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import sys
sys.path.insert(0, '../')
import torch
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.dataloader import default_collate
import pandas as pd
from util import tokenize, get_qsn_type, group, load_file
import os.path as osp
import random as rd
import numpy as np
import h5py
from dataloader.prepare_video import *
rd.seed(1)
class VideoQADataset(Dataset):
def __init__(
self,
csv_path,
features_path,
qmax_words=20,
amax_words=5,
tokenizer=None,
a2id=None,
max_feats=20,
mc=0,
feat_type='CLIP',
vg_loss=0
):
"""
:param csv_path: path to a csv containing columns video_id, question, answer
:param features_path: dictionary to video frames
:param qmax_words: maximum number of words for a question
:param amax_words: maximum number of words for an answer
:param tokenizer: BERT tokenizer
:param a2id: answer to index mapping
:param ivqa: whether to use iVQA or not
:param max_feats: maximum frames to sample from a video
"""
# self.anno_path = osp.dirname(csv_path)
self.data = pd.read_csv(csv_path)
self.dset = csv_path.split('/')[-2]
self.video_feature_path = features_path
self.feat_type = feat_type
self.use_frame = True
self.use_mot = False
self.qmax_words = qmax_words
self.amax_words = amax_words
self.a2id = a2id
self.tokenizer = tokenizer
self.v_questions = {}
self.max_feats = max_feats
self.mc = mc
self.vg = vg_loss
self.mode = osp.basename(csv_path).split('.')[0] #train, val or test
self.agu = False
if self.mode not in ['val', 'test']:
self.all_answers = set(self.data['answer'])
self.all_questions = set(self.data['question'])
self.ans_group, self.qsn_group = group(self.data, gt=False)
if self.agu:
anno_path = osp.dirname(csv_path)
agu_file = osp.join(anno_path, 'train_gpt4_sub.json')
self.qsn_agu = load_file(agu_file)
self._gather_by_v()
app_feat_file = osp.join(self.video_feature_path, f'{feat_type}/{feat_type}_I_{self.mode}.h5')
print('Load {}...'.format(app_feat_file))
self.frame_feats = {}
with h5py.File(app_feat_file, 'r') as fp:
vids = fp['vid']
feat_key = f'{feat_type}_I' if feat_type != 'Swin' else 'swin_2d'
feats = fp[feat_key]
print(feats.shape) #v_num, clip_num, feat_dim
for id, (vid, feat) in enumerate(zip(vids, feats)):
vid = vid.decode("utf-8")
self.frame_feats[str(vid)] = feat
# with h5py.File(app_feat_file, 'r') as fp:
# vqids = fp['qid']
# feat_key = f'{feat_type}_I'
# feats = fp[feat_key]
# print(feats.shape) #v_num, clip_num, feat_dim
# for id, (vqid, feat) in enumerate(zip(vqids, feats)):
# vqid = vqid.decode("utf-8")
# self.frame_feats[str(vqid)] = feat
def __len__(self):
return len(self.data)
def _gather_by_v(self):
for idx, row in self.data.iterrows():
vid, qsn, qtype = str(row['video_id']), row['question'], row['type']
if qtype[0] == 'D': continue #omit descriptive question
if vid not in self.v_questions:
self.v_questions[vid] = [qsn]
else:
self.v_questions[vid].append(qsn)
def get_vid_frames(self, vid_id):
#deprecated as extracting features offline is much more effcient
sp_mode = 'uniC' if self.mode == 'train' else 'uniC'
vid_path = osp.join(self.video_feature_path, vid_id)
frames = video_sampling(vid_path, mode=sp_mode, frame_num=self.max_feats)
video_inputs = prepare_input(frames)
return video_inputs
def get_vid_feats(self, vid_id):
feat = self.frame_feats[vid_id]
fnum = feat.shape[0]
sp_fids = np.linspace(0, fnum-1, self.max_feats, dtype=int)
feat = feat[sp_fids]
return feat
def get_vqid_feats(self, vqid):
