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文章目录
YOLO v5加入注意力机制、swin-head、解耦头部（回归源码）
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在YOLO v5的backbone和head引入全局注意力机制（GAM attention）、检测头引入解耦头部、SwinTransformer_Layer层，部分commom.py代码参考地址为：
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一、YOLO v5 简介
YOLO v5由输入端、Backbone、Neck、Head四部分组成。YOLO v5输入端是对图像进行预处理操作,达到对输入图片进行数据增强的效果；Backbone采用了Conv复合卷积模块、C3模块以及SPPF模块组成，Neck部分主则采用 FPN+PAN的特征金字塔结构，增加多尺度的语义表达，从而增强不同尺度上的表达能力；Head部分采用三种损失函数分别计算位置、分类和置信度损失。
二、全局注意力机制的引入
在YOLO v5 d的commom.py加入以下全局注意力机制的代码。
class GAM_Attention(nn.Module):#https://paperswithcode.com/paper/global-attention-mechanism-retain-information
def __init__(self, c1, c2, group=True, rate=4):super(GAM_Attention, self).__init__()
self.channel_attention = nn.Sequential(
nn.Linear(c1,int(c1 / rate)),
nn.ReLU(inplace=True),
nn.Linear(int(c1 / rate), c1))
self.spatial_attention = nn.Sequential(
nn.Conv2d(c1, c1 // rate, kernel_size=7, padding=3, groups=rate) if group else nn.Conv2d(c1, int(c1 / rate),
kernel_size=7,
padding=3),
nn.BatchNorm2d(int(c1 / rate)),
nn.ReLU(inplace=True),
nn.Conv2d(c1 // rate, c2, kernel_size=7, padding=3, groups=rate) if group else nn.Conv2d(int(c1 / rate), c2,
kernel_size=7,
padding=3),
nn.BatchNorm2d(c2))
def forward(self, x):
b, c, h, w = x.shape
x_permute = x.permute(0,2,3,1).view(b,-1, c)
x_att_permute = self.channel_attention(x_permute).view(b, h, w, c)
x_channel_att = x_att_permute.permute(0,3,1,2)#x_channel_att=channel_shuffle(x_channel_att,4) #last shuffle
x = x * x_channel_att
x_spatial_att = self.spatial_attention(x).sigmoid()
x_spatial_att =channel_shuffle(x_spatial_att,4) # last shuffle
out = x * x_spatial_att
#out=channel_shuffle(out,4) #last shufflereturn out
def channel_shuffle(x, groups=2): ##shuffle channel
#RESHAPE----->transpose------->Flatten
B, C, H, W = x.size()
out = x.view(B, groups, C // groups, H, W).permute(0, 2, 1, 3, 4).contiguous()
out = out.view(B, C, H, W)return out
三、引入SwinTransformer_Layer层
在commom.py加入以下代码。代码较多
def window_reverse(windows, window_size:int, H:int, W:int):"""
将window还原成一个feature map
Args:
windows:(num_windows*B, window_size, window_size, C)window_size(int): Window size(M)H(int): Height of image
W(int): Width of image
Returns:
x:(B, H, W, C)"""
B =int(windows.shape[0]/(H * W / window_size / window_size))#view:[B*num_windows, Mh, Mw, C]->[B, H//Mh, W//Mw, Mh, Mw, C]
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)#permute:[B, H//Mh, W//Mw, Mh, Mw, C] -> [B, H//Mh, Mh, W//Mw, Mw, C]#view:[B, H//Mh, Mh, W//Mw, Mw, C] -> [B, H, W, C]
x = x.permute(0,1,3,2,4,5).contiguous().view(B, H, W,-1)return x
class PatchMerging(nn.Module):""" Patch Merging Layer
Args:dim(int): Number of input channels.norm_layer(nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, dim, norm_layer=nn.LayerNorm):super().__init__()
self.dim = dim
self.reduction = nn.Linear(4* dim,2* dim, bias=False)
self.norm =norm_layer(4* dim)
def forward(self, x, H, W):""" Forward function.
Args:
x: Input feature, tensor size(B, H*W, C).
H, W: Spatial resolution of the input feature."""
