Pros of vision_transformer

• Official implementation from Google Research, likely more authoritative
• Supports multiple model variants (ViT-B, ViT-L, ViT-H)
• Includes pre-trained weights and evaluation scripts

Cons of vision_transformer

• Written in TensorFlow, which may be less familiar to PyTorch users
• Less focused on ease of use and integration with other projects
• Fewer examples and documentation for quick start

Code Comparison

vision_transformer:

class Encoder1D(tf.keras.layers.Layer):
  """Transformer Encoder."""

  def __init__(self,
               num_layers,
               mlp_dim,
               num_heads,
               dropout_rate=0.1,
               attention_dropout_rate=0.1,
               add_position_embedding=True,
               name='encoder'):
    super().__init__(name=name)
    self.num_layers = num_layers
    self.mlp_dim = mlp_dim
    self.num_heads = num_heads
    self.dropout_rate = dropout_rate
    self.attention_dropout_rate = attention_dropout_rate
    self.add_position_embedding = add_position_embedding

ViT-pytorch:

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
            ]))

Pros of vit-pytorch

• More modular and flexible implementation
• Includes additional features like distillation and various attention mechanisms
• Better documentation and examples

Cons of vit-pytorch

• May be more complex for beginners
• Less focus on reproducing original ViT paper results

Code Comparison

ViT-pytorch:

x = self.patch_embedding(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embedding[:, :(n + 1)]
x = self.dropout(x)

vit-pytorch:

x = self.to_patch_embedding(x)
b, n, _ = x.shape
cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b = b)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embedding[:, :(n + 1)]

Both repositories implement Vision Transformers (ViT) in PyTorch, but they have different approaches and features. ViT-pytorch focuses on reproducing the original paper's results, while vit-pytorch offers a more flexible and feature-rich implementation. The code comparison shows similarities in the basic structure, but vit-pytorch uses more concise and modular code. Overall, the choice between the two depends on the user's specific needs and level of expertise.

Pros of pytorch-image-models

• Extensive collection of pre-trained models and architectures
• Regular updates and active community support
• Comprehensive documentation and examples

Cons of pytorch-image-models

• Larger codebase, potentially more complex for beginners
• May include unnecessary features for those focused solely on ViT

Code Comparison

ViT-pytorch:

model = VisionTransformer(
    img_size=224,
    patch_size=16,
    num_classes=1000,
    embed_dim=768,
    depth=12,
    num_heads=12,
)

pytorch-image-models:

model = timm.create_model('vit_base_patch16_224', pretrained=True)
model.head = nn.Linear(model.head.in_features, num_classes)

The pytorch-image-models example demonstrates easier model creation and fine-tuning, while ViT-pytorch offers more granular control over model parameters.

pytorch-image-models provides a more comprehensive suite of tools and models, making it suitable for a wider range of computer vision tasks. However, ViT-pytorch's focused approach on Vision Transformers may be preferable for those specifically working with this architecture.

Both repositories offer valuable resources for implementing Vision Transformers in PyTorch, with the choice depending on the user's specific needs and preferences.

Pros of Swin-Transformer

• Hierarchical structure allows for better performance on various vision tasks
• More efficient computation and memory usage for high-resolution images
• Supports a wider range of vision applications beyond image classification

Cons of Swin-Transformer

• More complex architecture, potentially harder to understand and implement
• May require more computational resources for training and inference
• Less straightforward to adapt for non-vision tasks compared to ViT-pytorch

Code Comparison

ViT-pytorch:

class Transformer(nn.Module):
    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
        super().__init__()
        self.layers = nn.ModuleList([])
        for _ in range(depth):
            self.layers.append(nn.ModuleList([
                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
            ]))

Swin-Transformer:

class BasicLayer(nn.Module):
    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.depth = depth
        self.use_checkpoint = use_checkpoint

Pros of DeiT

• More comprehensive and feature-rich implementation
• Includes distillation techniques for improved performance
• Better documentation and examples for usage

Cons of DeiT

• More complex codebase, potentially harder to understand for beginners
• Requires more computational resources due to additional features

Code Comparison

ViT-pytorch:

class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5

DeiT:

class Attention(nn.Module):
    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

The main difference in the code is that DeiT allows for a custom qk_scale parameter, providing more flexibility in attention scaling.

Pros of transformers

• Comprehensive library with support for multiple architectures and tasks
• Extensive documentation and community support
• Regular updates and maintenance

Cons of transformers

• Larger codebase, potentially more complex for beginners
• May include unnecessary components for specific use cases

Code Comparison

ViT-pytorch:

import torch
from vit_pytorch import ViT

v = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(1, 3, 256, 256)
preds = v(img) # (1, 1000)

transformers:

from transformers import ViTFeatureExtractor, ViTForImageClassification
import torch

feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')

inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits

ViT-pytorch offers a more straightforward implementation for Vision Transformer models, while transformers provides a more versatile and feature-rich environment for working with various transformer architectures, including ViT.