feat = self.frame_feats[vqid]
vlen = feat.shape[0]
return feat, vlen
def __getitem__(self, index):
cur_sample = self.data.loc[index]
vid_id = cur_sample["video_id"]
vid_id = str(vid_id)
qid = str(cur_sample['qid'])
# vid_frames = self.get_vid_frames(vid_id)
vid_qid = f'{vid_id}_{qid}'
# vid_frames = self.frame_feats[vid_qid]
vid_frames = self.get_vid_feats(vid_id)
vlen = self.max_feats
question_txt = cur_sample['question']
# print(question_txt)
if self.mc >= 0:
qsn_tk_id = torch.tensor(
self.tokenizer.encode(
question_txt,
add_special_tokens=True,
padding="longest",
max_length=self.qmax_words,
truncation=True,
),
dtype=torch.long
)
q_len = torch.tensor([len(qsn_tk_id)], dtype=torch.long)
else:
qsn_tk_id = torch.tensor([0], dtype=torch.long)
q_len = torch.tensor([0], dtype=torch.long)
qtype, ans_token_ids, answer_len = 0, 0, 0
# max_seg_num = self.amax_words
# seg_feats = torch.zeros(self.mc, max_seg_num, 2048)
# seg_num = torch.LongTensor(self.mc)
qsns_id , qsns_token_ids, qsns_seq_len = 0, 0, 0
qtype = 'null' if 'type' not in cur_sample else cur_sample['type']
if self.mode == 'train' and self.agu:
if vid_qid in self.qsn_agu:
agus = self.qsn_agu[vid_qid]['gen']
#agus.append(question_txt)
question_txt = rd.sample(agus, 1)[0].rstrip('?') if rd.random()<0.3 else question_txt
#question_txt = question_txt.rstrip('?')
if self.vg and self.mode not in ['val','test']:
try:
qtype = get_qsn_type(question_txt, qtype)
except:
print(vid_qid, question_txt)
neg_num = 5
if qtype not in self.qsn_group or len(self.qsn_group[qtype]) < neg_num-1:
valid_qsncans = self.all_questions
else:
valid_qsncans = self.qsn_group[qtype]
if rd.random() < 0.3:
same_v_qsn = set(self.v_questions[vid_id])
same_v_other_qsn = list(same_v_qsn - set(question_txt))
num_other = len(same_v_other_qsn)
if num_other >= self.mc-1:
qchoices = rd.sample(same_v_other_qsn, self.mc-1)
else:
cand_qsn = valid_qsncans - same_v_qsn
if len(cand_qsn) < self.mc-1-num_other:
cand_qsn = set(self.all_question) - same_v_qsn
qchoices = same_v_other_qsn + rd.sample(list(cand_qsn), self.mc-1-num_other)
else:
cand_qsns = valid_qsncans - set(question_txt)
qchoices = rd.sample(list(cand_qsns), self.mc-1)
"""
same_v_qsn = set(self.v_questions[vid_id])
same_v_other_qsn = list(same_v_qsn - set(question_txt))
num_other = len(same_v_other_qsn)
cand_qsn = valid_qsncans - same_v_qsn
if num_other >= 2:
qchoices = rd.sample(same_v_other_qsn, 2)
else:
add = rd.sample(list(cand_qsn), 2-num_other)
qchoices = same_v_other_qsn + add
cand_qsn = cand_qsn - set(add)
qchoices.extend(rd.sample(list(cand_qsn), 2))
"""
qchoices.append(question_txt)
rd.shuffle(qchoices)
qsns_id = qchoices.index(question_txt)
qsns_token_ids, qsn_tokens = tokenize(
qchoices,
self.tokenizer,
add_special_tokens=True,
max_length=self.qmax_words,
dynamic_padding=False,
truncation=True
)
qsns_seq_len = torch.tensor([len(qsn) for qsn in qsns_token_ids], dtype=torch.long)
question_id = vid_id +'_'+str(cur_sample["qid"])
if self.mc:
ans = cur_sample['answer']
choices = [str(cur_sample["a" + str(i)]) for i in range(self.mc)]
answer_id = choices.index(ans) if ans in choices else -1
if self.mode not in ['val', 'test'] and rd.random() < 0.3:
try:
qtype = get_qsn_type(question_txt, qtype)
except:
print(vid_qid, question_txt)
if qtype not in self.ans_group or len(self.ans_group[qtype]) < self.mc-1:
valid_anscans = self.all_answers
else:
valid_anscans = self.ans_group[qtype]
cand_answers = valid_anscans - set(ans)