B, C, H, W = x.shape
#print('------------------------PatchMErging input shape:',x.size())#H=L**0.5#W=H#assertL == H * W,"input feature has wrong size"#assertH==W ,"input feature has wrong size"
x = x.view(B,int(H),int(W), C)#padding
pad_input =(H %2==1)or(W %2==1)if pad_input:
x = F.pad(x,(0,0,0, W %2,0, H %2))
x0 = x[:,0::2,0::2,:] # B H/2 W/2 C 左上
x1 = x[:,1::2,0::2,:] # B H/2 W/2 C 左下
x2 = x[:,0::2,1::2,:] # B H/2 W/2 C 右上
x3 = x[:,1::2,1::2,:] # B H/2 W/2 C 右下
x = torch.cat([x0, x1, x2, x3],-1) # B H/2 W/24*C
x = x.view(B,-1,4* C) # B H/2*W/24*C
x = self.norm(x)
x = self.reduction(x) # B H/2*W/22*C
#print('PatchMerging output shape:',x.size())return x
class Mlp(nn.Module):""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
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.Linear(in_features, hidden_features)
self.act =act_layer()
self.drop1 = nn.Dropout(drop)
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop2 = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)return x
class WindowAttention(nn.Module):""" Window based multi-head self attention(W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:dim(int): Number of input channels.window_size(tuple[int]): The height and width of the window.num_heads(int): Number of attention heads.qkv_bias(bool, optional): If True, add a learnable bias to query, key, value. Default: True
attn_drop(float, optional): Dropout ratio of attention weight. Default:0.0proj_drop(float, optional): Dropout ratio of output. Default:0.0"""
def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
meta_network_hidden_features=256):super().__init__()
self.dim = dim
self.window_size = window_size # [Mh, Mw]
self.num_heads = num_heads
head_dim = dim // num_heads#self.scale = head_dim **-0.5#defineaparameter table of relative position bias
self.relative_position_bias_weight = nn.Parameter(
torch.zeros((2* window_size[0]-1)*(2* window_size[1]-1), num_heads)) # [2*Mh-1*2*Mw-1, nH]
# 获取窗口内每对token的相对位置索引
#getpair-wise relative position index for each token inside the window#coords_h = torch.arange(self.window_size[0])#coords_w = torch.arange(self.window_size[1])#coords= torch.stack(torch.meshgrid([coords_h, coords_w])) # [2, Mh, Mw]indexing="ij"#coords_flatten = torch.flatten(coords,1) # [2, Mh*Mw]
# # [2, Mh*Mw,1]-[2,1, Mh*Mw]#relative_coords = coords_flatten[:,:, None]- coords_flatten[:, None,:] # [2, Mh*Mw, Mh*Mw]#relative_coords = relative_coords.permute(1,2,0).contiguous() # [Mh*Mw, Mh*Mw,2]#relative_coords[:,:,0]+= self.window_size[0]-1 # shift to start from 0#relative_coords[:,:,1]+= self.window_size[1]-1#relative_coords[:,:,0]*=2* self.window_size[1]-1#relative_position_index = relative_coords.sum(-1) # [Mh*Mw, Mh*Mw]#self.register_buffer("relative_position_index", relative_position_index)#Init meta network for positional encodings
self.meta_network: nn.Module = nn.Sequential(
nn.Linear(in_features=2, out_features=meta_network_hidden_features, bias=True),
nn.ReLU(inplace=True),
nn.Linear(in_features=meta_network_hidden_features, out_features=num_heads, bias=True))
self.qkv = nn.Linear(dim, dim *3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
nn.init.trunc_normal_(self.relative_position_bias_weight, std=.02)
self.softmax = nn.Softmax(dim=-1)#Init tau
self.register_parameter("tau", nn.Parameter(torch.zeros(1, num_heads,1,1)))#Init pair-wise relative positions(log-spaced)
indexes = torch.arange(self.window_size[0], device=self.tau.device)
coordinates = torch.stack(torch.meshgrid([indexes, indexes]), dim=0)
coordinates = torch.flatten(coordinates, start_dim=1)
