import torch
from open_clip import create_model
from transformers import PretrainedConfig, PreTrainedModel
from transformers.models.siglip.modeling_siglip import SiglipOutput
from typing import Optional, Tuple, Union, List
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessorMixin
from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from transformers.utils import TensorType
import string
import ftfy
import html
def basic_clean(text):
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
return text.strip()
def canonicalize_text(
text,
*,
keep_punctuation_exact_string=None,
trans_punctuation: dict = str.maketrans("", "", string.punctuation),
):
"""Returns canonicalized `text` (lowercase and punctuation removed).
From: https://github.com/google-research/big_vision/blob/53f18caf27a9419231bbf08d3388b07671616d3d/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94
Args:
text: string to be canonicalized.
keep_punctuation_exact_string: If provided, then this exact string kept.
For example providing '{}' will keep any occurrences of '{}' (but will
still remove '{' and '}' that appear separately).
"""
text = text.replace("_", " ")
if keep_punctuation_exact_string:
text = keep_punctuation_exact_string.join(
part.translate(trans_punctuation)
for part in text.split(keep_punctuation_exact_string)
)
else:
text = text.translate(trans_punctuation)
text = text.lower()
text = " ".join(text.split())
return text.strip()
def _clean_canonicalize(x):
# basic, remove whitespace, remove punctuation, lower case
return canonicalize_text(basic_clean(x))
class MarqoFashionSigLIPConfig(PretrainedConfig):
def __init__(
self,
open_clip_model_name: str = "",
**kwargs,
):
super().__init__(**kwargs)
self.open_clip_model_name = open_clip_model_name
class MarqoFashionSigLIPProcessor(ProcessorMixin):
r"""
Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor.
[`SiglipProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`SiglipTokenizer`]. See the
[`~SiglipProcessor.__call__`] and [`~SiglipProcessor.decode`] for more information.
Args:
image_processor ([`SiglipImageProcessor`]):
The image processor is a required input.
tokenizer ([`T5TokenizerFast`]):
The tokenizer is a required input.
"""
attributes = ["image_processor", "tokenizer"]
image_processor_class = "SiglipImageProcessor"
tokenizer_class = "T5TokenizerFast"
def __init__(self, image_processor, tokenizer):
super().__init__(image_processor, tokenizer)
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
images: ImageInput = None,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: int = None,
return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
) -> BatchFeature:
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to SiglipTokenizer's [`~SiglipTokenizer.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `images` argument to
SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
of the above two methods for more information.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
(pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last formats are supported.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Select a strategy to pad the returned sequences (according to the model's padding side and padding
index) among:
- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
sequence if provided).
- `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
max_length (`int`, *optional*):
Maximum length of the returned list and optionally padding length (see above).
truncation (`bool`, *optional*):
Activates truncation to cut input sequences longer than `max_length` to `max_length`.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors of a particular framework. Acceptable values are:
- `'tf'`: Return TensorFlow `tf.constant` objects.
- `'pt'`: Return PyTorch `torch.Tensor` objects.
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with the following fields:
- **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
- **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
`return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
`None`).
- **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
"""
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none.")
if text is not None:
if isinstance(text, str):
text = [text]
text = [_clean_canonicalize(raw_text) for raw_text in text]
encoding = self.tokenizer(
text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
)
if images is not None:
try:
images = [image.convert('RGB') for image in images] if isinstance(images, list) else images.convert('RGB')
except:
images = images
image_features = self.image_processor(images, return_tensors=return_tensors)
if text is not None and images is not None:
encoding["pixel_values"] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchFeature(data=dict(**image_features), tensor_type=return_tensors)
def decode(self, *args, **kwargs):
"""
This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
the docstring of this method for more information.
"""
return self.tokenizer.decode(*args, **kwargs)
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method for more information.
"""
return self.tokenizer.batch_decode(*args, **kwargs)
@property
# Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Siglip, T5->Siglip
def model_input_names(self):
tokenizer_input_names = self.tokenizer.model_input_names
image_processor_input_names = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
class MarqoFashionSigLIP(PreTrainedModel):
config_class = MarqoFashionSigLIPConfig
def __init__(self, config: MarqoFashionSigLIPConfig):
super().__init__(config)
self.config = config
self.model = create_model(config.open_clip_model_name, output_dict=True)
self.model.eval()
self.model.to(self.device)
def get_image_features(
self,
pixel_values: torch.FloatTensor,
normalize: bool = False,
**kwargs
) -> torch.FloatTensor:
with torch.inference_mode():
image_features = self.model.encode_image(pixel_values, normalize=normalize)
return image_features
def get_text_features(
self,
input_ids: torch.Tensor,
normalize: bool = False,
**kwargs
) -> torch.FloatTensor:
with torch.inference_mode():
text_features = self.model.encode_text(input_ids, normalize=normalize)
return text_features
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, SiglipOutput]:
vision_outputs = self.get_image_features(pixel_values=pixel_values, normalize=True)
text_outputs = self.get_text_features(input_ids=input_ids, normalize=True)
logits_per_text = text_outputs @ vision_outputs.T
logits_per_image = logits_per_text.T
if not return_dict:
return logits_per_image, logits_per_text, text_outputs, vision_outputs
return SiglipOutput(
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
text_embeds=text_outputs,
image_embeds=vision_outputs
)