File size: 12,049 Bytes
b4942cf |
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 |
from typing import Union, Optional
import PIL.Image
import torch
from torch.nn.functional import softmax, gumbel_softmax, pad
from transformers import PretrainedConfig, PreTrainedModel, AutoImageProcessor, AutoModel, AutoConfig
from ovis.util.constants import IMAGE_INDICATOR_IDS, IMAGE_ATOM_ID
class BaseVisualTokenizerConfig(PretrainedConfig):
def __init__(
self,
vocab_size=16384,
tokenize_function="softmax",
tau=1.0,
depths=None,
drop_cls_token=False,
backbone_config: Optional[Union[PretrainedConfig, dict]] = None,
hidden_stride: int = 1,
**kwargs
):
super().__init__(**kwargs)
self.vocab_size = vocab_size
self.tokenize_function = tokenize_function
self.tau = tau
if isinstance(depths, str):
depths = [int(x) for x in depths.split('|')]
self.depths = depths
self.backbone_kwargs = {}
self.drop_cls_token = drop_cls_token
if backbone_config is not None:
assert isinstance(backbone_config, (PretrainedConfig, dict)), \
f"expect `backbone_config` to be instance of PretrainedConfig or dict, but got {type(backbone_config)} type"
if not isinstance(backbone_config, PretrainedConfig):
model_type = backbone_config['model_type']
backbone_config.pop('model_type')
backbone_config = AutoConfig.for_model(model_type, **backbone_config)
self.backbone_config = backbone_config
self.hidden_stride = hidden_stride
class BaseVisualTokenizer(PreTrainedModel):
base_model_prefix = "backbone"
main_input_name = None
_image_processor_class = None
_image_processor_kwargs = {}
_backbone_class = None
_backbone_name_or_path = None
def __init__(self, config: BaseVisualTokenizerConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
if kwargs.get('train_from_scratch'):
self.image_processor = self._image_processor_class.from_pretrained(self._backbone_name_or_path,
**self._image_processor_kwargs)
self.backbone = self._backbone_class.from_pretrained(self._backbone_name_or_path,
**self.config.backbone_kwargs)
self.config.backbone_config = self.backbone.config
else:
self.image_processor = AutoImageProcessor.from_pretrained(kwargs['image_processor_name_or_path'])
self.backbone = AutoModel.from_config(self.config.backbone_config)
head_dim = self.config.vocab_size - len(IMAGE_INDICATOR_IDS) # reserved tokens for IMAGE_INDICATORS
self.head = torch.nn.Sequential(
torch.nn.Linear(
self.backbone.config.hidden_size * self.config.hidden_stride * self.config.hidden_stride, head_dim,
bias=False
),
torch.nn.LayerNorm(head_dim)
)
assert all((self.image_processor.do_resize,
not getattr(self.image_processor, 'do_center_crop', False),
self.image_processor.do_rescale,
self.image_processor.do_normalize
)), f"image_processor `{self.image_processor}` is not supported currently"
def get_backbone(self):
return self.backbone
def get_monitor_tensors(self):
raise NotImplementedError
def get_image_processor(self):
return self.image_processor
def mock_input(self):
height, width = self.get_image_size()
return torch.zeros(1, 3, height, width), self.construct_image_placeholders((1, 1))
def get_head(self):
return self.head
def get_image_size(self):
raise NotImplementedError
@staticmethod
def construct_image_placeholders(grid):
image_placeholders = [IMAGE_INDICATOR_IDS[0], IMAGE_ATOM_ID, IMAGE_INDICATOR_IDS[1]]
if grid[0] * grid[1] > 1:
for r in range(grid[0]):
for c in range(grid[1]):
image_placeholders.append(IMAGE_ATOM_ID)
if c < grid[1] - 1:
image_placeholders.append(IMAGE_INDICATOR_IDS[2])
if r < grid[0] - 1:
image_placeholders.append(IMAGE_INDICATOR_IDS[3])
image_placeholders.append(IMAGE_INDICATOR_IDS[4])
return image_placeholders
def preprocess_image(self, image: PIL.Image.Image, max_partition=9, covering_threshold=0.9, convert_to_rgb=True):
def _preprocess(img: PIL.Image.Image, side):
# first resize and preprocess
w, h = img.size
if w == h:
new_width = new_height = side
elif w > h:
new_width = side
new_height = int(h / w * new_width)
else:
new_height = side
new_width = int(w / h * new_height)
new_size = dict(height=new_height, width=new_width)
pixel_values = self.image_processor.preprocess(img, size=new_size, return_tensors='pt')['pixel_values']
# then pad to square
square_values = torch.zeros([1, 3, side, side], dtype=pixel_values.dtype, device=pixel_values.device)
new_height, new_width = pixel_values.shape[2:]
if new_height == new_width:
square_values[:, :, :, :] = pixel_values
elif new_height > new_width:
from_index = (side - new_width) // 2
square_values[:, :, :, from_index:from_index + new_width] = pixel_values
else:
from_index = (side - new_height) // 2
square_values[:, :, from_index:from_index + new_height, :] = pixel_values
return square_values
def _partition(img, grid):
