An error occurred when predicting las data, without any prompt.
lqlnbcr opened this issue · 3 comments
I'm using the latest version of the main branch myria3d. The las data dimensions I use for prediction include ['X','Y','Z','intensity','return_number','number_of_returns','synthetic','key_point','withheld','overlap','scanner_channel','scan_direction_flag','edge_of_flight_line','classification','user_data','scan_angle','point_source_id','gps_time','red','green','blue','Infrared'].
When I tried overfit on the toy dataset everything works fine with the code. But when I try to use the ckpt and yaml of "proto151_V2.0_epoch_100_Myria3DV3.1.0" in "trained_model_assets" for inference, an unknown error occurred. Terminates without any prompt.
My input:
First:
python -m myria3d.predict --config-path /trained_model_assets/ --config-name proto151_V2.0_epoch_100_Myria3DV3.1.0_predict_config_V3.2.0.yaml predict.src_las=/tests/data/Sydney_dataset/newdata.las predict.output_dir=/tests/data/output_dir predict.ckpt_path=/trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0.ckpt predict.gpus=0 datamodule.batch_size=2 hydra.run.dir=/tests/data/log_dir
Second:
python run.py \ task.task_name=predict \ predict.src_las=tests/data/Sydney_dataset/newdata.las \ predict.output_dir=/tests/data/output_dir/ \ predict.gpus=0 \ datamodule.batch_size=2
The first input terminates without any return at all.
The second input returns the complete config information and a string of characters "0it [00:00, ?it/s]", but I don't know what it means.
very much hope to get your help, thanks.
input：
python run.py \
task.task_name=predict \
predict.src_las=tests/data/Sydney_dataset/newdata.las \
predict.output_dir=/tests/data/output_dir/ \
predict.gpus=0 \
datamodule.batch_size=2
return：
CONFIG
├── task
│ └── task_name: predict
│
├── seed
│ └── 12345
├── 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: '[V3.0.2-BS10xMAX40000pts] RandLaNet_base_run_FR_pyg_randla_net_NoRS'
│ auto_log_co2: false
│ disabled: false
│
├── trainer
│ └── _target_: pytorch_lightning.Trainer
│ gpus:
│ - 0
│ - 1
│ min_epochs: 100
│ max_epochs: 150
│ log_every_n_steps: 1
│ weights_summary: null
│ progress_bar_refresh_rate: 1
│ auto_lr_find: false
│ num_sanity_val_steps: 2
│ accumulate_grad_batches: 3
│ strategy: ddp
│ accelerator: gpu
│ num_processes: 2
│
├── model
│ └── optimizer:
│ _target_: functools.partial
│ _args_:
│ - ${get_method:torch.optim.Adam}
│ lr: ${model.lr}
│ lr_scheduler:
│ _target_: functools.partial
│ _args_:
│ - ${get_method:torch.optim.lr_scheduler.ReduceLROnPlateau}
│ mode: min
│ factor: 0.5
│ patience: 20
│ cooldown: 5
│ verbose: true
│ criterion:
│ _target_: torch.nn.CrossEntropyLoss
│ label_smoothing: 0.0
│ _target_: myria3d.models.model.Model
│ d_in: ${dataset_description.d_in}
│ num_classes: ${dataset_description.num_classes}
│ ckpt_path: null
│ 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
│ iou:
│ _target_: functools.partial
│ _args_:
│ - ${get_method:torchmetrics.JaccardIndex}
│ - ${model.num_classes}
│ absent_score: 1.0
│ momentum: 0.9
│ monitor: val/loss_epoch
│ lr: 0.003933709606504788
│
├── datamodule
│ └── transforms:
│ preparations:
│ train:
│ TargetTransform:
│ _target_: myria3d.pctl.transforms.transforms.TargetTransform
│ _args_:
│ - ${dataset_description.classification_preprocessing_dict}
│ - ${dataset_description.classification_dict}
│ DropPointsByClass:
│ _target_: myria3d.pctl.transforms.transforms.DropPointsByClass
│ 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
