# Determine the paths to your SMART data and experiment repositories. DATA_DVC_DPATH=$(geowatch_dvc --tags='phase2_data' --hardware=auto) EXPT_DVC_DPATH=$(geowatch_dvc --tags='phase2_expt' --hardware=auto) echo " EXPT_DVC_DPATH=$EXPT_DVC_DPATH DATA_DVC_DPATH=$DATA_DVC_DPATH " # The baseline model is checked into the experiment DVC repo. This is the # model we used in the November delievery. You may need to pull it from DVC if # you haven't already. # BASELINE_PACKAGE_FPATH="$EXPT_DVC_DPATH"/training/horologic/connor.greenwell/Aligned-Drop4-2022-08-08-TA1-S2-WV-PD-ACC/runs/Drop4_SC_UNet/lightning_logs/version_5/checkpoints/epoch\=1092-step\=14209.pt # BASELINE_PACKAGE_FPATH="$EXPT_DVC_DPATH"/models/fusion/Drop4-BAS/packages/Drop4_TuneV323_BAS_30GSD_BGRNSH_V2/package_epoch0_step41.pt.pt # BASELINE_PACKAGE_FPATH="$EXPT_DVC_DPATH"/models/fusion/Drop4-BAS/packages/Drop4_BAS_15GSD_BGRNSH_invar_V8/Drop4_BAS_15GSD_BGRNSH_invar_V8_epoch=16-step=8704.pt BASELINE_PACKAGE_FPATH="$EXPT_DVC_DPATH"/models/fusion/Drop4-BAS/packages/Drop4_BAS_2022_12_15GSD_BGRN_V10/Drop4_BAS_2022_12_15GSD_BGRN_V10_epoch=0-step=4305.pt # NOTE: # the feature_fusion_tutorial curently just runs the baseline on # ``data_vali.kwcoco.json`` but here we run that file through ``split_videos`` # first which breaks it up into a kwcoco file per region. We then run the # evaluation on each region separately. We will likely want to adopt this # strategy for running evaluations so we can compare results at a more # granular level. # python -m geowatch.cli.split_videos "$DATA_DVC_DPATH"/Drop4-BAS/data_vali.kwcoco.json python -m geowatch.mlops.schedule_evaluation \ --params=" matrix: bas_pxl.package_fpath: - $BASELINE_PACKAGE_FPATH bas_pxl.test_dataset: #- $DATA_DVC_DPATH/Drop4-BAS/data_vali.kwcoco.json - $DATA_DVC_DPATH/Drop4-BAS/data_vali_KR_R001.kwcoco.json - $DATA_DVC_DPATH/Drop4-BAS/data_vali_KR_R002.kwcoco.json - $DATA_DVC_DPATH/Drop4-BAS/data_vali_US_R007.kwcoco.json - $DATA_DVC_DPATH/Drop4-BAS/BR_R001.kwcoco.json - $DATA_DVC_DPATH/Drop4-BAS/BR_R002.kwcoco.json - $DATA_DVC_DPATH/Drop4-BAS/AE_R001.kwcoco.json #- $DATA_DVC_DPATH/Drop4-BAS/data_vali_KR_R001_uky_invariants.kwcoco.json #- $DATA_DVC_DPATH/Drop4-BAS/data_vali_KR_R002_uky_invariants.kwcoco.json #- $DATA_DVC_DPATH/Drop4-BAS/data_vali_US_R007_uky_invariants.kwcoco.json #- $DATA_DVC_DPATH/Drop4-BAS/data_train_BR_R001_uky_invariants.kwcoco.json #- $DATA_DVC_DPATH/Drop4-BAS/data_train_BR_R002_uky_invariants.kwcoco.json #- $DATA_DVC_DPATH/Drop4-BAS/data_train_AE_R001_uky_invariants.kwcoco.json bas_pxl.channels: auto bas_pxl.chip_dims: 128, 128 bas_pxl.chip_overlap: 0.3 bas_pxl.window_space_scale: auto bas_pxl.output_space_scale: auto bas_pxl.input_space_scale: auto bas_pxl.time_span: auto bas_pxl.time_sampling: auto bas_poly.moving_window_size: null bas_poly.min_area_sqkm: null bas_poly.max_area_sqkm: null bas_poly.max_area_behavior: 'ignore' bas_poly.response_thresh: null bas_poly.time_thresh: null bas_poly.morph_kernel: 0 bas_poly.thresh: #- 0.025 - 0.05 #- 0.075 - 0.1 #- 0.125 - 0.15 #- 0.175 - 0.2 #- 0.225 - 0.25 #- 0.275 - 0.3 #- 0.325 - 0.35 #- 0.375 - 0.4 #- 0.425 - 0.45 #- 0.475 - 0.5 - 0.55 - 0.6 - 0.65 - 0.7 - 0.75 - 0.8 - 0.85 - 0.9 bas_poly.agg_fn: - probs - rescaled_probs #- binary #- rescaled_binary bas_pxl.enabled: 1 bas_poly.enabled: 1 bas_poly_eval.enabled: 1 bas_pxl_eval.enabled: 1 bas_poly_viz.enabled: 0 " \ --root_dpath="$EXPT_DVC_DPATH/_evaluations/normalized_expt2" \ --devices="1," --queue_size=6 \ --backend=tmux --queue_name "baseline-queue" \ --virtualenv_cmd="conda activate watch_py3.9" \ --pipeline=bas \ --run=1