import json import os import random import torch from datasets import Dataset as HFDataset from datasets import DatasetDict, load_from_disk from PIL import Image from torch.utils.data import Dataset from pycocotools import mask import numpy as np import copy from xtuner.registry import BUILDER from xtuner.dataset.huggingface import process_hf_dataset, build_origin_dataset import torchvision.transforms as T from xtuner.utils import DEFAULT_IMAGE_TOKEN from torchvision.transforms.functional import InterpolationMode from .encode_fn import video_lisa_encode_fn from .utils import dynamic_preprocess from .grand_process import glamm_grand_map_fn class GranDDataset(Dataset): 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, image_folder, json_folder=None, tokenizer=None, max_length=8196, special_tokens=None, template_map_fn=None, extra_image_processor=None, lazy=True, repeats=1, single_image_mode=False, image_list_save_path='./work_dirs/grand_image.json', json_list_save_path='./work_dirs/grand_jsons.json', ): super().__init__() assert lazy self.lazy = lazy self.max_length = max_length self.image_list_save_path = image_list_save_path self.json_list_save_path = json_list_save_path json_files, image_path_dict = self.json_file_preprocess(image_folder, json_folder) self.json_data = json_files self.image_path_dict = image_path_dict self.image_folder = image_folder self.tokenizer = BUILDER.build(tokenizer) if special_tokens is not None: self.tokenizer.add_tokens(special_tokens, special_tokens=True) self.template_map_fn = template_map_fn if isinstance(self.template_map_fn, dict) and self.lazy: _type = self.template_map_fn['type'] del self.template_map_fn['type'] self.template_map_fn = _type(**self.template_map_fn) if extra_image_processor is not None: self.extra_image_processor = BUILDER.build(extra_image_processor) self.repeats = repeats self._system = '' 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) ]) if special_tokens is not None: self.tokenizer.add_tokens(special_tokens, special_tokens=True) self.single_image_mode = single_image_mode def json_file_preprocess(self, image_folder, json_folder): # list jsons print("Processing GRAND json files !!!") if os.path.exists(self.json_list_save_path): with open(self.json_list_save_path, 'r') as f: json_files = json.load(f) else: json_files = os.listdir(json_folder) _json_files = [] for _file in json_files: if '.json' in _file: _json_files.append(os.path.join(json_folder, _file)) json_files = _json_files with open(self.json_list_save_path, 'w') as f: json.dump(json_files, f) print(f"Finished, {len(json_files)} json files !") # list images print("Processing GRAND image files !!!") if os.path.exists(self.image_list_save_path): with open(self.image_list_save_path, 'r') as f: image_path_dict = json.load(f) else: sub_folders = os.listdir(image_folder) _sub_folders = [] for folder_name in sub_folders: if 'sa_00' in folder_name: _sub_folders.append(folder_name) sub_folders = _sub_folders sub_folders = [os.path.join(image_folder, folder_name) for folder_name in sub_folders] image_path_dict = {} for sub_folder in sub_folders: files = os.listdir(sub_folder) for _file in files: if '.jpg' in _file: image_path_dict[_file] = os.path.join(sub_folder, _file) with open(self.image_list_save_path, 'w') as f: json.dump(image_path_dict, f) print(f"Finished, {len(image_path_dict)} image files !") return json_files, image_path_dict @property def modality_length(self): length_list = [10000] * len(self.json_data) return length_list * self.repeats def __len__(self): return len(self.json_data) * self.repeats def real_len(self): return len(self.json_data) def decode_mask(self, object_masks, ori_height, ori_width): binary_masks = [] for object_mask in object_masks: binary_mask = np.zeros((ori_height, ori_width), dtype=np.uint8) for seg in object_mask: m = mask.decode(seg) m = m.astype(np.uint8) binary_mask += m.squeeze() binary_masks.append(binary_mask) if len(binary_masks) == 0: return None masks = np.stack(binary_masks, axis=0) masks = torch.from_numpy(masks) return masks def dataset_map_fn(self, data_dict): data_dict = glamm_grand_map_fn(data_dict) return data_dict def replace_image_str(self, data_dict, image_str): data_dict['conversation'][0]['input'] = \ data_dict['conversation'][0]['input'].replace(DEFAULT_IMAGE_TOKEN, image_str) return data_dict def __getitem__(self, index): index = index % self.real_len() json_file_path = self.json_data[index] with open(json_file_path, 'r') as f: json_dict = json.load(f) image_name = list(json_dict.keys())[0] if image_name not in self.image_path_dict.keys(): return self.__getitem__(random.randint(0, len(self.json_data) - 1)) image_path = self.image_path_dict[image_name] json_dict = json_dict[image_name] # parse datasets result = self.dataset_map_fn(json_dict) json_dict.update(result) data_dict = json_dict data_dict['image'] = image_path # process image image_file = data_dict['image'] try: image = Image.open(os.path.join(self.image_folder, image_file)).convert('RGB') except: return self.__getitem__(random.randint(0, len(self.json_data) - 1)) ori_width, ori_height = image.size 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() 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) pixel_values = [self.transformer(image) for image in images] pixel_values = torch.stack(pixel_values) 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}' data_dict = self.replace_image_str(data_dict, image_token_str) result = self.template_map_fn(data_dict) data_dict.update(result) result = video_lisa_encode_fn(data_dict, tokenizer=self.tokenizer, max_length=self.max_length, with_image_token=True) data_dict.update(result) # process mask data_dict['masks'] = self.decode_mask(data_dict['masks'], ori_height=ori_height, ori_width=ori_width) if data_dict['masks'] is None: return self.__getitem__(random.randint(0, len(self.json_data) - 1)) return data_dict