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from typing import Union
import PIL.Image
import torch
import torch.nn.functional as F
from torch import nn
from einops import rearrange
import PIL
from torchvision.transforms.v2 import (
Compose,
Resize,
InterpolationMode,
ToImage,
ToDtype,
Normalize,
)
from transformers.utils import is_flash_attn_2_available
try:
if is_flash_attn_2_available():
from flash_attn.modules.mha import FlashSelfAttention
else:
FlashSelfAttention = None
except ImportError:
FlashSelfAttention = None
class Attention(nn.Module):
def __init__(self, dim, num_heads=16, use_flash_attn=False):
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)
if use_flash_attn and FlashSelfAttention is not None:
self.flash_attn = FlashSelfAttention()
else:
self.flash_attn = None
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:
if self.flash_attn is not None:
qkv = self.qkv(x)
qkv = rearrange(
qkv, "... (three h d) -> ... three h d", three=3, h=self.num_heads
)
attn_output = self.flash_attn(qkv)
output = rearrange(attn_output, "... h d -> ... (h d)")
output = self.proj(output)
return output
else:
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, use_flash_attn=False):
super().__init__()
self.attn = Attention(embed_dim, use_flash_attn=use_flash_attn)
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, use_flash_attn=False):
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, use_flash_attn=use_flash_attn) 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, use_flash_attn=False):
super().__init__()
self.model = nn.ModuleDict({"visual": VisionTransformer(use_flash_attn)})
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):
b, c, hp1, wp2 = x.shape
p1, p2 = 14, 14
h, w = hp1 // p1, wp2 // p2
x = x.reshape(b, c, h, p1, w, p2)
x = x.permute(0, 2, 4, 1, 3, 5)
x = x.reshape(b, h * w, c * p1 * p2)
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 * 2, hidden_dim, model_dim)
@property
def device(self):
return self.mlp.fc1.weight.device
def forward(self, x):
return self.mlp(x)
def create_patches(image, patch_size=(378, 378)):
assert image.dim() == 3, "Image must be in CHW format"
_, height, width = image.shape # Channels, Height, Width
patch_height, patch_width = patch_size
if height == patch_height and width == patch_width:
return []
# Iterate over the image and create patches
patches = []
for i in range(0, height, patch_height):
row_patches = []
for j in range(0, width, patch_width):
patch = image[:, i : i + patch_height, j : j + patch_width]
row_patches.append(patch)
patches.append(torch.stack(row_patches))
return patches
class VisionEncoder(nn.Module):
def __init__(self, use_flash_attn=False):
super().__init__()
self.encoder = EncoderWrapper(use_flash_attn)
self.projection = VisionProjection()
self.supported_sizes = [(378, 378), (378, 756), (756, 378), (756, 756)]
@property
def device(self):
return self.projection.mlp.fc1.weight.device
@property
def dtype(self):
return self.projection.mlp.fc1.weight.dtype
def preprocess(self, image: PIL.Image.Image):
width, height = image.size
max_dim = max(width, height)
if max_dim < 512:
im_size = (378, 378)
else:
aspect_ratio = width / height
im_size = min(
self.supported_sizes,
key=lambda size: (
abs((size[1] / size[0]) - aspect_ratio),
abs(size[0] - width) + abs(size[1] - height),
),
)
return Compose(
[
Resize(size=im_size, interpolation=InterpolationMode.BICUBIC),
ToImage(),
ToDtype(torch.float32, scale=True),
Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
]
)(image)
def forward(
self, images: Union[PIL.Image.Image, list[PIL.Image.Image], torch.Tensor]
) -> torch.Tensor:
im_list = None
if isinstance(images, torch.Tensor):
# Input must have dimensions (B, C, H, W)
assert (
len(images.shape) == 4
), "Tensor input must have dimensions (B, C, H, W)"
im_list = list(images)
elif isinstance(images, PIL.Image.Image):
im_list = [images]
elif isinstance(images, list):
im_list = images
else:
raise ValueError(
"Input must be a PIL image, list of PIL images, or a tensor"
)
# Preprocess unless the images are already tensors (indicating that
# they have already been preprocessed)
if not isinstance(im_list[0], torch.Tensor):
im_list = [self.preprocess(im.convert("RGB")) for im in im_list]
patches = [create_patches(im) for im in im_list]
flat_patches = [patch for image_patches in patches for patch in image_patches]
# Images may be variable size, and need to be resized to a common size after
# creating patches.
resized_images = [
F.interpolate(im.unsqueeze(0), size=(378, 378), mode="bilinear")
for im in im_list
]
combined_images = torch.cat([*resized_images, *flat_patches], dim=0)
combined_images = combined_images.to(self.device, dtype=self.dtype)
combined_features = self.encoder(combined_images)
full_img_features = combined_features[: len(im_list)]
patch_features = (
combined_features[len(im_list) :].transpose(1, 2).view(-1, 1152, 27, 27)
)
# Reshape patch features back to their original structure
reshaped_patch_features = []
patch_idx = 0
for i, patch_set in enumerate(patches):
if len(patch_set) == 0:
reshaped_patch_features.append(
full_img_features[i].transpose(0, 1).view(1152, 27, 27)
)
else:
sample_features = []
for row_patches in patch_set:
row_len = len(row_patches)
row_features = patch_features[
patch_idx : patch_idx + row_len
] # row_len, T, C
row_features = torch.cat(
list(row_features), dim=2
) # T, C * row_len
patch_idx += row_len
sample_features.append(row_features)
sample_features = torch.cat(sample_features, dim=1)
sample_features = F.interpolate(
sample_features.unsqueeze(0), size=(27, 27), mode="bilinear"
).squeeze(0)
reshaped_patch_features.append(sample_features)
reshaped_patch_features = (
torch.stack(reshaped_patch_features).view(-1, 1152, 729).transpose(1, 2)
)
final_features = torch.cat([full_img_features, reshaped_patch_features], dim=2)
return self.projection(final_features)
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