gerardovaldez0113 commited on
Commit
3a2109f
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1 Parent(s): 8481f56

Initial model

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - clip
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+ - ecommerce
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+ - multimodal retrieval
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+ - transformers
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+ - openCLIP
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+ ---
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+
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+ # Product Embedding Model
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+ This model is a multimodal embedding model trained for ecommerce products.
config.json ADDED
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+ {"auto_map": {"AutoConfig": "customSigLIP.SigLIPConfig", "AutoModel": "customSigLIP.SigLIP", "AutoProcessor": "customSigLIP.SigLIPProcessor"}, "open_clip_model_name": "hf-hub:gerardovaldez0113/product-embeddings", "model_type": "siglip", "type": "siglip"}
customSigLIP.py ADDED
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+ import torch
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+ from open_clip import create_model
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+ from transformers import PretrainedConfig, PreTrainedModel
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+ from transformers.models.siglip.modeling_siglip import SiglipOutput
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+ from typing import Optional, Tuple, Union, List
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+ from transformers.feature_extraction_utils import BatchFeature
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+ from transformers.image_utils import ImageInput
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+ from transformers.processing_utils import ProcessorMixin
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+ from transformers.tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
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+ from transformers.utils import TensorType
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+ import string
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+ import ftfy
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+ import html
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+
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+ def basic_clean(text):
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+ text = ftfy.fix_text(text)
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+ text = html.unescape(html.unescape(text))
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+ return text.strip()
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+
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+ def canonicalize_text(
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+ text,
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+ *,
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+ keep_punctuation_exact_string=None,
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+ trans_punctuation: dict = str.maketrans("", "", string.punctuation),
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+ ):
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+ """Returns canonicalized `text` (lowercase and punctuation removed).
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+
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+ From: https://github.com/google-research/big_vision/blob/53f18caf27a9419231bbf08d3388b07671616d3d/big_vision/evaluators/proj/image_text/prompt_engineering.py#L94
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+
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+ Args:
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+ text: string to be canonicalized.
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+ keep_punctuation_exact_string: If provided, then this exact string kept.
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+ For example providing '{}' will keep any occurrences of '{}' (but will
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+ still remove '{' and '}' that appear separately).
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+ """
36
+ text = text.replace("_", " ")
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+ if keep_punctuation_exact_string:
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+ text = keep_punctuation_exact_string.join(
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+ part.translate(trans_punctuation)
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+ for part in text.split(keep_punctuation_exact_string)
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+ )
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+ else:
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+ text = text.translate(trans_punctuation)
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+ text = text.lower()
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+ text = " ".join(text.split())
46
+ return text.strip()
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+
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+ def _clean_canonicalize(x):
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+ # basic, remove whitespace, remove punctuation, lower case
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+ return canonicalize_text(basic_clean(x))
51
+
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+ class SigLIPConfig(PretrainedConfig):
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+ def __init__(
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+ self,
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+ open_clip_model_name: str = "",
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+ **kwargs,
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+ ):
58
+ super().__init__(**kwargs)
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+ self.open_clip_model_name = open_clip_model_name
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+
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+ class SigLIPProcessor(ProcessorMixin):
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+ r"""
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+ Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor.
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+
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+ [`SiglipProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`SiglipTokenizer`]. See the
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+ [`~SiglipProcessor.__call__`] and [`~SiglipProcessor.decode`] for more information.
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+
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+ Args:
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+ image_processor ([`SiglipImageProcessor`]):
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+ The image processor is a required input.
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+ tokenizer ([`T5TokenizerFast`]):
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+ The tokenizer is a required input.
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+ """
74
+
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+ attributes = ["image_processor", "tokenizer"]
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+ image_processor_class = "SiglipImageProcessor"
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+ tokenizer_class = "T5TokenizerFast"
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+
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+ def __init__(self, image_processor, tokenizer):
80
+ super().__init__(image_processor, tokenizer)
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+
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+ def __call__(
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+ self,
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+ text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
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+ images: ImageInput = None,
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+ padding: Union[bool, str, PaddingStrategy] = False,
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+ truncation: Union[bool, str, TruncationStrategy] = None,
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+ max_length: int = None,
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+ return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
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+ ) -> BatchFeature:
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+ """
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+ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
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+ and `kwargs` arguments to SiglipTokenizer's [`~SiglipTokenizer.__call__`] if `text` is not `None` to encode
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+ the text. To prepare the image(s), this method forwards the `images` argument to
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+ SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring
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+ of the above two methods for more information.
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+
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+ Args:
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+ text (`str`, `List[str]`, `List[List[str]]`):
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+ The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
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+ (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
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+ `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
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+ images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
104
+ The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
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+ tensor. Both channels-first and channels-last formats are supported.
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+ padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
107
+ Select a strategy to pad the returned sequences (according to the model's padding side and padding
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+ index) among:
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+ - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
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+ sequence if provided).
