_LSCSBD, OBB_LSDECD, OBB_RSCD):
args[2] = make_divisible(min(args[2], max_channels) * width, 8)
elif m is RTDETRDecoder: # special case, channels arg must be passed in index 1
args.insert(1, [ch[x] for x in f])
elif m is Fusion:
args[0] = d[args[0]]
c1, c2 = [ch[x] for x in f], (sum([ch[x] for x in f]) if args[0] == 'concat' else ch[f[0]])
args = [c1, args[0]]
elif m is CBLinear:
c2 = make_divisible(min(args[0][-1], max_channels) * width, 8)
c1 = ch[f]
args = [c1, [make_divisible(min(c2_, max_channels) * width, 8) for c2_ in args[0]], *args[1:]]
elif m is CBFuse:
c2 = ch[f[-1]]
elif isinstance(m, str):
t = m
if len(args) == 2:
m = timm.create_model(m, pretrained=args[0], pretrained_cfg_overlay={'file':args[1]}, features_only=True)
elif len(args) == 1:
m = timm.create_model(m, pretrained=args[0], features_only=True)
c2 = m.feature_info.channels()
elif m in {convnextv2_atto, convnextv2_femto, convnextv2_pico, convnextv2_nano, convnextv2_tiny, convnextv2_base, convnextv2_large, convnextv2_huge,
fasternet_t0, fasternet_t1, fasternet_t2, fasternet_s, fasternet_m, fasternet_l,
EfficientViT_M0, EfficientViT_M1, EfficientViT_M2, EfficientViT_M3, EfficientViT_M4, EfficientViT_M5,
efficientformerv2_s0, efficientformerv2_s1, efficientformerv2_s2, efficientformerv2_l,
vanillanet_5, vanillanet_6, vanillanet_7, vanillanet_8, vanillanet_9, vanillanet_10, vanillanet_11, vanillanet_12, vanillanet_13, vanillanet_13_x1_5, vanillanet_13_x1_5_ada_pool,
RevCol,
lsknet_t, lsknet_s,
SwinTransformer_Tiny,
repvit_m0_9, repvit_m1_0, repvit_m1_1, repvit_m1_5, repvit_m2_3,
CSWin_tiny, CSWin_small, CSWin_base, CSWin_large,
unireplknet_a, unireplknet_f, unireplknet_p, unireplknet_n, unireplknet_t, unireplknet_s, unireplknet_b, unireplknet_l, unireplknet_xl,
transnext_micro, transnext_tiny, transnext_small, transnext_base,
RMT_T, RMT_S, RMT_B, RMT_L,
PKINET_T, PKINET_S, PKINET_B,
MobileNetV4ConvSmall, MobileNetV4ConvMedium, MobileNetV4ConvLarge, MobileNetV4HybridMedium, MobileNetV4HybridLarge,
starnet_s050, starnet_s100, starnet_s150, starnet_s1, starnet_s2, starnet_s3, starnet_s4
}:
if m is RevCol:
args[1] = [make_divisible(min(k, max_channels) * width, 8) for k in args[1]]
args[2] = [max(round(k * depth), 1) for k in args[2]]
m = m(*args)
c2 = m.channel
elif m in {EMA, SpatialAttention, BiLevelRoutingAttention, BiLevelRoutingAttention_nchw,
TripletAttention, CoordAtt, CBAM, BAMBlock, LSKBlock, ScConv, LAWDS, EMSConv, EMSConvP,
SEAttention, CPCA, Partial_conv3, FocalModulation, EfficientAttention, MPCA, deformable_LKA,
EffectiveSEModule, LSKA, SegNext_Attention, DAttention, MLCA, TransNeXt_AggregatedAttention,
FocusedLinearAttention, LocalWindowAttention, ChannelAttention_HSFPN, ELA_HSFPN, CA_HSFPN, CAA_HSFPN,
DySample, CARAFE, CAA, ELA, CAFM, AFGCAttention, EUCB, ContrastDrivenFeatureAggregation, FSA}:
