#!/usr/bin/env bash


# gmflow-scale2-regrefine6, inference on image dir
CUDA_VISIBLE_DEVICES=1 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



