Hi there,

I am new to pytorch and trying to understand.

I have a pretrained model from a paper. The code in the paper is immense and hard to understand, how the network was actually defined.

Still, I managed to load the pretrained model and evaluate it, so I got the following structure:

```
basic_conv1d(
(0): Sequential(
(0): Sequential(
(0): Conv1d(12, 128, kernel_size=(8,), stride=(1,), padding=(3,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(1): Sequential(
(0): Sequential(
(0): Conv1d(128, 256, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(2): Sequential(
(0): Sequential(
(0): Conv1d(256, 128, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)
(1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(3): Sequential(
(0): AdaptiveConcatPool1d(
(ap): AdaptiveAvgPool1d(output_size=1)
(mp): AdaptiveMaxPool1d(output_size=1)
)
(1): Flatten()
(2): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(3): Dropout(p=0.25, inplace=False)
(4): Linear(in_features=256, out_features=128, bias=True)
(5): ReLU(inplace=True)
(6): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(7): Dropout(p=0.5, inplace=False)
(8): Linear(in_features=128, out_features=5, bias=True)
)
)
```

I would love to define the network code myself, so I don’t have to work with the

supplied and very incomprehensible code.

Is there some kind of parser, that takes such a network evaluation and builds the code definition?

Or could anybody help me with this?

Thank you very much!!