c2 = ch[f]
args = [c2, *args]
# print(args)
elif m in {SimAM, SpatialGroupEnhance}:
c2 = ch[f]
elif m is ContextGuidedBlock_Down:
c2 = ch[f] * 2
args = [ch[f], c2, *args]
elif m is BiFusion:
c1 = [ch[x] for x in f]
c2 = make_divisible(min(args[0], max_channels) * width, 8)
args = [c1, c2]
# --------------GOLD-YOLO--------------
elif m in {SimFusion_4in, AdvPoolFusion}:
c2 = sum(ch[x] for x in f)
elif m is SimFusion_3in:
c2 = args[0]
if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output)
c2 = make_divisible(min(c2, max_channels) * width, 8)
args = [[ch[f_] for f_ in f], c2]
elif m is IFM:
c1 = ch[f]
c2 = sum(args[0])
args = [c1, *args]
elif m is InjectionMultiSum_Auto_pool:
c1 = ch[f[0]]
c2 = args[0]
args = [c1, *args]
elif m is PyramidPoolAgg:
c2 = args[0]
args = [sum([ch[f_] for f_ in f]), *args]
elif m is TopBasicLayer:
c2 = sum(args[1])
# --------------GOLD-YOLO--------------
# --------------ASF--------------
elif m is Zoom_cat:
c2 = sum(ch[x] for x in f)
elif m is Add:
c2 = ch[f[-1]]
elif m in {ScalSeq, DynamicScalSeq}:
c1 = [ch[x] for x in f]
c2 = make_divisible(args[0] * width, 8)
args = [c1, c2]
elif m is asf_attention_model:
args = [ch[f[-1]]]
# --------------ASF--------------
elif m is SDI:
args = [[ch[x] for x in f]]
elif m is Multiply:
c2 = ch[f[0]]
elif m is FocusFeature:
c1 = [ch[x] for x in f]
c2 = int(c1[1] * 0.5 * 3)
args = [c1, *args]
elif m is DASI:
c1 = [ch[x] for x in f]
args = [c1, c2]
elif m is CSMHSA:
c1 = [ch[x] for x in f]
c2 = ch[f[-1]]
args = [c1, c2]
elif m is CFC_CRB:
c1 = ch[f]
c2 = c1 // 2
args = [c1, *args]
elif m is SFC_G2:
c1 = [ch[x] for x in f]
c2 = c1[0]
args = [c1]
elif m in {CGAFusion, CAFMFusion, SDFM, PSFM}:
c2 = ch[f[1]]
args = [c2, *args]
elif m in {ContextGuideFusionModule}:
c1 = [ch[x] for x in f]
c2 = 2 * c1[1]
args = [c1]
# elif m in {PSA}:
# c2 = ch[f]
# args = [c2, *args]
elif m in {SBA}:
c1 = [ch[x] for x in f]
c2 = c1[-1]
args = [c1, c2]
elif m in {WaveletPool}:
c2 = ch[f] * 4
elif m in {WaveletUnPool}:
c2 = ch[f] // 4
elif m in {CSPOmniKernel}:
c2 = ch[f]
args = [c2]
elif m in {ChannelTransformer, PyramidContextExtraction}:
c1 = [ch[x] for x in f]
c2 = c1
args = [c1]
elif m in {RCM}:
c2 = ch[f]
args = [c2, *args]
elif m in {DynamicInterpolationFusion}:
c2 = ch[f[0]]
args = [[ch[x] for x in f]]
elif m in {FuseBlockMulti}:
c2 = ch[f[0]]
args = [c2]
elif m in {CrossLayerChannelAttention, CrossLayerSpatialAttention}:
c2 = [ch[x] for x in f]
args = [c2[0], *args]
elif m in {FreqFusion}:
c2 = ch[f[0]]
args = [[ch[x] for x in f], *args]
elif m in {DynamicAlignFusion}:
c2 = args[0]
args = [[ch[x] for x in f], c2]
elif m in {ConvEdgeFusion}:
c2 = make_divisible(min(args[0], max_channels) * width, 8)
args = [[ch[x] for x in f], c2]
elif m in {MutilScaleEdgeInfoGenetator}:
c1 = ch[f]
