gmflow_demo.sh

#!/usr/bin/env bash


# gmflow-scale2-regrefine6, inference on image dir
CUDA_VISIBLE_DEVICES=0 python main_flow.py \
--inference_dir demo/flow-davis \
--resume pretrained/gmflow-scale2-regrefine6-mixdata-train320x576-4e7b215d.pth \
--output_path output/gmflow-scale2-regrefine6-davis \
--padding_factor 32 \
--upsample_factor 4 \
--num_scales 2 \
--attn_splits_list 2 8 \
--corr_radius_list -1 4 \
--prop_radius_list -1 1 \
--reg_refine \
--num_reg_refine 6


# gmflow-scale2-regrefine6, inference on video, save as video
CUDA_VISIBLE_DEVICES=0 python main_flow.py \
--inference_video demo/kitti.mp4 \
--resume pretrained/gmflow-scale2-regrefine6-kitti15-25b554d7.pth \
--output_path output/kitti \
--padding_factor 32 \
--upsample_factor 4 \
--num_scales 2 \
--attn_splits_list 2 8 \
--corr_radius_list -1 4 \
--prop_radius_list -1 1 \
--reg_refine \
--num_reg_refine 6 \
--save_video \
--concat_flow_img



# gmflow-scale1, inference on image dir
CUDA_VISIBLE_DEVICES=0 python main_flow.py \
--inference_dir demo/flow-davis \
--resume pretrained/gmflow-scale1-mixdata-train320x576-4c3a6e9a.pth \
--output_path output/gmflow-scale1-davis

# optional predict bidirection flow and forward-backward consistency check
#--pred_bidir_flow
#--fwd_bwd_check


# gmflow-scale2, inference on image dir
CUDA_VISIBLE_DEVICES=0 python main_flow.py \
--inference_dir demo/flow-davis \
--resume pretrained/gmflow-scale2-mixdata-train320x576-9ff1c094.pth \
--output_path output/gmflow-scale2-davis \
--padding_factor 32 \
--upsample_factor 4 \
--num_scales 2 \
--attn_splits_list 2 8 \
--corr_radius_list -1 4 \
--prop_radius_list -1 1