def __init__(self, args):
super(Trainer, self).__init__()
self.args = args
if cfg.SEED > 0:
random.seed(cfg.SEED)
torch.manual_seed(cfg.SEED)
torch.cuda.manual_seed_all(cfg.SEED)
self.num_gpus = torch.cuda.device_count()
self.distributed = self.num_gpus > 1
if self.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
backend="nccl", init_method="env://"
)
self.device = torch.device("cuda")
self.rl_stage = False
self.setup_logging()
self.setup_dataset()
self.setup_network()
self.val_evaler = Evaler(
eval_ids = cfg.DATA_LOADER.VAL_ID,
gv_feat = cfg.DATA_LOADER.VAL_GV_FEAT,
att_feats = cfg.DATA_LOADER.VAL_ATT_FEATS,
eval_annfile = cfg.INFERENCE.VAL_ANNFILE
)
self.test_evaler = Evaler(
eval_ids = cfg.DATA_LOADER.TEST_ID,
gv_feat = cfg.DATA_LOADER.TEST_GV_FEAT,
att_feats = cfg.DATA_LOADER.TEST_ATT_FEATS,
eval_annfile = cfg.INFERENCE.TEST_ANNFILE
)
self.scorer = Scorer()
def __init__(self, args):
super(Trainer, self).__init__()
self.args = args
if cfg.SEED > 0:
random.seed(cfg.SEED)
torch.manual_seed(cfg.SEED)
torch.cuda.manual_seed_all(cfg.SEED)
self.num_gpus = torch.cuda.device_count()
self.distributed = self.num_gpus > 1
if self.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
if args.local_rank == 0:
self.writer = SummaryWriter(args.summary_dir)
else:
self.writer = SummaryWriter(args.summary_dir)
self.device = torch.device("cuda")
self.rl_stage = False
self.setup_logging()
self.setup_dataset()
self.setup_network()
self.val_evaler, self.test_evaler = {}, {}
for dataset_name in self.dataset_dict:
if dataset_name == 'coco':
self.val_evaler[dataset_name] = Evaler(
eval_ids=cfg.COCO_DATA_LOADER.VAL_ID,
gv_feat=cfg.COCO_DATA_LOADER.VAL_GV_FEAT,
att_feats=cfg.COCO_DATA_LOADER.VAL_ATT_FEATS,
eval_annfile=cfg.INFERENCE.COCO_VAL_ANNFILE,
dataset_name='coco')
self.test_evaler[dataset_name] = Evaler(
eval_ids=cfg.COCO_DATA_LOADER.TEST_ID,
gv_feat=cfg.COCO_DATA_LOADER.TEST_GV_FEAT,
att_feats=cfg.COCO_DATA_LOADER.TEST_ATT_FEATS,
eval_annfile=cfg.INFERENCE.COCO_TEST_ANNFILE,
dataset_name='coco')
elif dataset_name == 'aic':
self.val_evaler[dataset_name] = Evaler(
eval_ids=cfg.AIC_DATA_LOADER.VAL_ID,
gv_feat=cfg.AIC_DATA_LOADER.VAL_GV_FEAT,
att_feats=cfg.AIC_DATA_LOADER.VAL_ATT_FEATS,
eval_annfile=cfg.INFERENCE.AIC_VAL_ANNFILE,
dataset_name='aic')
self.test_evaler[dataset_name] = Evaler(
eval_ids=cfg.AIC_DATA_LOADER.TEST_ID,
gv_feat=cfg.AIC_DATA_LOADER.TEST_GV_FEAT,
att_feats=cfg.AIC_DATA_LOADER.TEST_ATT_FEATS,
eval_annfile=cfg.INFERENCE.AIC_TEST_ANNFILE,
dataset_name='aic')
self.scorer = Scorer()
def __init__(self, args):
super(Tester, self).__init__()
self.args = args
self.device = torch.device("cuda")
self.setup_logging()
self.setup_network()
self.evaler = Evaler(eval_ids=cfg.DATA_LOADER.TEST_ID,
gv_feat=cfg.DATA_LOADER.TEST_GV_FEAT,
att_feats=cfg.DATA_LOADER.TEST_ATT_FEATS,
