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import json |
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import logging |
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import os |
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import torch |
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from datasets import Dataset as HFDataset |
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from datasets import DatasetDict, load_from_disk |
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from mmengine import print_log |
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from mmengine.config import Config, ConfigDict |
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from PIL import Image |
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from torch.utils.data import Dataset |
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from xtuner.registry import BUILDER |
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from xtuner.dataset.utils import expand2square |
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from .encode_fns import encode_fn |
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from xtuner.dataset.llava import load_jsonl |
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from xtuner.dataset.huggingface import build_origin_dataset |
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import copy |
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import numpy as np |
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from projects.omg_llava.dataset.utils import expand2square_mask |
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import torch.nn.functional as F |
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class RegionLLaVALazyDataset(Dataset): |
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def __init__(self, |
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image_folder, |
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image_processor, |
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data_path=None, |
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tokenizer=None, |
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offline_processed_text_folder=None, |
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max_dataset_length=None, |
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dataset_map_fn=None, |
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template_map_fn=None, |
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max_length=2048, |
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pad_image_to_square=False, |
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lazy=False, |
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feat_down_ratio=14, |
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repeats=1, |
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): |
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super().__init__() |
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assert offline_processed_text_folder or (data_path and tokenizer) |
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if offline_processed_text_folder and data_path: |
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print_log( |
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'Both `offline_processed_text_folder` and ' |
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'`data_path` are set, and we load dataset from' |
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'`offline_processed_text_folder` ' |
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f'({offline_processed_text_folder})', |
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logger='current', |
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level=logging.WARNING) |
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if offline_processed_text_folder is not None: |
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self.text_data = load_from_disk(offline_processed_text_folder) |
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else: |
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print("Loading {}!!!".format(data_path)) |
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if data_path.endswith('.json'): |
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json_data = json.load(open(data_path)) |
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elif data_path.endswith('.jsonl'): |
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json_data = load_jsonl(data_path) |
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else: |
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raise NotImplementedError |
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print("Loaded {}!!!".format(data_path)) |
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for idx in range(len(json_data)): |
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if "id" in json_data[idx].keys() and isinstance(json_data[idx]['id'], int): |
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json_data[idx]['id'] = str(json_data[idx]['id']) |
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json_data = DatasetDict({'train': HFDataset.from_list(json_data)}) |
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assert max_dataset_length is None, "max_dataset_length is not supported in Lazy mode" |
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self.text_data = build_origin_dataset(json_data, 'train') |
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self.image_folder = image_folder |
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if isinstance(image_processor, dict) or isinstance( |
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image_processor, Config) or isinstance(image_processor, |
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ConfigDict): |
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self.image_processor = BUILDER.build(image_processor) |
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else: |
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self.image_processor = image_processor |
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self.image_size = self.image_processor.crop_size |
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assert self.image_size['height'] == self.image_size['width'] |
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self.image_size = self.image_size['height'] |
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self.feat_size = self.image_size // feat_down_ratio |
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self.pad_image_to_square = pad_image_to_square |
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self.lazy = lazy |
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if lazy: |
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self.tokenizer = tokenizer |
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if isinstance(self.tokenizer, dict) or isinstance(self.tokenizer, Config) or isinstance(self.tokenizer, ConfigDict): |
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self.tokenizer = BUILDER.build(self.tokenizer) |
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self.max_length = max_length |
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self.dataset_map_fn = dataset_map_fn |
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if isinstance(template_map_fn, dict) or isinstance(template_map_fn, Config) or isinstance( |
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template_map_fn, ConfigDict): |
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template_map_fn = BUILDER.build(template_map_fn) |
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self.template_map_fn = template_map_fn |
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self.repeats = repeats |
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@property |
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def modality_length(self): |
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if self.lazy: |
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length_list = [1000] * len(self.text_data) * self.repeats |
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return length_list |
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length_list = [] |
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for data_dict in self.text_data: |
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if 'input_ids' in data_dict.keys(): |
