Converting a pytorch model to nn.Module for exporting to onnx for lens studio
I am trying to convert pix2pix to a pb or onnx that can run in Lens Studio. Lens studio has strict requirements for the models. I am trying to export this pytorch model to onnx using this guide provided by lens studio. The issue is the pytorch model found here uses its own base class, when in the example it uses Module.nn, and therefore doesnt have methods/variables that the torch.onnx.export function needs to run. So far Ive run into its missing a variable called training and a method called train
Would it be worth it to try to modify the base model, or should I try to build it from scratch using nn.Module? Is there a way to make the pix2pix model inherit from both the abstract base class and nn.module? Am I not understanding the situation? The reason why I want to do it using the lens studio tutorial is because I have gotten it to export onnx different ways but Lens Studio wont accept those for various reasons.
Also this is my first time asking a SO question (after 6 years of coding), let me know if I make any mistakes and I can correct them. Thank you.
This is the important code from the tutorial creating a pytorch model for Lens Studio:
import torch
import torch.nn as nn
class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.layer = nn.Conv2d(in_channels=3, out_channels=1, 
                               kernel_size=3, stride=2, padding=1)
        
    def forward(self, x):
        out = self.layer(x)
        out = nn.functional.interpolate(out, scale_factor=2, 
                                        mode='bilinear', align_corners=True)
        out = torch.nn.functional.softmax(out, dim=1)
        return out

I'm not going to include all the code from the pytorch model bc its large, but the beginning of the baseModel.py is
import os
import torch
from collections import OrderedDict
from abc import ABC, abstractmethod
from . import networks


class BaseModel(ABC):
    &quot;&quot;&quot;This class is an abstract base class (ABC) for models.
    To create a subclass, you need to implement the following five functions:
        -- &lt;__init__&gt;:                      initialize the class; first call BaseModel.__init__(self, opt).
        -- &lt;set_input&gt;:                     unpack data from dataset and apply preprocessing.
        -- &lt;forward&gt;:                       produce intermediate results.
        -- &lt;optimize_parameters&gt;:           calculate losses, gradients, and update network weights.
        -- &lt;modify_commandline_options&gt;:    (optionally) add model-specific options and set default options.
    &quot;&quot;&quot;

    def __init__(self, opt):
        &quot;&quot;&quot;Initialize the BaseModel class.

        Parameters:
            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions

        When creating your custom class, you need to implement your own initialization.
        In this function, you should first call &lt;BaseModel.__init__(self, opt)&gt;
        Then, you need to define four lists:
            -- self.loss_names (str list):          specify the training losses that you want to plot and save.
            -- self.model_names (str list):         define networks used in our training.
            -- self.visual_names (str list):        specify the images that you want to display and save.
            -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
        &quot;&quot;&quot;
        self.opt = opt
        self.gpu_ids = opt.gpu_ids
        self.isTrain = opt.isTrain
        self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')  # get device name: CPU or GPU
        self.save_dir = os.path.join(opt.checkpoints_dir, opt.name)  # save all the checkpoints to save_dir
        if opt.preprocess != 'scale_width':  # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark.
            torch.backends.cudnn.benchmark = True
        self.loss_names = []
        self.model_names = []
        self.visual_names = []
        self.optimizers = []
        self.image_paths = []
        self.metric = 0  # used for learning rate policy 'plateau'

and for pix2pix_model.py
import torch
from .base_model import BaseModel
from . import networks


class Pix2PixModel(BaseModel):
    &quot;&quot;&quot; This class implements the pix2pix model, for learning a mapping from input images to output images given paired data.

    The model training requires '--dataset_mode aligned' dataset.
    By default, it uses a '--netG unet256' U-Net generator,
    a '--netD basic' discriminator (PatchGAN),
    and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the orignal GAN paper).

    pix2pix paper: https://arxiv.org/pdf/1611.07004.pdf
    &quot;&quot;&quot;
    @staticmethod
    def modify_commandline_options(parser, is_train=True):
        &quot;&quot;&quot;Add new dataset-specific options, and rewrite default values for existing options.

        Parameters:
            parser          -- original option parser
            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.

        Returns:
            the modified parser.

        For pix2pix, we do not use image buffer
        The training objective is: GAN Loss + lambda_L1 * ||G(A)-B||_1
        By default, we use vanilla GAN loss, UNet with batchnorm, and aligned datasets.
        &quot;&quot;&quot;
        # changing the default values to match the pix2pix paper (https://phillipi.github.io/pix2pix/)
        parser.set_defaults(norm='batch', netG='unet_256', dataset_mode='aligned')
        if is_train:
            parser.set_defaults(pool_size=0, gan_mode='vanilla')
            parser.add_argument('--lambda_L1', type=float, default=100.0, help='weight for L1 loss')

        return parser

    def __init__(self, opt):
        &quot;&quot;&quot;Initialize the pix2pix class.

        Parameters:
            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
        &quot;&quot;&quot;

(Also sidenote if you see this and it looks like no easy way out let me know, I know what its like seeing someone getting started in something who goes in to deep too early on)

You can definitely have your model inherit from both the base class and torch.nn.Module (python allows multiple inheritance). However you should take care about the conflicts if both inherited class have functions with identical names (I can see at least one : their base provide the eval function and so to nn.module).
However since you do not need the CycleGan, and a lot of the code is compatibility with their training environment, you'd probably better just re-implement the pix2pix. Just steal the code, have it inherit from nn.Module, copy-paste useful/mandatory functions from the base class, and have everything translated into clean pytorch code. You already have the forward function (which is the only requirement for a pytorch module).
All the subnetworks they use (like the resnet blocks) seem to inherit from nn.Module already so there is nothing to change here (double check that though)
