import copy from PIL import Image import numpy as np import torch import torchvision.transforms as T from torchvision.transforms.functional import InterpolationMode from transformers import AutoProcessor, AutoTokenizer from xtuner.utils import IGNORE_INDEX def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): best_ratio_diff = float('inf') best_ratio = (1, 1) area = width * height for ratio in target_ratios: target_aspect_ratio = ratio[0] / ratio[1] ratio_diff = abs(aspect_ratio - target_aspect_ratio) if ratio_diff < best_ratio_diff: best_ratio_diff = ratio_diff best_ratio = ratio elif ratio_diff == best_ratio_diff: if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: best_ratio = ratio return best_ratio def dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False): orig_width, orig_height = image.size aspect_ratio = orig_width / orig_height # calculate the existing image aspect ratio target_ratios = {(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if i * j <= max_num and i * j >= min_num} target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) # find the closest aspect ratio to the target target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height, image_size) # calculate the target width and height target_width = image_size * target_aspect_ratio[0] target_height = image_size * target_aspect_ratio[1] blocks = target_aspect_ratio[0] * target_aspect_ratio[1] # resize the image resized_img = image.resize((target_width, target_height)) processed_images = [] for i in range(blocks): box = ((i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size) # split the image split_img = resized_img.crop(box) processed_images.append(split_img) assert len(processed_images) == blocks if use_thumbnail and len(processed_images) != 1: thumbnail_img = image.resize((image_size, image_size)) processed_images.append(thumbnail_img) return processed_images def total_image_token(orig_size, min_num=1, max_num=12, image_size=448, use_thumbnail=True): orig_width, orig_height = orig_size aspect_ratio = orig_width / orig_height # calculate the existing image aspect ratio target_ratios = {(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if max_num >= i * j >= min_num} target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) # find the closest aspect ratio to the target target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height, image_size) blocks = target_aspect_ratio[0] * target_aspect_ratio[1] if use_thumbnail: blocks += 1 return blocks class InternVLProcessor: IMG_CONTEXT_TOKEN = '' IMG_START_TOKEN = '' IMG_END_TOKEN = '' IMAGENET_MEAN = (0.485, 0.456, 0.406) IMAGENET_STD = (0.229, 0.224, 0.225) SYSTEM = '' template = dict( SYSTEM='<|system|>\n{system}<|end|>\n', # INSTRUCTION='<|user|>\n{input}<|end|>\n<|assistant|>\n', INSTRUCTION='<|user|>\n{input}<|end|><|assistant|>\n', SUFFIX='<|end|>', SUFFIX_AS_EOS=True, SEP='\n', STOP_WORDS=['<|end|>']) def __init__(self, max_length=8192, special_tokens=['[SEG]'], pretrained_model_name_or_path=None): self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path) if special_tokens: self.tokenizer.add_tokens(special_tokens, special_tokens=True) self.max_length = max_length self.min_dynamic_patch = 1 self.max_dynamic_patch = 12 self.downsample_ratio = 0.5 self.image_size = 448 self.use_thumbnail = True patch_size = 14 self.patch_token = int( (self.image_size // patch_size)**2 * (self.downsample_ratio**2)) self.transformer = T.Compose([ T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), T.Resize((self.image_size, self.image_size), interpolation=InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=self.IMAGENET_MEAN, std=self.IMAGENET_STD) ]) def get_inputid_labels(self, conversations, image_token_str) -> dict: input = '' out_conversation = [] while conversations and conversations[0]['from'] == 'gpt': conversations = conversations[1:] for msg in conversations: if msg['from'] == 'human': if image_token_str is None and '' in msg['value']: msg['value'] = msg['value'].replace('', '') if '' in msg['value']: msg['value'] = msg['value'].replace('', image_token_str).strip() input += msg['value'].strip() elif msg['from'] == 'gpt': out_conversation.append({ 'input': input, 'output': msg['value'].strip() }) input = '' else: raise NotImplementedError input_ids, labels = [], [] for i, single_turn_conversation in enumerate(out_conversation): input = single_turn_conversation.get('input', '') if input is None: input = '' input_text = self.template['INSTRUCTION'].format( input=input, round=i + 1) if i == 0: if self.SYSTEM: system = self.template['SYSTEM'].format(system=self.SYSTEM) input_text = system + input_text input_encode = self.tokenizer.encode( input_text, add_special_tokens=True) else: input_encode = self.tokenizer.encode( input_text, add_special_tokens=False) input_ids += input_encode labels += [IGNORE_INDEX] * len(input_encode) output_text = single_turn_conversation.get('output', '') if self.template.get('SUFFIX', None): output_text += self.template['SUFFIX'] output_encode = self.tokenizer.encode( output_text, add_special_tokens=False) input_ids += output_encode labels += copy.deepcopy(output_encode) if len(input_ids) > self.max_length: input_ids = input_ids[:self.max_length] labels = labels[:self.max_length] return {'input_ids': input_ids, 'labels': labels} def __call__(self, data_dict): out_data_dict = {} if data_dict.get('image', None) is not None: image_file = data_dict['image'] try: image = Image.open(image_file).convert('RGB') except Exception as e: return None images = dynamic_preprocess(image, self.min_dynamic_patch, self.max_dynamic_patch, self.image_size, self.use_thumbnail) pixel_values = [self.transformer(image) for image in images] pixel_values = torch.stack(pixel_values) out_data_dict['pixel_values'] = pixel_values num_image_tokens = pixel_values.shape[0] * self.patch_token image_token_str = f'{self.IMG_START_TOKEN}' \ f'{self.IMG_CONTEXT_TOKEN * num_image_tokens}' \ f'{self.IMG_END_TOKEN}' token_dict = self.get_inputid_labels(data_dict['conversations'], image_token_str) out_data_dict.update(token_dict) else: token_dict = self.get_inputid_labels(data_dict['conversations'], None) out_data_dict.update(token_dict) out_data_dict['pixel_values'] = torch.zeros(1, 3, self.image_size, self.image_size) return out_data_dict