网上有很多添加解耦头的博客，在此记录下我使用解耦头对YOLOv5改进，若侵权删
解耦头的介绍过段时间再写，先直接上添加方法（这篇文章写的很好，解释了解耦头的作用）
ASFF我没有使用过，但是按照下边的方法添加后也能够运行
我是在YOLOv5-7.0版本上进行修改，如果有什么不对的地方欢迎大佬指教
一、common.py文件中加入代码
这部分是解耦头的代码
对着图去看代码能更好的理解结构
#======================= 解耦头=============================#class DecoupledHead(nn.Module): def __init__(self, ch=256, nc=80, anchors=()): super().__init__() self.nc = nc # number of classes self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.merge = Conv(ch, 256 , 1, 1) self.cls_convs1 = Conv(256 , 256 , 3, 1, 1) self.cls_convs2 = Conv(256 , 256 , 3, 1, 1) self.reg_convs1 = Conv(256 , 256 , 3, 1, 1) self.reg_convs2 = Conv(256 , 256 , 3, 1, 1) self.cls_preds = nn.Conv2d(256 , self.nc * self.na, 1) # 一个1x1的卷积，把通道数变成类别数，比如coco 80类（主要对目标框的类别，预测分数） self.reg_preds = nn.Conv2d(256 , 4 * self.na, 1) # 一个1x1的卷积，把通道数变成4通道，因为位置是xywh self.obj_preds = nn.Conv2d(256 , 1 * self.na, 1) # 一个1x1的卷积，把通道数变成1通道，通过一个值即可判断有无目标（置信度） def forward(self, x): x = self.merge(x) x1 = self.cls_convs1(x) x1 = self.cls_convs2(x1) x1 = self.cls_preds(x1) x2 = self.reg_convs1(x) x2 = self.reg_convs2(x2) x21 = self.reg_preds(x2) x22 = self.obj_preds(x2) out = torch.cat([x21, x22, x1], 1) # 把分类和回归结果按channel维度，即dim=1拼接 return outclass Decoupled_Detect(nn.Module): stride = None # strides computed during build onnx_dynamic = False # ONNX export parameter export = False # export mode def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer super().__init__() self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [torch.zeros(1)] * self.nl # init grid self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) self.m = nn.ModuleList(DecoupledHead(x, nc, anchors) for x in ch) self.inplace = inplace # use in-place ops (e.g. slice assignment) def forward(self, x): z = [] # inference output for i in range(self.nl): x[i] = self.m[i](x[i]) # conv bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() if not self.training: # inference if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) y = x[i].sigmoid() if self.inplace: y[..., 0:2] = (y[..., 0:2] * 2 + self.grid[i]) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953 xy, wh, conf = y.split((2, 2, self.nc + 1), 4) # y.tensor_split((2, 4, 5), 4) # torch 1.8.0 xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh y = torch.cat((xy, wh, conf), 4) z.append(y.view(bs, -1, self.no)) return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) def _make_grid(self, nx=20, ny=20, i=0): d = self.anchors[i].device t = self.anchors[i].dtype shape = 1, self.na, ny, nx, 2 # grid shape y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) if check_version(torch.__version__, '1.10.0'): # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility yv, xv = torch.meshgrid(y, x, indexing='ij') else: yv, xv = torch.meshgrid(y, x) grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) return grid, anchor_grid这部分是ASFF代码
首先需要在common.py导入该段代码
import torch.nn.functional as F然后在common.py最下方加入ASFF的代码
#====================================== ASFF ===========================================#class ASFFV5(nn.Module): def __init__(self, level, multiplier=1, rfb=False, vis=False, act_cfg=True): """ ASFF version for YoloV5 . different than YoloV3 multiplier should be 1, 0.5 which means, the channel of ASFF can be 512, 256, 128 -> multiplier=1 256, 128, 64 -> multiplier=0.5 For even smaller, you need change code manually. """ super(ASFFV5, self).__init__() self.level = level self.dim = [int(1024*multiplier), int(512*multiplier), int(256*multiplier)] # print(self.dim) self.inter_dim = self.dim[self.level] if level == 0: self.stride_level_1 = Conv(int(512*multiplier), self.inter_dim, 3, 2)self.stride_level_2 = Conv(int(256*multiplier), self.inter_dim, 3, 2)self.expand = Conv(self.inter_dim, int( 1024*multiplier), 3, 1) elif level == 1: self.compress_level_0 = Conv( int(1024*multiplier), self.inter_dim, 1, 1) self.stride_level_2 = Conv( int(256*multiplier), self.inter_dim, 3, 2) self.expand = Conv(self.inter_dim, int(512*multiplier), 3, 1) elif level == 2: self.compress_level_0 = Conv( int(1024*multiplier), self.inter_dim, 1, 1) self.compress_level_1 = Conv( int(512*multiplier), self.inter_dim, 1, 1) self.expand = Conv(self.inter_dim, int( 256*multiplier), 3, 1) # when adding rfb, we use half number of channels to save memory compress_c = 8 if rfb else 16 self.weight_level_0 = Conv( self.inter_dim, compress_c, 1, 1) self.weight_level_1 = Conv( self.inter_dim, compress_c, 1, 1) self.weight_level_2 = Conv( self.inter_dim, compress_c, 1, 1) self.weight_levels = Conv( compress_c*3, 3, 1, 1) self.vis = vis def forward(self, x): #l,m,s """ # 128, 256, 512 512, 256, 128 from small -> large """ x_level_0=x[2] #l x_level_1=x[1] #m x_level_2=x[0] #s # print('x_level_0: ', x_level_0.shape) # print('x_level_1: ', x_level_1.shape) # print('x_level_2: ', x_level_2.shape) if self.level == 0: level_0_resized = x_level_0 level_1_resized = self.stride_level_1(x_level_1) level_2_downsampled_inter = F.max_pool2d( x_level_2, 3, stride=2, padding=1) level_2_resized = self.stride_level_2(level_2_downsampled_inter) elif self.level == 1: level_0_compressed = self.compress_level_0(x_level_0) level_0_resized = F.interpolate( level_0_compressed, scale_factor=2, mode='nearest') level_1_resized = x_level_1 level_2_resized = self.stride_level_2(x_level_2) elif self.level == 2: level_0_compressed = self.compress_level_0(x_level_0) level_0_resized = F.interpolate( level_0_compressed, scale_factor=4, mode='nearest') x_level_1_compressed = self.compress_level_1(x_level_1) level_1_resized = F.interpolate( x_level_1_compressed, scale_factor=2, mode='nearest') level_2_resized = x_level_2 # print('level: {}, l1_resized: {}, l2_resized: {}'.format(self.level, # level_1_resized.shape, level_2_resized.shape)) level_0_weight_v = self.weight_level_0(level_0_resized) level_1_weight_v = self.weight_level_1(level_1_resized) level_2_weight_v = self.weight_level_2(level_2_resized) # print('level_0_weight_v: ', level_0_weight_v.shape) # print('level_1_weight_v: ', level_1_weight_v.shape) # print('level_2_weight_v: ', level_2_weight_v.shape) levels_weight_v = torch.cat( (level_0_weight_v, level_1_weight_v, level_2_weight_v), 1) levels_weight = self.weight_levels(levels_weight_v) levels_weight = F.softmax(levels_weight, dim=1) fused_out_reduced = level_0_resized * levels_weight[:, 0:1, :, :] +\ level_1_resized * levels_weight[:, 1:2, :, :] +\ level_2_resized * levels_weight[:, 2:, :, :] out = self.expand(fused_out_reduced) if self.vis: return out, levels_weight, fused_out_reduced.sum(dim=1) else: return out class ASFF_Detect(nn.Module): #add ASFFV5 layer and Rfb stride = None # strides computed during build onnx_dynamic = False # ONNX export parameter export = False # export mode def __init__(self, nc=80, anchors=(), ch=(), multiplier=0.5,rfb=False,inplace=True): # detection layer super().__init__() self.nc = nc # number of classes self.no = nc + 5 # number of outputs per anchor self.nl = len(anchors) # number of detection layers self.na = len(anchors[0]) // 2 # number of anchors self.grid = [torch.zeros(1)] * self.nl # init grid self.l0_fusion = ASFFV5(level=0, multiplier=multiplier,rfb=rfb) self.l1_fusion = ASFFV5(level=1, multiplier=multiplier,rfb=rfb) self.l2_fusion = ASFFV5(level=2, multiplier=multiplier,rfb=rfb) self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv self.inplace = inplace # use in-place ops (e.g. slice assignment) def forward(self, x): z = [] # inference output result=[] result.append(self.l2_fusion(x)) result.append(self.l1_fusion(x)) result.append(self.l0_fusion(x)) x=result for i in range(self.nl): x[i] = self.m[i](x[i]) # conv bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() if not self.training: # inference if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) y = x[i].sigmoid() # https://github.com/iscyy/yoloair if self.inplace: y[..., 0:2] = (y[..., 0:2] * 2 + self.grid[i]) * self.stride[i] # xy y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953 xy, wh, conf = y.split((2, 2, self.nc + 1), 4) # y.tensor_split((2, 4, 5), 4) # torch 1.8.0 xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh y = torch.cat((xy, wh, conf), 4) z.append(y.view(bs, -1, self.no)) return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) def _make_grid(self, nx=20, ny=20, i=0): d = self.anchors[i].device t = self.anchors[i].dtype shape = 1, self.na, ny, nx, 2 # grid shape y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) if check_version(torch.__version__, '1.10.0'): # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility yv, xv = torch.meshgrid(y, x, indexing='ij') else: yv, xv = torch.meshgrid(y, x) grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) #print(anchor_grid) return grid, anchor_grid二、修改yolo.py
1）修改这部分，用下边的代码直接替换红框这行代码就行，注意看我代码所在的行数
if isinstance(m, (Detect, Segment,Decoupled_Detect,ASFF_Detect)):2） 在这里添加红框中的代码
if isinstance(m, Decoupled_Detect) or isinstance(m, ASFF_Detect): s = 256 # 2x min stride m.inplace = self.inplace m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward m.anchors /= m.stride.view(-1, 1, 1) check_anchor_order(m) self.stride = m.stride try: self._initialize_biases() # only run once LOGGER.info('initialize_biases done') except: LOGGER.info('decoupled no biase ')3）这里添加红框代码
elif m is ASFF_Detect: args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f) elif m is Decoupled_Detect: args.append([ch[x] for x in f]) if isinstance(args[1], int): # number of anchors args[1] = [list(range(args[1] * 2))] * len(f)三、配置文件
只需要更改最后一层的Detect，使用解耦头的时候用 Decoupled_Detect，使用ASFF的时候用ASFF_Detect
# Parametersnc: 80 # number of classesdepth_multiple: 0.33 # model depth multiplewidth_multiple: 0.50 # layer channel multipleanchors: - [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 backbonebackbone: # [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]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, SPPF, [1024, 5]], # 9 ]# YOLOv5 v6.0 headhead: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 13 [-1, 1, 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, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 1, 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, Decoupled_Detect, [nc, anchors]], # Detect(P3, P4, P5),解耦 ]我在电脑上可以运行，有什么错误可以在评论区指出
来源地址：https://blog.csdn.net/Zeng999212/article/details/129948677
