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import logging
import os
from collections import OrderedDict
import pycocotools.mask as maskUtils
import mmengine
import torch
from mmengine import print_log
import numpy as np
from mmengine.dist import master_only
from xtuner.registry import BUILDER
from vlm.datasets.evaluation.base_eval_dataset import BaseEvalDataset
from vlm.utils import VideoReader
from .encode_fn import video_lisa_encode_multi_conv_fn
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode, to_pil_image
SEG_QUESTIONS = [
"Can you segment the {class_name} in this image?",
"Please segment {class_name} in this image.",
"What is {class_name} in this image? Please respond with segmentation mask.",
"What is {class_name} in this image? Please output segmentation mask.",
"Can you segment the {class_name} in this image",
"Please segment {class_name} in this image",
"What is {class_name} in this image? Please respond with segmentation mask",
"What is {class_name} in this image? Please output segmentation mask",
"Could you provide a segmentation mask for the {class_name} in this image?",
"Please identify and segment the {class_name} in this image.",
"Where is the {class_name} in this picture? Please respond with a segmentation mask.",
"Can you highlight the {class_name} in this image with a segmentation mask?",
"Could you provide a segmentation mask for the {class_name} in this image",
"Please identify and segment the {class_name} in this image",
"Where is the {class_name} in this picture? Please respond with a segmentation mask",
"Can you highlight the {class_name} in this image with a segmentation mask",
]
ANSWER_LIST = [
"It is [SEG].",
"Sure, [SEG].",
"Sure, it is [SEG].",
"Sure, the segmentation result is [SEG].",
"[SEG].",
]
def decode_masklet(masklet):
masks = []
for _rle in masklet:
mask = maskUtils.decode(_rle)
masks.append(mask)
return masks
def multi_template_fn(conversations, template_map):
for conv in conversations:
for i, single_turn_conversation in enumerate(conv):
input = single_turn_conversation.get('input', '')
if input is None:
input = ''
input_text = template_map.INSTRUCTION.format(input=input, round=i + 1)
system = single_turn_conversation.get('system', '')
if system != '' and system is not None:
system = template_map.SYSTEM.format(system=system)
input_text = system + input_text
single_turn_conversation['input'] = input_text
if template_map.get('SUFFIX', None):
output_text = single_turn_conversation.get('output', '')
output_text += template_map.SUFFIX
single_turn_conversation['output'] = output_text
# SUFFIX_AS_EOS is False ==> need_eos_token is True
single_turn_conversation['need_eos_token'] = \
not template_map.get('SUFFIX_AS_EOS', False)
single_turn_conversation['sep'] = template_map.get('SEP', '')
class VideoCustomDataset(BaseEvalDataset):
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
IMG_CONTEXT_TOKEN = '<IMG_CONTEXT>'
IMG_START_TOKEN = '<img>'
IMG_END_TOKEN = '</img>'
FAST_IMG_CONTEXT_TOKEN = '<FAST_IMG_CONTEXT>'
FAST_IMG_START_TOKEN = '<fast_img>'
FAST_IMG_END_TOKEN = '</fast_img>'
METAINFO: dict = dict(name='custom')
def __init__(self,
image_folder,
expression_file,
extra_image_processor=None,
tokenizer=None,
offline_processed_text_folder=None,
template_map_fn=None,
max_length=2048,
lazy=True,
special_tokens=None,
# eval settings
num_frames=5,
# eval name
eval_name=None,
# fast cfg
use_fast=False,
fast_pool_size=2,
n_fast_images=50,
fast_token_after_question=False,
):
super().__init__()
# check the config
assert lazy is True
self.tokenizer = BUILDER.build(tokenizer)
self.lazy = lazy
self.max_length = max_length
self.template_map = template_map_fn['template']
if offline_processed_text_folder and expression_file:
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:
exp_json_file = mmengine.load(expression_file)
vid_names = mmengine.list_dir_or_file(image_folder, list_dir=False, suffix='mp4')
vid_tags = list(map(lambda x: x.split('.')[0], vid_names))
json_data = OrderedDict()
for vid_tag in vid_tags:
assert vid_tag not in json_data
if not vid_tag in exp_json_file:
continue
exp_json_current = exp_json_file[vid_tag]
json_data[vid_tag] = {
'video_id': vid_tag,
'video_path': os.path.join(image_folder, f"{vid_tag}.mp4"),
'anno_path': os.path.join(image_folder, f"{vid_tag}_manual.json"),
'objects': exp_json_current['objects'],
}
self.data_infos = json_data
self.index2key = list(self.data_infos.keys())
self.image_folder = image_folder
if extra_image_processor is not None:
self.extra_image_processor = BUILDER.build(extra_image_processor)
self._system = ''
self.downsample_ratio = 0.5
self.image_size = 448
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.num_frames = num_frames
self.use_fast = use_fast
self.fast_pool_size = fast_pool_size
self.fast_token_after_question = fast_token_after_question
self.n_fast_images = n_fast_images # Dummy
# save to json
if eval_name is None:
eval_name = 'results'
self.eval_name = eval_name
# vid
self.vid_interval = 4
def __len__(self):
return len(self.data_infos)
@property
def modality_length(self):
length_list = []
for data_dict in self.data_infos:
cur_len = 10000
length_list.append(cur_len)
return length_list
def dataset_map_fn(self, text_prompts, num_frames, num_fast_frames=0):
text_dict = self.prepare_text(num_frames, text_prompts, num_image_tokens=self.patch_token, num_fast_frames=num_fast_frames)
ret = {'conversation': text_dict['conversation']}
return ret
def prepare_text(self, n_frames, expressions, num_image_tokens=256, num_fast_frames=0):
if self.use_fast and not self.fast_token_after_question:
fast_frame_token_str = f'{self.FAST_IMG_START_TOKEN}' \
f'{self.FAST_IMG_CONTEXT_TOKEN * num_fast_frames * self.fast_pool_size * self.fast_pool_size}' \
f'{self.FAST_IMG_END_TOKEN}' + '\n'
else:
fast_frame_token_str = ''
frame_token_str = f'{self.IMG_START_TOKEN}' \
f'{self.IMG_CONTEXT_TOKEN * num_image_tokens}' \
f'{self.IMG_END_TOKEN}'
if self.fast_token_after_question:
assert self.use_fast
after_question_str = f'{self.FAST_IMG_START_TOKEN}' \
f'{self.FAST_IMG_CONTEXT_TOKEN * num_fast_frames * self.fast_pool_size * self.fast_pool_size}' \
f'{self.FAST_IMG_END_TOKEN}'
else:
after_question_str = ''
questions = []
for i, exp in enumerate(expressions):
# the exp is a question
if '?' in exp:
questions.append(exp)
else:
exp = exp.replace('.', '').strip()