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+ - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
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+ acceptable input length for the model if that argument is not provided.
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+ - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
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+ lengths).
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+ max_length (`int`, *optional*):
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+ Maximum length of the returned list and optionally padding length (see above).
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+ truncation (`bool`, *optional*):
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+ Activates truncation to cut input sequences longer than `max_length` to `max_length`.
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+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
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+ If set, will return tensors of a particular framework. Acceptable values are:
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+
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+ - `'tf'`: Return TensorFlow `tf.constant` objects.
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+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
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+ - `'np'`: Return NumPy `np.ndarray` objects.
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+ - `'jax'`: Return JAX `jnp.ndarray` objects.
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+
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+ Returns:
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+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
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+
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+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
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+ `None`).
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+ - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
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+ """
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+
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+ if text is None and images is None:
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+ raise ValueError("You have to specify either text or images. Both cannot be none.")
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+
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+ if text is not None:
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+ if isinstance(text, str):
142
+ text = [text]
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+ text = [_clean_canonicalize(raw_text) for raw_text in text]
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+ encoding = self.tokenizer(
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+ text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
146
+ )
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+
148
+ if images is not None:
149
+ try:
150
+ images = [image.convert('RGB') for image in images] if isinstance(images, list) else images.convert('RGB')
151
+ except:
152
+ images = images
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+ image_features = self.image_processor(images, return_tensors=return_tensors)
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+
155
+ if text is not None and images is not None:
156
+ encoding["pixel_values"] = image_features.pixel_values
157
+ return encoding
158
+ elif text is not None:
159
+ return encoding
160
+ else:
161
+ return BatchFeature(data=dict(**image_features), tensor_type=return_tensors)
162
+
163
+ def decode(self, *args, **kwargs):
164
+ """
165
+ This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to
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+ the docstring of this method for more information.
167
+ """
168
+ return self.tokenizer.decode(*args, **kwargs)
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+
170
+ def batch_decode(self, *args, **kwargs):
171
+ """
172
+ This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
173
+ refer to the docstring of this method for more information.
174
+ """
175
+ return self.tokenizer.batch_decode(*args, **kwargs)
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+
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+ @property
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+ # Copied from transformers.models.clip.processing_clip.CLIPProcessor.model_input_names with CLIP->Siglip, T5->Siglip
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+ def model_input_names(self):
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+ tokenizer_input_names = self.tokenizer.model_input_names
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+ image_processor_input_names = self.image_processor.model_input_names
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+ return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
183
+
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+ class SigLIP(PreTrainedModel):
185
+ config_class = SigLIPConfig
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+
187
+ def __init__(self, config: SigLIPConfig):
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+ super().__init__(config)
189
+ self.config = config
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+ self.model = create_model(config.open_clip_model_name, output_dict=True)
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+ self.model.eval()
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+ self.model.to(self.device)
193
+
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+ def get_image_features(
195
+ self,
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+ pixel_values: torch.FloatTensor,
197
+ normalize: bool = False,
198
+ **kwargs
199
+ ) -> torch.FloatTensor:
200
+
201
+ with torch.inference_mode():
202
+ image_features = self.model.encode_image(pixel_values, normalize=normalize)
203
+ return image_features
204
+
205
+ def get_text_features(
206
+ self,
207
+ input_ids: torch.Tensor,
208
+ normalize: bool = False,
209
+ **kwargs
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+ ) -> torch.FloatTensor:
211
+
212
+ with torch.inference_mode():
213
+ text_features = self.model.encode_text(input_ids, normalize=normalize)
214
+ return text_features
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+
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+ def forward(
217
+ self,
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+ input_ids: Optional[torch.LongTensor] = None,
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+ pixel_values: Optional[torch.FloatTensor] = None,
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+ return_dict: Optional[bool] = None,
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+ ) -> Union[Tuple, SiglipOutput]:
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+
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+ vision_outputs = self.get_image_features(pixel_values=pixel_values, normalize=True)
224
+ text_outputs = self.get_text_features(input_ids=input_ids, normalize=True)
225
+
226
+ logits_per_text = text_outputs @ vision_outputs.T
227
+ logits_per_image = logits_per_text.T
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+
229
+ if not return_dict:
230
+ return logits_per_image, logits_per_text, text_outputs, vision_outputs
231
+
232
+ return SiglipOutput(
233
+ logits_per_image=logits_per_image,
234
+ logits_per_text=logits_per_text,
235
+ text_embeds=text_outputs,
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+ image_embeds=vision_outputs
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+ )
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+ {
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+ "model_cfg": {
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+ "embed_dim": 1024,
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+ "init_logit_bias": -10,
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+ "custom_text": true,
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+ "vision_cfg": {
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+ "image_size": 256,
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+ "timm_model_name": "vit_large_patch16_siglip_256",
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+ "timm_model_pretrained": false,
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+ "timm_pool": "map",
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+ "timm_proj": "none"
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+ },
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+ "text_cfg": {
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+ "context_length": 64,
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+ "vocab_size": 32000,
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+ "hf_tokenizer_name": "timm/ViT-B-16-SigLIP",
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+ "tokenizer_kwargs": {
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+ "clean": "canonicalize"
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+ },
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+ "width": 1024,
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+ "heads": 16,
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+ "layers": 24,
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+ "no_causal_mask": true,
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+ "proj_bias": true,
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+ "pool_type": "last",
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+ "norm_kwargs": {
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+ "eps": 1e-06
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+ }
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+ }
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+ },
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+ "preprocess_cfg": {
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+ 0.5
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+ ],
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+ "interpolation": "bicubic",
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+ "resize_mode": "squash"
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+ }
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+ }
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The diff for this file is too large to render. See raw diff
 
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