目录
1.ASFF介绍
2.ASFF加入Yolov5提升检测精度
2.1 ASFF加入common.py中：
2.2 ASFF加入yolo.py中：
2.3 修改yolov5s_asff.yaml
2.4 与cbam结合 进一步提升检测精度
1.ASFF介绍
Learning Spatial Fusion for Single-Shot Object Detection
论文地址：https://arxiv.org/pdf/1911.09516v2.pdf
多尺度特征特别是特征金字塔FPN是解决目标检测中跨尺度目标的最常用有效的解决方法，但是不同特征尺度中存在的不一致性限制了（基于特征金字塔的）single-shot检测器的性能。本文提出一种特征金字塔融合方法ASFF，它自动学习去抑制不同尺度特征在融合时空间上可能存在不一致；
2.ASFF加入Yolov5提升检测精度
2.1 ASFF加入common.py中：
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# ------------------------------------asff -----end--------------------------------2.2 ASFF加入yolo.py中：
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() 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, torch_1_10=check_version(torch.__version__, '1.10.0')): 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 torch_1_10: # 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_gridclass DetectionModel(BaseModel):下加入 （PS:建议直接搜索Detect关键词）
m = self.model[-1] # Detect() if isinstance(m, (Detect, Segment,ASFF_Detect)):def parse_model(d, ch): # model_dict, input_channels(3)
# TODO: channel, gw, gd elif m in {Detect, Segment,ASFF_Detect}: args.append([ch[x] for x in f])class BaseModel(nn.Module):
def _apply(self, fn): # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers self = super()._apply(fn) m = self.model[-1] # Detect() if isinstance(m, (Detect, Segment,ASFF_Detect)):2.3 修改yolov5s_asff.yaml
# YOLOv5 ? by Ultralytics, GPL-3.0 license# Parametersnc: 1 # 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, ASFF_Detect, [nc, anchors]], # Detect(P3, P4, P5) ]2.4 与cbam结合 进一步提升检测精度
cbam介绍：https://blog.csdn.net/m0_63774211/article/details/129611391
# Parametersnc: 1 # number of classesdepth_multiple: 0.67 # model depth multiplewidth_multiple: 0.75 # layer channel multiple# anchorsanchors: - [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, CBAM, [1024]], #9 [-1, 1, SPPF, [1024, 5]], #10 ]# 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]], # 14 [-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]], # 18 (P3/8-small) [-1, 1, CBAM, [256]], #19 [-1, 1, Conv, [256, 3, 2]], [[-1, 14], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 22 (P4/16-medium) [-1, 1, CBAM, [512]], [-1, 1, Conv, [512, 3, 2]], [[-1, 10], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 25 (P5/32-large) [-1, 1, CBAM, [1024]], [[19, 23, 27], 1, ASFF_Detect, [nc, anchors]], # Detect(P3, P4, P5) ]