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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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IGNORE_INDEX = -100 |
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IMAGE_TOKEN_INDEX = -200 |
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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_for_batch(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: |
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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 |
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def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool: |
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outputs = [] |
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for i in range(output_ids.shape[0]): |
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outputs.append(self.call_for_batch(output_ids[i].unsqueeze(0), scores)) |
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return all(outputs) |
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