relative_coordinates = coordinates[:,:, None]- coordinates[:, None,:]
relative_coordinates = relative_coordinates.permute(1,2,0).reshape(-1,2).float()
relative_coordinates_log = torch.sign(relative_coordinates) \
* torch.log(1.+ relative_coordinates.abs())
self.register_buffer("relative_coordinates_log", relative_coordinates_log)
def get_relative_positional_encodings(self):"""
Method computes the relative positional encodings
:return: Relative positional encodings [1, number of heads, window size **2, window size **2]"""
relative_position_bias = self.meta_network(self.relative_coordinates_log)
relative_position_bias = relative_position_bias.permute(1,0)
relative_position_bias = relative_position_bias.reshape(self.num_heads,
self.window_size[0]* self.window_size[1], \
self.window_size[0]* self.window_size[1])return relative_position_bias.unsqueeze(0)
def forward(self, x, mask=None):"""
Args:
x: input features with shape of(num_windows*B, Mh*Mw, C)
mask:(0/-inf) mask with shape of(num_windows, Wh*Ww, Wh*Ww) or None
"""
# [batch_size*num_windows, Mh*Mw, total_embed_dim]
B_, N, C = x.shape
#qkv():->[batch_size*num_windows, Mh*Mw,3* total_embed_dim]#reshape:->[batch_size*num_windows, Mh*Mw,3, num_heads, embed_dim_per_head]#permute:->[3, batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]
qkv = self.qkv(x).reshape(B_, N,3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
# [batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]
q, k, v = qkv.unbind(0) # make torchscript happy(cannot use tensor as tuple)
attn = torch.einsum("bhqd, bhkd -> bhqk", q, k) \
/ torch.maximum(torch.norm(q, dim=-1, keepdim=True)* torch.norm(k, dim=-1, keepdim=True).transpose(-2,-1),
torch.tensor(1e-06, device=q.device, dtype=q.dtype))#transpose:->[batch_size*num_windows, num_heads, embed_dim_per_head, Mh*Mw]
# @: multiply ->[batch_size*num_windows, num_heads, Mh*Mw, Mh*Mw]#q= q * self.scale#cosine-->dot?????? Scaled cosine attention：cosine(q,k)/tau 也许理解的不准确： 控制数值范围有利于训练稳定 （残差块的累加 导致深层难以稳定训练）#attn=(q @ k.transpose(-2,-1))#q= torch.norm(q, p=2, dim=-1)#k= torch.norm(k, p=2, dim=-1)#attn/= q.unsqueeze(-1)#attn/= k.unsqueeze(-2)#attn=attention_map#print('attn shape:',attn.size())#print('attn2 shape:',attention_map.size())
attn /= self.tau.clamp(min=0.01)#relative_position_bias_table.view:[Mh*Mw*Mh*Mw,nH]->[Mh*Mw,Mh*Mw,nH]#relative_position_bias = self.relative_position_bias_weight[self.relative_position_index.view(-1)].view(#self.window_size[0]* self.window_size[1], self.window_size[0]* self.window_size[1],-1)#relative_position_bias = relative_position_bias.permute(2,0,1).contiguous() # [nH, Mh*Mw, Mh*Mw]#print("net work new positional_enco:",self.__get_relative_positional_encodings().size())#print('attn shape:',attn.size())#attn= attn + relative_position_bias.unsqueeze(0)
attn = attn + self.get_relative_positional_encodings()if mask is not None:#mask:[nW, Mh*Mw, Mh*Mw]
nW = mask.shape[0] # num_windows
#attn.view:[batch_size, num_windows, num_heads, Mh*Mw, Mh*Mw]#mask.unsqueeze:[1, nW,1, Mh*Mw, Mh*Mw]
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
# @: multiply ->[batch_size*num_windows, num_heads, Mh*Mw, embed_dim_per_head]#transpose:->[batch_size*num_windows, Mh*Mw, num_heads, embed_dim_per_head]#reshape:->[batch_size*num_windows, Mh*Mw, total_embed_dim]#x=(attn @ v).transpose(1,2).reshape(B_, N, C)##float()
x = torch.einsum("bhal, bhlv -> bhav", attn, v)#x= self.proj(x)#x= self.proj_drop(x)#print('out shape:',x.size())return x
class SwinTransformerBlock(nn.Module):
r""" Swin Transformer Block.
