计算机视觉：预训练模型（ViT/Swin Transformer）原理与实践
随着深度学习技术的飞速发展，计算机视觉领域取得了显著的成果。近年来，预训练模型在图像分类、目标检测、图像分割等任务中表现出色。其中，基于Transformer的视觉Transformer（ViT）和Swin Transformer模型因其优越的性能和简洁的架构而备受关注。本文将围绕ViT和Swin Transformer的原理与实践展开讨论，旨在帮助读者深入理解这两种预训练模型。
ViT：视觉Transformer的原理与实践
1. ViT的原理
ViT（Vision Transformer）是一种基于Transformer的视觉模型，它将图像分割成固定大小的patch，并将这些patch视为序列中的token。ViT的核心思想是将图像转换为序列，然后利用Transformer进行特征提取和分类。
1.1 Patch Embedding
将图像分割成固定大小的patch，例如16x16像素。然后，将每个patch转换为向量，这个过程称为Patch Embedding。
python
import torch
import torch.nn as nn
class PatchEmbed(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = (img_size // patch_size) 2
self.proj = nn.Linear(in_chans patch_size 2, embed_dim)
def forward(self, x):
x = x.flatten(2).transpose(1, 2)
x = self.proj(x)
return x
1.2 Positional Encoding
由于Transformer模型本身没有位置信息，因此需要添加位置编码来表示每个patch的位置。
python
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=5000):
super().__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position div_term)
pe[:, 1::2] = torch.cos(position div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return x
1.3 Transformer Encoder
ViT使用多个Transformer编码器层来提取特征。每个编码器层包含多头自注意力机制和前馈神经网络。
python
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.dropout = nn.Dropout(dropout)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
def forward(self, src, src_mask=None, src_key_padding_mask=None):
src2 = self.norm1(src)
q = k = v = src2
src2, _ = self.self_attn(q, k, v, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)
src = src + self.dropout1(src2)
src2 = self.norm2(src)
src2 = self.linear2(self.dropout(self.linear1(src2)))
src = src + self.dropout2(src2)
return src
2. ViT的实践
在实际应用中，可以使用PyTorch框架实现ViT模型。以下是一个简单的ViT模型实现：
python
class ViT(nn.Module):
def __init__(self, num_classes=1000, img_size=224, patch_size=16, in_chans=3, num_layers=12, num_heads=12, dim_feedforward=768):
super().__init__()
self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=dim_feedforward)
self.positional_encoding = PositionalEncoding(dim_feedforward)
self.transformer = nn.Sequential([TransformerEncoderLayer(dim_feedforward, num_heads, dim_feedforward) for _ in range(num_layers)])
self.norm = nn.LayerNorm(dim_feedforward)
self.classifier = nn.Linear(dim_feedforward, num_classes)
def forward(self, x):
x = self.patch_embed(x)
x = self.positional_encoding(x)
x = self.transformer(x)[0]
x = self.norm(x)
x = x[:, 0, :]
x = self.classifier(x)
return x
Swin Transformer：原理与实践
1. Swin Transformer的原理
Swin Transformer是一种改进的Transformer模型，它通过引入分块窗口机制来提高计算效率。Swin Transformer将图像分割成多个大小不同的块，并在每个块内进行特征提取。
1.1 分块窗口机制
Swin Transformer使用分块窗口机制来减少计算量。具体来说，它将图像分割成多个大小不同的块，并在每个块内进行特征提取。
python
class Block(nn.Module):
def __init__(self, in_chans, out_chans, stride=1, expand_ratio=4):
super().__init__()
self.stride = stride
self.expand_ratio = expand_ratio
self.depth = in_chans expand_ratio // 64
self.use_depthwise = self.depth == in_chans
self.conv1 = nn.Conv2d(in_chans, in_chans, kernel_size=3, stride=stride, padding=1)
self.norm1 = nn.BatchNorm2d(in_chans)
self.relu = nn.ReLU(inplace=True)
if self.use_depthwise:
self.conv2 = nn.Conv2d(in_chans, self.depth, kernel_size=3, stride=1, padding=1, groups=in_chans)
else:
self.conv2 = nn.Conv2d(in_chans, self.depth, kernel_size=3, stride=1, padding=1)
self.norm2 = nn.BatchNorm2d(self.depth)
self.conv3 = nn.Conv2d(self.depth, out_chans, kernel_size=1)
self.downsample = None
if stride != 1 or in_chans != out_chans:
self.downsample = nn.Sequential(
nn.Conv2d(in_chans, out_chans, kernel_size=1, stride=stride),
nn.BatchNorm2d(out_chans),
)
def forward(self, x):
x = self.conv1(x)
x = self.norm1(x)
x = self.relu(x)
if self.use_depthwise:
x = self.conv2(x)
else:
x = self.conv2(x)
x = self.norm2(x)
x = self.relu(x)
x = self.conv3(x)
if self.downsample is not None:
x = self.downsample(x)
return x
1.2 Swin Transformer Encoder
Swin Transformer使用多个Block层来构建编码器。每个Block层包含多个分块窗口，用于提取不同尺度的特征。
python
class SwinTransformer(nn.Module):
def __init__(self, num_classes=1000, img_size=224, patch_size=4, in_chans=3, num_layers=18, num_heads=12, dim_feedforward=768):
super().__init__()
self.patch_embed = PatchEmbed(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=dim_feedforward)
self.positional_encoding = PositionalEncoding(dim_feedforward)
self.layers = nn.Sequential([Block(in_chans, in_chans, stride=2, expand_ratio=4) for _ in range(num_layers)])
self.norm = nn.LayerNorm(dim_feedforward)
self.classifier = nn.Linear(dim_feedforward, num_classes)
def forward(self, x):
x = self.patch_embed(x)
x = self.positional_encoding(x)
x = self.layers(x)[0]
x = self.norm(x)
x = x[:, 0, :]
x = self.classifier(x)
return x
2. Swin Transformer的实践
与ViT类似，Swin Transformer也可以使用PyTorch框架实现。以下是一个简单的Swin Transformer模型实现：
python
...（与ViT实现类似，此处省略）
总结
本文介绍了ViT和Swin Transformer两种预训练模型的原理与实践。ViT通过将图像分割成patch并使用Transformer进行特征提取，而Swin Transformer则通过分块窗口机制提高计算效率。这两种模型在计算机视觉领域取得了显著的成果，为后续研究提供了新的思路和方法。
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