TypeError: forward() missing 2 required positional arguments: 'y_orders' and 'beta'


I am using the following code from hgraph2graph and I am getting an error message for missing arguments. Can you please help to solve this error.


Traceback (most recent call last):
File “/apps/hgraph2graph/20210428/hgraph2graph/train_translator.py”, line 86, in
loss, kl_div, wacc, iacc, tacc, sacc = model(*batch)
File “/apps/hgraph2graph/20210428/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 1110, in _call_impl
return forward_call(*input, **kwargs)
TypeError: forward() missing 2 required positional arguments: ‘y_orders’ and ‘beta’

import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader

import math, random, sys
import numpy as np
import argparse

from hgraph import *
import rdkit

lg = rdkit.RDLogger.logger()

parser = argparse.ArgumentParser()
parser.add_argument(‘–train’, required=True)
parser.add_argument(‘–vocab’, required=True)
parser.add_argument(‘–atom_vocab’, default=common_atom_vocab)
parser.add_argument(‘–save_dir’, required=True)
parser.add_argument(‘–load_epoch’, type=int, default=-1)

parser.add_argument(‘–conditional’, action=‘store_true’)
parser.add_argument(‘–cond_size’, type=int, default=4)

parser.add_argument(‘–rnn_type’, type=str, default=‘LSTM’)
parser.add_argument(‘–hidden_size’, type=int, default=270)
parser.add_argument(‘–embed_size’, type=int, default=270)
parser.add_argument(‘–batch_size’, type=int, default=32)
parser.add_argument(‘–latent_size’, type=int, default=4)
parser.add_argument(‘–depthT’, type=int, default=20)
parser.add_argument(‘–depthG’, type=int, default=20)
parser.add_argument(‘–diterT’, type=int, default=1)
parser.add_argument(‘–diterG’, type=int, default=3)
parser.add_argument(‘–dropout’, type=float, default=0.0)

parser.add_argument(‘–lr’, type=float, default=1e-3)
parser.add_argument(‘–clip_norm’, type=float, default=20.0)
parser.add_argument(‘–beta’, type=float, default=0.3)

parser.add_argument(‘–epoch’, type=int, default=12)
parser.add_argument(‘–anneal_rate’, type=float, default=0.9)
parser.add_argument(‘–print_iter’, type=int, default=50)
parser.add_argument(‘–save_iter’, type=int, default=-1)

args = parser.parse_args()

vocab = [x.strip("\r\n ").split() for x in open(args.vocab)]
args.vocab = PairVocab(vocab)
if args.conditional:
model = HierCondVGNN(args).cuda()
model = HierVGNN(args).cuda()

for param in model.parameters():
if param.dim() == 1:
nn.init.constant_(param, 0)

if args.load_epoch >= 0:
model.load_state_dict(torch.load(args.save_dir + “/model.” + str(args.load_epoch)))

print(“Model #Params: %dK” % (sum([x.nelement() for x in model.parameters()]) / 1000,))

optimizer = optim.Adam(model.parameters(), lr=args.lr)
scheduler = lr_scheduler.ExponentialLR(optimizer, args.anneal_rate)

param_norm = lambda m: math.sqrt(sum([p.norm().item() ** 2 for p in m.parameters()]))
grad_norm = lambda m: math.sqrt(sum([p.grad.norm().item() ** 2 for p in m.parameters() if p.grad is not None]))

total_step = 0
beta = args.beta
meters = np.zeros(6)

for epoch in range(args.load_epoch + 1, args.epoch):
dataset = DataFolder(args.train, args.batch_size)

for batch in dataset:
    total_step += 1
    batch = batch + (beta,)
    loss, kl_div, wacc, iacc, tacc, sacc = model(*batch)

    nn.utils.clip_grad_norm_(model.parameters(), args.clip_norm)

    meters = meters + np.array([kl_div, loss.item(), wacc.cpu() * 100, iacc.cpu() * 100, tacc.cpu() * 100, sacc.cpu() * 100])

meters = meters + np.array([kl_div, loss.item(), wacc * 100, iacc * 100, tacc * 100, sacc * 100])

    if total_step % args.print_iter == 0:
        meters /= args.print_iter
        print("[%d] Beta: %.3f, KL: %.2f, loss: %.3f, Word: %.2f, %.2f, Topo: %.2f, Assm: %.2f, PNorm: %.2f, GNorm: %.2f" % (total_step, beta, meters[0], meters[1], meters[2], meters[3], meters[4], meters[5], param_norm(model), grad_norm(model)))
        meters *= 0
   if args.save_iter >= 0 and total_step % args.save_iter == 0:
        n_iter = total_step // args.save_iter - 1
        torch.save(model.state_dict(), args.save_dir + "/model." + str(n_iter))
        print("learning rate: %.6f" % scheduler.get_lr()[0])

del dataset
if args.save_iter == -1:
    torch.save(model.state_dict(), args.save_dir + "/model." + str(epoch))
    print("learning rate: %.6f" % scheduler.get_lr()[0])

What does the model’s forward() method look like that is called by this line?

How can I get this information?