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40
config/bert_base_6layer_6conect.json
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40
config/bert_base_6layer_6conect.json
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{
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"max_position_embeddings": 512,
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"type_vocab_size": 2,
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"vocab_size": 30522,
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"v_feature_size": 2048,
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"v_target_size": 1601,
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"v_hidden_size": 1024,
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"v_num_hidden_layers": 6,
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"v_num_attention_heads": 8,
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"v_intermediate_size": 1024,
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"bi_hidden_size": 1024,
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"bi_num_attention_heads": 8,
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"bi_intermediate_size": 1024,
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"bi_attention_type": 1,
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"v_attention_probs_dropout_prob": 0.1,
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"v_hidden_act": "gelu",
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"v_hidden_dropout_prob": 0.1,
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"v_initializer_range": 0.02,
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"pooling_method": "mul",
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"v_biattention_id": [0, 1, 2, 3, 4, 5],
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"t_biattention_id": [6, 7, 8, 9, 10, 11],
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"gnn_act": "gelu",
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"num_v_gnn_layers": 2,
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"num_q_gnn_layers": 2,
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"num_h_gnn_layers": 2,
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"num_gnn_attention_heads": 4,
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"gnn_dropout_prob": 0.1,
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"v_gnn_edge_dim": 12,
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"q_gnn_edge_dim": 48,
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"v_gnn_ids": [0, 1, 2, 3, 4, 5],
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"t_gnn_ids": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
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}
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33
config/ensemble.conf
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config/ensemble.conf
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test = {
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split = test
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skip_mrr_eval = true
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# data
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visdial_test_data = data/visdial_1.0_test.json
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# directory
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log_dir = logs/vdgr_ensemble
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pred_dir = logs/vdgr
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visdial_output_dir = visdial_output
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processed = [
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false,
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false,
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false,
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false,
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false,
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false,
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false,
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false
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]
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models = [
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"P2_K1_v1.0_CE",
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"P2_K2_v1.0_CE",
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"P2_K3_v1.0_CE",
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"P2_K4_v1.0_CE",
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"P2_K1_v1.0_LISTNET",
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"P2_K2_v1.0_LISTNET",
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"P2_K3_v1.0_LISTNET",
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"P2_K4_v1.0_LISTNET",
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]
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}
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188
config/vdgr.conf
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config/vdgr.conf
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# Phase 1
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P1 {
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use_cpu = false
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visdial_version = 1.0
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train_on_dense = false
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metrics_to_maximize = mrr
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# visdial data
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visdial_image_feats = data/visdial_img_feat.lmdb
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visdial_image_adj_matrices = data/img_adj_matrices
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visdial_question_adj_matrices = data/question_adj_matrices
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visdial_history_adj_matrices = data/history_adj_matrices
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visdial_train = data/visdial_1.0_train.json
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visdial_val = data/visdial_1.0_val.json
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visdial_test = data/visdial_1.0_test.json
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visdial_val_dense_annotations = data/visdial_1.0_val_dense_annotations.json
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visdial_train_09 = data/visdial_0.9_train.json
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visdial_val_09 = data/visdial_0.9_val.json
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visdial_test_09 = data/visdial_0.9_test.json
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visdialconv_val = data/visdial_conv.json
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visdialconv_val_dense_annotations = data/visdialconv_dense_annotations.json
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visdialvispro_val = data/vispro.json
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visdialvispro_val_dense_annotations = data/vispro_dense_annotations.json
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visdial_question_parse_vocab = data/parse_vocab.pkl
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# init
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start_path = ckpt/vdgr_visdial_v1.0_after_warmup_K2.ckpt
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model_config = config/bert_base_6layer_6conect.json
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# visdial training
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freeze_vilbert = false
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visdial_tot_rounds = 11
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num_negative_samples = 1
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sequences_per_image = 2
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batch_size = 8
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lm_loss_coeff = 1
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nsp_loss_coeff = 1
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img_loss_coeff = 1
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batch_multiply = 1
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use_trainval = false
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dense_loss = ce
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dense_loss_coeff = 0
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dataloader_text_only = false
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rlv_hst_only = false
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rlv_hst_dense_round = false
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# visdial model
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mask_prob = 0.1
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image_mask_prob = 0.1
