|
|
|
|
|
from pdb import set_trace as st |
|
import torch |
|
import torch.nn.functional as F |
|
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.): |
|
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.): |
|
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.dropout = nn.Dropout(dropout) |
|
|
|
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, mask = None): |
|
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 |
|
|
|
if mask is not None: |
|
mask = rearrange(mask, 'b ... -> b (...)') |
|
mask = F.pad(mask, (x.shape[-2] - mask.shape[-1], 0), value = True) |
|
dots = dots.masked_fill(~mask, -torch.finfo(dots.dtype).max) |
|
|
|
attn = self.attend(dots) |
|
attn = self.dropout(attn) |
|
|
|
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.): |
|
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, mask = None): |
|
for attn, ff in self.layers: |
|
x = attn(x, mask = mask) + 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., emb_dropout = 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) |
|
|
|
st() |
|
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, mask = None): |
|
x = self.to_patch_embedding(img) |
|
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)] |
|
x = self.dropout(x) |
|
|
|
x = self.transformer(x, mask = mask) |
|
|
|
x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0] |
|
|
|
x = self.to_latent(x) |
|
return self.mlp_head(x) |
|
|
|
if __name__ == '__main__': |
|
x = torch.randn(1, 3, 256, 256) |
|
mask = torch.ones(1, 16, 16).bool() |
|
|
|
vit = ViT( |
|
dim = 512, |
|
depth = 6, |
|
heads = 8, |
|
mlp_dim = 1024, |
|
image_size = 256, |
|
patch_size = 16, |
|
num_classes = 10 |
|
) |
|
|
|
out = vit(x, mask = mask) |
|
|