# EVAL: Use the first question all the time.
# question_template = random.choice(SEG_QUESTIONS)
question_template = SEG_QUESTIONS[0]
questions.append(question_template.format(class_name=exp.lower()))
eval_conversation_list = []
for i, question in enumerate(questions):
qa_list = []
frame_tokens = frame_token_str + '\n'
frame_tokens = frame_tokens * n_frames
frame_tokens = frame_tokens.strip()
qa_list.append(
{'from': 'human', 'value': fast_frame_token_str + frame_tokens + question + after_question_str}
)
qa_list.append(
{'from': 'gpt', 'value': ''}
)
assert len(qa_list) == 2
input = ''
conversation = []
for msg in qa_list:
if msg['from'] == 'human':
input += msg['value']
elif msg['from'] == 'gpt':
if msg['value'] == '':
conversation.append({'input': input,})
else:
conversation.append({'input': input, 'output': msg['value']})
input = ''
else:
raise NotImplementedError
# add system information
conversation[0].update({'system': self._system})
eval_conversation_list.append(conversation)
return {'conversation': eval_conversation_list}
def __getitem__(self, index):
data_info = self.data_infos[self.index2key[index]]
obj_ids = data_info['objects'].keys()
video_path = data_info['video_path']
vid_frames = VideoReader(video_path)[::self.vid_interval]
mask_json_file = data_info['anno_path']
if os.path.exists(mask_json_file):
mask_data = mmengine.load(mask_json_file)
else:
mask_data = None
gt_masks = []
text_prompts = []
for obj_id in obj_ids:
# obj_id_int = int(obj_id)
# mask_ind = mask_data['masklet_id'].index(obj_id_int)
# masks = decode_masklet([_[mask_ind] for _ in mask_data['masklet']])
text_prompt = data_info['objects'][obj_id]['exp']
# gt_masks.append(masks)
text_prompts.append(text_prompt)
data_dict = self.dataset_map_fn(text_prompts, self.num_frames, num_fast_frames=len(vid_frames))
multi_template_fn(data_dict['conversation'], self.template_map)
result = video_lisa_encode_multi_conv_fn(data_dict, input_ids_with_output=False, tokenizer=self.tokenizer, max_length=self.max_length)
data_dict.update(result)
pixel_values = []
extra_pixel_values = []
if self.use_fast:
fast_pixel_values = []
ori_width, ori_height = None, None
for frame_idx, frame_image in enumerate(vid_frames):
if ori_height is None:
ori_height, ori_width = frame_image.shape[0], frame_image.shape[1]
else:
assert ori_height == frame_image.shape[0]
assert ori_width == frame_image.shape[1]
frame_image = frame_image[..., ::-1] # BGR (opencv system) to RGB (numpy system)
if self.extra_image_processor is not None:
g_image = np.array(frame_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()
extra_pixel_values.append(g_pixel_values)
if self.use_fast:
img = to_pil_image(frame_image, mode='RGB')
img = self.transformer(img)
fast_pixel_values.append(img)
if frame_idx < self.num_frames:
img = to_pil_image(frame_image, mode='RGB')
img = self.transformer(img)
pixel_values.append(img)
pixel_values = torch.stack(pixel_values, dim=0) # (n_f, 3, h, w)
data_dict['pixel_values'] = pixel_values
if self.use_fast:
fast_pixel_values = torch.stack(fast_pixel_values, dim=0) # (n_f, 3, h, w)
data_dict['fast_pixel_values'] = fast_pixel_values
if self.extra_image_processor is not None:
data_dict['g_pixel_values'] = extra_pixel_values
data_dict['type'] = 'video'
data_dict['video_id'] = index
data_dict['text_prompts'] = text_prompts
data_dict['image_folder'] = self.image_folder
data_dict['ori_height'] = ori_height
data_dict['ori_width'] = ori_width
data_dict['video_path'] = video_path
return data_dict
@master_only
def evaluate(self, results, work_dir):
return {"Dummy": 0}
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