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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-04-03 17:24:13 +02:00
parent a59caf053a
commit efc662351b
1 changed files with 6 additions and 6 deletions
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models.py
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@ -137,17 +137,17 @@ class YOLOLayer(nn.Module):
anchor_wh = self.anchor_wh.repeat((1, 1, nG, nG, 1)).view((1, -1, 2)) / nG anchor_wh = self.anchor_wh.repeat((1, 1, nG, nG, 1)).view((1, -1, 2)) / nG
# p = p.view(-1, 5 + self.nC) # p = p.view(-1, 5 + self.nC)
# xy = xy + self.grid_xy[0] # x, y # xy = torch.sigmoid(p[..., 0:2]) + grid_xy[0] # x, y
# wh = torch.exp(wh) * self.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:], 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 / nG, 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
wh = torch.exp(p[..., 2:4]) * anchor_wh # width, height wh = torch.exp(p[..., 2:4]) * anchor_wh # width, height
p_conf = torch.sigmoid(p[..., 4:5]) # Conf p_conf = torch.sigmoid(p[..., 4:5]) # Conf
p_cls = p[..., 5:] p_cls = p[..., 5:85]
# Broadcasting only supported on first dimension in CoreML. See onnx-coreml/_operators.py # Broadcasting only supported on first dimension in CoreML. See onnx-coreml/_operators.py
# p_cls = F.softmax(p_cls, 2) * p_conf # SSD-like conf # p_cls = F.softmax(p_cls, 2) * p_conf # SSD-like conf
p_cls = torch.exp(p_cls).permute((2, 1, 0)) p_cls = torch.exp(p_cls).permute((2, 1, 0))
@ -203,8 +203,8 @@ class Darknet(nn.Module):
layer_outputs.append(x) layer_outputs.append(x)
if ONNX_EXPORT: if ONNX_EXPORT:
output = torch.cat(output, 1) # merge the 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
return output[5:].t(), output[:4].t() # ONNX scores, boxes return output[5:85].t(), output[:4].t() # ONNX scores, boxes
else: else:
return output if self.training else torch.cat(output, 1) return output if self.training else torch.cat(output, 1)