143 lines
7.1 KiB
Python
143 lines
7.1 KiB
Python
# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Image/Text processor class for SigLIP.
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"""
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from typing import List, Optional, Union
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from ...feature_extraction_utils import BatchFeature
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from ...image_utils import ImageInput
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from ...processing_utils import ProcessorMixin
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from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
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from ...utils import TensorType
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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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[`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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Args:
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image_processor ([`SiglipImageProcessor`]):
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The image processor is a required input.
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tokenizer ([`SiglipTokenizer`]):
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The tokenizer is a required input.
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "SiglipImageProcessor"
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tokenizer_class = "SiglipTokenizer"
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def __init__(self, image_processor, tokenizer):
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super().__init__(image_processor, tokenizer)
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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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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]`):
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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`):
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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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- `'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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Returns:
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[`BatchFeature`]: A [`BatchFeature`] with the following fields:
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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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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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if text is not None:
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encoding = self.tokenizer(
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text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
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)
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if images is not None:
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image_features = self.image_processor(images, return_tensors=return_tensors)
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if text is not None and images is not None:
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encoding["pixel_values"] = image_features.pixel_values
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return encoding
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elif text is not None:
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return encoding
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else:
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return BatchFeature(data=dict(**image_features), tensor_type=return_tensors)
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def decode(self, *args, **kwargs):
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"""
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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.
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"""
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return self.tokenizer.decode(*args, **kwargs)
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def batch_decode(self, *args, **kwargs):
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"""
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This method forwards all its arguments to SiglipTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
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refer to the docstring of this method for more information.
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"""
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return self.tokenizer.batch_decode(*args, **kwargs)
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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))
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