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cur_len = len(data_dict['input_ids']) |
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else: |
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cur_len = 1000 |
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if data_dict.get('image', None) is None: |
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cur_len = -cur_len |
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length_list.append(cur_len) |
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return length_list |
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def __len__(self): |
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return len(self.text_data) * self.repeats |
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def __getitem__(self, index): |
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index = index % len(self.text_data) |
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data_dict = copy.deepcopy(self.text_data[index]) |
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if 'image' not in data_dict.keys() and 'image_name' in data_dict.keys(): |
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data_dict['image'] = data_dict['image_name'] |
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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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image = Image.open(os.path.join(self.image_folder, |
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image_file)).convert('RGB') |
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if self.pad_image_to_square: |
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image = expand2square( |
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image, |
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tuple( |
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int(x * 255) for x in self.image_processor.image_mean)) |
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image = self.image_processor.preprocess( |
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image, return_tensors='pt')['pixel_values'][0] |
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data_dict['pixel_values'] = image |
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else: |
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if hasattr(self.image_processor, 'crop_size'): |
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crop_size = self.image_processor.crop_size |
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else: |
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crop_size = self.image_processor.size |
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data_dict['pixel_values'] = torch.zeros(3, crop_size['height'], |
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crop_size['width']) |
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result = self.dataset_map_fn(data_dict) |
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data_dict.update(result) |
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result = self.template_map_fn(data_dict) |
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data_dict.update(result) |
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result = encode_fn(data_dict, tokenizer=self.tokenizer, max_length=self.max_length, with_image_token=True) |
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data_dict.update(result) |
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if 'region_masks' not in data_dict.keys(): |
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region_masks = np.ones((1, self.feat_size, self.feat_size), dtype=np.uint8) |
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region_masks = torch.from_numpy(region_masks) |
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else: |
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region_masks = data_dict['region_masks'] |
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if self.pad_image_to_square: |
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region_masks = expand2square_mask(region_masks) |
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region_masks = torch.from_numpy(region_masks) |
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region_masks = F.interpolate(region_masks.unsqueeze(0), |
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size=(self.feat_size, self.feat_size), mode='nearest').squeeze(0) |
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data_dict['region_masks'] = region_masks |
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return data_dict |
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class CombineDataset(Dataset): |
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def __init__(self, |
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datasets_cfgs, |
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): |
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super().__init__() |
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self.datasets = [] |
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self.datasets_length = [] |
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self.tokenizer = datasets_cfgs[0].tokenizer |
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tokenizer_type = self.tokenizer['type'] |
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del self.tokenizer['type'] |
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self.tokenizer = tokenizer_type(**self.tokenizer) |
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for i in range(len(datasets_cfgs)): |
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datasets_cfgs[i].tokenizer = self.tokenizer |
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for dataset_cfg in datasets_cfgs: |
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dataset = dataset_cfg['type'] |
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del dataset_cfg['type'] |
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dataset = dataset(**dataset_cfg) |
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self.datasets.append(dataset) |
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self.datasets_length.append(len(dataset)) |
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self.dataset_threthold = [] |
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for i, length in enumerate(self.datasets_length): |
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if i == 0: |
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self.dataset_threthold.append(length) |
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else: |
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self.dataset_threthold.append(length + self.dataset_threthold[i - 1]) |
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np.random.seed(42) |
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self.shuffled_index = np.arange(self.dataset_threthold[-1]) |
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np.random.shuffle(self.shuffled_index) |
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@property |
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def modality_length(self): |
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length_list = [] |
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for dataset in self.datasets: |
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length_list += dataset.modality_length |
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return length_list |
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def __len__(self): |
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return self.dataset_threthold[-1] |
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def __getitem__(self, index): |
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index = int(self.shuffled_index[index]) |
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for i, thred in enumerate(self.dataset_threthold): |
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if index < thred: |
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break |
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if i == 0: |
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_index = index |
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else: |
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_index = index - self.dataset_threthold[i - 1] |
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return self.datasets[i][_index] |
