Computation requirement for training

[HimangiM]

Hi,

First of all, great contribution towards the field of image manipulation. Could you please provide information on how many GPUs and how much duration it took to train the model?

Thanks,
Himangi

[lsh3163]

Dear HimangiM,

Thanks for your interest, it takes one day to train the model with one single GPU because we use the fixed weights of CLIP image encoder and text encoder.

Sincerely,

[Allencheng97]

Hi, Dear author,
Thank you for your great work. May I ask what is your final loss (text_contrastive_loss and image_contrastive_loss) in the final?
I tried to train my own model but it seems the loss decrease very slow.

I am looking forward to hearing from you soon.

[lsh3163]

I agree. The convergence is slow, so I got the best audio representation around 30 epochs. Interpolation between image and text embeddings is also a good option. Here is the code I used:

for idx, (batch_audio, batch_audio_aug, batch_img, batch_text) in enumerate(train_dataloader):
   audio_embedding = audioencoder(batch_audio.cuda())
   audio_aug_embedding = audioencoder(batch_audio_aug.cuda())
   text_tokens = torch.cat([clip.tokenize(text) for text in batch_text])
   with torch.no_grad():
      text_embedding = clip_model.encode_text(text_tokens.to(device))
      text_embedding = text_embedding / text_embedding.norm(dim=-1, keepdim=True)
      image_embedding = clip_model.encode_image(batch_img.to(device))
      image_embedding = image_embedding / image_embedding.norm(dim=-1, keepdim=True)

   audio_embedding = audio_embedding / audio_embedding.norm(dim=-1, keepdim=True)
   audio_aug_embedding = audio_aug_embedding / audio_aug_embedding.norm(dim=-1, keepdim=True)

   loss = 0

   projection_audio_text = (audio_embedding @ text_embedding.T) * math.exp(0.07)
   projection_audio_img = (audio_embedding @ image_embedding.T) * math.exp(0.07)
   projection_self_audio = (audio_embedding @ audio_aug_embedding.T) * math.exp(0.07)

   label = torch.arange(args.batch_size, dtype=torch.long).cuda()

   audio_contrastive_loss = ce(projection_audio_text, label) + ce(projection_audio_text.T, label) + ce(projection_audio_img, label) + ce(projection_audio_img.T, label)
   self_contrastive_loss = ce(projection_self_audio, label) + ce(projection_self_audio.T, label)
   loss = (audio_contrastive_loss + self_contrastive_loss) / 4