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import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange
from torchvision.transforms.v2 import (
Compose,
Resize,
InterpolationMode,
ToImage,
ToDtype,
Normalize,
)
class Attention(nn.Module):
def __init__(self, dim, num_heads=16):
super().__init__()
assert dim % num_heads == 0, "dim should be divisible by num_heads"
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = nn.Linear(dim, dim * 3)
self.proj = nn.Linear(dim, dim)
torch.nn.init.kaiming_normal_(
self.qkv.weight, mode="fan_in", nonlinearity="relu"
)
torch.nn.init.kaiming_normal_(
self.proj.weight, mode="fan_in", nonlinearity="relu"
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, self.head_dim)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv.unbind(0)
x = F.scaled_dot_product_attention(q, k, v)
x = x.transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
return x
class VitBlock(nn.Module):
def __init__(self, embed_dim):
super().__init__()
self.attn = Attention(embed_dim)
self.mlp = MLP(embed_dim, 4304)
self.norm1 = nn.LayerNorm(embed_dim)
self.norm2 = nn.LayerNorm(embed_dim)
def forward(self, x):
x = x + self.attn(self.norm1(x))
x = x + self.mlp(self.norm2(x))
return x
class VisionTransformer(nn.Module):
def __init__(self):
super().__init__()
embed_len = 729
embed_dim = 1152
self.patch_embed = LinearPatchEmbedding()
self.pos_embed = nn.Parameter(torch.randn(1, embed_len, embed_dim) * 0.02)
self.blocks = nn.Sequential(*[VitBlock(embed_dim) for _ in range(27)])
self.norm = nn.LayerNorm(embed_dim)
def forward(self, x):
x = self.patch_embed(x)
x = x + self.pos_embed
for block in self.blocks:
x = block(x)
return self.norm(x)
class EncoderWrapper(nn.Module):
def __init__(self):
super().__init__()
self.model = nn.ModuleDict({"visual": VisionTransformer()})
def forward(self, x):
return self.model["visual"](x)
class LinearPatchEmbedding(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(588, 1152)
def forward(self, x):
return self.linear(x)
class MLP(nn.Module):
def __init__(
self,
in_features: int,
hidden_features: int = None,
out_features: int = None,
) -> None:
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = nn.GELU(approximate="tanh")
self.fc2 = nn.Linear(hidden_features, out_features)
torch.nn.init.kaiming_normal_(
self.fc1.weight, mode="fan_in", nonlinearity="relu"
)
torch.nn.init.kaiming_normal_(
self.fc2.weight, mode="fan_in", nonlinearity="relu"
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
return x
class VisionProjection(nn.Module):
def __init__(self):
super().__init__()
image_embedding_dim = 1152
model_dim = 2048
hidden_dim = model_dim * 4
self.mlp = MLP(image_embedding_dim, hidden_dim, model_dim)
@property
def device(self):
return self.mlp.fc1.weight.device
def forward(self, x):
return self.mlp(x)
class VisionEncoder(nn.Module):
def __init__(self) -> None:
super().__init__()
self.encoder = EncoderWrapper()
self.projection = VisionProjection()
self.preprocess = Compose(
[
Resize(size=(378, 378), interpolation=InterpolationMode.BICUBIC),
ToImage(),
ToDtype(torch.float32, scale=True),
Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
]
)
@property
def device(self):
return self.projection.mlp.fc1.weight.device
@property
def dtype(self):
return self.projection.mlp.fc1.weight.dtype
def __call__(self, images) -> torch.Tensor:
if not isinstance(images, list):
images = [images]
with torch.no_grad():
x = torch.stack(
[self.preprocess(image.convert("RGB")) for image in images]
).to(self.device, dtype=self.dtype)
x = rearrange(x, "b c (h p1) (w p2) -> b (h w) (c p1 p2)", p1=14, p2=14)
x = self.encoder(x)
x = self.projection(x)
return x
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