change to light decoupled head
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Zhang Wei committed
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@@ -125,6 +125,80 @@ class Detect(nn.Module):
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grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5
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anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
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return grid, anchor_grid
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# 新增
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class DetectDecoupledLite(nn.Module):
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stride = None
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dynamic = False
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export = False
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def __init__(self, nc=80, anchors=(), ch=(), inplace=True):
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super().__init__()
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self.nc = nc
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self.no = nc + 5
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self.nl = len(anchors)
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self.na = len(anchors[0]) // 2
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self.grid = [torch.empty(0) for _ in range(self.nl)]
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self.anchor_grid = [torch.empty(0) for _ in range(self.nl)]
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self.register_buffer("anchors", torch.tensor(anchors).float().view(self.nl, -1, 2))
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self.inplace = inplace
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head_c = 128
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# stem
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self.stems = nn.ModuleList(Conv(c, head_c, 1, 1) for c in ch)
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# reg + obj shared trunk
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self.regobj_convs = nn.ModuleList(Conv(head_c, head_c, 3, 1) for _ in ch)
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# cls trunk
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self.cls_convs = nn.ModuleList(Conv(head_c, head_c, 3, 1) for _ in ch)
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# predictors
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self.box_preds = nn.ModuleList(nn.Conv2d(head_c, self.na * 4, 1) for _ in ch)
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self.obj_preds = nn.ModuleList(nn.Conv2d(head_c, self.na * 1, 1) for _ in ch)
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self.cls_preds = nn.ModuleList(nn.Conv2d(head_c, self.na * self.nc, 1) for _ in ch)
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def forward(self, x):
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z = []
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for i in range(self.nl):
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xi = self.stems[i](x[i])
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regobj_feat = self.regobj_convs[i](xi)
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cls_feat = self.cls_convs[i](xi)
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box = self.box_preds[i](regobj_feat)
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obj = self.obj_preds[i](regobj_feat)
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cls = self.cls_preds[i](cls_feat)
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# [bs, na*4 + na*1 + na*nc, ny, nx]
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out = torch.cat((box, obj, cls), 1)
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bs, _, ny, nx = out.shape
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out = out.view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
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x[i] = out
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if not self.training:
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if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
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self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
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xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
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xy = (xy * 2 + self.grid[i]) * self.stride[i]
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wh = (wh * 2) ** 2 * self.anchor_grid[i]
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y = torch.cat((xy, wh, conf), 4)
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z.append(y.view(bs, self.na * nx * ny, self.no))
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return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
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def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, "1.10.0")):
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d = self.anchors[i].device
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t = self.anchors[i].dtype
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shape = 1, self.na, ny, nx, 2
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y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
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yv, xv = torch.meshgrid(y, x, indexing="ij") if torch_1_10 else torch.meshgrid(y, x)
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grid = torch.stack((xv, yv), 2).expand(shape) - 0.5
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anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
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return grid, anchor_grid
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class Segment(Detect):
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@@ -207,7 +281,8 @@ class BaseModel(nn.Module):
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"""
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self = super()._apply(fn)
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m = self.model[-1] # Detect()
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if isinstance(m, (Detect, Segment)):
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# if isinstance(m, (Detect, Segment)):
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if isinstance(m, (Detect, DetectDecoupledLite, Segment)):
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m.stride = fn(m.stride)
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m.grid = list(map(fn, m.grid))
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if isinstance(m.anchor_grid, list):
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@@ -244,7 +319,8 @@ class DetectionModel(BaseModel):
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# Build strides, anchors
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m = self.model[-1] # Detect()
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if isinstance(m, (Detect, Segment)):
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# if isinstance(m, (Detect, Segment)):
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if isinstance(m, (Detect, DetectDecoupledLite, Segment)):
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def _forward(x):
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"""Passes the input 'x' through the model and returns the processed output."""
