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