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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 pycocotools import mask |
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import numpy as np |
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import torch.nn.functional as F |
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import copy |
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from xtuner.registry import BUILDER |
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from .utils import expand2square, expand2square_mask |
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from xtuner.dataset.huggingface import process_hf_dataset, build_origin_dataset |
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from xtuner.dataset.utils import encode_fn |
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class DecoupledGCGDataset(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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num_proc=32, |
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lazy=False, |
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repeats=1, |
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mode='given_description'): |
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super().__init__() |
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assert offline_processed_text_folder or (data_path and tokenizer) |
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self.lazy = lazy |
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self.max_length = max_length |
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self.dataset_map_fn = dataset_map_fn |
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self.template_map_fn = template_map_fn |
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if isinstance(self.template_map_fn, dict) and self.lazy: |
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_type = self.template_map_fn['type'] |
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del self.template_map_fn['type'] |
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self.template_map_fn = _type(**self.template_map_fn) |
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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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raise NotImplementedError |
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else: |
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json_data = self.json_file_preprocess(data_path) |
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json_data = DatasetDict({'train': HFDataset.from_list(json_data)}) |
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if self.lazy: |
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self.text_data = build_origin_dataset(json_data, 'train') |
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else: |
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self.text_data = process_hf_dataset( |
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dataset=json_data, |
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tokenizer=tokenizer, |
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max_length=max_length, |
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dataset_map_fn=dataset_map_fn, |
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template_map_fn=template_map_fn, |
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split='train', |
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max_dataset_length=max_dataset_length, |
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remove_unused_columns=False, |
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pack_to_max_length=False, |
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with_image_token=True, |
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map_num_proc=num_proc, |
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) |
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self.image_folder = image_folder |
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size = image_processor.crop_size |
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if isinstance(size, int): |
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self.image_h, self.image_w = size, size |
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else: |
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self.image_w, self.image_h = size |
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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.pad_image_to_square = pad_image_to_square |
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self.down_ratio = 1 |
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self.repeats = repeats |
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self.mode = mode |
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self.tokenizer = tokenizer |
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def json_file_preprocess(self, data_path): |
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with open(data_path, 'r') as f: |
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json_data = json.load(f) |
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return json_data |
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@property |
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def modality_length(self): |
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length_list = [] |
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for data_dict in self.text_data: |
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if self.lazy: |
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cur_len = 100 |
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else: |
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cur_len = len(data_dict['input_ids']) |
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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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length_list = length_list * self.repeats |
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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 real_len(self): |
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return len(self.text_data) |
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def decode_mask(self, object_masks, ori_height, ori_width): |
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binary_masks = [] |
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for object_mask in object_masks: |
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binary_mask = np.zeros((ori_height, ori_width), dtype=np.uint8) |
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for seg in object_mask: |
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rles = mask.frPyObjects([seg], ori_height, ori_width) |
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m = mask.decode(rles) |
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m = m.astype(np.uint8) |
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binary_mask += m.squeeze() |
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binary_masks.append(binary_mask) |
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if len(binary_masks) == 0: |
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return None |
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masks = np.stack(binary_masks, axis=0) |
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if self.pad_image_to_square: |
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masks = expand2square_mask(masks) |
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masks = torch.from_numpy(masks) |
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masks = F.interpolate(masks.unsqueeze(0), size=(self.image_h // self.down_ratio, self.image_w // self.down_ratio), mode='nearest').squeeze(0) |
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return masks |
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def __getitem__(self, index): |
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index = index % self.real_len() |
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data_dict = copy.deepcopy(self.text_data[index]) |
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if self.lazy: |
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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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assert 'image' in data_dict.keys() |
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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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ori_width, ori_height = image.size |
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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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data_dict['masks'] = self.decode_mask(data_dict['masks'], ori_height=ori_height, ori_width=ori_width) |
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assert self.mode in ['given_objects', 'given_description'] |
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if self.mode == 'given_objects': |
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data_dict['regions'] = copy.deepcopy(data_dict['masks']) |
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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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data_dict['masks'] = None |
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return data_dict |
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class DecoupledRefCOCOgGCGDataset(DecoupledGCGDataset): |
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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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repeats=1, |
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mode='given_description', |
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): |
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super().__init__( |
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image_folder=image_folder, |
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image_processor=image_processor, |
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data_path=data_path, |
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tokenizer=tokenizer, |
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offline_processed_text_folder=offline_processed_text_folder, |
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max_dataset_length=max_dataset_length, |
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dataset_map_fn=dataset_map_fn, |
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template_map_fn=template_map_fn, |
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max_length=max_length, |
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pad_image_to_square=pad_image_to_square, |
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lazy=lazy, |
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repeats=repeats, |
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mode=mode, |
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) |
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def json_file_preprocess(self, data_path): |
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json_data = json.load(open(data_path)) |
