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Integrate offset into grid (#7262) 

Eliminate 1 op during training and inference.
pull/7265/head
Glenn Jocher 2022-04-03 23:40:23 +02:00 committed by GitHub
parent 8bc839ed8e
commit ea72b84f5e
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1 changed files with 3 additions and 3 deletions
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models/yolo.py
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@ -64,11 +64,11 @@ class Detect(nn.Module):
y = x[i].sigmoid()
if self.inplace:
y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
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] - 0.5)) * self.stride[i] # xy
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))
@ -82,7 +82,7 @@ class Detect(nn.Module):
yv, xv = torch.meshgrid(torch.arange(ny, device=d), torch.arange(nx, device=d), indexing='ij')
else:
yv, xv = torch.meshgrid(torch.arange(ny, device=d), torch.arange(nx, device=d))
grid = torch.stack((xv, yv), 2).expand(shape).float()
grid = torch.stack((xv, yv), 2).expand(shape).float() - 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).float()
return grid, anchor_grid