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35 lines
1.0 KiB
35 lines
1.0 KiB
#!/bin/bash -l |
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SCRIPTPATH=$(dirname $(readlink -f "$0")) |
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PROJECT_DIR="${SCRIPTPATH}/../../" |
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# conda activate loftr |
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export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH |
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cd $PROJECT_DIR |
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TRAIN_IMG_SIZE=640 |
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# to reproduced the results in our paper, please use: |
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# TRAIN_IMG_SIZE=840 |
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data_cfg_path="configs/data/megadepth_trainval_${TRAIN_IMG_SIZE}.py" |
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main_cfg_path="configs/loftr/outdoor/loftr_ot_dense.py" |
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n_nodes=1 |
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n_gpus_per_node=4 |
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torch_num_workers=4 |
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batch_size=1 |
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pin_memory=true |
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exp_name="outdoor-ot-${TRAIN_IMG_SIZE}-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))" |
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python -u ./train.py \ |
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${data_cfg_path} \ |
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${main_cfg_path} \ |
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--exp_name=${exp_name} \ |
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--gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \ |
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--batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \ |
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--check_val_every_n_epoch=1 \ |
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--log_every_n_steps=1 \ |
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--flush_logs_every_n_steps=1 \ |
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--limit_val_batches=1. \ |
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--num_sanity_val_steps=10 \ |
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--benchmark=True \ |
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--max_epochs=30
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