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import json
import os
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 .gcg_process import glamm_openpsg_map_fn, glamm_flickr_map_fn, glamm_granf_map_fn, glamm_refcocog_map_fn
class GCGDataset(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,
data_path=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,
):
super().__init__()
assert lazy
self.lazy = lazy
self.max_length = max_length
json_data = self.json_file_preprocess(data_path)
json_data = DatasetDict({'train': HFDataset.from_list(json_data)})
self.text_data = build_origin_dataset(json_data, 'train')
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, data_path):
with open(data_path, 'r') as f:
json_data = json.load(f)
return json_data
@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)
masks = torch.from_numpy(masks)
return masks
def dataset_map_fn(self, data_dict):
data_dict = glamm_refcocog_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()
data_dict = copy.deepcopy(self.text_data[index])
# parse datasets
result = self.dataset_map_fn(data_dict)
data_dict.update(result)
# process image
image_file = data_dict['image']
image = Image.open(os.path.join(self.image_folder,
image_file)).convert('RGB')
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__(0)
return data_dict
class RefCOCOgGCGDataset(GCGDataset):
def __init__(self,
image_folder,
data_path=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,
):
super().__init__(
image_folder=image_folder,
data_path=data_path,
tokenizer=tokenizer,
max_length=max_length,
special_tokens=special_tokens,
template_map_fn=template_map_fn,
extra_image_processor=extra_image_processor,
lazy=lazy,
repeats=repeats,
single_image_mode=single_image_mode,
)
def json_file_preprocess(self, data_path):
json_data = json.load(open(data_path))
# convert {id: dict} to dict(..., id=xx)
for idx in range(len(json_data)):
id = list(json_data[idx].keys())[0]
json_data[idx] = json_data[idx][id]
json_data[idx].update({'id': id})
return json_data
class GranDfGCGDataset(GCGDataset):
def __init__(self,
image_folder,
data_path=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,
):
super().__init__(
image_folder=image_folder,
data_path=data_path,
tokenizer=tokenizer,
max_length=max_length,
special_tokens=special_tokens,
template_map_fn=template_map_fn,
extra_image_processor=extra_image_processor,
lazy=lazy,
repeats=repeats,
single_image_mode=single_image_mode,
)
def dataset_map_fn(self, data_dict):
data_dict = glamm_granf_map_fn(data_dict)
return data_dict
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 rle in object_mask:
m = mask.decode(rle).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
class OpenPsgGCGDataset(GranDfGCGDataset):
def __init__(self,
image_folder,
data_path=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,
):
super().__init__(
image_folder=image_folder,
data_path=data_path,
tokenizer=tokenizer,
max_length=max_length,
special_tokens=special_tokens,
template_map_fn=template_map_fn,
extra_image_processor=extra_image_processor,
lazy=lazy,
repeats=repeats,
single_image_mode=single_image_mode,
)
def dataset_map_fn(self, data_dict):
data_dict = glamm_openpsg_map_fn(data_dict)
return data_dict
class FlickrGCGDataset(GCGDataset):
def __init__(self,
image_folder,
data_path=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,
):
super().__init__(
image_folder=image_folder,
data_path=data_path,
tokenizer=tokenizer,
max_length=max_length,
special_tokens=special_tokens,
template_map_fn=template_map_fn,
extra_image_processor=extra_image_processor,
lazy=lazy,
repeats=repeats,
single_image_mode=single_image_mode,
)
def dataset_map_fn(self, data_dict):
data_dict = glamm_flickr_map_fn(data_dict)
return data_dict
def json_file_preprocess(self, data_path):
def filter_images(data_infos, min_size):
return [i for i, info in enumerate(data_infos) if min(info['width'], info['height']) >= min_size]
# convert {id: dict} to dict(..., id=xx)
from pycocotools.coco import COCO
self.coco = COCO(data_path)
self.image_ids = self.coco.getImgIds()
data_infos = []
total_ann_ids = []
removed_img_count = 0
for img_id in self.image_ids:
info = self.coco.loadImgs([img_id])[0]
if len(info['caption'].split(' ')) < 3:
removed_img_count += 1
continue
info['filename'] = info['file_name'].split('_')[-1]
info['height'] = int(info['height'])
info['width'] = int(info['width'])
data_infos.append(info)
ann_ids = self.coco.getAnnIds(imgIds=[img_id])
total_ann_ids.extend(ann_ids)
assert len(set(total_ann_ids)) == len(total_ann_ids), f"Non-unique annotation IDs in '{data_path}'!"
print(f'Removed {removed_img_count} images.')
data_infos = [data_infos[i] for i in filter_images(data_infos, min_size=32)]
# obtain_annotations
for data_info in data_infos:
ann_ids = self.coco.getAnnIds(imgIds=data_info['id'])
ann_info = self.coco.loadAnns(ann_ids)
data_info.update({'ann_info': ann_info})
return data_infos
def decode_mask(self, object_masks, ori_height, ori_width):
binary_masks = []
for object_mask in object_masks:
binary_mask = mask.decode(object_mask).astype(np.uint8)
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 |