w, h = img.size
row_height = h // grid[0]
col_width = w // grid[1]
partition = []
for row in range(grid[0]):
for col in range(grid[1]):
left = col * col_width
upper = row * row_height
right = w if col == grid[1] - 1 else (col + 1) * col_width
lower = h if row == grid[0] - 1 else (row + 1) * row_height
partition.append((left, upper, right, lower))
return partition
def _covering_area(left, upper, right, lower, side):
w = right - left
h = lower - upper
w, h = max(w, h), min(w, h)
if w > side:
h = h / w * side
w = side
return w * h
def _get_best_grid(img, side):
img_area = img.size[0] * img.size[1]
candidate_grids = []
for i in range(1, max_partition + 1):
for j in range(1, max_partition + 1):
if i * j <= max_partition:
candidate_grids.append((i, j))
all_grids = []
good_grids = []
for grid in candidate_grids:
partition = _partition(img, grid)
covering_ratio = sum([_covering_area(*p, side) for p in partition]) / img_area
assert covering_ratio <= 1.0
all_grids.append((grid, covering_ratio))
if covering_ratio > covering_threshold:
good_grids.append((grid, covering_ratio))
if len(good_grids) > 0:
# pick the good partition with minimum #sub_images and break the tie using covering_ratio
return sorted(good_grids, key=lambda x: (x[0][0] * x[0][1], -x[1]))[0][0]
else:
# pick the partition with maximum covering_ratio and break the tie using #sub_images
return sorted(all_grids, key=lambda x: (-x[1], x[0][0] * x[0][1]))[0][0]
if convert_to_rgb and image.mode != 'RGB':
image = image.convert('RGB')
sides = self.get_image_size()
if sides[0] != sides[1]:
raise ValueError('get_image_size() returns non-square size')
side = sides[0]
grid = _get_best_grid(image, side)
partition = _partition(image, grid)
crops = [image.crop(p) for p in partition]
if len(crops) > 1:
crops.insert(0, image)
pixel_values = torch.cat([_preprocess(crop, side) for crop in crops], dim=0)
image_placeholders = self.construct_image_placeholders(grid)
return pixel_values, image_placeholders
def get_backbone_layer(self, index):
return self.backbone.vision_model.encoder.layers[index]
def tokenize(self, logits):
def st_argmax(y_soft, dim): # straight-through softmax
index = y_soft.max(dim, keepdim=True)[1]
y_hard = torch.zeros_like(y_soft, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
ret = y_hard - y_soft.detach() + y_soft
return ret
if self.config.tokenize_function == 'softmax':
tokens = softmax(logits, dim=-1)
elif self.config.tokenize_function == 'gumbel_argmax':
tokens = gumbel_softmax(logits, tau=self.config.tau, hard=True)
elif self.config.tokenize_function == 'st_argmax':
tokens = st_argmax(logits, dim=-1)
else:
raise ValueError(
f'Invalid `max_type`, expected softmax or gumbel_argmax or st_argmax, but got {self.config.tokenize_function}')
return tokens
def encode(self, pixel_values):
output = self.backbone(pixel_values, output_hidden_states=True, return_dict=True)
features = output.hidden_states[-1]
if self.config.drop_cls_token:
features = features[:, 1:, :]
# merge number of `hidden_stride * hidden_stride` hidden states together to reduce token sequence length
# e.g., for hidden_stride=3, this leads to a token length reduction: 729 -> 81 for siglip
if self.config.hidden_stride > 1:
n, l, d = features.shape # this `d` maybe different from the above `d
sqrt_l = int(l ** 0.5)
assert sqrt_l ** 2 == l, "The token sequence length should be a perfect square."
features = features.reshape(n, sqrt_l, sqrt_l, d)
pl = (self.config.hidden_stride - (sqrt_l % self.config.hidden_stride)) % self.config.hidden_stride
features = pad(features, (0, 0, 0, pl, 0, pl), "constant", 0)
sqrt_l += pl
features = features.reshape(n, sqrt_l // self.config.hidden_stride, self.config.hidden_stride,
sqrt_l // self.config.hidden_stride, self.config.hidden_stride, d)
features = features.permute(0, 1, 3, 2, 4, 5) # [n, sqrt_l/hs, sqrt_l/hs, hs, hs, d]
features = features.flatten(3) # [n, sqrt_l/hs, sqrt_l/hs, hs*hs*d]
features = features.reshape(
n, -1, self.config.hidden_stride * self.config.hidden_stride * d)
return features
def forward(self, pixel_values) -> torch.Tensor: # [BatchSize, ImageShape] -> [BatchSize, #Token, VocabSize]
features = self.encode(pixel_values)
logits = self.head(features)
tokens = self.tokenize(logits)
# tokens' shape is [BatchSize, #Token, VocabSize-5], so padding with [BatchSize, #Token, 5], after
# which, tokens' shape should become [BatchSize, #Token, VocabSize]
batch_size, token_len, _ = tokens.shape
padding_tensor = torch.zeros(size=(batch_size, token_len, len(IMAGE_INDICATOR_IDS)),
dtype=tokens.dtype,
device=tokens.device,
layout=tokens.layout,
requires_grad=False)
tokens = torch.cat((tokens, padding_tensor), dim=2)
return tokens
|