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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-04-21 23:49:10 +02:00
parent cfe354064c
commit 37799efa0b
1 changed files with 5 additions and 4 deletions
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9
models.py
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@ -131,16 +131,17 @@ class YOLOLayer(nn.Module):
return p return p
elif ONNX_EXPORT: elif ONNX_EXPORT:
ngu = self.ng.view((1, 1, 2)) # Constants CAN NOT BE BROADCAST, ensure correct shape!
ngu = self.ng.repeat((1, self.na * self.nx * self.ny, 1))
grid_xy = self.grid_xy.repeat((1, self.na, 1, 1, 1)).view((1, -1, 2)) grid_xy = self.grid_xy.repeat((1, self.na, 1, 1, 1)).view((1, -1, 2))
anchor_wh = self.anchor_wh.repeat((1, 1, self.nx, self.ny, 1)).view((1, -1, 2)) / self.nx anchor_wh = self.anchor_wh.repeat((1, 1, self.nx, self.ny, 1)).view((1, -1, 2)) / ngu
# p = p.view(-1, 5 + self.nc) # p = p.view(-1, 5 + self.nc)
# xy = torch.sigmoid(p[..., 0:2]) + grid_xy[0] # x, y # xy = torch.sigmoid(p[..., 0:2]) + grid_xy[0] # x, y
# wh = torch.exp(p[..., 2:4]) * anchor_wh[0] # width, height # wh = torch.exp(p[..., 2:4]) * anchor_wh[0] # width, height
# p_conf = torch.sigmoid(p[:, 4:5]) # Conf # p_conf = torch.sigmoid(p[:, 4:5]) # Conf
# p_cls = F.softmax(p[:, 5:85], 1) * p_conf # SSD-like conf # p_cls = F.softmax(p[:, 5:85], 1) * p_conf # SSD-like conf
# return torch.cat((xy / ng, wh, p_conf, p_cls), 1).t() # return torch.cat((xy / ngu[0], wh, p_conf, p_cls), 1).t()
p = p.view(1, -1, 5 + self.nc) p = p.view(1, -1, 5 + self.nc)
xy = torch.sigmoid(p[..., 0:2]) + grid_xy # x, y xy = torch.sigmoid(p[..., 0:2]) + grid_xy # x, y
@ -152,7 +153,7 @@ class YOLOLayer(nn.Module):
p_cls = torch.exp(p_cls).permute((2, 1, 0)) p_cls = torch.exp(p_cls).permute((2, 1, 0))
p_cls = p_cls / p_cls.sum(0).unsqueeze(0) * p_conf.permute((2, 1, 0)) # F.softmax() equivalent p_cls = p_cls / p_cls.sum(0).unsqueeze(0) * p_conf.permute((2, 1, 0)) # F.softmax() equivalent
p_cls = p_cls.permute(2, 1, 0) p_cls = p_cls.permute(2, 1, 0)
return torch.cat((xy / self.nx, wh, p_conf, p_cls), 2).squeeze().t() return torch.cat((xy / ngu, wh, p_conf, p_cls), 2).squeeze().t()
else: # inference else: # inference
io = p.clone() # inference output io = p.clone() # inference output