Baseline 2024-06-11 BAS ----------------------- The following is the EVAL23 baseline MLOPs grid for BAS-only. .. code:: python from geowatch.mlops.smart_pipeline import * # NOQA dag = make_smart_pipeline('bas') # Show the graph structure of inputs and outputs dag.print_graphs() # List what known parameters are configurable dag.inspect_configurables() .. code:: bash DVC_DATA_DPATH=$(geowatch_dvc --tags='phase3_data' --hardware=auto) DVC_EXPT_DPATH=$(geowatch_dvc --tags='phase3_expt' --hardware=auto) TRUTH_DPATH=$DVC_DATA_DPATH/annotations/drop8 MLOPS_NAME=_bas_only_baseline MLOPS_DPATH=$DVC_EXPT_DPATH/$MLOPS_NAME # Set this to the GPU numbers you want to use. DEVICES="1,2" MODEL_SHORTLIST=" - $DVC_EXPT_DPATH/models/fusion/Drop8-Median10GSD-V1/packages/Drop8_Median10GSD_allsensors_scratch_V7/Drop8_Median10GSD_allsensors_scratch_V7_epoch187_step2632.pt - $DVC_EXPT_DPATH/models/fusion/uconn/D7-V2-COLD-candidate/epoch=203-step=4488.pt " mkdir -p "$MLOPS_DPATH" echo "$MODEL_SHORTLIST" > "$MLOPS_DPATH/shortlist.yaml" cat "$MLOPS_DPATH/shortlist.yaml" geowatch schedule --params=" pipeline: bas matrix: bas_pxl.package_fpath: $MLOPS_DPATH/shortlist.yaml bas_pxl.test_dataset: - $DVC_DATA_DPATH/Drop8-ARA-Median10GSD-V1/KR_R002/imganns-KR_R002-rawbands.kwcoco.zip - $DVC_DATA_DPATH/Drop8-ARA-Median10GSD-V1/CN_C000/imganns-CN_C000-rawbands.kwcoco.zip - $DVC_DATA_DPATH/Drop8-ARA-Median10GSD-V1/KW_C001/imganns-KW_C001-rawbands.kwcoco.zip - $DVC_DATA_DPATH/Drop8-ARA-Median10GSD-V1/CO_C001/imganns-CO_C001-rawbands.kwcoco.zip bas_pxl.chip_overlap: 0.3 bas_pxl.chip_dims: auto bas_pxl.time_span: auto bas_pxl.time_sampling: soft4 bas_poly.thresh: #- 0.10 #- 0.30 - 0.35 #- 0.4 bas_poly.inner_window_size: 1y bas_poly.inner_agg_fn: mean bas_poly.norm_ord: inf bas_poly.polygon_simplify_tolerance: 1 bas_poly.agg_fn: probs bas_poly.time_thresh: - 0.8 #- 0.6 bas_poly.resolution: 10GSD bas_poly.moving_window_size: null bas_poly.poly_merge_method: 'v2' bas_poly.time_pad_after: 3 months bas_poly.time_pad_before: 3 months bas_poly.min_area_square_meters: 7200 bas_poly.max_area_square_meters: 8000000 bas_poly.boundary_region: $TRUTH_DPATH/region_models bas_poly_eval.true_site_dpath: $TRUTH_DPATH/site_models bas_poly_eval.true_region_dpath: $TRUTH_DPATH/region_models bas_pxl.enabled: 1 bas_pxl_eval.enabled: 1 bas_poly_viz.enabled: 0 bas_poly.enabled: 1 bas_poly_eval.enabled: 1 " \ --root_dpath="$MLOPS_DPATH" \ --devices="$DEVICES" --tmux_workers=4 \ --backend=tmux --queue_name "$MLOPS_NAME" \ --skip_existing=1 \ --run=1 The process graph for this pipeline looks like: .. code:: Process Graph ╙── bas_pxl ├─╼ bas_pxl_eval └─╼ bas_poly ├─╼ bas_poly_eval └─╼ bas_poly_viz To report your scores: .. code:: bash DVC_EXPT_DPATH=$(geowatch_dvc --tags='phase3_expt' --hardware=auto) MLOPS_DPATH=$DVC_EXPT_DPATH/_bas_only_baseline echo "DVC_EXPT_DPATH = $DVC_EXPT_DPATH" python -m geowatch.mlops.aggregate \ --pipeline=bas \ --target " - $MLOPS_DPATH " \ --export_tables=0 \ --output_dpath="$MLOPS_DPATH/aggregate" \ --resource_report=0 \ --eval_nodes=" - bas_poly_eval #- bas_pxl_eval " \ --plot_params=" enabled: 0 stats_ranking: 0 min_variations: 1 #params_of_interest: # - params.bas_poly.thresh # - resolved_params.bas_pxl.channels " \ --stdout_report=" top_k: 10 per_group: 1 macro_analysis: 0 analyze: 0 print_models: True reference_region: final concise: 1 show_csv: 0 " \ --rois="KR_R002,CN_C000,KW_C001,CO_C001"