Args:dim(int): Number of input channels.num_heads(int): Number of attention heads.window_size(int): Window size.shift_size(int): Shift size for SW-MSA.mlp_ratio(float): Ratio of mlp hidden dim to embedding dim.qkv_bias(bool, optional): If True, add a learnable bias to query, key, value. Default: True
drop(float, optional): Dropout rate. Default:0.0attn_drop(float, optional): Attention dropout rate. Default:0.0drop_path(float, optional): Stochastic depth rate. Default:0.0act_layer(nn.Module, optional): Activation layer. Default: nn.GELU
norm_layer(nn.Module, optional): Normalization layer. Default: nn.LayerNorm
"""
def __init__(self, dim, num_heads, window_size=7, shift_size=0,
mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
act_layer=nn.GELU, norm_layer=nn.LayerNorm, Global=False):super().__init__()
self.dim = dim
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
assert 0<= self.shift_size < self.window_size,"shift_size must in 0-window_size"#patches_resolution =[img_size[0]// patch_size[0], img_size[1] // patch_size[1]]
self.norm1 =norm_layer(dim)
self.attn =WindowAttention(
dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, qkv_bias=qkv_bias,
attn_drop=attn_drop, proj_drop=drop)#ifGlobal elseGlobal_WindowAttention(#dim, window_size=(self.window_size, self.window_size),input_resolution=() num_heads=num_heads, qkv_bias=qkv_bias,#attn_drop=attn_drop, proj_drop=drop)
self.drop_path =DropPath(drop_path)if drop_path >0.else nn.Identity()
self.norm2 =norm_layer(dim)
mlp_hidden_dim =int(dim * mlp_ratio)
self.mlp =Mlp(in_features=dim, hidden_features=mlp_hidden_dim, out_features=dim, act_layer=act_layer,
drop=drop)
def forward(self, x, attn_mask):#H, W = self.H, self.W#print("org-input block shape:",x.size())
x = x.permute(0,3,2,1).contiguous() # B,H,W,C
B, H, W, C = x.shape
#B, L, C = x.shape#assertL == H * W,"input feature has wrong size"
shortcut = x
#H,W=int(H),int(W)#x= self.norm1(x)#x= x.view(B, H, W, C)#padfeature maps to multiples of window size
# 把feature map给pad到window size的整数倍
#ifmin(H, W)< self.window_size or H % self.window_size!=0:#Padding = True
pad_l =pad_t=0
pad_r =(self.window_size - W % self.window_size)% self.window_size
pad_b =(self.window_size - H % self.window_size)% self.window_size
x = F.pad(x,(0,0, pad_l, pad_r,pad_t, pad_b))
_, Hp, Wp, _ = x.shape
#cyclicshiftif self.shift_size >0:
shifted_x = torch.roll(x, shifts=(-self.shift_size,-self.shift_size), dims=(1,2))else:
shifted_x = x
attn_mask = None
#partitionwindows
x_windows =window_partition(shifted_x, self.window_size) # [nW*B, Mh, Mw, C]
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # [nW*B, Mh*Mw, C]#W-MSA/SW-MSA
attn_windows = self.attn(x_windows, mask=attn_mask) # [nW*B, Mh*Mw, C]#mergewindows
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) # [nW*B, Mh, Mw, C]
shifted_x =window_reverse(attn_windows, self.window_size, Hp, Wp) # [B, H', W', C]#reversecyclic shiftif self.shift_size >0:
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1,2))else:
x = shifted_x
if pad_r >0 or pad_b >0:
# 把前面pad的数据移除掉
x = x[:,:H,:W,:].contiguous()
x = self.norm1(x) # pos-norm.1#x= x.view(B, H * W, C)#FFN
x = shortcut + self.drop_path(x)
x = x + self.drop_path(self.norm2(self.mlp(x))) # pos-norm.2
x = x.permute(0,3,2,1).contiguous()#print("swinblock ouput——shape：",x.size())return x
def window_partition(x, window_size:int):"""
将feature map按照window_size划分成一个个没有重叠的window
Args:
x:(B, H, W, C)window_size(int): window size(M)
Returns:
windows:(num_windows*B, window_size, window_size, C)"""
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)#permute:[B, H//Mh, Mh, W//Mw, Mw, C] -> [B, H//Mh, W//Mh, Mw, Mw, C]#view:[B, H//Mh, W//Mw, Mh, Mw, C] -> [B*num_windows, Mh, Mw, C]
windows = x.permute(0,1,3,2,4,5).contiguous().view(-1, window_size, window_size, C)return windows
class SwinTransformer_Layer(nn.Module):"""
A basic Swin Transformer layer for one stage.