eval_annfile=cfg.INFERENCE.TEST_ANNFILE)
def __init__(self, args):
super(Trainer, self).__init__()
self.args = args
if cfg.SEED > 0:
np.random.seed(int(cfg.SEED))
random.seed(cfg.SEED)
torch.manual_seed(cfg.SEED)
torch.cuda.manual_seed_all(cfg.SEED)
self.setup_logging()
self.setup_dataset()
self.setup_network()
self.val_evaler = Evaler(eval_ids=cfg.DATA_LOADER.VAL_ID,
gv_feat=cfg.DATA_LOADER.VAL_GV_FEAT,
att_feats=cfg.DATA_LOADER.VAL_ATT_FEATS,
eval_annfile=cfg.INFERENCE.VAL_ANNFILE)
self.test_evaler = Evaler(eval_ids=cfg.DATA_LOADER.TEST_ID,
gv_feat=cfg.DATA_LOADER.TEST_GV_FEAT,
att_feats=cfg.DATA_LOADER.TEST_ATT_FEATS,
eval_annfile=cfg.INFERENCE.TEST_ANNFILE)
self.scorer = Scorer()
self._init_ppo()
def __init__(self, args):
super(Tester, self).__init__()
self.args = args
self.device = torch.device("cuda")
self.setup_logging()
self.setup_network()
if self.args.test_raw_image:
eval_ids = cfg.RAW_DATA_LOADER.TEST_IMG_DIR
self.raw_evaler = Evaler(
eval_ids=eval_ids,
gv_feat=None,
att_feats=cfg.RAW_DATA_LOADER.TEST_ATT_FEATS,
dataset_name='raw',
eval_annfile=None)
self.evaler = {'raw': self.raw_evaler}
#self.output_list = [img.split('.')[0] for img in os.listdir(eval_ids)]
else:
self.coco_evaler = Evaler(
eval_ids=cfg.COCO_DATA_LOADER.TEST_ID,
gv_feat=cfg.COCO_DATA_LOADER.TEST_GV_FEAT,
att_feats=cfg.COCO_DATA_LOADER.TEST_ATT_FEATS,
eval_annfile=cfg.INFERENCE.COCO_TEST_ANNFILE,
dataset_name='coco')
self.aic_evaler = Evaler(
eval_ids=cfg.AIC_DATA_LOADER.TEST_ID,
gv_feat=cfg.AIC_DATA_LOADER.TEST_GV_FEAT,
att_feats=cfg.AIC_DATA_LOADER.TEST_ATT_FEATS,
eval_annfile=cfg.INFERENCE.AIC_TEST_ANNFILE,
dataset_name='aic')
self.evaler = {'coco': self.coco_evaler, 'aic': self.aic_evaler}
if self.args.output_attention_list:
with open(self.args.output_attention_list, 'r') as f:
self.output_list = f.readlines()
self.output_list = [id_.strip() for id_ in self.output_list]
else:
self.output_list = None
def __init__(self, args):
super(Trainer, self).__init__()
self.args = args
if cfg.SEED > 0:
random.seed(cfg.SEED)
torch.manual_seed(cfg.SEED)
torch.cuda.manual_seed_all(cfg.SEED)
self.num_gpus = torch.cuda.device_count()
self.distributed = self.num_gpus > 1
if self.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl",
init_method="env://")
self.device = torch.device("cuda")
# self.device = 'cpu'
self.rl_stage = False
self.setup_logging()
self.setup_dataset()
self.setup_network()
self.val_evaler = Evaler(datasets.create(
name=args.dataset_name,
image_dir=args.image_dir,
ann_path=args.ann_path,
tokenizer=self.tokenizer,
split='val',
args=args,
),
tokenizer=self.tokenizer) # TODO
self.test_evaler = Evaler(datasets.create(name=args.dataset_name,
image_dir=args.image_dir,
ann_path=args.ann_path,
tokenizer=self.tokenizer,
split='test',
args=args),
tokenizer=self.tokenizer) # TODO
self.scorer = Scorer()