vit-pytorch-implementation 1.0.2

Vision Transformer (ViT) - Pytorch

Pytorch Implementation of ViT

Original Paper link: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale(Alexey Dosovitskiy et al.)

Install

$ pip install vit-pytorch-implementation

#Usage:

import torch
from vit_pytorch import lilViT

v = lilViT(
                 img_size=224, 
                 in_channels=3,
                 patch_size=16, 
                 num_transformer_layers=12,
                 embedding_dim=768,
                 mlp_size=3072,
                 num_heads=12, 
                 attn_dropout=0,
                 mlp_dropout=0.1,
                 embedding_dropout=0.1,
                 num_classes=1000
)

img = torch.randn(1, 3, 224, 224)

preds = v(img) # (1, 1000)
preds.shape

Parameters

• img_size: int.
  Image resolution. Default=224(224x224)
• in_channels: int.
  Image channels. Default 3
• patch_size: int.
  Size of patches. image_size must be divisible by patch_size.
  The number of patches is: n = (image_size // patch_size) ** 2 and n must be greater than 16. Default 16
• num_transformer_layers: int.
  Depth(number of transformer blocks). Default 12
• embedding_dim: int.
  Embedding dimension. Default 768
• mlp_size: int.
  MLP size. Default 3072
• num_heads: int.
  Number of heads in Multi-head Attention layer. Default 12
• attn_dropout: float.
  Dropout for attention projection. Default 0
• mlp_dropout: float
  Dropout for dense/MLP layers. Default 0.1
• embedding_dropout: float.
  Dropout for patch and position embeddings.Default 0.1
• num_classes: int.
  Number of classes to classify. Default 1000