我正在尝试将PyTorch VAE转换为onnx,但是我得到:torch.onnx.symbolic.normal does not exist
问题似乎源自reparametrize()
函数:
def reparametrize(self, mu, logvar):
std = logvar.mul(0.5).exp_()
if self.have_cuda:
eps = torch.normal(torch.zeros(std.size()),torch.ones(std.size())).cuda()
else:
eps = torch.normal(torch.zeros(std.size()),torch.ones(std.size()))
return eps.mul(std).add_(mu)
我也尝试过:
eps = torch.cuda.FloatTensor(std.size()).normal_()
产生错误:
Schema not found for node. File a bug report.
Node: %173 : Float(1, 20) = aten::normal(%169, %170, %171, %172), scope: VAE
Input types:Float(1, 20), float, float, Generator
和
eps = torch.randn(std.size()).cuda()
产生错误:
builtins.TypeError: i_(): incompatible function arguments. The following argument types are supported:
1. (self: torch._C.Node, arg0: str, arg1: int) -> torch._C.Node
Invoked with: %137 : Tensor = onnx::RandomNormal(), scope: VAE, 'shape', 133 defined in (%133 : int[] = prim::ListConstruct(%128, %132), scope: VAE) (occurred when translating randn)
我正在使用cuda
。
任何想法都值得赞赏。也许我需要对onnx采用不同的z
/ latent方法?
注意:逐步了解,我发现它正在为RandomNormal()
找到torch.randn()
,应该是正确的。但是那时我真的没有访问参数的权限,那么我该如何解决呢?
答案 0 :(得分:1)
简而言之,下面的代码可能有效。 (至少在我的环境中,它没有错误)。
似乎.size()
运算符可能返回变量,而不是常量,因此会导致onnx编译错误。 (更改为使用.size()时,我遇到了相同的错误)
import torch
import torch.utils.data
from torch import nn
from torch.nn import functional as F
IN_DIMS = 28 * 28
BATCH_SIZE = 10
FEATURE_DIM = 20
class VAE(nn.Module):
def __init__(self):
super(VAE, self).__init__()
self.fc1 = nn.Linear(784, 400)
self.fc21 = nn.Linear(400, FEATURE_DIM)
self.fc22 = nn.Linear(400, FEATURE_DIM)
self.fc3 = nn.Linear(FEATURE_DIM, 400)
self.fc4 = nn.Linear(400, 784)
def encode(self, x):
h1 = F.relu(self.fc1(x))
return self.fc21(h1), self.fc22(h1)
def reparameterize(self, mu, logvar):
std = torch.exp(0.5*logvar)
eps = torch.randn(BATCH_SIZE, FEATURE_DIM, device='cuda')
return eps.mul(std).add_(mu)
def decode(self, z):
h3 = F.relu(self.fc3(z))
return torch.sigmoid(self.fc4(h3))
def forward(self, x):
mu, logvar = self.encode(x)
z = self.reparameterize(mu, logvar)
recon_x = self.decode(z)
return recon_x
model = VAE().cuda()
dummy_input = torch.randn(BATCH_SIZE, IN_DIMS, device='cuda')
torch.onnx.export(model, dummy_input, "vae.onnx", verbose=True)