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Zero
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import copy
import random
import glob
import json
import logging
import os
import torch
from mmengine import print_log
from mmengine.config import Config, ConfigDict
from PIL import Image
from torch.utils.data import Dataset
import numpy as np
import torch.nn.functional as F
from pycocotools.coco import COCO
from pycocotools import mask as mask_utils
from xtuner.registry import BUILDER
from xtuner.dataset.utils import encode_fn
from xtuner.dataset.map_fns import llava_map_fn
from projects.glamm.datasets.utils.utils import expand2square
from projects.glamm.datasets.utils.utils import SEG_QUESTIONS, ANSWER_LIST
from projects.glamm.utils import DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from third_parts.mmdet.datasets.refcoco import RefCocoDataset
class ReferSegmDataset(RefCocoDataset):
def __init__(self,
data_root,
ann_file=None,
split_file=None,
image_processor=None,
extra_image_processor=None,
data_prefix=dict(img_path='train2014/'),
tokenizer=None,
template_map_fn=None,
max_length=2048,
pad_image_to_square=False,
num_classes_per_sample=3):
super().__init__(
data_root=data_root,
data_prefix=data_prefix,
pipeline=None,
ann_file=ann_file,
split_file=split_file,
)
self.begin_str = f"""{DEFAULT_IMAGE_TOKEN} provides an overview of the picture.\n"""
self.question_templates = SEG_QUESTIONS
if extra_image_processor is not None:
self.extra_image_processor = BUILDER.build(extra_image_processor)
self.num_classes_per_sample = num_classes_per_sample
self.tokenizer = BUILDER.build(tokenizer)
self.tokenizer.add_tokens(
[DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True
)
reg_tokens = ['<bbox>', '<point>']
segmentation_tokens = ['[SEG]']
phrase_tokens = ['<p>', '</p>']
special_tokens = reg_tokens + segmentation_tokens + phrase_tokens
self.tokenizer.add_tokens(special_tokens, special_tokens=True)
self.max_length = max_length
self.template_map_fn = BUILDER.build(template_map_fn)
self.image_processor = BUILDER.build(image_processor)
size = self.image_processor.crop_size
if isinstance(size, dict):
self.image_w, self.image_h = size['width'], size['height']
self.pad_image_to_square = pad_image_to_square
@property
def modality_length(self):
import pickle
length_list = []
for idx in range(len(self)):
length_list.append(100)
# for idx in range(len(self)):
# if self.serialize_data:
# start_addr = 0 if idx == 0 else self.data_address[idx - 1].item()
# end_addr = self.data_address[idx].item()
# bytes = memoryview(
# self.data_bytes[start_addr:end_addr]) # type: ignore
# data_dict = pickle.loads(bytes)
# else:
# data_dict = copy.deepcopy(self.data_list[idx])
return length_list
def _parse_annotations(self, ann_info):
image_path = ann_info['img_path']
image = Image.open(image_path).convert('RGB')
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()
ann_info['g_pixel_values'] = g_pixel_values
width, 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]
ann_info['pixel_values'] = image
masks, phrases = [], []
instances, text = ann_info['instances'], ann_info['text']
index = np.random.choice(range(len(instances)), min(
len(instances), self.num_classes_per_sample))
for idx in index:
inst = instances[idx]
phrase = text[idx].lower()
phrases.append(phrase)
binary_mask = np.zeros((height, width), dtype=np.uint8)
for seg in inst["mask"]:
rles = mask_utils.frPyObjects([seg], height, width)
m = mask_utils.decode(rles)
m = m.astype(np.uint8)
binary_mask += m.squeeze()
masks.append(binary_mask)
ann_info.update({
'masks': masks,
'phrases': phrases,
})
return ann_info
def __getitem__(self, idx):
data_dict = {}
ann_info = super().__getitem__(idx)
ann_info = self._parse_annotations(ann_info)
data_dict['g_pixel_values'] = ann_info.pop('g_pixel_values')
data_dict['pixel_values'] = ann_info.pop('pixel_values')
if len(ann_info['masks']) == 0:
return self.__getitem__(0)
data_dict['masks'] = torch.from_numpy(
np.stack(ann_info['masks'], axis=0))
conversation = []
for i, phrase in enumerate(ann_info['phrases']):
question = random.choice(SEG_QUESTIONS).format(class_name=phrase)
conversation.append(
{'input': question, 'output': random.choice(ANSWER_LIST)})
data_dict['conversation'] = conversation
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)
return data_dict
if __name__ == '__main__':
from transformers import CLIPImageProcessor, AutoTokenizer
from third_parts.segment_anything.utils.transforms import ResizeLongestSide
pretrained_model = 'MBZUAI/GLaMM-GranD-Pretrained'
llm_name_or_path = 'lmsys/vicuna-7b-v1.5'
tokenizer = dict(
type=AutoTokenizer.from_pretrained,
pretrained_model_name_or_path=llm_name_or_path)
image_processor = dict(
type=CLIPImageProcessor.from_pretrained,
pretrained_model_name_or_path='openai/clip-vit-large-patch14-336')
extra_image_processor = dict(
type=ResizeLongestSide,
target_length=1024,
)
from xtuner.utils.templates import PROMPT_TEMPLATE
prompt_template = PROMPT_TEMPLATE.vicuna
from xtuner.dataset.map_fns import llava_map_fn, template_map_fn_factory, template_map_fn
from projects.glamm.datasets.collate_fns.glamm_collate_fn import glamm_collate_fn
dataset = ReferSegmDataset(
tokenizer=tokenizer,
image_processor=image_processor,
template_map_fn=dict(
type=template_map_fn_factory, template=prompt_template),
extra_image_processor=extra_image_processor,
data_root='data/coco/',
data_prefix=dict(img_path='train2014/'),
ann_file='refcoco+/instances.json',
split_file='refcoco+/refs(unc).p',
)
for i in range(1000):
dataset[i]
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