How to apply multiple data loaders to one model?

I am trying to train one model using multiple data loaders.

An example I thought of is below.

for epoch in epochs:
train_loader = zip(train_loader_list[0], train_loader_list[1], train_loader_list[2])
    for batch_idx, sample in enumerate(train_loader):
    for batch_idx in range(len(train_loader)):

        sample = batch_queue.get()
        data_time.update(time.time() - end)

        for i in range(0, len(sample)):
            input = sample[i]['image'].cuda()
            target = sample[i]['label'].cuda()
            target_exist = sample[i]['exist'].cuda()            

            output, output_exist = model(input)  # output_mid           
            loss_seg = criterion(torch.nn.functional.log_softmax(output, dim=1), target)
            loss_exist = criterion_exist(output_exist, target_exist)
            loss = loss_seg + loss_exist * 0.1            

            loss_avg += loss
        loss_avg = loss_avg / len(sample)

        losses.update(, input.size(0))


I want to average the loss values of 3 data loaders and backward them.

I use DistributedDataParallel

“RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [32]] is at version 5; expected version 4 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).”

An above error occurs when proceeding in the following way.

I don’t know which part is the problem.

Please help me.

Thank you