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import os
import copy
import pickle
import random
import json
import glob
from uniperceiver.utils import comm
from numpy.random import choice
import pyarrow as pa
from PIL import Image
from torchvision import transforms
import numpy as np
from uniperceiver.config import configurable
from uniperceiver.functional import read_np, dict_as_tensor, boxes_to_locfeats
from uniperceiver.tokenization import ClipTokenizer
from ..build import DATASETS_REGISTRY
import torch
from uniperceiver.datasets.custom_transforms import clip_transforms
__all__ = ["VQADataset"]
memorycache = False
try:
if "SLURM_JOB_ID" in os.environ:
import mc
import io
memorycache = True
# print("VQA using memory cache")
else:
# print("missing memory cache")
pass
except:
# print("missing memory cache")
pass
@DATASETS_REGISTRY.register()
class VQADataset:
@configurable
def __init__(
self,
cfg,
dataset_name,
task_type,
stage: str,
anno_folder: str,
ans2label_path: str,
label2ans_path: str,
feats_folder: str,
max_feat_num: int,
max_seq_len: int,
use_global_v: bool,
tokenizer,
tokenizer_name,
use_ceph,
transform,
as_gen,
inf_input,
single_class,
small_val,
block_vq,
data_percentage,
two_eot,
):
self.stage = stage
self.anno_folder = anno_folder
self.ans2label = pickle.load(open(ans2label_path, "rb"))
self.label2ans = pickle.load(open(label2ans_path, "rb"))
self.feats_folder = feats_folder
self.max_feat_num = max_feat_num
self.max_seq_len = max_seq_len
self.use_global_v = use_global_v
self.tokenizer = tokenizer
self.tokenizer_name = tokenizer_name
self.num_labels = len(self.ans2label)
self.cfg = cfg
self.dataset_name = dataset_name
self.task_type = task_type
self.id2path = self.load_img_info(self.anno_folder)
self.initialized = False
self.transform = transform
self.as_gen = as_gen
self.inf_input = inf_input
self.single_class = single_class
self.small_val = small_val
self.block_vq = block_vq
self.data_percentage = data_percentage
self.two_eot = two_eot
# if as_retrieval:
if self.tokenizer_name == "clip":
self.mask_tokens = [tokenizer.encoder["<|spe|>"]]
else:
raise NotImplementedError
# remove the first null answer, we are not using complementay dataset
self.answer_tokens = self.tokenize_answer()
self.answer_type_tokens = [np.zeros(len(x), dtype=np.int64) for x in self.answer_tokens]
self.use_ceph = use_ceph
if self.use_ceph:
self.feats_folder = "s3://coco"
print('debug info for vqa {}'.format( self.feats_folder))
from uniperceiver.datasets import TCSLoader
if 'SLURM_PROCID' in os.environ:
tcs_conf_path = cfg.DATALOADER.get("TCS_CONF_PATH", "slurm_tools/petreloss.config")
else:
# dev machine
tcs_conf_path = "slurm_tools/petreloss_local.config"
self.tcs_loader = TCSLoader(tcs_conf_path)
self.load_data(self.cfg)
self.task_info = {
'task_type' : self.cfg.DATASETS.TASK_TYPE,
'dataset_name' : self.cfg.DATASETS.DATASET_NAME,
'batch_size' : self.cfg.DATALOADER.TRAIN_BATCH_SIZE if self.stage == 'train' else self.cfg.DATALOADER.TEST_BATCH_SIZE,
'sampling_weight': self.cfg.DATALOADER.SAMPLING_WEIGHT,
'single_class' : self.cfg.DATALOADER.SINGLE_CLASS
}
def _init_memcached(self):
if not self.initialized:
server_list_config_file = "/mnt/cache/share/memcached_client/server_list.conf"
client_config_file = "/mnt/cache/share/memcached_client/client.conf"
self.mclient = mc.MemcachedClient.GetInstance(server_list_config_file, client_config_file)
self.initialized = True
@classmethod
def from_config(cls, cfg, stage: str = "train"):
ans2label_path = os.path.join(cfg.DATALOADER.ANNO_FOLDER, "trainval_ans2label.pkl")
label2ans_path = os.path.join(cfg.DATALOADER.ANNO_FOLDER, "trainval_label2ans.pkl")
feats_folder = cfg.DATALOADER.FEATS_FOLDER
# if stage == "test":
# feats_folder = feats_folder + "/test2015"
if getattr(cfg.DATALOADER, 'TRANSFORM', None) == 'clip_transforms':
transform = clip_transforms(stage, flip_prob=0.0)
else:
transform = transforms.Compose([
transforms.Resize([224, 224]),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))]
)
ret = {
'cfg': cfg,
'dataset_name': cfg.DATASETS.DATASET_NAME,
'task_type': cfg.DATASETS.TASK_TYPE,
"stage": stage,
"anno_folder": cfg.DATALOADER.ANNO_FOLDER,
"ans2label_path": ans2label_path,
"label2ans_path": label2ans_path,
"feats_folder": feats_folder,
"max_feat_num": cfg.DATALOADER.MAX_FEAT_NUM,
