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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
The text was updated successfully, but these errors were encountered:
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)
I hope you can help me with my questions!
Thanks again for making your code publicly avaiable and greetings from Germany
The text was updated successfully, but these errors were encountered: