import copy import random import glob import json import logging import os from typing import Literal import torch from mmengine import print_log from mmengine.config import Config, ConfigDict from PIL import Image from torch.utils.data import Dataset import numpy as np import torch.nn.functional as F import torchvision.transforms as T from torchvision.transforms.functional import InterpolationMode from pycocotools.coco import COCO from pycocotools import mask as mask_utils from xtuner.registry import BUILDER from xtuner.utils import IGNORE_INDEX from xtuner.dataset.utils import encode_fn from xtuner.dataset.map_fns import llava_map_fn from projects.glamm.datasets.utils.utils import expand2square from projects.glamm.datasets.utils.utils import SEG_QUESTIONS, ANSWER_LIST from projects.glamm.utils import DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from third_parts.mmdet.datasets.refcoco import RefCocoDataset from .utils import dynamic_preprocess class ReferSegmDataset(RefCocoDataset): os.environ['TOKENIZERS_PARALLELISM'] = 'true' 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) def __init__(self, data_root, ann_file=None, split_file=None, special_tokens=None, prompt_template=None, extra_image_processor=None, data_prefix=dict(img_path='train2014/'), tokenizer=None, max_length=2048, num_classes_per_sample=3, single_image_mode=False, arch_type: Literal['intern_vl', 'qwen'] = 'intern_vl', preprocessor=None, **kwargs): super().__init__( data_root=data_root, data_prefix=data_prefix, pipeline=None, ann_file=ann_file, split_file=split_file, **kwargs, ) self.begin_str = f'{DEFAULT_IMAGE_TOKEN}\n' if extra_image_processor is not None: self.extra_image_processor = BUILDER.build(extra_image_processor) self.arch_type = arch_type if self.arch_type == 'qwen': self.IMG_CONTEXT_TOKEN = '<|image_pad|>' self.IMG_START_TOKEN = '<|vision_start|>' self.IMG_END_TOKEN = '<|vision_end|>' elif self.arch_type == 'llava': self.IMG_CONTEXT_TOKEN = '' self.IMG_START_TOKEN = '' self.IMG_END_TOKEN = '' self.tokenizer = BUILDER.build(tokenizer) if special_tokens is not None: self.tokenizer.add_tokens(special_tokens, special_tokens=True) self.image_folder = data_root self.template = prompt_template self.max_length = max_length if self.arch_type == 'intern_vl': # self._system = '你是由上海人工智能实验室联合商汤科技开发的书生多模态大模型，英文名叫InternVL, 是一个有用无害的人工智能助手。' self._system = '' self.template['INSTRUCTION'] = '<|user|>\n{input}<|end|><|assistant|>\n' elif self.arch_type == 'qwen': self._system = '' elif self.arch_type == 'llava': self._system = '' self.num_classes_per_sample = num_classes_per_sample self.min_dynamic_patch = 1 self.max_dynamic_patch = 12 self.downsample_ratio = 0.5 if self.arch_type == 'llava': self.downsample_ratio = 1 self.image_size = 448 if self.arch_type == 'llava': self.image_size = 336 self.use_thumbnail = True patch_size = 14 self.patch_token = int((self.image_size // patch_size) ** 2 * (self.downsample_ratio ** 2)) if preprocessor is None: 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) ]) self.preprocessor = None else: self.transformer = None self.preprocessor = BUILDER.build(preprocessor) self.arch_type = arch_type self.single_image_mode = single_image_mode self._max_refetch = 1000 print("Image RES dataset, include {} items.".format(len(self))) @property def modality_length(self): import pickle length_list = [] for idx in range(len(self)): length_list.append(100) return length_list def _parse_annotations(self, ann_info): image_path = ann_info['img_path'] image = Image.open(image_path).convert('RGB') width, height = image.size masks, phrases = [], [] instances, text = ann_info['instances'], ann_info['text'] # index = np.random.choice(range(len(instances)), min( # len(instances), self.num_classes_per_sample)) index = np.random.choice(range(len(instances)), self.num_classes_per_sample, replace=True) for idx in index: inst = instances[idx] phrase = text[idx].lower() if '.' == phrase[-1]: phrase = phrase[:-1] phrases.append(phrase) binary_mask = np.zeros((height, width), dtype=np.uint8) for seg in inst["mask"]: rles = mask_utils.frPyObjects([seg], height, width) m = mask_utils.decode(rles) m = m.astype(np.uint8) binary_mask += m.squeeze() masks.append(binary_mask) conversation = [] for i, phrase in enumerate(phrases): question = random.choice(SEG_QUESTIONS).format(class_name=phrase) if i == 0: question = self.begin_str + question conversation.append({'from': 'human', 'value': question}) conversation.append({'from': 'gpt', 'value': random.choice(ANSWER_LIST)}) masks = torch.stack([torch.from_numpy(mask) for mask in masks], dim=0) ann_info.update({ 'masks': masks, 'conversations': conversation, 'image': image_path }) return ann_info def prepare_data(self, index): data_dict = super().prepare_data(index) data_dict = self._parse_annotations(data_dict) if data_dict is None: return None out_data_dict = {} if 'masks' in data_dict: out_data_dict['masks'] = data_dict['masks'] 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: print(f'Error: {e}', flush=True) print_log(f'Error: {e}', logger='current') return None if hasattr(self, 'extra_image_processor'): g_image = np.array(image) # for grounding g_image = self.extra_image_processor.apply_image(g_image) g_pixel_values = torch.from_numpy(g_image).permute(2, 0, 1).contiguous() out_data_dict['g_pixel_values'] = g_pixel_values if self.single_image_mode: images = [image] else: images = dynamic_preprocess(image, self.min_dynamic_patch, self.max_dynamic_patch, self.image_size, self.use_thumbnail) if self.preprocessor is not None: if self.arch_type == 'qwen': _data_dict = self.preprocessor(images, do_resize=True) _data_dict['pixel_values'] = torch.tensor(_data_dict['pixel_values'], dtype=torch.float) _data_dict['image_grid_thw'] = torch.tensor(_data_dict['image_grid_thw'], dtype=torch.int) num_image_tokens = int(_data_dict['image_grid_thw'][0].prod() * (self.downsample_ratio ** 2)) elif self.arch_type == 'llava': _data_dict = self.preprocessor(images, do_resize=True, size=(self.image_size, self.image_size)) _data_dict['pixel_values'] = np.stack(_data_dict['pixel_values'], axis=0) _data_dict['pixel_values'] = torch.tensor(_data_dict['pixel_values'], dtype=torch.float) num_image_tokens = _data_dict['pixel_values'].shape[0] * self.patch_token else: raise NotImplementedError out_data_dict.update(_data_dict) else: 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 def get_inputid_labels(self, conversations, image_token_str) -> dict: input = '' out_conversation = [] while conversations and conversations[0]['from'] == 'gpt': # Skip the first one if it is 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 != '' and self._system is not None: 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] # print('len_ids: ', len(input_ids)) return {'input_ids': input_ids, 'labels': labels} def __getitem__(self, index): for _ in range(self._max_refetch + 1): data = self.prepare_data(index) # Broken images may cause the returned data to be None if data is None: index = self._rand_another() continue return data if __name__ == '__main__': from transformers import CLIPImageProcessor, AutoTokenizer from third_parts.segment_anything.utils.transforms import ResizeLongestSide pretrained_model = 'MBZUAI/GLaMM-GranD-Pretrained' llm_name_or_path = 'lmsys/vicuna-7b-v1.5' tokenizer = dict( type=AutoTokenizer.from_pretrained, pretrained_model_name_or_path=llm_name_or_path) image_processor = dict( type=CLIPImageProcessor.from_pretrained, pretrained_model_name_or_path='openai/clip-vit-large-patch14-336') extra_image_processor = dict( type=ResizeLongestSide, target_length=1024, ) from xtuner.utils.templates import PROMPT_TEMPLATE prompt_template = PROMPT_TEMPLATE.vicuna from xtuner.dataset.map_fns import llava_map_fn, template_map_fn_factory, template_map_fn from projects.glamm.datasets.collate_fns.glamm_collate_fn import glamm_collate_fn dataset = ReferSegmDataset( tokenizer=tokenizer, special_tokens=['[SEG]'], extra_image_processor=extra_image_processor, prompt_template=prompt_template, data_root='data/coco/', data_prefix=dict(img_path='train2014/'), ann_file='refcoco+/instances.json', split_file='refcoco+/refs(unc).p', ) for i in range(1000): dataset[i]