File size: 10,214 Bytes
21f112d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 |
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)
|