c2 = [make_divisible(min(i, max_channels) * width, 8) for i in args[0]]
args = [c1, c2]
elif m in {MultiScaleGatedAttn}:
c1 = [ch[x] for x in f]
c2 = min(c1)
args = [c1]
elif m in {WFU, MultiScalePCA, MultiScalePCA_Down}:
c1 = [ch[x] for x in f]
c2 = c1[0]
args = [c1]
elif m in {GetIndexOutput}:
c2 = ch[f][args[0]]
elif m is HyperComputeModule:
c1, c2 = ch[f], args[0]
c2 = make_divisible(min(c2, max_channels) * width, 8)
args = [c1, c2, threshold]
else:
c2 = ch[f]
if isinstance(c2, list) and m not in {ChannelTransformer, PyramidContextExtraction, CrossLayerChannelAttention, CrossLayerSpatialAttention, MutilScaleEdgeInfoGenetator}:
is_backbone = True
m_ = m
m_.backbone = True
else:
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
t = str(m)[8:-2].replace('__main__.', '') # module type
m.np = sum(x.numel() for x in m_.parameters()) # number params
m_.i, m_.f, m_.type = i + 4 if is_backbone else i, f, t # attach index, 'from' index, type
if verbose:
LOGGER.info(f"{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f} {t:<60}{str(args):<50}") # print
save.extend(x % (i + 4 if is_backbone else i) for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
if i == 0:
ch = []
if isinstance(c2, list) and m not in {ChannelTransformer, PyramidContextExtraction, CrossLayerChannelAttention, CrossLayerSpatialAttention, MutilScaleEdgeInfoGenetator}:
ch.extend(c2)
for _ in range(5 - len(ch)):
ch.insert(0, 0)
else:
ch.append(c2)
return nn.Sequential(*layers), sorted(save)第③步：yolov8.yaml文件修改
在下述文件夹中创立yolov8-lsknet.yaml
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call 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 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
# 0-P1/2
# 1-P2/4
# 2-P3/8
# 3-P4/16
# 4-P5/32
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, lsknet_t, []] # 4
- [-1, 1, SPPF, [1024, 5]] # 5
# YOLOv8.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 6
- [[-1, 3], 1, Concat, [1]] # 7 cat backbone P4
- [-1, 3, C2f, [512]] # 8
- [-1, 1, nn.Upsample, [None, 2, 'nearest']] # 9
- [[-1, 2], 1, Concat, [1]] # 10 cat backbone P3
- [-1, 3, C2f, [256]] # 11 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]] # 12
- [[-1, 8], 1, Concat, [1]] # 13 cat head P4
- [-1, 3, C2f, [512]] # 14 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]] # 15
- [[-1, 5], 1, Concat, [1]] # 16 cat head P5
- [-1, 3, C2f, [1024]] # 17 (P5/32-large)
- [[11, 14, 17], 1, Detect, [nc]] # Detect(P3, P4, P5)
第④步：验证是否加入成功
将train.py中的配置文件进行修改，并运行
🏋不是每一粒种子都能开花，但播下种子就比荒芜的旷野强百倍🏋
🍁YOLOv8入门+改进专栏🍁
【YOLOv8改进系列】：
【YOLOv8】YOLOv8结构解读
YOLOv8改进系列（1）----替换主干网络之EfficientViT
YOLOv8改进系列（2）----替换主干网络之FasterNet
YOLOv8改进系列（3）----替换主干网络之ConvNeXt V2
YOLOv8改进系列（4）----替换C2f之FasterNet中的FasterBlock替换C2f中的Bottleneck
YOLOv8改进系列（5）----替换主干网络之EfficientFormerV2
YOLOv8改进系列（6）----替换主干网络之VanillaNet
