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Miscellaneous config changes. #96

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Miscellaneous config changes. #96

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4 changes: 2 additions & 2 deletions data/maldives_ecosystem_mapping/config_sentinel2.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ model:
data:
class_path: rslearn.train.data_module.RslearnDataModule
init_args:
path: gcs://rslearn-eai/datasets/maldives_ecosystem_mapping/dataset_v1/20240924/
path: gcs://rslearn-eai/datasets/maldives_ecosystem_mapping/dataset_v1/20241017/
inputs:
sentinel2:
data_type: "raster"
Expand Down Expand Up @@ -146,7 +146,7 @@ trainer:
logging_interval: "epoch"
- class_path: rslearn.train.prediction_writer.RslearnWriter
init_args:
path: gcs://rslearn-eai/datasets/maldives_ecosystem_mapping/dataset_v1/20240924/
path: gcs://rslearn-eai/datasets/maldives_ecosystem_mapping/dataset_v1/20241017/
output_layer: output
selector: ["segment"]
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
Expand Down
19 changes: 13 additions & 6 deletions one_off_projects/minderoo/classify/config.yaml
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Expand Up @@ -17,14 +17,15 @@ model:
num_fc_layers: 1
- class_path: rslearn.train.tasks.classification.ClassificationHead
lr: 0.0001
plateau_factor: 0.1
plateau_patience: 10
plateau: true
plateau_factor: 0.5
plateau_patience: 2
plateau_min_lr: 0
plateau_cooldown: 0
plateau_cooldown: 5
data:
class_path: rslearn.train.data_module.RslearnDataModule
init_args:
root_dir: /data/favyenb/dvim/rslearn_classify/
path: /data/favyenb/dvim/rslearn_classify/
inputs:
image:
data_type: "raster"
Expand Down Expand Up @@ -88,8 +89,14 @@ trainer:
logging_interval: "epoch"
- class_path: rslearn.train.prediction_writer.RslearnWriter
init_args:
root_dir: /data/favyenb/dvim/rslearn_classify/
path: /data/favyenb/dvim/rslearn_classify/
output_layer: output
selector: ["class"]
- class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
save_top_k: 1
save_last: true
monitor: val_class/accuracy
mode: max
rslp_project: minderoo_classify
rslp_experiment: data_20240811_satlaspretrain_flip_256_00
rslp_experiment: data_20241028_satlaspretrain_flip_256_00
95 changes: 0 additions & 95 deletions one_off_projects/minderoo/classify/config_infer.yaml
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2 changes: 1 addition & 1 deletion rslp/landsat_vessels/predict_pipeline.py
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Expand Up @@ -215,7 +215,7 @@ def run_classifier(
window.save_layer_datas(dict(LANDSAT_LAYER_NAME=layer_data))

logger.info("materialize dataset")
apply_windows_args = ApplyWindowsArgs(group=group)
apply_windows_args = ApplyWindowsArgs(group=group, workers=32)
materialize_pipeline_args = MaterializePipelineArgs(
disabled_layers=[],
prepare_args=PrepareArgs(apply_windows_args=apply_windows_args),
Expand Down
3 changes: 0 additions & 3 deletions rslp/sentinel2_vessels/job_launcher.py
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Expand Up @@ -61,10 +61,7 @@ def launch_job(image_name: str, tasks: list[PredictionTask]) -> None:
],
constraints=Constraints(
cluster=[
# Don't use Renton cirrascale since it loads very slowly from Weka.
"ai2/prior-elanding",
"ai2/jupiter-cirrascale-2",
"ai2/neptune-cirrascale",
]
),
preemptible=True,
Expand Down
5 changes: 1 addition & 4 deletions rslp/sentinel2_vessels/predict_pipeline.py
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Expand Up @@ -137,7 +137,7 @@ def get_vessel_detections(
window.save_layer_datas(dict(SENTINEL2_LAYER_NAME=layer_data))

logger.info("Materialize dataset for Sentinel-2 Vessel Detection")
apply_windows_args = ApplyWindowsArgs(group=group, workers=1)
apply_windows_args = ApplyWindowsArgs(group=group, workers=32)
materialize_pipeline_args = MaterializePipelineArgs(
disabled_layers=[],
prepare_args=PrepareArgs(apply_windows_args=apply_windows_args),
Expand Down Expand Up @@ -297,9 +297,6 @@ def predict_pipeline(tasks: list[PredictionTask], scratch_path: str) -> None:
with crop_fname.open("wb") as f:
Image.fromarray(image.transpose(1, 2, 0)).save(f, format="PNG")

if scene_id not in json_vessels_by_scene:
json_vessels_by_scene[scene_id] = []

json_vessels_by_scene[scene_id].append(
dict(
longitude=lon,
Expand Down
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