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from PIL import Image
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from io import BytesIO
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import base64
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import torch
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from transformers import StoppingCriteria
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from llava.constants import IMAGE_TOKEN_INDEX
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def load_image_from_base64(image):
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return Image.open(BytesIO(base64.b64decode(image)))
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def expand2square(pil_img, background_color):
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width, height = pil_img.size
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if width == height:
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return pil_img
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elif width > height:
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result = Image.new(pil_img.mode, (width, width), background_color)
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result.paste(pil_img, (0, (width - height) // 2))
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return result
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else:
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result = Image.new(pil_img.mode, (height, height), background_color)
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result.paste(pil_img, ((height - width) // 2, 0))
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return result
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def process_images(images, image_processor, model_cfg):
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image_aspect_ratio = getattr(model_cfg, "image_aspect_ratio", None)
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new_images = []
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if image_aspect_ratio == 'pad':
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for image in images:
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image = expand2square(image, tuple(int(x*255) for x in image_processor.image_mean))
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image = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
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new_images.append(image)
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else:
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return image_processor(images, return_tensors='pt')['pixel_values']
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if all(x.shape == new_images[0].shape for x in new_images):
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new_images = torch.stack(new_images, dim=0)
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return new_images
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def tokenizer_image_token(prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, return_tensors=None):
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prompt_chunks = [tokenizer(chunk).input_ids for chunk in prompt.split('<image>')]
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def insert_separator(X, sep):
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return [ele for sublist in zip(X, [sep]*len(X)) for ele in sublist][:-1]
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input_ids = []
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offset = 0
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if len(prompt_chunks) > 0 and len(prompt_chunks[0]) > 0 and prompt_chunks[0][0] == tokenizer.bos_token_id:
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offset = 1
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input_ids.append(prompt_chunks[0][0])
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for x in insert_separator(prompt_chunks, [image_token_index] * (offset + 1)):
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input_ids.extend(x[offset:])
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if return_tensors is not None:
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if return_tensors == 'pt':
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return torch.tensor(input_ids, dtype=torch.long)
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raise ValueError(f'Unsupported tensor type: {return_tensors}')
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return input_ids
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def get_model_name_from_path(model_path):
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model_path = model_path.strip("/")
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model_paths = model_path.split("/")
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if model_paths[-1].startswith('checkpoint-'):
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return model_paths[-2] + "_" + model_paths[-1]
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else:
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return model_paths[-1]
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class KeywordsStoppingCriteria(StoppingCriteria):
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def __init__(self, keywords, tokenizer, input_ids):
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self.keywords = keywords
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self.keyword_ids = []
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self.max_keyword_len = 0
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for keyword in keywords:
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cur_keyword_ids = tokenizer(keyword).input_ids
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if len(cur_keyword_ids) > 1 and cur_keyword_ids[0] == tokenizer.bos_token_id:
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cur_keyword_ids = cur_keyword_ids[1:]
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if len(cur_keyword_ids) > self.max_keyword_len:
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self.max_keyword_len = len(cur_keyword_ids)
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self.keyword_ids.append(torch.tensor(cur_keyword_ids))
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self.tokenizer = tokenizer
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self.start_len = input_ids.shape[1]
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def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
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assert output_ids.shape[0] == 1, "Only support batch size 1 (yet)"
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offset = min(output_ids.shape[1] - self.start_len, self.max_keyword_len)
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self.keyword_ids = [keyword_id.to(output_ids.device) for keyword_id in self.keyword_ids]
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for keyword_id in self.keyword_ids:
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if (output_ids[0, -keyword_id.shape[0]:] == keyword_id).all():
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return True
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outputs = self.tokenizer.batch_decode(output_ids[:, -offset:], skip_special_tokens=True)[0]
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for keyword in self.keywords:
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if keyword in outputs:
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return True
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return False |