Args:dim(int): Number of input channels.depth(int): Number of blocks.num_heads(int): Number of attention heads.window_size(int): Local window size:7 or 8mlp_ratio(float): Ratio of mlp hidden dim to embedding dim.qkv_bias(bool, optional): If True, add a learnable bias to query, key, value. Default: True
drop(float, optional): Dropout rate. Default:0.0attn_drop(float, optional): Attention dropout rate. Default:0.0drop_path(float| tuple[float], optional): Stochastic depth rate. Default:0.0norm_layer(nn.Module, optional): Normalization layer. Default: nn.LayerNorm
downsample(nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
use_checkpoint(bool): Whether to use checkpointing to save memory. Default: False."""
def __init__(self, dim, depth, num_heads, last_layer=False, window_size=7,
mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
drop_path=0., norm_layer=nn.LayerNorm, downsample=PatchMerging, use_checkpoint=False):super().__init__()
self.dim = dim
self.depth = depth
self.last_layer = last_layer
self.window_size = window_size
self.use_checkpoint = use_checkpoint
self.shift_size = window_size // 2#buildblocks
self.blocks = nn.ModuleList([SwinTransformerBlock(
dim=dim,
num_heads=num_heads,
window_size=window_size,
shift_size=0if(i %2==0)else self.shift_size,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
drop=drop,
attn_drop=attn_drop,
drop_path=drop_path[i]ifisinstance(drop_path, list)else drop_path,
norm_layer=norm_layer,
Global=False)for i in range(depth)])#patchmerging layerif self.last_layer is False:#print('开始进行patchmergin------打印层深度：',depth)
self.downsample =downsample(dim=dim, norm_layer=norm_layer)else:#print('最后1层默认没有Patchmerging：',depth)#self.norm =norm_layer(self.num_features)#self.avgpool = nn.AdaptiveAvgPool1d(1)
self.downsample = None
self.avgpool = nn.AdaptiveAvgPool1d(1)
def create_mask(self, x, H, W):#calculateattention mask for SW-MSA
# 保证Hp和Wp是window_size的整数倍
Hp =int(np.ceil(H / self.window_size))* self.window_size
Wp =int(np.ceil(W / self.window_size))* self.window_size
# 拥有和feature map一样的通道排列顺序，方便后续window_partition
img_mask = torch.zeros((1, Hp, Wp,1), device=x.device) # [1, Hp, Wp,1]
h_slices =(slice(0,-self.window_size),slice(-self.window_size,-self.shift_size),slice(-self.shift_size, None))
w_slices =(slice(0,-self.window_size),slice(-self.window_size,-self.shift_size),slice(-self.shift_size, None))
cnt =0for h in h_slices:for w in w_slices:
img_mask[:, h, w,:]= cnt
cnt +=1
mask_windows =window_partition(img_mask, self.window_size) # [nW, Mh, Mw,1]
mask_windows = mask_windows.view(-1, self.window_size * self.window_size) # [nW, Mh*Mw]
attn_mask = mask_windows.unsqueeze(1)- mask_windows.unsqueeze(2) # [nW,1, Mh*Mw]-[nW, Mh*Mw,1]
# [nW, Mh*Mw, Mh*Mw]
attn_mask = attn_mask.masked_fill(attn_mask !=0,float(-100.0)).masked_fill(attn_mask ==0,float(0.0))return attn_mask
def forward(self, x):#print('swinlayers input shape:',x.size())
B, C, H, W = x.size()#H=int(L**0.5)#W=H#assertL == H * W,"input feature has wrong size"
attn_mask = self.create_mask(x, H, W) # [nW, Mh*Mw, Mh*Mw]for blk in self.blocks:
blk.H, blk.W = H, W
if not torch.jit.is_scripting() and self.use_checkpoint:
x = checkpoint.checkpoint(blk, x, attn_mask)else:
x =blk(x, attn_mask)if self.downsample is not None:
x = self.downsample(x, H, W)
H, W =(H +1)// 2, (W + 1) // 2#ifself.last_layer:#x=x.view(B,H,W,C)#x=x.transpose(1,3)#x= self.norm(x) # [B, L, C]#x= self.avgpool(x.transpose(1,2)) # [B, C,1]#x= x.view(B,-1,H,W)#x=window_reverse(x, self.window_size, H, W) # [B, H', W', C]#x= torch.flatten(x,1)
x = x.view(B,-1, H, W) #
#print("Swin-Transform 层 ------------------------输出维度:",x.size())return x
class DropPath(nn.Module):"""Drop paths(Stochastic Depth) per sample(when applied in main path of residual blocks)."""
def __init__(self, drop_prob=None):super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):returndrop_path_f(x, self.drop_prob, self.training)
def drop_path_f(x, drop_prob:float=0., training: bool = False):"""Drop paths(Stochastic Depth) per sample(when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument."""
