What the mean of "list index out of range"and 'params set for InstanceNormalization layer'?This is my first time to use cubemx,so I'm confused.Could anyone help me?Thanks!

[MYChen]

When I use cubemx.ai to analyze a onnx model ,I get a error tells list index out of range,what should I do? When I analyze another onnx model ,it occur another error 'params set for InstanceNormalization layer'Could anyone help me?Thanks:confounded_face:

[MYChen]

The model I use is a model which is transformed form pytorch to onnx.The model has no problem in pytorch and onnx.

[MYChen]

error1:params set for InstanceNormalization layererror2:list index out of range 

[MYChen]

The error about wish can be fixed by 'sudo apt-get install tk'.However I don't know why it will occur error 'list out of range'

[fauvarque.daniel]

Can you share the model reproducing the error (or a dummy untrained one)

Thanks

Daniel

[MYChen]

Thank you for your kindness. I'll write these down below.

These models use pytorch：

1.

class Encoder(nn.Module):

def __init__(self,input_nc=1,ngf=64,C_channel=4, n_downsampling=3,norm_layer=nn.InstanceNorm2d,max_ngf=256, Conv_type="DW"):

assert(n_downsampling>=0)

super(Encoder, self).__init__()

activation = nn.ReLU(True)

model = [nn.ReflectionPad2d(3), my_Conv(input_nc, ngf, kernel_size=7, padding=0, Conv_type=Conv_type, norm_layer=norm_layer, activation=activation), norm_layer(ngf), activation]

##downsample

for i in range(n_downsampling):

mult = 2**i

model += [my_Conv(min(ngf * mult,max_ngf), min(ngf * mult * 2,max_ngf), kernel_size=3, stride=2, padding=1, Conv_type=Conv_type, norm_layer=norm_layer, activation=activation),

norm_layer(min(ngf * mult * 2, max_ngf)), activation]

self.model = nn.Sequential(*model)

self.projection = nn.Sequential(*[my_Conv(min(ngf*(2**n_downsampling),max_ngf),C_channel,kernel_size=3,stride=1,padding=1, Conv_type=Conv_type, norm_layer=norm_layer, activation=activation),norm_layer(C_channel),nn.Sigmoid()])

def forward(self, input):

z = self.model(input)

return self.projection(z)

​

2.

class Decoder(nn.Module):

def __init__(self,ngf=64,C_channel=4, n_downsampling=3,output_nc=1,n_blocks=3, norm_layer=nn.InstanceNorm2d,padding_type="reflect",max_ngf=256, Conv_type="DW", Dw_Index=None):

assert (n_blocks>=0)

super(Decoder, self).__init__()

activation = nn.ReLU(True)

mult = 2 ** n_downsampling

ngf_dim = min(ngf * mult, max_ngf)

model = [my_Conv(C_channel,ngf_dim,kernel_size=3,stride=1,padding=1, Conv_type=Conv_type, activation=activation, norm_layer=norm_layer),norm_layer(ngf_dim),activation]

​

for i in range(n_blocks):

model += [ResnetBlock(ngf_dim, padding_type=padding_type, activation=activation, norm_layer=norm_layer, Conv_type=Conv_type)]

​

for i in range(n_downsampling):

mult = 2 ** (n_downsampling - i)

model += [my_Deconv(min(ngf * mult,max_ngf), min(ngf * mult //2, max_ngf), Conv_type=Conv_type, norm_layer=norm_layer, activation=activation),

norm_layer(min(ngf * mult //2,max_ngf)), activation]

if Conv_type == "NC":

model += [nn.ReflectionPad2d(3), nn.Conv2d(ngf, output_nc,kernel_size=7, padding=0), nn.Tanh()]

else:

model += [nn.ReflectionPad2d(3), DEPTHWISECONV(ngf, output_nc, kernel_size=7, padding=0), nn.Tanh()]

self.model = nn.Sequential(*model)

def forward(self, input):

return self.model(input)

​

I successfully transformed them form pytorch into onnx.The first problem about 'list index' arises when I use cubemx. AI to convert the Encoder model,and the problem adout 'params set for InstanceNormalization layer' arises when I use cubemx. AI to convert the Decoder model.

[YYin.2]

I also have a similar problem. The code is in the attachment.

Thank you!

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