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| import torch
from torch import nn
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
def pair(t):
return t if isinstance(t, tuple) else (t, t)
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(self.norm(x), **kwargs)
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout=0.0):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout),
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, heads=8, dim_head=64, dropout=0.0):
super().__init__()
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head**-0.5
self.attend = nn.Softmax(dim=-1)
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
self.to_out = (
nn.Sequential(nn.Linear(inner_dim, dim), nn.Dropout(dropout))
if project_out
else nn.Identity()
)
def forward(self, x):
qkv = self.to_qkv(x).chunk(3, dim=-1)
q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=self.heads), qkv)
dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
attn = self.attend(dots)
out = torch.matmul(attn, v)
out = rearrange(out, "b h n d -> b n (h d)")
return self.to_out(out)
class Transformer(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.0):
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)),
]
)
)
def forward(self, x):
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x
return x
class ViT(nn.Module):
def __init__(
self,
*,
image_size,
patch_size,
num_classes,
dim,
depth,
heads,
mlp_dim,
pool="cls",
channels=3,
dim_head=64,
dropout=0.0,
emb_dropout=0.0
):
super().__init__()
image_height, image_width = pair(image_size)
patch_height, patch_width = pair(patch_size)
assert (
image_height % patch_height == 0 and image_width % patch_width == 0
), "Image dimensions must be divisible by the patch size."
num_patches = (image_height // patch_height) * (image_width // patch_width)
patch_dim = channels * patch_height * patch_width
assert pool in {
"cls",
"mean",
}, "pool type must be either cls (cls token) or mean (mean pooling)"
self.to_patch_embedding = nn.Sequential(
Rearrange(
"b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
p1=patch_height,
p2=patch_width,
),
nn.Linear(patch_dim, dim),
)
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
self.dropout = nn.Dropout(emb_dropout)
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
self.pool = pool
self.to_latent = nn.Identity()
self.mlp_head = nn.Sequential(nn.LayerNorm(dim), nn.Linear(dim, num_classes))
def forward(self, img):
x = self.to_patch_embedding(img)
b, n, _ = x.shape
cls_tokens = repeat(self.cls_token, "() n d -> b n d", b=b)
x = torch.cat((cls_tokens, x), dim=1)
x += self.pos_embedding[:, : (n + 1)]
x = self.dropout(x)
x = self.transformer(x)
x = x.mean(dim=1) if self.pool == "mean" else x[:, 0]
x = self.to_latent(x)
return self.mlp_head(x)
vit = ViT(
image_size=100,
patch_size=10,
num_classes=10,
dim=128,
depth=12,
heads=8,
mlp_dim=256,
)
img = torch.randn(2, 3, 100, 100)
out = vit(img)
print(out.shape)
|
