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main_qamc_tsv_mlm_gen_ans_idx.py
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main_qamc_tsv_mlm_gen_ans_idx.py
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from utils.lib import *
from main_qamc_tsv_mlm_head import Dataset_QAMC_MLM_Head, VIOLET_QAMC_MLM_Head, Agent_QAMC_MLM_Head, get_tsv_dls
from utils.args import get_args
from utils.logger import LOGGER, add_log_to_file
from utils.dist import NoOp, is_main_process, get_rank, get_world_size, iter_tqdm
class Dataset_QAMC_MLM_Head_GEN(Dataset_QAMC_MLM_Head):
def __init__(self, args, img_tsv_path, txt, id2lineidx, split, tokzr=None):
super().__init__(args, img_tsv_path, txt, id2lineidx, split, tokzr=tokzr)
self.ans_tok_ids = self.tokzr.convert_tokens_to_ids([f"{i}" for i in range(self.args.size_option)])
def append_mask(self, tokens, padding_len):
tokens = [self.tokzr.cls_token]+tokens+[self.tokzr.mask_token]+[self.tokzr.sep_token]+[self.tokzr.pad_token]*(padding_len)
return tokens
def prepend_mask(self, tokens, padding_len):
tokens = [self.tokzr.mask_token, self.tokzr.cls_token]+tokens+[self.tokzr.sep_token]+[self.tokzr.pad_token]*(padding_len)
return tokens
def replace_cls(self, tokens, padding_len):
tokens = [self.tokzr.mask_token]+tokens+[self.tokzr.sep_token]+[self.tokzr.pad_token]*(padding_len)
return tokens
def insert_mask(self, tokens, padding_len):
tokens = [self.tokzr.cls_token]+tokens+[self.tokzr.sep_token]+[self.tokzr.pad_token]*(padding_len)
if len(tokens)<10: tokens += [self.tokzr.mask_token]
else: tokens = tokens[:10]+[self.tokzr.mask_token]+tokens[10:]
return tokens
def str2txt(self, s):
tokens = self.tokzr.tokenize(s)
tokens = tokens[:self.args.size_txt-1]
padding_len = self.args.size_txt-len(tokens)
if self.args.mask_pos=="append": tokens = self.append_mask(tokens, padding_len)
elif self.args.mask_pos=="prepend": tokens = self.prepend_mask(tokens, padding_len)
elif self.args.mask_pos=="insert": tokens = self.insert_mask(tokens, padding_len)
elif self.args.mask_pos=="replace": tokens = self.replace_cls(tokens, padding_len)
txt = self.tokzr.convert_tokens_to_ids(tokens)
mask = [1 if w != self.pad_token_id else 0 for w in txt]
mask = T.LongTensor(mask)
txt = T.LongTensor(txt)
return txt, mask
def __getitem__(self, idx):
item = self.txt[idx]
video_id = item['video']
lineidx = self.id2lineidx[video_id]
b = self.seek_img_tsv(lineidx)[2:]
img = self.get_img_or_video(b)
ans_idx = item['answer']
ans_tok_id = self.tokzr.convert_tokens_to_ids([f"{ans_idx}"])[0]
question = item['question']
for i in range(self.args.size_option):
answer = item[f'option_{i}']
answer = f"option {i}: "+answer
question = self.concat_txt(question, answer)
txt, mask = self.str2txt(question)
mask_ans = T.ones(txt.shape).long()*-1
mask_ans[txt==self.mask_token_id] = ans_tok_id
return img, txt, mask, mask_ans, ans_idx
@property
def prompt_text(self):
return "which answer is correct, from "+f"{list(range(self.args.size_option))}?"
