Decoupled_Detect
一、common.py文件中加入DecoupledHead
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) self.reg_preds = nn.Conv2d(256 , 4 * self.na, 1) self.obj_preds = nn.Conv2d(256 , 1 * self.na, 1) 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) return out
二、yolo.py文件中加入加入Decoupled_Detect
class 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, 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) yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility 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
在yolo.py文件Model类中做如下修改
在yolo.py文件parse_model函数下做如下修改
三、yaml文件中的Detect改为Decoupled_Detect
ASFF_Detect
一、common.py文件中加入ASFFV5
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
二、yolo.py文件中加入加入ASFF_Detect
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_grid
在yolo.py文件Model类中做如下修改
在yolo.py文件parse_model函数下做如下修改
三、yaml文件中的Detect改为ASFF_Detect
