I wanted to build multimodal models for a while now and what better way that to start with Image Captioning, which is kinda like the hello world of multimodal.
I used the following 2 models:
-
ViT Base, patch size = 16, image size = 224
-
GPT2 small
-
I prepared the architecturealmostfrom scratch
-
I extracted the useful ViT layers from the
timmpackage and used it as the encoder with the pretrained weights. -
As for GPT2, I coded the entirety from scratch, added a new
Cross Attentionlayer in the decoder block to get a standardencoder-decodertransformer. -
GPT2 weights were loaded via HuggingFace. Refer toNanoGPT.
The dataset I used wasCOCO 2017with options forFlickr30kandFlickr8k.
- The dataset preparation was also done from scratch
- my code goes in detail about how to prepare the labels for causal language modeling, calculating the loss while ignoring special tokens, etc.
- Dynamic padding with custom collate function to pad sequences based on the batch and not the max length of the model.
- The training loop was written from scratch, the metric I used was
perplexity = e^loss - I trained it with mixed-precision fp16 using
torch.amp. - I initially trained the randomly initialized cross-attention layers, then in further epochs, I finetuned the entire GPT2 and in further epochs I finetuned the entire ViT-GPT2 model.
- Standard
torch.multinomialsampling based generation with temperature control. - Support for deterministic generation with
torch.argmax - The results are good not great, I only trained on about 30% of the training samples in COCO.
| Epoch | Train Loss | Train Perplexity | Val Loss | Val Perplexity |
|---|---|---|---|---|
| 0 | 5.164732 | 174.990611 | 3.288565 | 26.804375 |
| 1 | 2.668888 | 14.423919 | 2.341017 | 10.391795 |
| 2 | 2.30841 | 10.058415 | 2.201064 | 9.034617 |
| 3 | 2.033982 | 7.64447 | 2.099659 | 8.163385 |
| 4 | 1.855595 | 6.395501 | 2.08667 | 8.058035 |
See morehere
Psalm 32:8