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import torch
import torch.nn as nn
import torch.nn.functional as F
class PatchEmbed(nn.Module):
""" 将图像分成patch并进行embedding """
def __init__(self, img_size=32, patch_size=4, in_chans=3, embed_dim=96):
super().__init__()
self.img_size = img_size
self.patch_size = patch_size
self.n_patches = (img_size // patch_size) ** 2
self.proj = nn.Conv2d(
in_chans, embed_dim,
kernel_size=patch_size, stride=patch_size
)
def forward(self, x):
x = self.proj(x) # (B, E, H/P, W/P)
x = x.flatten(2) # (B, E, N)
x = x.transpose(1, 2) # (B, N, E)
return x
class Attention(nn.Module):
""" 多头自注意力机制 """
def __init__(self, dim, n_heads=8, qkv_bias=True, attn_p=0., proj_p=0.):
super().__init__()
self.n_heads = n_heads
self.dim = dim
self.head_dim = dim // n_heads
self.scale = self.head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_p)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_p)
def forward(self, x):
n_samples, n_tokens, dim = x.shape
if dim != self.dim:
raise ValueError
qkv = self.qkv(x) # (n_samples, n_patches + 1, 3 * dim)
qkv = qkv.reshape(
n_samples, n_tokens, 3, self.n_heads, self.head_dim
) # (n_samples, n_patches + 1, 3, n_heads, head_dim)
qkv = qkv.permute(2, 0, 3, 1, 4) # (3, n_samples, n_heads, n_patches + 1, head_dim)
q, k, v = qkv[0], qkv[1], qkv[2] # each with shape (n_samples, n_heads, n_patches + 1, head_dim)
k_t = k.transpose(-2, -1) # (n_samples, n_heads, head_dim, n_patches + 1)
dp = (q @ k_t) * self.scale # (n_samples, n_heads, n_patches + 1, n_patches + 1)
attn = dp.softmax(dim=-1) # (n_samples, n_heads, n_patches + 1, n_patches + 1)
attn = self.attn_drop(attn)
weighted_avg = attn @ v # (n_samples, n_heads, n_patches + 1, head_dim)
weighted_avg = weighted_avg.transpose(1, 2) # (n_samples, n_patches + 1, n_heads, head_dim)
weighted_avg = weighted_avg.flatten(2) # (n_samples, n_patches + 1, dim)
x = self.proj(weighted_avg) # (n_samples, n_patches + 1, dim)
x = self.proj_drop(x) # (n_samples, n_patches + 1, dim)
return x
class MLP(nn.Module):
""" 多层感知机 """
def __init__(self, in_features, hidden_features, out_features, p=0.):
super().__init__()
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = nn.GELU()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(p)
def forward(self, x):
x = self.fc1(x) # (n_samples, n_patches + 1, hidden_features)
x = self.act(x) # (n_samples, n_patches + 1, hidden_features)
x = self.drop(x) # (n_samples, n_patches + 1, hidden_features)
x = self.fc2(x) # (n_samples, n_patches + 1, out_features)
x = self.drop(x) # (n_samples, n_patches + 1, out_features)
return x
class Block(nn.Module):
""" Transformer编码器块 """
def __init__(self, dim, n_heads, mlp_ratio=4.0, qkv_bias=True,
p=0., attn_p=0.):
super().__init__()
self.norm1 = nn.LayerNorm(dim, eps=1e-6)
self.attn = Attention(
dim,
n_heads=n_heads,
qkv_bias=qkv_bias,
attn_p=attn_p,
proj_p=p
)
self.norm2 = nn.LayerNorm(dim, eps=1e-6)
hidden_features = int(dim * mlp_ratio)
self.mlp = MLP(
in_features=dim,
hidden_features=hidden_features,
out_features=dim,
)
def forward(self, x):
x = x + self.attn(self.norm1(x))
x = x + self.mlp(self.norm2(x))
return x
class ViT(nn.Module):
""" Vision Transformer """
def __init__(
self,
img_size=32,
patch_size=4,
in_chans=3,
n_classes=10,
embed_dim=96,
depth=12,
n_heads=8,
mlp_ratio=4.,
qkv_bias=True,
p=0.,
attn_p=0.,
):
super().__init__()
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
)
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.pos_embed = nn.Parameter(
torch.zeros(1, 1 + self.patch_embed.n_patches, embed_dim)
)
self.pos_drop = nn.Dropout(p=p)
self.blocks = nn.ModuleList([
Block(
dim=embed_dim,
n_heads=n_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
p=p,
attn_p=attn_p,
)
for _ in range(depth)
])
self.norm = nn.LayerNorm(embed_dim, eps=1e-6)
self.head = nn.Linear(embed_dim, n_classes)
def forward(self, x):
n_samples = x.shape[0]
x = self.patch_embed(x)
cls_token = self.cls_token.expand(n_samples, -1, -1)
x = torch.cat((cls_token, x), dim=1)
x = x + self.pos_embed
x = self.pos_drop(x)
for block in self.blocks:
x = block(x)
x = self.norm(x)
cls_token_final = x[:, 0]
x = self.head(cls_token_final)
return x
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