Comprehension Question - Negating Input-Images

[42elenz]

Thank you for you awesome code and work.
I have a question:
In your train_batch function in the run_train.py you are negating the images with -images in the forward pass:
logits_per_image, logits_per_text = model(-images, texts)

Why do you do so?

Also in the train function you wanted to get the "highest_val_auc" which you never used. How would you define the AUC in such a scenario?

`def train(model, loader, device, criterion, optimizer, config):
model_save_dir = os.path.join(config.save_dir, config.model_name)
if not os.path.exists(model_save_dir):
# Create a new folder if not exists
os.makedirs(model_save_dir)

# Run training
total_batches = len(loader) * config.epochs
example_ct = 0  # number of examples seen
batch_ct = 0
report_freq = config.log_interval
highest_val_auc = 0 # save highest mean auc

for epoch in range(config.epochs):
    running_loss = 0.0 # running loss over batch
    for data in tqdm(loader):
        # get the images
        images = data['img']

        texts = data['txt']
        texts = preprocess_text(texts, model) 
        
        # perform step for a single batch
        loss = train_batch(images, texts, model, device, criterion, optimizer)
        example_ct +=  len(images)
        batch_ct += 1
        running_loss += loss.item()

        # Report metrics every `report_freq` batch
        if (batch_ct % report_freq) == 0:
            train_log(running_loss / report_freq, example_ct, epoch)
            running_loss = 0.0
        
        if (batch_ct % config.save_interval) == 0: 
            model_path = os.path.join(model_save_dir, "checkpoint_{batch_ct}.pt".format(
                batch_ct=str(batch_ct), 
            ))
            print("Saved checkpoint to: ", model_path)
            save(model, model_path)`

I hope you can help me with my questions!

Thanks again for making your code publicly avaiable and greetings from Germany