写一段关于ViT的背景介绍
时间: 2024-05-21 12:15:24 浏览: 8
ViT(Vision Transformer)是一种基于Transformer的图像分类器,由Google Brain团队在2020年提出。在过去的几年中,卷积神经网络(CNNs)已经成为计算机视觉领域中最成功的工具之一。然而,CNNs的局限性在于它们是基于固定大小的图像块进行卷积的,这使得它们不适用于处理变形的图像或较大的图像。ViT被设计为解决这些问题,并将视觉任务的处理方法归结为仅使用Transformer。
ViT的核心思想是将图像分解为一系列的小图像块(patch),然后将这些图像块视为序列输入到Transformer中进行处理。通过这种方式,ViT可以处理变形的图像,并且可以处理比CNNs更大的图像。ViT在许多计算机视觉任务中具有与CNNs相当的性能,并且已经成为计算机视觉领域中的研究热点之一。
相关问题
写一段VIT模型的pytorch代码
好的,以下是使用PyTorch实现Vision Transformer(VIT)模型的示例代码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class ViT(nn.Module):
def __init__(self, img_size, patch_size, emb_size, num_heads, num_layers, num_classes):
super(ViT, self).__init__()
self.patch_size = patch_size
self.num_patches = (img_size // patch_size) ** 2
self.proj = nn.Conv2d(3, emb_size, kernel_size=patch_size, stride=patch_size)
self.cls_token = nn.Parameter(torch.randn(1, 1, emb_size))
self.pos_emb = nn.Parameter(torch.randn(1, self.num_patches + 1, emb_size))
self.blocks = nn.ModuleList([TransformerBlock(emb_size, num_heads) for _ in range(num_layers)])
self.mlp_head = nn.Sequential(
nn.LayerNorm(emb_size),
nn.Linear(emb_size, num_classes)
)
def forward(self, x):
x = self.proj(x)
x = x.flatten(2).transpose(1, 2)
cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_emb[:, :(self.num_patches + 1)]
for block in self.blocks:
x = block(x)
x = x.mean(dim=1)
x = self.mlp_head(x)
return x
class TransformerBlock(nn.Module):
def __init__(self, emb_size, num_heads, mlp_ratio=4.0):
super(TransformerBlock, self).__init__()
self.attention = nn.MultiheadAttention(emb_size, num_heads)
self.mlp = nn.Sequential(
nn.Linear(emb_size, int(emb_size * mlp_ratio)),
nn.GELU(),
nn.Linear(int(emb_size * mlp_ratio), emb_size),
nn.Dropout(0.1)
)
self.norm1 = nn.LayerNorm(emb_size)
self.norm2 = nn.LayerNorm(emb_size)
def forward(self, x):
residual = x
x = self.norm1(x)
x = x.permute(1, 0, 2)
x, _ = self.attention(x, x, x)
x = x.permute(1, 0, 2)
x += residual
residual = x
x = self.norm2(x)
x = self.mlp(x)
x += residual
return x
```
pytorch写一个vit
非常感谢您的提问。以下是使用 PyTorch 实现 ViT 的代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class PatchEmbedding(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
super().__init__()
img_size = (img_size, img_size) if isinstance(img_size, int) else img_size
patch_size = (patch_size, patch_size) if isinstance(patch_size, int) else patch_size
num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
self.img_size = img_size
self.patch_size = patch_size
self.num_patches = num_patches
self.projection = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
B, C, H, W = x.shape
assert H == self.img_size[0] and W == self.img_size[1], \
f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.projection(x).flatten(2).transpose(1, 2)
return x
class MLP(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, dropout=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = nn.GELU()
self.fc2 = nn.Linear(hidden_features, out_features)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.dropout(x)
x = self.fc2(x)
x = self.dropout(x)
return x
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
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0., drop_path=0.):
super().__init__()
self.norm1 = nn.LayerNorm(dim)
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = nn.Dropout(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = nn.LayerNorm(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = MLP(in_features=dim, hidden_features=mlp_hidden_dim, out_features=dim, dropout=drop)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class VisionTransformer(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000,
embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=True,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.):
super().__init__()
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim
self.patch_embed = PatchEmbedding(img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i])
for i in range(depth)])
self.norm = nn.LayerNorm(embed_dim)
self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward(self, x):
x = self.patch_embed(x)
cls_token = self.cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_token, x), dim=1)
x = x + self.pos_embed
x = self.pos_drop(x)
for blk in self.blocks:
x = blk(x)
x = self.norm(x[:, 0])
x = self.head(x)
return x
```
希望这个代码示例能够帮助您实现 ViT。如果您有任何问题,请随时问我。