pytorch中的while for 循环 导出onnx的问题-解决方案：

时间：2024-04-06 16:54:22

torch.jit.script

例如：

class LoopAdd(torch.nn.Module):
    def __init__(self):
        super().__init__()
    def forward(self, x):
        h = x
        for i in range(x.size(0)):
            h = h + 1
        return h
input_1 = torch.ones(3, 16)
model = LoopAdd()
traced_model = torch.jit.trace(model, (input_1, ))
print(traced_model.graph)

graph(%self : __torch__.LoopAdd,
      %x : Float(3, 16, strides=[16, 1], requires_grad=0, device=cpu)):
  %7 : Long(requires_grad=0, device=cpu) = prim::Constant[value={1}]() # /home/mark.yj/GPT-SoVITS/b.py:8:0
  %8 : int = prim::Constant[value=1]() # /home/mark.yj/GPT-SoVITS/b.py:8:0
  %h.1 : Float(3, 16, strides=[16, 1], requires_grad=0, device=cpu) = aten::add(%x, %7, %8) # /home/mark.yj/GPT-SoVITS/b.py:8:0
  %10 : Long(requires_grad=0, device=cpu) = prim::Constant[value={1}]() # /home/mark.yj/GPT-SoVITS/b.py:8:0
  %11 : int = prim::Constant[value=1]() # /home/mark.yj/GPT-SoVITS/b.py:8:0
  %h : Float(3, 16, strides=[16, 1], requires_grad=0, device=cpu) = aten::add(%h.1, %10, %11) # /home/mark.yj/GPT-SoVITS/b.py:8:0
  %13 : Long(requires_grad=0, device=cpu) = prim::Constant[value={1}]() # /home/mark.yj/GPT-SoVITS/b.py:8:0
  %14 : int = prim::Constant[value=1]() # /home/mark.yj/GPT-SoVITS/b.py:8:0
  %15 : Float(3, 16, strides=[16, 1], requires_grad=0, device=cpu) = aten::add(%h, %13, %14) # /home/mark.yj/GPT-SoVITS/b.py:8:0
  return (%15)

改造后：

class LoopAdd(torch.jit.ScriptModule):
    def __init__(self):
        super().__init__()
    @torch.jit.script_method
    def forward(self, x):
        h = x
        for i in range(x.size(0)):
            h = h + 1
        return h
input_1 = torch.ones(3, 16)
model = LoopAdd()
traced_model = torch.jit.trace(model, (input_1, ))
print(traced_model.graph)

graph(%self : __torch__.LoopAdd,
      %x.1 : Tensor):
  %8 : bool = prim::Constant[value=1]() # /home/mark.yj/GPT-SoVITS/b.py:18:8
  %4 : int = prim::Constant[value=0]() # /home/mark.yj/GPT-SoVITS/b.py:18:30
  %11 : int = prim::Constant[value=1]() # /home/mark.yj/GPT-SoVITS/b.py:19:20
  %5 : int = aten::size(%x.1, %4) # /home/mark.yj/GPT-SoVITS/b.py:18:23
  %h : Tensor = prim::Loop(%5, %8, %x.1) # /home/mark.yj/GPT-SoVITS/b.py:18:8
    block0(%i : int, %h.9 : Tensor):
      %h.3 : Tensor = aten::add(%h.9, %11, %11) # /home/mark.yj/GPT-SoVITS/b.py:19:16
      -> (%8, %h.3)
  return (%h)

可以看到 prim::Loop ，说明不再是固定参数的静态图了。

转ScriptModule

将模型转换为 torch.jit.ScriptModule
使用 torch.jit.trace_module() 跟踪模型并输入样本
使用 torch.onnx.export() 导出 ONNX 模型

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