DATA: dataset: refcocog_u train_lmdb: /home/s1/chaeyunkim/VerbCentric_CY/datasets/lmdb/refcocog_u/train.lmdb train_split: train val_lmdb: /home/s1/chaeyunkim/VerbCentric_CY/datasets/lmdb/refcocog_u/val.lmdb val_split: val mask_root: /home/s1/chaeyunkim/VerbCentric_CY/datasets/masks/refcocog_u TRAIN: # Base Arch clip_pretrain: pretrain/RN50.pt input_size: 416 word_len: 22 word_dim: 1024 vis_dim: 512 fpn_in: [512, 1024, 1024] fpn_out: [256, 512, 1024] sync_bn: True freeze: True # Decoder num_layers: 3 num_head: 8 dim_ffn: 2048 dropout: 0.1 intermediate: False # Training Setting workers: 4 # data loader workers workers_val: 4 epochs: 50 milestones: [35] start_epoch: 0 batch_size: 32 # batch size for training batch_size_val: 32 # batch size for validation during training, memory and speed tradeoff base_lr: 0.0001 lr_decay: 0.1 lr_multi: 0.1 weight_decay: 0. max_norm: 0. manual_seed: 0 print_freq: 100 # metric learning args metric_learning: True # specific metric learning args metric_mode : 'hardpos_only_rev' # Choice : ['hardpos_only', 'hardpos_only_rev', 'both'] exclude_multiobj : True # exclude multiobj (nobj >= 3) exclude_pos : True # exclude multiobj w/ positional query expression (nobj >=2 && positional query) loss_option : 'ACL_verbonly' # Choice : ['AML_verbonly', 'AML', 'ACL', 'ACL_verbonly'] metric_loss_weight: 0.1 hn_prob: 0.0 hn_celoss: False # Angular Margin Contrastive Loss argument margin_value : 10 temperature : 0.05 # Resume & Save exp_name: CRIS_AML_verbonly_pos25_b32 output_folder: exp/refcocog_u/exclude_multiobj save_freq: 1 weight: # path to initial weight (default: none) resume: "latest" # path to latest checkpoint (default: none) evaluate: True # evaluate on validation set, extra gpu memory needed and small batch_size_val is recommend Distributed: dist_url: tcp://localhost:2298 dist_backend: 'nccl' multiprocessing_distributed: True world_size: 1 rank: 0 TEST: test_split: val-test test_lmdb: /home/s1/chaeyunkim/VerbCentric_CY/datasets/lmdb/refcocog_u/val.lmdb visualize: False