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max_seq_len = 256
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num_options = 100
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num_options_dense = 100
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use_embedding = joint
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# visdial evaluation
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eval_visdial_on_test = true
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eval_batch_size = 1
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eval_line_batch_size = 200
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skip_mrr_eval = false
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skip_ndcg_eval = false
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skip_visdial_eval = false
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eval_visdial_every = 1
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eval_dataset = visdial # visdial_vispro # choices = [visdial, visdial_conv, visdial_vispro ]
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continue_evaluation = false
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eval_at_start = false
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eval_before_training = false
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initializer = normal
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bert_cased = false
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# restore ckpt
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loads_best_ckpt = false
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loads_ckpt = false
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restarts = false
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resets_max_metric = false
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uses_new_optimizer = false
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sets_new_lr = false
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loads_start_path = false
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# logging
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random_seed = 42
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next_logging_pct = 1.0
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next_evaluating_pct = 50.0
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max_ckpt_to_keep = 1
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num_epochs = 20
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early_stop_epoch = 5
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skip_saving_ckpt = false
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dp_type = apex
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stack_gr_data = false
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master_port = 5122
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stop_epochs = -1
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train_each_round = false
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drop_last_answer = false
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num_samples = -1
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# predicting
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predict_split = test
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predict_each_round = false
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predict_dense_round = false
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num_test_dialogs = 8000
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num_val_dialogs = 2064
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save_score = false
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# optimizer
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reset_optim = none
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learning_rate_bert = 5e-6
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learning_rate_gnn = 2e-4
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gnn_weight_decay = 0.01
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use_diff_lr_gnn = true
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min_lr = 0
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decay_method_bert = linear
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decay_method_gnn = linear
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decay_exp = 2
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max_grad_norm = 1.0
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task_optimizer = adam
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warmup_ratio = 0.1
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# directory
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log_dir = logs/vdgr
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data_dir = data
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visdial_output_dir = visdial_output
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bert_cache_dir = transformers
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# keep track of other hparams in bert json
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v_gnn_edge_dim = 12 # 11 classes + hub_node
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q_gnn_edge_dim = 48 # 47 classes + hub_node
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num_v_gnn_layers = 2
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num_q_gnn_layers = 2
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num_h_gnn_layers = 2
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num_gnn_attention_heads = 4
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v_gnn_ids = [0, 1, 2, 3, 4, 5]
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t_gnn_ids = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
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}
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# Phase 2
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P2_CE = ${P1} {
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# basic
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train_on_dense = true
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use_trainval = true
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metrics_to_maximize = ndcg
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visdial_train_dense = data/visdial_1.0_train_dense.json
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visdial_train_dense_annotations = data/visdial_1.0_train_dense_annotations.json
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visdial_val_dense = data/visdial_1.0_val.json
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tr_graph_idx_mapping = data/tr_dense_mapping.json
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val_graph_idx_mapping = data/val_dense_mapping.json
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test_graph_idx_mapping = data/test_dense_mapping.json
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visdial_val = data/visdial_1.0_val.json
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visdial_val_dense_annotations = data/visdial_1.0_val_dense_annotations.json
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# data
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start_path = logs/vdgr/P1_K2_v1.0/epoch_best.ckpt
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rlv_hst_only = false
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# visdial training
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nsp_loss_coeff = 0
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dense_loss_coeff = 1
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batch_multiply = 10
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batch_size = 1
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# visdial model
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num_options_dense = 100
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# visdial evaluation
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eval_batch_size = 1
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eval_line_batch_size = 100
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skip_mrr_eval = true
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# training
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stop_epochs = 3
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dp_type = dp
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dense_loss = ce
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# optimizer
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learning_rate_bert = 1e-4
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}
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P2_LISTNET = ${P2_CE} {
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dense_loss = listnet
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}
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