"max_seq_len": cfg.MODEL.MAX_SEQ_LEN,
"use_global_v": cfg.DATALOADER.USE_GLOBAL_V,
"use_ceph": getattr(cfg.DATALOADER, 'USE_CEPH', False),
"transform": transform,
"as_gen": cfg.DATALOADER.DO_AS_GEN,
"inf_input": cfg.DATALOADER.VQA_INPUT,
"single_class": cfg.DATALOADER.SINGLE_CLASS,
"small_val": cfg.DATALOADER.SMALL_VAL,
"block_vq": cfg.DATALOADER.BLOCK_VQ,
"data_percentage": cfg.DATALOADER.DATA_PERCENTAGE,
"two_eot": cfg.DATALOADER.TWO_EOT,
}
ret['tokenizer'] = ClipTokenizer()
ret['tokenizer_name'] = "clip"
return ret
def load_img_info(self, anno_folder):
id2path = {}
coco_map = json.load(open(os.path.join(anno_folder, "coco_map.json")))
for k, v in coco_map.items():
id2path[int(k)] = v
return id2path
def load_data(self, cfg):
cache_path = os.path.join(
self.anno_folder, "cache",
"VQA_sep_%s_%s_%d%s.pkl" % (self.tokenizer_name, self.stage, self.max_seq_len, "_full_val" if self.stage == "val" and not self.small_val else "")
)
if not os.path.exists(os.path.dirname(cache_path)):
os.makedirs(os.path.dirname(cache_path))
if not os.path.exists(cache_path):
datalist = self.load_raw_data(cfg)
self.tokenize(datalist)
pickle.dump(datalist, open(cache_path, "wb"))
datalist = pickle.load(open(cache_path, "rb"))
if self.data_percentage < 1.0 and self.stage == "train":
labels2l = dict()
for data in datalist:
if not data['answer']['labels']:
continue
ans = data['answer']['labels'][0]
if ans not in labels2l:
labels2l[ans] = list()
labels2l[ans].append(data)
datalist = []
for v in labels2l.values():
datalist.extend(random.sample(v, k=int(self.data_percentage * len(v)+1)))
self.database = pa.array(datalist)
self.datalist = datalist
if comm.is_main_process():
import sys
print(f"!!! Dataset {self.dataset_name} with task {self.task_type}:")
print('!!! length of _temp_list: ', len(datalist))
print('!!! size of _temp_list: ', sys.getsizeof(datalist))
print('!!! size of pa database: ', sys.getsizeof(self.database))
del datalist
def tokenize(self, datalist):
for entry in datalist:
tokens = self.tokenizer.encode(entry["question"])
tokens = tokens[: self.max_seq_len - 2]
# tokens = self.tokenizer.add_special_tokens_single_sentence(tokens)
entry["question"] = tokens
def tokenize_answer(self):
output = list()
for answer in self.label2ans:
answer_tokens = self.tokenizer.encode(answer + " <|endoftext|>")
# answer_tokens = self.tokenizer.add_special_tokens_single_sentence(answer_tokens)
output.append(answer_tokens)
return output
def load_raw_data(self, cfg):
if self.stage == 'train': # trainval mode
question_path_train = os.path.join(self.anno_folder, "v2_OpenEnded_mscoco_train2014_questions.json")
questions_train = sorted(
json.load(open(question_path_train))["questions"],
key=lambda x: x["question_id"],
)
answer_path_train = os.path.join(self.anno_folder, "train_target.pkl")
answers_train = pickle.load(open(answer_path_train, "rb"))
answers_train = sorted(answers_train, key=lambda x: x["question_id"])
question_path_val = os.path.join(self.anno_folder, "v2_OpenEnded_mscoco_val2014_questions.json")
questions_val = sorted(
json.load(open(question_path_val))["questions"],
key=lambda x: x["question_id"],
)
answer_path_val = os.path.join(self.anno_folder, "val_target.pkl")
answers_val = pickle.load(open(answer_path_val, "rb"))
answers_val = sorted(answers_val, key=lambda x: x["question_id"])
# VG
vg_question_path_train = os.path.join(self.anno_folder, "VG_questions2.json")
vg_questions_train = sorted(
json.load(open(vg_question_path_train))["questions"],
key=lambda x: x["question_id"],
)
vg_answer_path_train = os.path.join(self.anno_folder, "vg_target.pkl")
vg_answers_train = pickle.load(open(vg_answer_path_train, "rb"))
vg_answers_train = sorted(vg_answers_train, key=lambda x: x["question_id"])
questions = questions_train + questions_val[:-3000] + vg_questions_train
answers = answers_train + answers_val[:-3000] + vg_answers_train
elif self.stage == "val": # minval
question_path_val = os.path.join(self.anno_folder, "v2_OpenEnded_mscoco_val2014_questions.json")
questions_val = sorted(
json.load(open(question_path_val))["questions"],
key=lambda x: x["question_id"],
)
answer_path_val = os.path.join(self.anno_folder, "val_target.pkl")
answers_val = pickle.load(open(answer_path_val, "rb"))
answers_val = sorted(answers_val, key=lambda x: x["question_id"])
