Source code for transformers.models.clip.feature_extraction_clip

# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
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"""Feature extractor class for CLIP."""

from typing import List, Optional, Union

import numpy as np
from PIL import Image

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...file_utils import TensorType
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import logging

logger = logging.get_logger(__name__)

[docs]class CLIPFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin): r""" Constructs a CLIP feature extractor. This feature extractor inherits from :class:`~transformers.FeatureExtractionMixin` which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: do_resize (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to resize the input to a certain :obj:`size`. size (:obj:`int`, `optional`, defaults to 224): Resize the input to the given size. Only has an effect if :obj:`do_resize` is set to :obj:`True`. resample (:obj:`int`, `optional`, defaults to :obj:`PIL.Image.BICUBIC`): An optional resampling filter. This can be one of :obj:`PIL.Image.NEAREST`, :obj:`PIL.Image.BOX`, :obj:`PIL.Image.BILINEAR`, :obj:`PIL.Image.HAMMING`, :obj:`PIL.Image.BICUBIC` or :obj:`PIL.Image.LANCZOS`. Only has an effect if :obj:`do_resize` is set to :obj:`True`. do_center_crop (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether to crop the input at the center. If the input size is smaller than :obj:`crop_size` along any edge, the image is padded with 0's and then center cropped. crop_size (:obj:`int`, `optional`, defaults to 224): Desired output size when applying center-cropping. Only has an effect if :obj:`do_center_crop` is set to :obj:`True`. do_normalize (:obj:`bool`, `optional`, defaults to :obj:`True`): Whether or not to normalize the input with :obj:`image_mean` and :obj:`image_std`. image_mean (:obj:`List[int]`, defaults to :obj:`[0.485, 0.456, 0.406]`): The sequence of means for each channel, to be used when normalizing images. image_std (:obj:`List[int]`, defaults to :obj:`[0.229, 0.224, 0.225]`): The sequence of standard deviations for each channel, to be used when normalizing images. """ model_input_names = ["pixel_values"] def __init__( self, do_resize=True, size=224, resample=Image.BICUBIC, do_center_crop=True, crop_size=224, do_normalize=True, image_mean=None, image_std=None, **kwargs ): super().__init__(**kwargs) self.do_resize = do_resize self.size = size self.resample = resample self.do_center_crop = do_center_crop self.crop_size = crop_size self.do_normalize = do_normalize self.image_mean = image_mean if image_mean is not None else [0.48145466, 0.4578275, 0.40821073] self.image_std = image_std if image_std is not None else [0.26862954, 0.26130258, 0.27577711] def __call__( self, images: Union[ Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"] # noqa ], return_tensors: Optional[Union[str, TensorType]] = None, **kwargs ) -> BatchFeature: """ Main method to prepare for the model one or several image(s). .. warning:: NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass PIL images. Args: images (:obj:`PIL.Image.Image`, :obj:`np.ndarray`, :obj:`torch.Tensor`, :obj:`List[PIL.Image.Image]`, :obj:`List[np.ndarray]`, :obj:`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 (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`, defaults to :obj:`'np'`): If set, will return tensors of a particular framework. Acceptable values are: * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects. * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects. * :obj:`'np'`: Return NumPy :obj:`np.ndarray` objects. * :obj:`'jax'`: Return JAX :obj:`jnp.ndarray` objects. Returns: :class:`~transformers.BatchFeature`: A :class:`~transformers.BatchFeature` with the following fields: - **pixel_values** -- Pixel values to be fed to a model. """ # Input type checking for clearer error valid_images = False # Check that images has a valid type if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images): valid_images = True elif isinstance(images, (list, tuple)): if len(images) == 0 or isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]): valid_images = True if not valid_images: raise ValueError( "Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example)," "`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)." ) is_batched = bool( isinstance(images, (list, tuple)) and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0])) ) if not is_batched: images = [images] # transformations (resizing + center cropping + normalization) if self.do_resize and self.size is not None and self.resample is not None: images = [self.resize(image=image, size=self.size, resample=self.resample) for image in images] if self.do_center_crop and self.crop_size is not None: images = [self.center_crop(image, self.crop_size) for image in images] if self.do_normalize: images = [self.normalize(image=image, mean=self.image_mean, std=self.image_std) for image in images] # return as BatchFeature data = {"pixel_values": images} encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors) return encoded_inputs
[docs] def center_crop(self, image, size): """ Crops :obj:`image` to the given size using a center crop. Note that if the image is too small to be cropped to the size is given, it will be padded (so the returned result has the size asked). Args: image (:obj:`PIL.Image.Image` or :obj:`np.ndarray` or :obj:`torch.Tensor`): The image to resize. size (:obj:`int` or :obj:`Tuple[int, int]`): The size to which crop the image. """ self._ensure_format_supported(image) if not isinstance(size, tuple): size = (size, size) if not isinstance(image, Image.Image): image = self.to_pil_image(image) image_width, image_height = image.size crop_height, crop_width = size crop_top = int((image_height - crop_height + 1) * 0.5) crop_left = int((image_width - crop_width + 1) * 0.5) return image.crop((crop_left, crop_top, crop_left + crop_width, crop_top + crop_height))
[docs] def resize(self, image, size, resample=Image.BICUBIC): """ Resizes :obj:`image`. Note that this will trigger a conversion of :obj:`image` to a PIL Image. Args: image (:obj:`PIL.Image.Image` or :obj:`np.ndarray` or :obj:`torch.Tensor`): The image to resize. size (:obj:`int` or :obj:`Tuple[int, int]`): The size to use for resizing the image. If :obj:`int` it will be resized to match the shorter side resample (:obj:`int`, `optional`, defaults to :obj:`PIL.Image.BILINEAR`): The filter to user for resampling. """ self._ensure_format_supported(image) if not isinstance(image, Image.Image): image = self.to_pil_image(image) if isinstance(size, tuple): new_w, new_h = size else: width, height = image.size short, long = (width, height) if width <= height else (height, width) if short == size: return image new_short, new_long = size, int(size * long / short) new_w, new_h = (new_short, new_long) if width <= height else (new_long, new_short) return image.resize((new_w, new_h), resample)