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import torch
from torch import nn
import torch.nn.functional as F
from einops import rearrange
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(x, **kwargs) + x
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):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
nn.GELU(),
nn.Linear(hidden_dim, dim)
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, heads=8):
super().__init__()
self.heads = heads
self.scale = dim ** -0.5
self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
self.to_out = nn.Linear(dim, dim)
def forward(self, x, mask = None):
b, n, _, h = *x.shape, self.heads
qkv = self.to_qkv(x)
q, k, v = rearrange(qkv, 'b n (qkv h d) -> qkv b h n d', qkv=3, h=h)
dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
if mask is not None:
mask = F.pad(mask.flatten(1), (1, 0), value = True)
assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions'
mask = mask[:, None, :] * mask[:, :, None]
dots.masked_fill_(~mask, float('-inf'))
del mask
attn = dots.softmax(dim=-1)
out = torch.einsum('bhij,bhjd->bhid', attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
out = self.to_out(out)
return out
class Transformer(nn.Module):
def __init__(self, dim, depth, heads, mlp_dim):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
Residual(PreNorm(dim, Attention(dim, heads = heads))),
Residual(PreNorm(dim, FeedForward(dim, mlp_dim)))
]))
def forward(self, x, mask=None):
for attn, ff in self.layers:
x = attn(x, mask=mask)
x = ff(x)
return x
class CViT(nn.Module):
def __init__(self, image_size=224, patch_size=7, num_classes=2, channels=512,
dim=1024, depth=6, heads=8, mlp_dim=2048):
super().__init__()
assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
self.features = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=32),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=32),
nn.ReLU(),
nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=64),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=64),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=64),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=128),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=128),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=256),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=256),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=256),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=256),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=512),
nn.ReLU(),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=512),
nn.ReLU(),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=512),
nn.ReLU(),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(num_features=512),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2)
)
num_patches = (image_size // patch_size) ** 2
self.max_sequence_length = num_patches+1
patch_dim = channels * patch_size ** 2
self.patch_size = patch_size
self.pos_embedding = nn.Parameter(torch.randn(1, self.max_sequence_length, dim))
self.patch_to_embedding = nn.Linear(patch_dim, dim)
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
self.transformer = Transformer(dim, depth, heads, mlp_dim)
self.to_cls_token = nn.Identity()
self.mlp_head = nn.Sequential(
nn.Linear(dim, mlp_dim),
nn.ReLU(),
nn.Linear(mlp_dim, num_classes)
)
def forward(self, img, mask=None):
p = self.patch_size
x = self.features(img)
y = rearrange(x, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)
y = self.patch_to_embedding(y)
cls_tokens = self.cls_token.expand(y.shape[0], -1, -1)
x = torch.cat((cls_tokens, y), dim=1)
x += self.pos_embedding[:, :x.size(1)]
x = self.transformer(x, mask)
x = self.to_cls_token(x[:, 0])
return self.mlp_head(x) |