│ Center:
│ _target_: torch_geometric.transforms.Center
│ 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
│ augmentations:
│ x_flip:
│ _target_: torch_geometric.transforms.RandomFlip
│ _args_:
│ - 0
│ p: 0.5
│ y_flip:
│ _target_: torch_geometric.transforms.RandomFlip
│ _args_:
│ - 1
│ p: 0.5
│ normalizations:
│ NullifyLowestZ:
│ _target_: myria3d.pctl.transforms.transforms.NullifyLowestZ
│ NormalizePos:
│ _target_: myria3d.pctl.transforms.transforms.NormalizePos
│ subtile_width: ${datamodule.subtile_width}
│ StandardizeRGBAndIntensity:
│ _target_: myria3d.pctl.transforms.transforms.StandardizeRGBAndIntensity
│ augmentations_list: '${oc.dict.values: datamodule.transforms.augmentations}'
│ preparations_train_list: '${oc.dict.values: datamodule.transforms.preparations.train}'
│ preparations_eval_list: '${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
│ data_dir: null
│ split_csv_path: null
│ hdf5_file_path: /var/data/cgaydon/data/20220607_151_dalles_proto.myria3d-V2.4.hdf5
│ 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: 50
│ tile_width: 1000
│ subtile_width: 50
│ subtile_shape: square
│ subtile_overlap_train: 0
│ subtile_overlap_predict: ${predict.subtile_overlap}
│ batch_size: 2
│ num_workers: 3
│ prefetch_factor: 3
│
├── dataset_description
│ └── _convert_: all
│ classification_preprocessing_dict:
│ 3: 5
│ 4: 5
│ 160: 64
│ 161: 64
│ 162: 64
│ 0: 1
│ 7: 1
│ 46: 1
│ 47: 1
│ 48: 1
│ 49: 1
│ 50: 1
│ 51: 1
│ 52: 1
│ 53: 1
│ 54: 1
│ 55: 1
│ 56: 1
│ 57: 1
│ 58: 1
│ 64: 1
│ 66: 1
│ 67: 1
│ 77: 1
│ 155: 1
│ 204: 1
│ classification_dict:
│ 1: unclassified
│ 2: ground
│ 5: vegetation
│ 6: building
│ 9: water
│ 17: bridge
│ 64: lasting_above
│ class_weights:
│ - 0.25
│ - 0.1
│ - 0.1
│ - 0.5
│ - 2.0
│ - 2.0
│ - 2.0
│ 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
│ log_iou_by_class:
│ _target_: myria3d.callbacks.logging_callbacks.LogIoUByClass
│ classification_dict: ${dataset_description.classification_dict}
│ 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
│
└── predict
└── src_las: tests/data/Sydney_dataset/newdata.las
output_dir: /tests/data/output_dir/
ckpt_path: /home/lqlnbcr/myria3d/trained_model_assets/proto151_V2.0_epoch_100_Myria3DV3.1.0.ckpt
subtile_overlap: 0
gpus: 0
interpolator:
_target_: myria3d.models.interpolation.Interpolator
interpolation_k: 10
classification_dict: ${dataset_description.classification_dict}
probas_to_save:
- building
- ground
predicted_classification_channel: confidence
entropy_channel: entropy
0it [00:00, ?it/s]
Hi @lqlnbcr,
Using python -m myria3d.predict is deprecated so you were right to use python run.py task.task_name=predict following the updated doc :)
It seems that the predict.output_dir=/tests/data/output_dir/ uses an absolute path that starts with "/", when you probably meant the relative "tests/data/output_dir/".
This alone would create an error as your command would not have writing credentials to create the /tests/ folder (this happens at the end of the inference here).
0it [00:00, ?it/s] comes from the tqdm here. This means you were at the 0th batch when the system crashed. If your las is small (it fits in a single batch) this is probably normal.
Feel free to make a small PR to make its meaning more explicit with e.g. unit=batches as a tqdm parameter :)
Hi @CharlesGaydon , thank you for your reply.
I found that code can't use relative paths, and it can be run by replacing all of relative paths with complete absolute paths. The results were excellent.