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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-12-23 23:34:30 -08:00
parent 05a9a6205f
commit 1e1cffae8b
1 changed files with 6 additions and 7 deletions
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13
models.py
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@ -182,11 +182,10 @@ class YOLOLayer(nn.Module):
anchor_wh = self.anchor_wh.repeat((1, 1, self.nx, self.ny, 1)).view(1, m, 2) / ngu anchor_wh = self.anchor_wh.repeat((1, 1, self.nx, self.ny, 1)).view(1, m, 2) / ngu
p = p.view(m, self.no) p = p.view(m, self.no)
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_cls = F.softmax(p[:, 5:self.no], 1) * torch.sigmoid(p[:, 4:5]) # SSD-like conf
p_cls = F.softmax(p[:, 5:self.no], 1) * p_conf # SSD-like conf return torch.cat((xy / ngu[0], wh, p_cls), 1).t()
return torch.cat((xy / ngu[0], wh, p_conf, p_cls), 1).t()
# p = p.view(1, m, self.no) # p = p.view(1, m, self.no)
# xy = torch.sigmoid(p[..., 0:2]) + grid_xy # x, y # xy = torch.sigmoid(p[..., 0:2]) + grid_xy # x, y
@ -203,7 +202,7 @@ class YOLOLayer(nn.Module):
else: # inference else: # inference
# s = 1.5 # scale_xy (pxy = pxy * s - (s - 1) / 2) # s = 1.5 # scale_xy (pxy = pxy * s - (s - 1) / 2)
io = p.clone() # inference output io = p.clone() # inference output
io[..., 0:2] = torch.sigmoid(io[..., 0:2]) + self.grid_xy # xy io[..., :2] = torch.sigmoid(io[..., :2]) + self.grid_xy # xy
io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh # wh yolo method io[..., 2:4] = torch.exp(io[..., 2:4]) * self.anchor_wh # wh yolo method
# io[..., 2:4] = ((torch.sigmoid(io[..., 2:4]) * 2) ** 3) * self.anchor_wh # wh power method # io[..., 2:4] = ((torch.sigmoid(io[..., 2:4]) * 2) ** 3) * self.anchor_wh # wh power method
io[..., :4] *= self.stride io[..., :4] *= self.stride
@ -270,7 +269,7 @@ class Darknet(nn.Module):
elif ONNX_EXPORT: elif ONNX_EXPORT:
output = torch.cat(output, 1) # cat 3 layers 85 x (507, 2028, 8112) to 85 x 10647 output = torch.cat(output, 1) # cat 3 layers 85 x (507, 2028, 8112) to 85 x 10647
nc = self.module_list[self.yolo_layers[0]].nc # number of classes nc = self.module_list[self.yolo_layers[0]].nc # number of classes
return output[5:5 + nc].t(), output[0:4].t() # ONNX scores, boxes return output[4:4 + nc].t(), output[0:4].t() # ONNX scores, boxes
else: else:
io, p = list(zip(*output)) # inference output, training output io, p = list(zip(*output)) # inference output, training output
return torch.cat(io, 1), p return torch.cat(io, 1), p