if self.small_val:
questions = questions_val[-3000:]
answers = answers_val[-3000:]
else:
questions = questions_val
answers = answers_val
else:
question_path_test = os.path.join(self.anno_folder, "v2_OpenEnded_mscoco_test2015_questions.json")
# question_path_test = os.path.join(self.anno_folder, "v2_OpenEnded_mscoco_test-dev2015_questions.json")
questions_test = sorted(
json.load(open(question_path_test))["questions"],
key=lambda x: x["question_id"],
)
questions = questions_test
datalist = []
if self.stage == "test":
for question in questions:
datalist.append({
"question_id": str(question["question_id"]),
"image_id": str(question["image_id"]),
"question": question["question"],
})
else:
assert len(questions) == len(answers)
for question, answer in zip(questions, answers):
assert question["question_id"] == answer["question_id"]
assert question["image_id"] == answer["image_id"]
answer.pop("image_id")
answer.pop("question_id")
datalist.append({
"question_id": str(question["question_id"]),
"image_id": str(question["image_id"]),
"question": question["question"],
"answer": answer,
})
return datalist
def __len__(self):
return len(self.database)
def __getitem__(self, index):
for i_try in range(100):
try:
dataset_dict = self.database[index].as_py()
image_id = dataset_dict['image_id']
question_id = dataset_dict["question_id"]
global memorycache
image_path = os.path.join(self.feats_folder, self.id2path[int(image_id)])
### LOAD IMAGE ###
if self.use_ceph:
img = self.tcs_loader(image_path).convert('RGB')
elif not memorycache:
img = Image.open(image_path).convert("RGB")
else:
# memcached
self._init_memcached()
value = mc.pyvector()
self.mclient.Get(image_path, value)
value_str = mc.ConvertBuffer(value)
buff = io.BytesIO(value_str)
img = Image.open(buff).convert("RGB")
except Exception as e:
print(
"Failed to load video from {} with error {} ; trial {}".format(
image_path, e, i_try
)
)
# let's try another one
index = random.randint(0, len(self.datalist) - 1)
dataset_dict = self.datalist[index]
continue
img = self.transform(img)
prob = random.random()
if prob > 0.5 and self.stage == 'train':
# img = img[:, :, ::-1]
img = torch.flip(img, [2])
question = dataset_dict["question"]
if self.as_gen:
if self.two_eot:
question = question + self.tokenizer.encode("<|endoftext|>")
question = question + self.tokenizer.encode("<|spe|> <|endoftext|>")
index = len(question) - 2
question = np.array(question, dtype=np.int64)
#######################################################
if prob > 0.5 and self.stage == 'train':
for i in range(1, len(question)):
if self.tokenizer_name == "clip":
left = self.tokenizer.encoder["left"]
right = self.tokenizer.encoder["right"]
if question[i] == left:
question[i] = right
elif question[i] == right:
question[i] = left
else:
raise NotImplementedError
if 'image' in self.inf_input:
ret = {
'input_sample': [{
'data' : img,
'invalid_mask': None,
'modality' : 'image',
'data_type' : 'input',
'sample_info' : {
'id': image_id,
'path': image_path
}
}]
}
self.target_set = self.cfg.DATASETS.TARGET_SET
target = 0
if "answer" in dataset_dict:
answer = dataset_dict["answer"]
labels = answer["labels"]
scores = answer["scores"]
#######################################################
if prob > 0.5 and self.stage == 'train':
for i in range(len(labels)):
if labels[i] == self.ans2label['left']:
labels[i] = self.ans2label['right']
elif labels[i] == self.ans2label['right']:
labels[i] = self.ans2label['left']
#######################################################
if self.single_class:
if len(labels) < 1:
target = 0
else:
s = sum(scores)
# probabilty
p = [t / s for t in scores]
# sample
target = choice(labels, 1, p=p).item()
else:
target = np.zeros(self.num_labels)
if len(labels) > 0:
for label, score in zip(labels, scores):
target[label] = score
target = np.array(target, dtype=np.float32)
if self.as_gen:
# caption like
ret['input_sample'].append({
'data': [question],
'invalid_mask': None,
'modality': 'text',
'data_type': 'input',
'sample_info': {
'spe_index': index,
'question_id': question_id
}
})
ret.update({
'target_sample': [],
'target_idx' : [target],
'target_set' : copy.deepcopy(self.target_set),
'task_info' : copy.deepcopy(self.task_info)
})
dict_as_tensor(ret)
return ret
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