choices = rd.sample(list(cand_answers), self.mc-1)
choices.append(ans)
rd.shuffle(choices)
answer_id = choices.index(ans)
# print(question_txt, choices, ans)
answer_txts = [question_txt+f' {self.tokenizer.sep_token} '+ opt for opt in choices]
try:
ans_token_ids, answer_tokens = tokenize(
answer_txts,
self.tokenizer,
add_special_tokens=True,
max_length=self.amax_words,
dynamic_padding=False,
truncation=True
)
except:
print('Fail to tokenize: '+answer_txts)
qas_len = torch.tensor([len(ans) for ans in ans_token_ids], dtype=torch.long)
else:
answer_txts = cur_sample["answer"]
answer_id = self.a2id.get(answer_txts, -1) # answer_id -1 if not in top answers, that will be considered as wrong prediction during evaluation
return {
"video_id": vid_id,
"video_frames": vid_frames,
"video_len": vlen,
"question": qsn_tk_id,
"question_txt": question_txt,
"type": qtype,
"answer_id": answer_id,
"answer_txt": answer_txts,
"answer": ans_token_ids,
"qas_len": qas_len,
"question_id": question_id,
"qsns_id": qsns_id,
"qsns_token_ids": qsns_token_ids,
"qsns_seq_len": qsns_seq_len,
"q_len": q_len
}
def videoqa_collate_fn(batch):
"""
:param batch: [dataset[i] for i in N]
:return: tensorized batch with the question and the ans candidates padded to the max length of the batch
"""
qmax_len = max(len(batch[i]["question"]) for i in range(len(batch)))
for i in range(len(batch)):
if len(batch[i]["question"]) < qmax_len:
batch[i]["question"] = torch.cat(
[
batch[i]["question"],
torch.zeros(qmax_len - len(batch[i]["question"]), dtype=torch.long),
],
0,
)
if not isinstance(batch[0]["answer"], int):
amax_len = max(x["answer"].size(1) for x in batch)
for i in range(len(batch)):
if batch[i]["answer"].size(1) < amax_len:
batch[i]["answer"] = torch.cat(
[
batch[i]["answer"],
torch.zeros(
(
batch[i]["answer"].size(0),
amax_len - batch[i]["answer"].size(1),
),
dtype=torch.long,
),
],
1,
)
return default_collate(batch)
def get_videoqa_loaders(args, features_path, a2id, tokenizer, test_mode):
if test_mode != 'train':
test_dataset = VideoQADataset(
csv_path=args.val_csv_path if test_mode == 'val' else args.test_csv_path,
features_path=features_path,
qmax_words=args.qmax_words,
amax_words=args.amax_words,
tokenizer=tokenizer,
a2id=a2id,
max_feats=args.max_feats,
mc=args.mc,
feat_type =args.feat_type
)
test_loader = DataLoader(
test_dataset,
batch_size=args.batch_size_val,
num_workers=args.num_thread_reader,
shuffle=False,
drop_last=False,
collate_fn=videoqa_collate_fn,
)
train_loader, val_loader = None, None
else:
train_dataset = VideoQADataset(
csv_path=args.train_csv_path,
features_path=features_path,
qmax_words=args.qmax_words,
amax_words=args.amax_words,
tokenizer=tokenizer,
a2id=a2id,
max_feats=args.max_feats,
mc=args.mc,
feat_type =args.feat_type,
vg_loss=args.vg_loss
)
train_loader = DataLoader(
train_dataset,
batch_size=args.batch_size,
num_workers=args.num_thread_reader,
shuffle=True,
drop_last=True,
collate_fn=videoqa_collate_fn,
)
val_dataset = VideoQADataset(
csv_path=args.val_csv_path,
features_path=features_path,
qmax_words=args.qmax_words,
amax_words=args.amax_words,
tokenizer=tokenizer,
a2id=a2id,
max_feats=args.max_feats,
mc=args.mc,
feat_type =args.feat_type,
)
val_loader = DataLoader(
val_dataset,
batch_size=args.batch_size_val,
num_workers=args.num_thread_reader,
shuffle=False,
collate_fn=videoqa_collate_fn,
)
test_loader = None
return (train_loader, val_loader, test_loader)
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