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import torch
import torch.nn.functional as F
from torch import nn
import math
from torch import Tensor
class Reduce(nn.Module):
def forward(self, x):
return x.mean(dim=1)
class PatchEmbedding(nn.Module):
def __init__(self, in_channels: int = 3, patch_size: int = 16, emb_size: int = 768, img_size: int = 224):
self.patch_size = patch_size
super().__init__()
self.projection = nn.Conv2d(in_channels, emb_size, kernel_size=patch_size, stride=patch_size)
self.cls_token = nn.Parameter(torch.zeros(1, 1, emb_size))
self.positions = nn.Parameter(torch.zeros((img_size // patch_size) * (img_size // patch_size) + 1, emb_size))
def forward(self, x: Tensor) -> Tensor:
b, _, _, _ = x.shape
x = self.projection(x)
x = x.flatten(2).transpose(1, 2)
cls_tokens = self.cls_token.expand(b, -1, -1)
# prepend the cls token to the input
x = torch.cat([cls_tokens, x], dim=1)
# add position embedding
x += self.positions
return x
class MultiHeadAttention(nn.Module):
def __init__(self, d_model = 123, num_heads = 6, dropout = 0.):
super().__init__()
assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
self.d_model = d_model
self.num_heads = num_heads
self.head_dim = d_model // num_heads
self.query = nn.Linear(d_model, d_model)
self.key = nn.Linear(d_model, d_model)
self.value = nn.Linear(d_model, d_model)
self.out = nn.Linear(d_model, d_model)
self.dropout = nn.Dropout(dropout)
def split_heads(self, x, batch_size):
x = x.view(batch_size, -1, self.num_heads, self.head_dim)
return x.transpose(1, 2)
def forward(self, x):
batch_size = x.size(0)
query = self.query(x)
key = self.key(x)
value = self.value(x)
query = self.split_heads(query, batch_size)
key = self.split_heads(key, batch_size)
value = self.split_heads(value, batch_size)
scores = torch.matmul(query, key.transpose(-2, -1))
scores = scores / 8
attn_weights = F.softmax(scores, dim=-1)
attn_weights = self.dropout(attn_weights)
attn_output = torch.matmul(attn_weights, value)
attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)
attn_output = self.out(attn_output)
return attn_output
class ResidualAdd(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
res = x
x = self.fn(x, **kwargs)
x += res
return x
class FeedForwardBlock(nn.Sequential):
def __init__(self, emb_size: int, expansion: int = 2, drop_p: float = 0.):
super().__init__(
nn.Linear(emb_size, expansion * emb_size),
nn.ReLU(),
nn.Dropout(drop_p),
nn.Linear(expansion * emb_size, emb_size),
)
class TransformerEncoderBlock(nn.Sequential):
def __init__(self,
emb_size: int = 768,
drop_p: float = 0.,
forward_expansion: int = 2,
forward_drop_p: float = 0.,
** kwargs):
super().__init__(
ResidualAdd(nn.Sequential(
nn.LayerNorm(emb_size),
MultiHeadAttention(emb_size, **kwargs),
nn.Dropout(drop_p)
)),
ResidualAdd(nn.Sequential(
nn.LayerNorm(emb_size),
FeedForwardBlock(
emb_size, expansion=forward_expansion, drop_p=forward_drop_p),
nn.Dropout(drop_p)
)
))
class TransformerEncoder(nn.Sequential):
def __init__(self, depth: int = 1, **kwargs):
super().__init__(*[TransformerEncoderBlock(**kwargs) for _ in range(depth)])
class ClassificationHead(nn.Sequential):
def __init__(self, emb_size: int = 768, n_classes: int = 1000):
super().__init__(
Reduce(),
nn.LayerNorm(emb_size),
nn.Linear(emb_size, n_classes))
class ViT_1_3(nn.Sequential):
def __init__(self,
in_channels: int = 3,
num_heads: int = 3,
patch_size: int = 16,
emb_size: int = 48,
img_size: int = 32,
depth: int = 1,
n_classes: int = 10,
**kwargs):
super().__init__(
PatchEmbedding(in_channels, patch_size, emb_size, img_size),
TransformerEncoder(depth, emb_size=emb_size, num_heads=num_heads, **kwargs),
ClassificationHead(emb_size, n_classes)
)
class ViT_1_6(nn.Sequential):
def __init__(self,
in_channels: int = 3,
num_heads: int = 6,
patch_size: int = 16,
emb_size: int = 96,
img_size: int = 32,
depth: int = 1,
n_classes: int = 10,
**kwargs):
super().__init__(
PatchEmbedding(in_channels, patch_size, emb_size, img_size),
TransformerEncoder(depth, emb_size=emb_size, num_heads=num_heads, **kwargs),
ClassificationHead(emb_size, n_classes)
)
class ViT_2_3(nn.Sequential):
def __init__(self,
in_channels: int = 3,
num_heads: int = 3,
patch_size: int = 16,
emb_size: int = 48,
img_size: int = 32,
depth: int = 2,
n_classes: int = 10,
**kwargs):
super().__init__(
PatchEmbedding(in_channels, patch_size, emb_size, img_size),
TransformerEncoder(depth, emb_size=emb_size, num_heads=num_heads, **kwargs),
ClassificationHead(emb_size, n_classes)
)
class ViT_2_6(nn.Sequential):
def __init__(self,
in_channels: int = 3,
num_heads: int = 6,
patch_size: int = 16,
emb_size: int = 48,
img_size: int = 32,
depth: int = 2,
n_classes: int = 10,
**kwargs):
super().__init__(
PatchEmbedding(in_channels, patch_size, emb_size, img_size),
TransformerEncoder(depth, emb_size=emb_size, num_heads=num_heads, **kwargs),
ClassificationHead(emb_size, n_classes)
) |