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| import torch | |
| from PIL import Image | |
| from torchvision import transforms | |
| from transformers import ProcessorMixin, BatchEncoding | |
| from transformers.image_processing_utils import BatchFeature | |
| OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073) | |
| OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711) | |
| def make_list_of_images(x): | |
| if not isinstance(x, list): | |
| return [x] | |
| return x | |
| def get_image_transform(config): | |
| config = config.vision_config | |
| transform = transforms.Compose( | |
| [ | |
| transforms.ToTensor(), | |
| transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC), | |
| transforms.CenterCrop(224), | |
| transforms.Normalize(OPENAI_DATASET_MEAN, OPENAI_DATASET_STD) # assume image | |
| ] | |
| ) | |
| return transform | |
| def load_and_transform_image(image_path, transform): | |
| image = Image.open(image_path).convert('RGB') if isinstance(image_path, str) else image_path | |
| image_outputs = transform(image) | |
| return image_outputs | |
| class LanguageBindImageProcessor(ProcessorMixin): | |
| attributes = [] | |
| tokenizer_class = ("LanguageBindImageTokenizer") | |
| def __init__(self, config, tokenizer=None, **kwargs): | |
| super().__init__(**kwargs) | |
| self.config = config | |
| self.transform = get_image_transform(config) | |
| self.image_processor = load_and_transform_image | |
| self.tokenizer = tokenizer | |
| self.image_mean = OPENAI_DATASET_MEAN | |
| self.crop_size = {'height': 224, 'width': 224} | |
| def __call__(self, images=None, text=None, context_length=77, return_tensors=None, **kwargs): | |
| 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, max_length=context_length, padding='max_length', | |
| truncation=True, return_tensors=return_tensors, **kwargs) | |
| if images is not None: | |
| images = make_list_of_images(images) | |
| image_features = [self.image_processor(image, self.transform) for image in images] | |
| image_features = torch.stack(image_features) | |
| if text is not None and images is not None: | |
| encoding["pixel_values"] = image_features | |
| return encoding | |
| elif text is not None: | |
| return encoding | |
| else: | |
| return {"pixel_values": image_features} | |
| def preprocess(self, images, return_tensors): | |
| return self.__call__(images=images, return_tensors=return_tensors) | |
| def batch_decode(self, skip_special_tokens=True, *args, **kwargs): | |
| """ | |
| This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please | |
| refer to the docstring of this method for more information. | |
| """ | |
| return self.tokenizer.batch_decode(*args, skip_special_tokens=skip_special_tokens, **kwargs) | |
| def decode(self, skip_special_tokens=True, *args, **kwargs): | |
| """ | |
| This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to | |
| the docstring of this method for more information. | |
| """ | |
| return self.tokenizer.decode(*args, skip_special_tokens=skip_special_tokens, **kwargs) | |