Cannot running GNN+Transformer model

[yrq3027]

Hi,
This error happened only in GNN+Transformer model.

Let's use 1 GPU!
--- Data Preparation ---
--- Go for Training ---
Traceback (most recent call last):
File "Pharos_DeepPurpose_ATFP_clf_demo.py", line 103, in
trainModel(coding)
File "Pharos_DeepPurpose_ATFP_clf_demo.py", line 96, in trainModel
model.train(train, val, test)
File "/home/data/aidd/DeepPurpose/DeepPurpose/DTI.py", line 436, in train
score = self.model(v_d, v_p)
File "/home/AIDD/anaconda3/envs/DeepPurpose/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "/home/data/aidd/DeepPurpose/DeepPurpose/DTI.py", line 52, in forward
v_f = torch.cat((v_D, v_P), 1)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 512 but got size 2 for tensor number 1 in the list.

[kexinhuang12345]

Could you send code to reproduce error?

[yrq3027]

Thank you for reply. I definited my own dataset, also ,the same error happened when I run the benchmark dataset.
import os
import pandas as pd
import numpy as np
os.environ['CUDA_VISIBLE_DEVICES'] = "1"

import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
from DeepPurpose import utils, DTI, dataset

CODING_TYPE = ["0"]

def dataLoader():

X_drugs, X_targets, y = dataset.load_Pharos(binary=True)
return X_drugs, X_targets, y

def createModel(coding):

X_drugs, X_targets, y = dataLoader()

if coding == "0":
    
    drug_encoding = 'DGL_GIN_AttrMasking'
    target_encoding = 'transformer'
    train, val, test = utils.data_process(X_drugs, X_targets, y, 
                            drug_encoding, target_encoding, 
                            split_method='random',frac=[0.7,0.1,0.2],
                            random_seed = 1)
    config = utils.generate_config(drug_encoding = drug_encoding, 
                     target_encoding = target_encoding, 
                     cls_hidden_dims = [1024,1024,512], 
                     train_epoch = 150, 
                     LR = 0.001, 
                     batch_size = 512,
                     hidden_dim_drug = 128,
                     mpnn_hidden_size = 128,
                     mpnn_depth = 3, 
                     cnn_target_filters = [32,64,96],
                     cnn_target_kernels = [4,8,12]
                    )
    model = models.model_initialize(**config)


return train, val, test, drug_encoding, target_encoding, model

def trainModel(coding):

train, val, test, drug_encoding, target_encoding, model = createModel(coding)
model.train(train, val, test)
model.save_model('./Pharos_model/'+drug_encoding+"-"+target_encoding+"_clf")

if name == "main":

for coding in CODING_TYPE:
    
    trainModel(coding)

[kexinhuang12345]

Are you using multiple GPUs? could you try a single GPU? also, does other combination work?