Rename FRACTAL-LidarHD_7cl_randlanet-inference-Myria3DV3.3.yaml to FRACTAL-LidarHD_7cl_randlanet-inference-Myria3DV3.8.yaml
30fc749
verified
| seed: 12345 | |
| work_dir: ${hydra:runtime.cwd} | |
| debug: false | |
| print_config: true | |
| ignore_warnings: true | |
| datamodule: | |
| transforms: | |
| preparations: | |
| eval: | |
| TargetTransform: | |
| _target_: myria3d.pctl.transforms.transforms.TargetTransform | |
| _args_: | |
| - ${dataset_description.classification_preprocessing_dict} | |
| - ${dataset_description.classification_dict} | |
| DropPointsByClass: | |
| _target_: myria3d.pctl.transforms.transforms.DropPointsByClass | |
| CopyFullPos: | |
| _target_: myria3d.pctl.transforms.transforms.CopyFullPos | |
| CopyFullPreparedTargets: | |
| _target_: myria3d.pctl.transforms.transforms.CopyFullPreparedTargets | |
| GridSampling: | |
| _target_: torch_geometric.transforms.GridSampling | |
| _args_: | |
| - 0.25 | |
| MinimumNumNodes: | |
| _target_: myria3d.pctl.transforms.transforms.MinimumNumNodes | |
| _args_: | |
| - 300 | |
| MaximumNumNodes: | |
| _target_: myria3d.pctl.transforms.transforms.MaximumNumNodes | |
| _args_: | |
| - 40000 | |
| CopySampledPos: | |
| _target_: myria3d.pctl.transforms.transforms.CopySampledPos | |
| Center: | |
| _target_: torch_geometric.transforms.Center | |
| predict: | |
| DropPointsByClass: | |
| _target_: myria3d.pctl.transforms.transforms.DropPointsByClass | |
| CopyFullPos: | |
| _target_: myria3d.pctl.transforms.transforms.CopyFullPos | |
| GridSampling: | |
| _target_: torch_geometric.transforms.GridSampling | |
| _args_: | |
| - 0.25 | |
| MinimumNumNodes: | |
| _target_: myria3d.pctl.transforms.transforms.MinimumNumNodes | |
| _args_: | |
| - 300 | |
| MaximumNumNodes: | |
| _target_: myria3d.pctl.transforms.transforms.MaximumNumNodes | |
| _args_: | |
| - 40000 | |
| CopySampledPos: | |
| _target_: myria3d.pctl.transforms.transforms.CopySampledPos | |
| Center: | |
| _target_: torch_geometric.transforms.Center | |
| normalizations: | |
| NullifyLowestZ: | |
| _target_: myria3d.pctl.transforms.transforms.NullifyLowestZ | |
| NormalizePos: | |
| _target_: myria3d.pctl.transforms.transforms.NormalizePos | |
| subtile_width: ${datamodule.subtile_width} | |
| StandardizeRGBAndIntensity: | |
| _target_: myria3d.pctl.transforms.transforms.StandardizeRGBAndIntensity | |
| preparations_eval_list: '${oc.dict.values: datamodule.transforms.preparations.eval}' | |
| preparations_predict_list: '${oc.dict.values: datamodule.transforms.preparations.predict}' | |
| normalizations_list: '${oc.dict.values: datamodule.transforms.normalizations}' | |
| _target_: myria3d.pctl.datamodule.hdf5.HDF5LidarDataModule | |
| epsg: 2154 | |
| data_dir: null | |
| split_csv_path: null | |
| hdf5_file_path: null | |
| points_pre_transform: | |
| _target_: functools.partial | |
| _args_: | |
| - ${get_method:myria3d.pctl.points_pre_transform.lidar_hd.lidar_hd_pre_transform} | |
| pre_filter: | |
| _target_: functools.partial | |
| _args_: | |
| - ${get_method:myria3d.pctl.dataset.utils.pre_filter_below_n_points} | |
| min_num_nodes: 1 | |
| tile_width: 1000 | |
| subtile_width: 50 | |
| subtile_overlap_predict: ${predict.subtile_overlap} | |
| batch_size: 10 | |
| num_workers: 3 | |
| prefetch_factor: 3 | |
| dataset_description: | |
| _convert_: all | |
| classification_preprocessing_dict: | |
| 3: 5 | |
| 4: 5 | |
| 66: 65 | |
| classification_dict: | |
| 1: unclassified | |
| 2: ground | |
| 5: vegetation | |
| 6: building | |
| 9: water | |
| 17: bridge | |
| 64: lasting_above | |
| d_in: 9 | |
| num_classes: 7 | |
| callbacks: | |
| log_code: | |
| _target_: myria3d.callbacks.comet_callbacks.LogCode | |
| code_dir: ${work_dir}/myria3d | |
| log_logs_dir: | |
| _target_: myria3d.callbacks.comet_callbacks.LogLogsPath | |
| lr_monitor: | |
| _target_: pytorch_lightning.callbacks.LearningRateMonitor | |
| logging_interval: step | |
| log_momentum: true | |
| model_checkpoint: | |
| _target_: pytorch_lightning.callbacks.ModelCheckpoint | |
| monitor: val/loss_epoch | |
| mode: min | |
| save_top_k: 1 | |
| save_last: true | |
| verbose: true | |
| dirpath: checkpoints/ | |
| filename: epoch_{epoch:03d} | |
| auto_insert_metric_name: false | |
| early_stopping: | |
| _target_: pytorch_lightning.callbacks.EarlyStopping | |
| monitor: val/loss_epoch | |
| mode: min | |
| patience: 6 | |
| min_delta: 0 | |
| model: | |
| _target_: myria3d.models.model.Model | |
| d_in: ${dataset_description.d_in} | |
| num_classes: ${dataset_description.num_classes} | |
| classification_dict: ${dataset_description.classification_dict} | |
| ckpt_path: FRACTAL-LidarHD_7cl_randlanet.ckpt | |
| neural_net_class_name: PyGRandLANet | |
| neural_net_hparams: | |
| num_features: ${model.d_in} | |
| num_classes: ${model.num_classes} | |
| num_neighbors: 16 | |
| decimation: 4 | |
| return_logits: true | |
| interpolation_k: ${predict.interpolator.interpolation_k} | |
| num_workers: 4 | |
| logger: | |
| comet: | |
| _target_: pytorch_lightning.loggers.comet.CometLogger | |
| api_key: ${oc.env:COMET_API_TOKEN} | |
| workspace: ${oc.env:COMET_WORKSPACE} | |
| project_name: ${oc.env:COMET_PROJECT_NAME} | |
| experiment_name: DATAPAPER-LidarHD-20240416_100k_fractal-6GPUs | |
| auto_log_co2: false | |
| disabled: false | |
| task: | |
| task_name: predict | |
| predict: | |
| src_las: /path/to/input.las | |
| output_dir: /path/to/output_dir/ | |
| ckpt_path: FRACTAL-LidarHD_7cl_randlanet.ckpt | |
| gpus: 0 | |
| subtile_overlap: 0 | |
| interpolator: | |
| _target_: myria3d.models.interpolation.Interpolator | |
| interpolation_k: 10 | |
| classification_dict: ${dataset_description.classification_dict} | |
| probas_to_save: [building,ground] | |
| predicted_classification_channel: PredictedClassification | |
| entropy_channel: entropy | |