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import logging
import os
import torch
from datasets import Dataset as HFDataset
from datasets import DatasetDict, load_from_disk
from mmengine import print_log
from mmengine.config import Config, ConfigDict
from PIL import Image
from torch.utils.data import Dataset
from pycocotools import mask
import numpy as np
import torch.nn.functional as F
from xtuner.registry import BUILDER
from .utils import expand2square, expand2square_mask, expand2square_points
from xtuner.dataset.huggingface import process_hf_dataset, build_origin_dataset
import copy
from xtuner.dataset.utils import encode_fn
class MDPVPointDetailedCaptionDataset(Dataset):
def __init__(self,
image_folder,
image_processor,
data_path=None,
tokenizer=None,
offline_processed_text_folder=None,
max_dataset_length=None,
dataset_map_fn=None,
template_map_fn=None,
max_length=2048,
pad_image_to_square=False,
num_proc=32,
lazy=False,
repeats=1):
super().__init__()
assert offline_processed_text_folder or (data_path and tokenizer)
self.lazy = lazy
self.max_length = max_length
self.dataset_map_fn = dataset_map_fn
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 offline_processed_text_folder and data_path:
print_log(
'Both `offline_processed_text_folder` and '
'`data_path` are set, and we load dataset from'
'`offline_processed_text_folder` '
f'({offline_processed_text_folder})',
logger='current',
level=logging.WARNING)
if offline_processed_text_folder is not None:
raise NotImplementedError
else:
json_data = self.json_file_preprocess(data_path)
self.json_data = json_data
json_data = self.filter_hf_require_infos(json_data)
hf_json_data = DatasetDict({'train': HFDataset.from_list(json_data)})
if self.lazy:
self.text_data = build_origin_dataset(json_data, 'train')
else:
self.text_data = process_hf_dataset(
dataset=hf_json_data,
tokenizer=tokenizer,
max_length=max_length,
dataset_map_fn=dataset_map_fn,
template_map_fn=template_map_fn,
split='train',
max_dataset_length=max_dataset_length,
remove_unused_columns=False,
pack_to_max_length=False,
with_image_token=True,
map_num_proc=num_proc, # because limited mem
)
self.image_folder = image_folder
size = image_processor.crop_size
if isinstance(size, int):
self.image_h, self.image_w = size, size
else:
self.image_w, self.image_h = size
if isinstance(image_processor, dict) or isinstance(
image_processor, Config) or isinstance(image_processor,
ConfigDict):
self.image_processor = BUILDER.build(image_processor)
else:
self.image_processor = image_processor
self.pad_image_to_square = pad_image_to_square
self.down_ratio = 1
self.repeats = repeats
self.tokenizer = tokenizer
def filter_hf_require_infos(self, dataset_infos):
ret = []
for dataset_info in dataset_infos:
conversations = dataset_info["conversations"]
image = dataset_info['image'].split('/')[-1]
num_marks = len(dataset_info['points'])
required_info = {'image': image,
'conversations': conversations,
'num_marks': num_marks}
ret.append(required_info)
return ret
def json_file_preprocess(self, data_path):
with open(data_path, 'r') as f:
json_file = json.load(f)
return json_file
@property
def modality_length(self):
length_list = []
for data_dict in self.text_data:
if self.lazy:
cur_len = 100
else:
cur_len = len(data_dict['input_ids'])
if data_dict.get('image', None) is None:
cur_len = -cur_len
length_list.append(cur_len)
return length_list * self.repeats
def __len__(self):
return len(self.text_data) * self.repeats
def real_len(self):
return len(self.text_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:
rles = mask.frPyObjects([seg], ori_height, ori_width)
m = mask.decode(rles)
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)
if self.pad_image_to_square:
masks = expand2square_mask(masks)
masks = torch.from_numpy(masks)
masks = F.interpolate(masks.unsqueeze(0), size=(self.image_h // self.down_ratio, self.image_w // self.down_ratio), mode='nearest').squeeze(0)
return masks
def __getitem__(self, index):
index = index % self.real_len()
data_dict = copy.deepcopy(self.json_data[index])
data_dict.update(self.text_data[index])
if self.lazy:
result = self.dataset_map_fn(data_dict)
data_dict.update(result)
result = self.template_map_fn(data_dict)
data_dict.update(result)
result = encode_fn(data_dict, tokenizer=self.tokenizer, max_length=self.max_length, with_image_token=True)
data_dict.update(result)
assert 'image' in data_dict.keys()
if data_dict.get('image', None) is not None:
image_file = data_dict['image']
image_path = os.path.join(self.image_folder, image_file)
if not os.path.exists(image_path) and "VG" in self.image_folder:
image_path = os.path.join(self.image_folder + "_2", image_file)
image = Image.open(image_path).convert('RGB')
ori_width, ori_height = image.size
if self.pad_image_to_square:
image = expand2square(
image,
tuple(
int(x * 255) for x in self.image_processor.image_mean))
image = self.image_processor.preprocess(
image, return_tensors='pt')['pixel_values'][0]
data_dict['pixel_values'] = image
# process and get masks
points = data_dict["points"]
points = np.array(points)
if self.pad_image_to_square:
points = expand2square_points(points, height=ori_height, width=ori_width)
points[:, 0] = points[:, 0] / max(ori_height, ori_width) * self.image_w
points[:, 1] = points[:, 1] / max(ori_height, ori_width) * self.image_h
else:
points[:, 0] = points[:, 0] / ori_width * self.image_w
points[:, 1] = points[:, 1] / ori_height * self.image_h
data_dict['points'] = torch.from_numpy(points)
if data_dict['points'] is None:
return self.__getitem__(0)
data_dict['masks'] = None
data_dict['regions'] = None
else:
if hasattr(self.image_processor, 'crop_size'):
crop_size = self.image_processor.crop_size
else:
crop_size = self.image_processor.size
data_dict['pixel_values'] = torch.zeros(3, crop_size['height'],
crop_size['width'])
data_dict['masks'] = None
data_dict['regions'] = None
data_dict['points'] = None
return data_dict
class MDPVPointBriefCaptionDataset(MDPVPointDetailedCaptionDataset):
def __init__(self,
image_folder,
image_processor,
data_path=None,
tokenizer=None,
offline_processed_text_folder=None,
max_dataset_length=None,
dataset_map_fn=None,
template_map_fn=None,
max_length=2048,
pad_image_to_square=False,
num_proc=32,
lazy=False,
repeats=1):
super().__init__(
image_folder=image_folder,
image_processor=image_processor,
data_path=data_path,
tokenizer=tokenizer,
offline_processed_text_folder=offline_processed_text_folder,
max_dataset_length=max_dataset_length,
dataset_map_fn=dataset_map_fn,
template_map_fn=template_map_fn,
max_length=max_length,
pad_image_to_square=pad_image_to_square,
num_proc=num_proc,
lazy=lazy,
repeats=repeats
)
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