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Running
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Zero
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#!/bin/sh
set -x
set -e
input_rgb_root=/path/to/input/RGB/root_directory # The parent directory that contaning [sintel, scannet, KITTI, bonn, NYUv2] input RGB
saved_root=/path/to/saved/root_directory # The parent directory that saving [sintel, scannet, KITTI, bonn, NYUv2] prediction
gpus=0,1,2,3 # Using 4 GPU, you can adjust it according to your device
# infer sintel
python benchmark/infer/infer_batch.py \
--meta_path ./eval/csv/meta_sintel.csv \
--saved_root ${saved_root} \
--saved_dataset_folder results_sintel \
--input_rgb_root ${input_rgb_root} \
--process_length 50 \
--gpus ${gpus} \
--dataset sintel \
# infer scannet
python benchmark/infer/infer_batch.py \
--meta_path ./eval/csv/meta_scannet_test.csv \
--saved_root ${saved_root} \
--saved_dataset_folder results_scannet \
--input_rgb_root ${input_rgb_root} \
--process_length 90 \
--gpus ${gpus} \
--dataset scannet \
# infer kitti
python benchmark/infer/infer_batch.py \
--meta_path ./eval/csv/meta_kitti_val.csv \
--saved_root ${saved_root} \
--saved_dataset_folder results_kitti \
--input_rgb_root ${input_rgb_root} \
--process_length 110 \
--gpus ${gpus} \
--dataset kitti \
# infer bonn
python benchmark/infer/infer_batch.py \
--meta_path ./eval/csv/meta_bonn.csv \
--saved_root ${saved_root} \
--saved_dataset_folder results_bonn \
--input_rgb_root ${input_rgb_root} \
--process_length 110 \
--gpus ${gpus} \
--dataset bonn \
# infer nyu
python benchmark/infer/infer_batch.py \
--meta_path ./eval/csv/meta_nyu_test.csv \
--saved_root ${saved_root} \
--saved_dataset_folder results_nyu \
--input_rgb_root ${input_rgb_root} \
--process_length 1 \
--gpus ${gpus} \
--overlap 0 \
--dataset nyu \
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