Source code for deepke.relation_extraction.multimodal.models.clip.processing_clip

# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
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Image/Text processor class for CLIP
from transformers.tokenization_utils_base import BatchEncoding
from .feature_extraction_clip import CLIPFeatureExtractor
from .tokenization_clip import CLIPTokenizer

[docs]class CLIPProcessor: r""" Constructs a CLIP processor which wraps a CLIP feature extractor and a CLIP tokenizer into a single processor. [`CLIPProcessor`] offers all the functionalities of [`CLIPFeatureExtractor`] and [`CLIPTokenizer`]. See the [`~CLIPProcessor.__call__`] and [`~CLIPProcessor.decode`] for more information. Args: feature_extractor ([`CLIPFeatureExtractor`]): The feature extractor is a required input. tokenizer ([`CLIPTokenizer`]): The tokenizer is a required input. """ def __init__(self, feature_extractor, tokenizer): if not isinstance(feature_extractor, CLIPFeatureExtractor): raise ValueError( f"`feature_extractor` has to be of type CLIPFeatureExtractor, but is {type(feature_extractor)}" ) if not isinstance(tokenizer, CLIPTokenizer): raise ValueError(f"`tokenizer` has to be of type CLIPTokenizer, but is {type(tokenizer)}") self.feature_extractor = feature_extractor self.tokenizer = tokenizer self.current_processor = self.feature_extractor
[docs] def save_pretrained(self, save_directory): """ Save a CLIP feature extractor object and CLIP tokenizer object to the directory `save_directory`, so that it can be re-loaded using the [`~CLIPProcessor.from_pretrained`] class method. <Tip> This class method is simply calling [`~PreTrainedFeatureExtractor.save_pretrained`] and [`~tokenization_utils_base.PreTrainedTokenizer.save_pretrained`]. Please refer to the docstrings of the methods above for more information. </Tip> Args: save_directory (`str` or `os.PathLike`): Directory where the feature extractor JSON file and the tokenizer files will be saved (directory will be created if it does not exist). """ self.feature_extractor._set_processor_class(self.__class__.__name__) self.feature_extractor.save_pretrained(save_directory) self.tokenizer._set_processor_class(self.__class__.__name__) self.tokenizer.save_pretrained(save_directory)
[docs] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, **kwargs): r""" Instantiate a [`CLIPProcessor`] from a pretrained CLIP processor. <Tip> This class method is simply calling CLIPFeatureExtractor's [`~PreTrainedFeatureExtractor.from_pretrained`] and CLIPTokenizer's [`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`]. Please refer to the docstrings of the methods above for more information. </Tip> Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained feature_extractor hosted inside a model repo on Valid model ids can be located at the root-level, like `clip-vit-base-patch32`, or namespaced under a user or organization name, like `openai/clip-vit-base-patch32`. - a path to a *directory* containing a feature extractor file saved using the [`~PreTrainedFeatureExtractor.save_pretrained`] method, e.g., `./my_model_directory/`. - a path or url to a saved feature extractor JSON *file*, e.g., `./my_model_directory/preprocessor_config.json`. **kwargs Additional keyword arguments passed along to both [`PreTrainedFeatureExtractor`] and [`PreTrainedTokenizer`] """ feature_extractor = CLIPFeatureExtractor.from_pretrained(pretrained_model_name_or_path, **kwargs) tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, **kwargs) return cls(feature_extractor=feature_extractor, tokenizer=tokenizer)
def __call__(self, text=None, images=None, return_tensors=None, **kwargs): """ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to CLIPTokenizer's [`~CLIPTokenizer.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to CLIPFeatureExtractor's [`~CLIPFeatureExtractor.__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. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width. return_tensors (`str` or [`~file_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: [`BatchEncoding`]: A [`BatchEncoding`] 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: encoding = self.tokenizer(text, return_tensors=return_tensors, **kwargs) if images is not None: image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs) 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 BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
[docs] def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to CLIPTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs)
[docs] def decode(self, *args, **kwargs): """ This method forwards all its arguments to CLIPTokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs)