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Zero
File size: 8,978 Bytes
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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_CONTEXT>'
IMG_START_TOKEN = '<img>'
IMG_END_TOKEN = '</img>'
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 |