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main_retmc_task_specific.py
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main_retmc_task_specific.py
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from utils.lib import *
from main_qamc_task_specific import Dataset_QAMC_TS
from model import LAVENDER_Base
from agent import Agent_Base
from dataset import 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, all_gather,
get_rank, get_world_size, iter_tqdm)
class Dataset_RetMC_TS(Dataset_QAMC_TS):
def __init__(self, args, img_tsv_path, txt, id2lineidx, split, tokzr=None):
super().__init__(
args, img_tsv_path, txt, id2lineidx, split, tokzr)
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)
q = item['question']
txt, mask = [], []
for i in range(self.args.size_option):
if len(q):
option = q + ' ' + item[f'option_{i}']
else:
option = item[f'option_{i}']
t, m = self.str2txt(option)
txt.append(t), mask.append(m)
txt = T.stack(txt)
mask = T.stack(mask)
return img, txt, mask, item['answer']
class LAVENDER_RetMC_TS(LAVENDER_Base):
def __init__(self, args, tokzr=None):
super().__init__(args, tokzr)
self.fc = T.nn.Sequential(
*[T.nn.Dropout(0.1),
T.nn.Linear(self.hidden_size, self.hidden_size*2),
T.nn.ReLU(inplace=True),
T.nn.Linear(self.hidden_size*2, 1)])
def forward(self, batch):
batch = defaultdict(lambda: None, batch)
img, txt, mask = [
batch[key] for key in ["img", "txt", "mask"]]
ans = batch["ans"]
(_B, _T, _, _H, _W), (_, _O, _X) = img.shape, txt.shape
_h, _w = _H//32, _W//32
feat_img, mask_img, feat_txt, mask_txt = self.go_feat(
img, txt.flatten(0, 1), mask.flatten(0, 1))
feat_img, mask_img = [
feat_img.unsqueeze(1).expand([-1, _O, -1, -1]).flatten(0, 1),
mask_img.unsqueeze(1).expand([-1, _O, -1]).flatten(0, 1)]
out, _ = self.go_cross(feat_img, mask_img, feat_txt, mask_txt)
out = self.fc(out[:, (1+_h*_w)*_T, :]).squeeze(dim=-1).view([_B, _O])
return out, ans
def reinit_head(self):
del self.fc
self.fc = T.nn.Sequential(
*[T.nn.Dropout(0.1),
T.nn.Linear(self.hidden_size, self.hidden_size*2),
T.nn.ReLU(inplace=True),
T.nn.Linear(self.hidden_size*2, 1)])
class Agent_RetMC_TS(Agent_Base):
def __init__(self, args, model):
super().__init__(args, model)
self.log = {'ls_tr': [], 'ac_vl': [], 'ac_ts': []}
def go_dl(self, ep, dl, is_train):
if is_train:
self.model.train()
else:
self.model.eval()
ret = []
idx = 0
for idx, batch in enumerate(dl):
if idx % self.args.logging_steps == 0 and is_train:
LOGGER.info(self.log_memory(ep, idx+1))
batch = self.prepare_batch(batch)
curr_ret = self.step(batch, is_train)
if isinstance(curr_ret, list):
ret.extend(curr_ret)
else:
ret.append(curr_ret)
if idx % self.args.logging_steps != 0 and is_train:
LOGGER.info(self.log_memory(ep, idx+1))
gathered_ret = []
for ret_per_rank in all_gather(ret):
gathered_ret.extend(ret_per_rank)
ret = float(np.average(gathered_ret))
return ret
def step(self, batch, is_train):
with T.cuda.amp.autocast(enabled=not self.args.deepspeed):
out = self.forward_step(batch)
out, ans = out
ls = self.loss_func(out, ans)
if is_train:
self.backward_step(ls)
return ls.item()
else:
out = T.argmax(out, dim=1)
ac = (out == ans).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_RetMC_TS, tokzr=tokzr)
if args.size_epoch == 0 or len(dl_tr) == 0:
args.max_iter = 1
else:
args.max_iter = len(dl_tr) * args.size_epoch
args.actual_size_test = len(dl_ts.dataset)
model = LAVENDER_RetMC_TS(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_RetMC_TS(args, model)
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()
# DIST.barrier()
LOGGER.info("Saved training meta infomation, start training ...")
if os.path.exists(args.path_ckpt):
if len(dl_vl):
ac_vl = agent.go_dl(0, dl_vl, False)
else:
ac_vl = -0.01
if len(dl_ts):
ac_ts = agent.go_dl(0, dl_ts, False)
else:
ac_ts = -0.01
LOGGER.info('ZS: %.2f %.2f' % (
ac_vl*100, ac_ts*100))
else:
LOGGER.info("No pre-trained weight, skip zero-shot Evaluation")
if args.size_epoch:
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)
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}')