Spaces:
Runtime error
Runtime error
File size: 7,194 Bytes
f949b3f 1a105d4 f949b3f c048c54 1a105d4 f949b3f f3a7d15 c048c54 1a105d4 f3a7d15 1a105d4 f3a7d15 c048c54 f3a7d15 c048c54 f3a7d15 1a105d4 c048c54 f3a7d15 |
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 |
import math
from typing import Any, Mapping
import torch
from torchvision.transforms.functional import to_pil_image
import torch.nn as nn
import kornia
import open_clip
from transformers import CLIPVisionModelWithProjection, AutoProcessor
from transformers.models.bit.image_processing_bit import BitImageProcessor
from einops import rearrange, repeat
# FFN
# from mamba_ssm import Mamba
class ImgEmbContextResampler(nn.Module):
def __init__(
self,
inner_dim=1280,
cross_attention_dim=1024,
expansion_factor=16,
**kwargs,
):
super().__init__()
self.context_embedding = nn.Sequential(
nn.Linear(cross_attention_dim, inner_dim),
nn.SiLU(),
nn.Linear(inner_dim, cross_attention_dim * expansion_factor),
)
self.expansion_factor = expansion_factor
self.cross_attention_dim = cross_attention_dim
def forward(self, x, batch_size=0):
if x.ndim == 2:
x = rearrange(x, "(B F) C -> B F C", B=batch_size)
assert x.ndim == 3
x = torch.mean(x, dim=1, keepdim=True)
x = self.context_embedding(x)
x = x.view(-1, self.expansion_factor, self.cross_attention_dim)
return x
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
self.embedding_dim = -1
self.num_tokens = -1
def encode(self, *args, **kwargs):
raise NotImplementedError
class FrozenOpenCLIPImageEmbedder(AbstractEncoder):
"""
Uses the OpenCLIP vision transformer encoder for images
"""
def __init__(
self,
arch="ViT-H-14",
version="laion2b_s32b_b79k",
device="cuda",
max_length=77,
freeze=True,
antialias=True,
ucg_rate=0.0,
unsqueeze_dim=False,
repeat_to_max_len=False,
num_image_crops=0,
output_tokens=False,
):
super().__init__()
model, _, _ = open_clip.create_model_and_transforms(
arch,
device=torch.device("cpu"),
pretrained=version,
)
del model.transformer
self.model = model
# self.model_t = CLIPVisionModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
# self.processor = AutoProcessor.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
self.max_crops = num_image_crops
self.pad_to_max_len = self.max_crops > 0
self.repeat_to_max_len = repeat_to_max_len and (not self.pad_to_max_len)
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.antialias = antialias
self.register_buffer(
"mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
)
self.register_buffer(
"std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
)
self.ucg_rate = ucg_rate
self.unsqueeze_dim = unsqueeze_dim
self.stored_batch = None
# self.model.visual.output_tokens = output_tokens
self.output_tokens = output_tokens
def preprocess(self, x):
# normalize to [0,1]
x = kornia.geometry.resize(
x,
(224, 224),
interpolation="bicubic",
align_corners=True,
antialias=self.antialias,
)
x = (x + 1.0) / 2.0
# renormalize according to clip
x = kornia.enhance.normalize(x, self.mean, self.std)
return x
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
# self.model_t = self.model_t.eval()
def forward(self, image, no_dropout=False):
z = self.encode_with_vision_transformer(image)
tokens = None
if self.output_tokens:
z, tokens = z[0], z[1]
z = z.to(image.dtype)
if self.ucg_rate > 0.0 and not no_dropout and not (self.max_crops > 0):
z = (
torch.bernoulli(
(1.0 - self.ucg_rate) * torch.ones(z.shape[0], device=z.device)
)[:, None]
* z
)
if tokens is not None:
tokens = (
expand_dims_like(
torch.bernoulli(
(1.0 - self.ucg_rate)
* torch.ones(tokens.shape[0], device=tokens.device)
),
tokens,
)
* tokens
)
if self.unsqueeze_dim:
z = z[:, None, :]
if self.output_tokens:
assert not self.repeat_to_max_len
assert not self.pad_to_max_len
return tokens, z
if self.repeat_to_max_len:
if z.dim() == 2:
z_ = z[:, None, :]
else:
z_ = z
return repeat(z_, "b 1 d -> b n d", n=self.max_length), z
elif self.pad_to_max_len:
assert z.dim() == 3
z_pad = torch.cat(
(
z,
torch.zeros(
z.shape[0],
self.max_length - z.shape[1],
z.shape[2],
device=z.device,
),
),
1,
)
return z_pad, z_pad[:, 0, ...]
return z
def encode_with_vision_transformer(self, img):
if self.max_crops > 0:
img = self.preprocess_by_cropping(img)
# pil_img = to_pil_image(img[0]*0.5 + 0.5)
# inputs = self.processor(images=pil_img, return_tensors="pt").to("cuda")
# outputs = self.model_t(**inputs)
# return outputs.image_embeds
if img.dim() == 5:
assert self.max_crops == img.shape[1]
img = rearrange(img, "b n c h w -> (b n) c h w")
img = self.preprocess(img)
if not self.output_tokens:
assert not self.model.visual.output_tokens
x = self.model.visual(img)
tokens = None
else:
assert self.model.visual.output_tokens
x, tokens = self.model.visual(img)
if self.max_crops > 0:
x = rearrange(x, "(b n) d -> b n d", n=self.max_crops)
# drop out between 0 and all along the sequence axis
x = (
torch.bernoulli(
(1.0 - self.ucg_rate)
* torch.ones(x.shape[0], x.shape[1], 1, device=x.device)
)
* x
)
if tokens is not None:
tokens = rearrange(tokens, "(b n) t d -> b t (n d)", n=self.max_crops)
print(
f"You are running very experimental token-concat in {self.__class__.__name__}. "
f"Check what you are doing, and then remove this message."
)
if self.output_tokens:
return x, tokens
return x
def encode(self, text):
return self(text) |