distillable-vision-transformer 1.0.1

A PyTorch vision transformer for distillation.

PyTorch Vision Transformers with Distillation

Based on the paper "Training data-efficient image transformers & distillation through attention".

This repository will allow you to use distillation techniques with vision transformers in PyTorch. Most importantly, you can use pretrained models for the teacher, the student, or even both! My motivation was to use transfer learning to decrease the amount of resources it takes to train a vision transformer.

Quickstart

Install with pip install distillable_vision_transformer and load a pretrained transformer with:

from distillable_vision_transformer import DistillableVisionTransformer
model = DistillableVisionTransformer.from_pretrained('ViT-B_16')

Installation

Install via pip:

pip install distillable_vision_transformer

Or install from source:

git clone https://github.com/Graeme22/DistillableVisionTransformer.git
cd DistillableVisionTransformer
pip install -e .

Usage

Load a model architecture:

from distillable_vision_transformer import DistillableVisionTransformer
model = DistillableVisionTransformer.from_name('ViT-B_16')

Load a pretrained model:

from distillable_vision_transformer import DistillableVisionTransformer
model = DistillableVisionTransformer.from_pretrained('ViT-B_16')

Default hyper parameters:

Param\Model	ViT-B_16	ViT-B_32	ViT-L_16	ViT-L_32
image_size	384, 384	384, 384	384, 384	384, 384
patch_size	16	32	16	32
emb_dim	768	768	1024	1024
mlp_dim	3072	3072	4096	4096
num_heads	12	12	16	16
num_layers	12	12	24	24
num_classes	1000	1000	1000	1000
attn_dropout_rate	0.0	0.0	0.0	0.0
dropout_rate	0.1	0.1	0.1	0.1

If you need to modify these hyperparameters, just overwrite them:

model = DistillableVisionTransformer.from_name('ViT-B_16', patch_size=64, emb_dim=2048, ...)

Training

Wrap the student (instance of DistillableVisionTransformer) and the teacher (any network that you want to use to train the student) with a DistillationTrainer:

from distillable_vision_transformer import DistillableVisionTransformer, DistillationTrainer

student = DistillableVisionTransformer.from_pretrained('ViT-B_16')
trainer = DistillationTrainer(teacher=teacher, student=student) # where teacher is some pretrained network, e.g. an EfficientNet

For the loss function, it is recommended that you use the DistilledLoss class, which is a kind of hybrid between cross-entropy and KL-divergence loss. It takes as arguments teacher_logits, student_logits, and distill_logits, which are obtained from the forward pass on DistillationTrainer, as well as the true labels labels.

from distillable_vision_transformer import DistilledLoss

loss_fn = DistilledLoss(alpha=0.5, temperature=3.0)
loss = loss_fn(teacher_logits, student_logits, distill_logits, labels)

Inference

For inference, we want to use the DistillableVisionTransformer instance, not its DistillationTrainer wrapper.

import torch
from distillable_vision_transformer import DistillableVisionTransformer

model = DistillableVisionTransformer.from_pretrained('ViT-B_16')
model.eval()

inputs = torch.rand(1, 3, *model.image_size)
# we can discard the distillation tokens, as they are only needed to calculate loss
outputs, _ = model(inputs)

Contributing

If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.