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@@ -256,12 +332,36 @@ class DetectionModel(BaseModel):
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check_anchor_order(m)
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m.anchors /= m.stride.view(-1, 1, 1)
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self.stride = m.stride
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self._initialize_biases() # only run once
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# self._initialize_biases() # only run once
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if isinstance(m, Detect):
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self._initialize_biases()
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elif isinstance(m, DetectDecoupledLite):
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self._initialize_decoupled_biases()
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# Init weights, biases
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initialize_weights(self)
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self.info()
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LOGGER.info("")
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#新增
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def _initialize_decoupled_biases(self, cf=None):
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"""Initialize biases for DetectDecoupledLite."""
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m = self.model[-1] # DetectDecoupledLite
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# objectness bias
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for mi, s in zip(m.obj_preds, m.stride):
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b = mi.bias.view(m.na, -1) # (na, 1)
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b.data += math.log(8 / (640 / s) ** 2)
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mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
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# classification bias
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for mi in m.cls_preds:
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b = mi.bias.view(m.na, -1) # (na, nc)
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if cf is None:
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b.data += math.log(0.6 / (m.nc - 0.99999))
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else:
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b.data += torch.log(cf / cf.sum())
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mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
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def forward(self, x, augment=False, profile=False, visualize=False):
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"""Performs single-scale or augmented inference and may include profiling or visualization."""
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@@ -434,7 +534,8 @@ def parse_model(d, ch):
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elif m is Concat:
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c2 = sum(ch[x] for x in f)
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# TODO: channel, gw, gd
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elif m in {Detect, Segment}:
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# elif m in {Detect, Segment}:
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elif m in {Detect, DetectDecoupledLite, Segment}:
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args.append([ch[x] for x in f])
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if isinstance(args[1], int): # number of anchors
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args[1] = [list(range(args[1] * 2))] * len(f)
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@@ -0,0 +1,62 @@
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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# Parameters
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nc: 100 # number of classes
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depth_multiple: 0.33 # model depth multiple
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width_multiple: 0.50 # layer channel multiple
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anchors:
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- [19, 27, 44, 40, 38, 94] # P3/8
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- [96, 68, 86, 152, 180, 137] # P4/16
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- [140, 301, 303, 264, 238, 542] # P5/32
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- [436, 615, 739, 380, 925, 792] # P6/64
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# YOLOv5 v6.0 backbone
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backbone:
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# [from, number, module, args]
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[
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[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
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[-1, 3, C3, [768]],
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[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
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[-1, 3, C3, [1024]],
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[-1, 1, SPPF, [1024, 5]], # 11
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]
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# YOLOv5 v6.0 head
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head: [
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[-1, 1, Conv, [768, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, "nearest"]],
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[[-1, 8], 1, Concat, [1]], # cat backbone P5
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[-1, 3, C3, [768, False]], # 15
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[-1, 1, Conv, [512, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, "nearest"]],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, C3, [512, False]], # 19
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[-1, 1, Conv, [256, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, "nearest"]],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, C3, [256, False]], # 23 (P3/8-small)
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 20], 1, Concat, [1]], # cat head P4
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[-1, 3, C3, [512, False]], # 26 (P4/16-medium)
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 16], 1, Concat, [1]], # cat head P5
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[-1, 3, C3, [768, False]], # 29 (P5/32-large)
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[-1, 1, Conv, [768, 3, 2]],
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[[-1, 12], 1, Concat, [1]], # cat head P6
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[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)
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# [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
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[[23, 26, 29, 32], 1, DetectDecoupledLite, [nc, anchors]], # Detect(P3, P4, P5, P6)
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]
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+3
-2
@@ -5,5 +5,6 @@ python /data6/zhangw/yolov5/train.py \
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--epochs 300 \
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--data /data6/zhangw/ZW_gra_project/CUB_100/cub100.yaml \
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--weights /data6/zhangw/yolov5/yolov5s6.pt \
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--project /data6/zhangw/ZW_gra_project/yolov5_result \
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--name G0_cub100_yolov5s6_raw_e300
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--project /data6/zhangw/ZW_gra_project/yolov5_result_pretrained \
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--name G8_decoupled_head_light_e300 \
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--cfg /data6/zhangw/yolov5/models/yolov5s6_decoupled_light.yaml
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