Spaces:
Sleeping
Sleeping
File size: 11,511 Bytes
929a772 | 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 | import os
import torch
import torch.nn as nn
from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
class CLIPVisionTower(nn.Module):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
if not delay_load:
self.load_model()
elif getattr(args, 'unfreeze_mm_vision_tower', False):
self.load_model()
else:
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
def load_model(self, device_map=None):
if self.is_loaded:
print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name))
return
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
# def feature_select(self, image_forward_outs):
# image_features = image_forward_outs.hidden_states[self.select_layer]
# if self.select_feature == 'patch':
# image_features = image_features[:, 1:]
# elif self.select_feature == 'cls_patch':
# image_features = image_features
# else:
# raise ValueError(f'Unexpected select feature: {self.select_feature}')
# return image_features
def feature_select_withcls(self, image_forward_outs):
image_features = image_forward_outs.hidden_states[self.select_layer]
image_features = image_features
return image_features
@torch.no_grad()
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
image_feature = self.feature_select_withcls(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select_withcls(image_forward_outs).to(images.dtype)
return image_features
def forward_select(self, images, token_num):
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
image_feature = self.feature_select_withcls(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True, output_attentions=True)
attn_weights = image_forward_outs.attentions[-2]
hidden_states = image_forward_outs.hidden_states[-2]
dominant_num = token_num
## Dominant Visual Tokens
cls_idx = 0
cls_attention = attn_weights[:, :, cls_idx, cls_idx+1:]
cls_attention_sum = cls_attention.sum(dim=1)
topk_indices = cls_attention_sum.topk(dominant_num, dim=1).indices
topk_indices_sorted = torch.sort(topk_indices, dim=1).values
return topk_indices_sorted
def forward_select_scope(self, images, token_num, alpha):
if type(images) is list:
image_features = []
for image in images:
image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
image_feature = self.feature_select_withcls(image_forward_out).to(image.dtype)
image_features.append(image_feature)
else:
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True, output_attentions=True)
attn_weights = image_forward_outs.attentions[-2]
hidden_states = image_forward_outs.hidden_states[-2]
dominant_num = token_num
## Dominant Visual Tokens
# cls_idx = 0
# cls_attention = attn_weights[:, :, cls_idx, cls_idx+1:]
# cls_attention_sum = cls_attention.sum(dim=1)
# topk_indices = cls_attention_sum.topk(dominant_num, dim=1).indices
# topk_indices_sorted = torch.sort(topk_indices, dim=1).values
cls_idx = 0
cls_attention = attn_weights[:, :, cls_idx, cls_idx+1:]
cls_attention_sum = cls_attention.sum(dim=1)
image_features = hidden_states[:, cls_idx + 1:]
bs = image_features.shape[0]
dominant_num = int(dominant_num /bs)
selected_idx, _ = SCOPE(image_features, dominant_num, cls_attention_sum, alpha)
# selected_idx += 1
all_indices = selected_idx
topk_indices_sorted = torch.sort(all_indices, dim=1).values
# hidden_states_save = dominant_tokens
return topk_indices_sorted
@property
def dummy_feature(self):
return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
@property
def dtype(self):
return self.vision_tower.dtype
@property
def device(self):
return self.vision_tower.device
@property
def config(self):
if self.is_loaded:
return self.vision_tower.config
else:
return self.cfg_only
@property
def hidden_size(self):
return self.config.hidden_size
@property
def num_patches_per_side(self):
return self.config.image_size // self.config.patch_size
@property
def num_patches(self):
return (self.config.image_size // self.config.patch_size) ** 2
class CLIPVisionTowerS2(CLIPVisionTower):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__(vision_tower, args, delay_load)
self.s2_scales = getattr(args, 's2_scales', '336,672,1008')
self.s2_scales = list(map(int, self.s2_scales.split(',')))
self.s2_scales.sort()
self.s2_split_size = self.s2_scales[0]
self.s2_image_size = self.s2_scales[-1]
try:
from s2wrapper import forward as multiscale_forward
except ImportError:
raise ImportError('Package s2wrapper not found! Please install by running: \npip install git+https://github.com/bfshi/scaling_on_scales.git')
self.multiscale_forward = multiscale_forward
# change resize/crop size in preprocessing to the largest image size in s2_scale
if not delay_load or getattr(args, 'unfreeze_mm_vision_tower', False):
self.image_processor.size['shortest_edge'] = self.s2_image_size
self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
def load_model(self, device_map=None):
if self.is_loaded:
print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name))
return
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
self.vision_tower.requires_grad_(False)
self.image_processor.size['shortest_edge'] = self.s2_image_size
self.image_processor.crop_size['height'] = self.image_processor.crop_size['width'] = self.s2_image_size
self.is_loaded = True
@torch.no_grad()
def forward_feature(self, images):
image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@torch.no_grad()
def forward(self, images):
if type(images) is list:
image_features = []
for image in images:
image_feature = self.multiscale_forward(self.forward_feature, image.unsqueeze(0), img_sizes=self.s2_scales, max_split_size=self.s2_split_size)
image_features.append(image_feature)
else:
image_features = self.multiscale_forward(self.forward_feature, images, img_sizes=self.s2_scales, max_split_size=self.s2_split_size)
return image_features
@property
def hidden_size(self):
return self.config.hidden_size * len(self.s2_scales)
def SCOPE(visual_feature_vectors, num_selected_token, cls_attn=None, alpha=1.0):
"""
Batched version of SCOPE that processes all batch elements simultaneously.
Args:
visual_feature_vectors: [B, N, D] batch of feature vectors
num_selected_token: Number of tokens to select per batch
cls_attn: [B, N] batch of attention weights
Returns:
selected_idx: [B, K] selected token indices for each batch
cosine_simi: [B, N, N] batch of cosine similarity matrices
"""
# Calculate cosine similarity for all batches at once
norm_vectors = visual_feature_vectors / visual_feature_vectors.norm(dim=-1, keepdim=True)
cosine_simi = torch.bmm(norm_vectors, norm_vectors.transpose(1, 2))
B, N = visual_feature_vectors.shape[:2]
device = visual_feature_vectors.device
dtype = visual_feature_vectors.dtype
# Pre-allocate tensors for all batches
selected = torch.zeros(B, N, dtype=torch.bool, device=device)
selected_idx = torch.empty(B, num_selected_token, dtype=torch.long, device=device)
cur_max = torch.zeros(B, N, dtype=dtype, device=device)
# Precompute cls_attn ** alpha for all batches
# alpha = float(os.environ.get('ALPHA', '1.0'))
if cls_attn is not None:
cls_attn_powered = cls_attn ** alpha
else:
cls_attn_powered = torch.ones(B, N, dtype=dtype, device=device)
for i in range(num_selected_token):
# Calculate gains for all batches simultaneously
unselected_mask = ~selected
gains = torch.maximum(
torch.zeros(1, dtype=dtype, device=device),
cosine_simi.masked_fill(~unselected_mask.unsqueeze(1), 0) -
cur_max.unsqueeze(2)
).sum(dim=1)
# Apply attention weights
combined = os.environ.get('COMBINED', 'multi')
if combined == 'multi':
gains = gains * cls_attn_powered
elif combined == 'add':
gains = gains + cls_attn_powered
else:
raise NotImplementedError
# Mask out already selected tokens
gains = gains.masked_fill(~unselected_mask, float('-inf'))
# Find best elements for all batches
best_idx = gains.argmax(dim=1)
# Update states for all batches
selected[torch.arange(B, device=device), best_idx] = True
selected_idx[:, i] = best_idx
cur_max = torch.maximum(cur_max, cosine_simi[torch.arange(B, device=device), best_idx])
return selected_idx, cosine_simi |