66 lines
1.3 KiB
Bash
66 lines
1.3 KiB
Bash
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train_alg=dagger
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features=vitbase
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ft_dim=768
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obj_features=vitbase
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obj_ft_dim=768
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ngpus=1
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seed=0
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name=${train_alg}-${features}
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name=${name}-seed.${seed}
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name=${name}-init.aug.45k
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outdir=${DATA_ROOT}/R2R/exprs_map/finetune/${name}
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flag="--root_dir ${DATA_ROOT}
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--dataset r4r
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--output_dir ${outdir}
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--world_size ${ngpus}
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--seed ${seed}
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--tokenizer bert
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--enc_full_graph
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--graph_sprels
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--fusion dynamic
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--expert_policy spl
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--train_alg ${train_alg}
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--num_l_layers 9
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--num_x_layers 4
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--num_pano_layers 2
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--max_action_len 15
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--max_instr_len 200
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--batch_size 8
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--lr 1e-5
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--iters 200000
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--log_every 1000
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--optim adamW
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--features ${features}
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--image_feat_size ${ft_dim}
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--angle_feat_size 4
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--ml_weight 0.2
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--feat_dropout 0.4
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--dropout 0.5
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--gamma 0."
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# train
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CUDA_VISIBLE_DEVICES='0' python r2r/main_nav.py $flag \
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--tokenizer bert \
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--bert_ckpt_file 'put the pretrained model (see pretrain_src) here' \
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--eval_first
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# test
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CUDA_VISIBLE_DEVICES='0' python r2r/main_nav.py $flag \
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--tokenizer bert \
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--resume_file ../datasets/R2R/trained_models/best_val_unseen \
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--test --submit |