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for idx in range(len(json_data)): |
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id = list(json_data[idx].keys())[0] |
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json_data[idx] = json_data[idx][id] |
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json_data[idx].update({'id': id}) |
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return json_data |
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class DecoupledGranDfGCGDataset(DecoupledGCGDataset): |
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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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num_proc=4, |
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lazy=False, |
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repeats=1, |
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mode='given_description'): |
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super().__init__( |
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image_folder=image_folder, |
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image_processor=image_processor, |
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data_path=data_path, |
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tokenizer=tokenizer, |
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offline_processed_text_folder=offline_processed_text_folder, |
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max_dataset_length=max_dataset_length, |
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dataset_map_fn=dataset_map_fn, |
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template_map_fn=template_map_fn, |
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max_length=max_length, |
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pad_image_to_square=pad_image_to_square, |
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num_proc=num_proc, |
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lazy=lazy, |
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repeats=repeats, |
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mode=mode |
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) |
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def decode_mask(self, object_masks, ori_height, ori_width): |
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binary_masks = [] |
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for object_mask in object_masks: |
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binary_mask = np.zeros((ori_height, ori_width), dtype=np.uint8) |
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for rle in object_mask: |
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m = mask.decode(rle).astype(np.uint8) |
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binary_mask += m.squeeze() |
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binary_masks.append(binary_mask) |
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if len(binary_masks) == 0: |
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return None |
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masks = np.stack(binary_masks, axis=0) |
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if self.pad_image_to_square: |
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masks = expand2square_mask(masks) |
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masks = torch.from_numpy(masks) |
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masks = F.interpolate(masks.unsqueeze(0), size=(self.image_h // self.down_ratio, self.image_w // self.down_ratio), mode='nearest').squeeze(0) |
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return masks |
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class DecoupledOpenPsgGCGDataset(DecoupledGranDfGCGDataset): |
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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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num_proc=4, |
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lazy=False, |
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repeats=1, |
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mode='given_description'): |
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super().__init__( |
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image_folder=image_folder, |
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image_processor=image_processor, |
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data_path=data_path, |
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tokenizer=tokenizer, |
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offline_processed_text_folder=offline_processed_text_folder, |
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max_dataset_length=max_dataset_length, |
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dataset_map_fn=dataset_map_fn, |
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template_map_fn=template_map_fn, |
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max_length=max_length, |
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pad_image_to_square=pad_image_to_square, |
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num_proc=num_proc, |
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lazy=lazy, |
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repeats=repeats, |
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mode=mode |
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) |
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class DecoupledFlickrGCGDataset(DecoupledGCGDataset): |
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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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num_proc=4, |
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lazy=False, |
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repeats=1, |
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mode='given_description' |
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): |
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super().__init__( |
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image_folder=image_folder, |
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image_processor=image_processor, |
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data_path=data_path, |
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tokenizer=tokenizer, |
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offline_processed_text_folder=offline_processed_text_folder, |
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max_dataset_length=max_dataset_length, |
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dataset_map_fn=dataset_map_fn, |
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template_map_fn=template_map_fn, |
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max_length=max_length, |
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pad_image_to_square=pad_image_to_square, |
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num_proc=num_proc, |
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lazy=lazy, |
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repeats=repeats, |
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mode=mode |
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) |
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def json_file_preprocess(self, data_path): |
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def filter_images(data_infos, min_size): |
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return [i for i, info in enumerate(data_infos) if min(info['width'], info['height']) >= min_size] |
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from pycocotools.coco import COCO |
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self.coco = COCO(data_path) |
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self.image_ids = self.coco.getImgIds() |
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data_infos = [] |
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total_ann_ids = [] |
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removed_img_count = 0 |
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for img_id in self.image_ids: |
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info = self.coco.loadImgs([img_id])[0] |
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if len(info['caption'].split(' ')) < 3: |
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removed_img_count += 1 |
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continue |
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info['filename'] = info['file_name'].split('_')[-1] |
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info['height'] = int(info['height']) |
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info['width'] = int(info['width']) |
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data_infos.append(info) |
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ann_ids = self.coco.getAnnIds(imgIds=[img_id]) |
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total_ann_ids.extend(ann_ids) |
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assert len(set(total_ann_ids)) == len(total_ann_ids), f"Non-unique annotation IDs in '{data_path}'!" |
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print(f'Removed {removed_img_count} images.') |
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data_infos = [data_infos[i] for i in filter_images(data_infos, min_size=32)] |
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for data_info in data_infos: |
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ann_ids = self.coco.getAnnIds(imgIds=data_info['id']) |
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ann_info = self.coco.loadAnns(ann_ids) |
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data_info.update({'ann_info': ann_info}) |
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return data_infos |
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def decode_mask(self, object_masks, ori_height, ori_width): |
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binary_masks = [] |
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for object_mask in object_masks: |
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binary_mask = mask.decode(object_mask).astype(np.uint8) |
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binary_masks.append(binary_mask) |
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if len(binary_masks) == 0: |
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return None |
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masks = np.stack(binary_masks, axis=0) |
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if self.pad_image_to_square: |
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masks = expand2square_mask(masks) |
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masks = torch.from_numpy(masks) |
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masks = F.interpolate(masks.unsqueeze(0), size=(self.image_h // self.down_ratio, self.image_w // self.down_ratio), mode='nearest').squeeze(0) |
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return masks |
