onnx2trt 踩坑总结

Parsing Error

Gather

1. Assertion failed: !(data->getType() == nvinfer1::DataType::kINT32 && nbDims == 1) && “Cannot perform gather on a shape tensor!”

• file: torch/nn/functional.py

    # 源码：
    if torch._C._get_tracing_state():
                return [(torch.floor(input.size(i + 2) * torch.tensor(float(scale_factors[i])))) for i in range(dim)]
            else:
                return [int(math.floor(int(input.size(i + 2)) * scale_factors[i])) for i in range(dim)]
  
    # 修改为：
    return [int(math.floor(input.size(i + 2) * scale_factors[i])) for i in range(dim)]
  

• file: custom

  
    x=x.view(x.size(),-1) -> x=x.flatten(1)
  
    # section 1
    # 源码
    class ShapeModel(nn.Module):
    def __init__(self):
        super(ShapeModel, self).__init__()
    def forward(self, x):
        return x.shape
  
    # 修改为
    class ShapeModel(nn.Module):
    def __init__(self):
        super(ShapeModel, self).__init__()
    def forward(self, x):
        return torch.tensor(x.shape)
  
    # section 2
    # 源码
    class ResizeModel(nn.Module):
    def __init__(self):
        super(ResizeModel, self).__init__()
    def forward(self, x):
        return F.interpolate(x, scale_factor=(2, 2), mode='nearest')
  
    # 修改为
    class ResizeModel(nn.Module):
    def __init__(self):
        super(ResizeModel, self).__init__()
    def forward(self, x):
        sh = torch.tensor(x.shape)
        return F.interpolate(x, size=(sh[2] * 2, sh[3] * 2), mode='nearest')
  

Upample

```python
#return F.upsample(x, size=(x.shape[2] * 2, x.shape[3] * 2), mode='bilinear', align_corners=True)
                        # RuntimeError: ONNX symbolic expected a constant value in the trace

#return F.interpolate(x, size=(x.shape[2] * 2, x.shape[3] * 2), mode='bilinear', align_corners=True)
                        # RuntimeError: ONNX symbolic expected a constant value in the trace

#return F.upsample(x, size=(600, 600), mode='bilinear', align_corners=False)
                        # UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.

#return F.interpolate(x, size=(600, 600), mode='bilinear', align_corners=True)
                        # UserWarning: ONNX export failed on upsample_bilinear2d because align_corners == True not supported
                        # RuntimeError: ONNX export failed: Couldn't export operator aten::upsample_bilinear2d

return F.interpolate(x, size=(600, 600), mode='bilinear', align_corners=False) #no warning, all clear
```