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from typing import Union, Optional
import PIL.Image
import torch
from torch.nn.functional import softmax, gumbel_softmax
from transformers import PretrainedConfig, PreTrainedModel, AutoImageProcessor, AutoModel, AutoConfig
class BaseVisualTokenizerConfig(PretrainedConfig):
def __init__(self,
vocab_size=16384,
tokenize_function="softmax",
tau=1.0,
depths=None,
use_indicators=False,
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.use_indicators = use_indicators
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)
self.head = None
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 get_head(self):
return self.head
def get_image_size(self):
raise NotImplementedError
def preprocess_image(self, image: PIL.Image.Image, convert_to_rgb=True):
if convert_to_rgb and image.mode != 'RGB':
image = image.convert('RGB')
# first resize and preprocess
sides = self.get_image_size()
if sides[0] != sides[1]:
raise ValueError('get_image_size() returns non-square size')
side = sides[0]
width, height = image.size
if width == height:
new_width = new_height = side
elif width > height:
new_width = side
new_height = int(height / width * new_width)
else:
new_height = side
new_width = int(width / height * new_height)
new_size = dict(height=new_height, width=new_width)
pixel_values = self.image_processor.preprocess(image, 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 get_layer_norm(self):
return self.layer_norm
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
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