def collate_batch(self, inputs):
img, txt, mask, mask_ans, ans_idx = map(list, unzip(inputs))
all_imgs = T.stack(img, dim=0)
all_mask_ans = T.stack(mask_ans, dim=0)
all_txts = T.stack(txt, dim=0)
all_masks = T.stack(mask, dim=0)
ans_idx = T.LongTensor(ans_idx)
batch = {"img": all_imgs, "txt": all_txts, "mask": all_masks, "mask_ans": all_mask_ans, "ans_idx": ans_idx}
return batch
class VIOLET_QAMC_MLM_Head_GEN(VIOLET_QAMC_MLM_Head):
def __init__(self, args, tokzr=None):
super().__init__(args, tokzr)
def forward(self, batch):
batch = defaultdict(lambda: None, batch)
img, txt, mask = [batch[key] for key in ["img", "txt", "mask"]]
ans = batch["mask_ans"]
(_B, _T, _, _H, _W), (_, _X) = img.shape, txt.shape
_h, _w = _H//32, _W//32
feat_img, mask_img, feat_txt, mask_txt = self.go_feat(img, txt, mask)
ans, mask_txt, feat_txt = self.prepro_txt_inputs(ans, mask_txt, feat_txt, task_name=batch["task_name"], prompt=batch["prompt"])
out, _ = self.go_cross(feat_img, mask_img, feat_txt, mask_txt)
if self.args.temporal_fusion=="mean": _T = 1
out = self.fc_mtm(out[:, (1+_h*_w)*_T:])
return out, ans
class Agent_QAMC_MLM_Head_GEN(Agent_QAMC_MLM_Head):
def __init__(self, args, model, ans_tok_ids):
super(Agent_QAMC_MLM_Head, self).__init__(args, model)
self.ans_tok_ids = ans_tok_ids
if args.freeze_violet: self.model.freeze()
def step(self, batch, is_train):
with T.cuda.amp.autocast(enabled=not self.args.deepspeed):
out = self.forward_step(batch)
out, ans = out
if is_train:
ans = ans.flatten(0, 1)
out = out.flatten(0, len(out.shape)-2)
ans = ans.flatten(0, len(ans.shape)-1)
ls = self.loss_func(out, ans)
self.backward_step(ls)
return ls.item()
else:
_B, _ = ans.shape
p_all_ans_toks = out[:, :, self.ans_tok_ids]
ans_mtm = ans
out_mtm = p_all_ans_toks[ans_mtm!=-1]
out_mtm = out_mtm/out_mtm.sum(dim=-1).view(_B, 1)
out_mtm = out_mtm.view(_B, -1)
out_mtm = T.argmax(out_mtm, dim=-1)
ans_idx = batch["ans_idx"]
ac = (out_mtm==ans_idx).float().tolist()
return ac
if __name__=='__main__':
args = get_args()
tokzr = transformers.AutoTokenizer.from_pretrained(args.tokenizer)
dl_tr, dl_vl, dl_ts = get_tsv_dls(args, Dataset_QAMC_MLM_Head_GEN, tokzr=tokzr)
print(len(dl_tr.dataset), len(dl_vl.dataset), len(dl_ts.dataset))
if args.size_epoch==0: args.max_iter = 1
else: args.max_iter = len(dl_tr)*args.size_epoch
model = VIOLET_QAMC_MLM_Head_GEN(args, tokzr=tokzr)
model.load_ckpt(args.path_ckpt)
if args.reinit_head: model.reinit_head()
model.cuda()
if args.distributed: LOGGER.info(f"n_gpu: {args.num_gpus}, rank: {get_rank()},"
f" world_size: {get_world_size()}")
args.path_output = '%s/_%s_%s'%(args.path_output, args.task, datetime.now().strftime('%Y%m%d%H%M%S'))
agent = Agent_QAMC_MLM_Head_GEN(args, model, dl_ts.dataset.ans_tok_ids)
if args.distributed: agent.prepare_dist_model()
agent.save_training_meta()
if is_main_process(): add_log_to_file('%s/stdout.txt'%(args.path_output))
else: LOGGER = NoOp()
LOGGER.info("Saved training meta infomation, start training ...")
if os.path.exists(args.path_ckpt):
LOGGER.info("Zero-shot Evaluation")
ac_vl = agent.go_dl(0, dl_vl, False)
ac_ts = agent.go_dl(0, dl_ts, False)
LOGGER.info('ZS: %.2f %.2f'%(ac_vl*100, ac_ts*100))
print('ZS:', ac_vl, ac_ts)
else: LOGGER.info("No pre-trained weight, skip zero-shot Evaluation")
if args.size_epoch:
agent.setup_wandb()
LOGGER.info("Start training....")
for e in iter_tqdm(range(args.size_epoch)):
ls_tr = agent.go_dl(e+1, dl_tr, True)
ac_vl = agent.go_dl(e+1, dl_vl, False)
ac_ts = agent.go_dl(e+1, dl_ts, False)
agent.log['ls_tr'].append(ls_tr)
agent.log['ac_vl'].append(ac_vl)
agent.log['ac_ts'].append(ac_ts)
agent.log_dict_to_wandb({"ac_vl": ac_vl})
agent.log_dict_to_wandb({"ac_ts": ac_ts})
LOGGER.info('Ep %d: %.6f %.2f %.2f'%(e+1, ls_tr, ac_vl*100, ac_ts*100))
agent.save_model(e+1)
best_vl, best_ts = agent.best_epoch()
LOGGER.info(f'Best val @ ep {best_vl[0]+1}, {best_vl[1]*100:.2f}')
LOGGER.info(f'Best test @ ep {best_ts[0]+1}, {best_ts[1]*100:.2f}')