if drop_prob ==0. or not training:return x
keep_prob =1- drop_prob
shape =(x.shape[0],)+(1,)*(x.ndim -1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x.div(keep_prob)* random_tensor
return output
四、引入解耦头部层
引入解耦头部的方法，就不在讲述了，其他比我厉害的博主也都有些写引入解耦头部的方法，大家可以去网上查看别的博客。
五、修改模型yaml文件
这里是我修改的模型yaml文件如下： 自己添加的注意力机制层数稍微多点。从下面可以看出在在head部分每一层的SwinTransformer_Layer都加了一层注意力机制。最后一层Detect部分采用Decoupled_Detect解耦头部。
#YOLOv5 🚀 by Ultralytics, GPL-3.0 license#Parameters
nc:1 # number of classes
depth_multiple:0.33 # model depth multiple
width_multiple:0.50 # layer channel multiple
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 v6.0 backbone
backbone:
# [from, number, module, args][[-1,1, Conv,[64,6,2,2]], # 0-P1/2[-1,1, Conv,[128,3,2]], # 1-P2/4[-1,3, C3,[128]], # 2[-1,1, Conv,[256,3,2]], # 3-P3/8[-1,1, SwinTransformer_Layer,[128,2,8,True,8]], # 4[-1,1, Conv,[512,3,2]], # 5-P4/16[-1,1, SwinTransformer_Layer,[256,2,8,True,8]], # 6[-1,1, Conv,[1024,3,2]], # 7-P5/32[-1,1, SwinTransformer_Layer,[512,2,8,True,4]], # 8[-1,1, GAM_Attention,[512,512]], # 9[-1,1, SPPF,[1024,5]], # 10]
head:[[-1,1, Conv,[512,1,1]], # 11[-1,1, nn.Upsample,[None,2,'nearest']], # 12[[-1,6],1, Concat,[1]], # 13[-1,3, C3,[512, False]], # 14[-1,1, Conv,[256,1,1]], # 15[-1,1, nn.Upsample,[None,2,'nearest']], # 16[[-1,4],1, Concat,[1]], # 17[-1,3, C3,[256, False]], # 18[-1,1,GAM_Attention,[128,128]], # 19[-1,1, Conv,[512,3,2]], # 20[[-1,6,13],1, Concat,[1]], # 21[-1,3, C3,[512, False]], # 22[-1,1, SwinTransformer_Layer,[256,2,2,True,8]], # 23[-1,1,GAM_Attention,[256,256]], # 24[-1,1, Conv,[1024,3,2]], # 25[[-1,10],1, Concat,[1]], # 26[-1,3, C3,[1024, False]], # 27(P4/16-medium)[-1,1, SwinTransformer_Layer,[512,2,2,True,4]], # 28[-1,1,GAM_Attention,[512,512]], # 29[[19,24,29],1,Decoupled_Detect,[nc, anchors]], # Detect(P3, P4, P5)]
六、运行代码
1.train.py报错问题
在修改后的YOLO v5中运行上述修改的yaml模型文件时候训练会报错，因为精度的问题，报错内容如下：RuntimeError: expected scalar type Half but found Float
在train.py的def parse_opt(known=False)中增加下面的最后一行代码：
添加上述代码还会继续报错，还是同样的问题，在对train.py文件进行继续修改，在下面两个部分添加half=not opt.swin_float这句代码修改后部分如下：
2.再次运行train.py
再次运行代码，–data部分是我自己的数据集yam文件，–cfg部分是上述修改后的模型yaml文件，这里权重设置为空，训练为200次，批次大小为4。终端运行训练代码如下：
python train.py --data huo.yaml --cfg yolov5s_swin_head.yaml --weights ' '--epoch 200--batch-size 4--swin_float
3.自己数据集实验结果
模型文件[email protected]:.95YOLO v575.8%45.9%YOLO swin-head76.1%47.9%
总结
第一次写这个，奈何自己才疏学浅，后期有新的想法会继续更新。还望大家积极批评指正。
commom.py部分代码参考：
链接: [link] (https://github.com/iloveai8086/YOLOC)
本文转载自: https://blog.csdn.net/m0_60890175/article/details/126459343
版权归原作者 小啊磊BLUE 所有， 如有侵权，请联系我们删除。
版权归原作者 小啊磊BLUE 所有， 如有侵权，请联系我们删除。