# lightning.pytorch==2.4.0 seed_everything: 0 trainer: accelerator: auto strategy: auto devices: auto num_nodes: 1 precision: 16-mixed logger: class_path: lightning.pytorch.loggers.TensorBoardLogger init_args: save_dir: /dccstor/geofm-finetuning/benchmark-geo-bench-paolo/ name: test2 log_graph: false default_hp_metric: true prefix: '' comment: '' max_queue: 10 flush_secs: 120 filename_suffix: '' callbacks: - class_path: lightning.pytorch.callbacks.RichProgressBar init_args: refresh_rate: 1 leave: false theme: description: white progress_bar: '#6206E0' progress_bar_finished: '#6206E0' progress_bar_pulse: '#6206E0' batch_progress: white time: grey54 processing_speed: grey70 metrics: white metrics_text_delimiter: ' ' metrics_format: .3f - class_path: lightning.pytorch.callbacks.LearningRateMonitor init_args: logging_interval: epoch log_momentum: false log_weight_decay: false - class_path: lightning.pytorch.callbacks.EarlyStopping init_args: monitor: val/loss min_delta: 0.0 patience: 20 verbose: false mode: min strict: true check_finite: true log_rank_zero_only: false fast_dev_run: false max_epochs: 50 max_steps: -1 overfit_batches: 0.0 check_val_every_n_epoch: 2 log_every_n_steps: 10 enable_checkpointing: true accumulate_grad_batches: 1 inference_mode: true use_distributed_sampler: true detect_anomaly: false barebones: false sync_batchnorm: false reload_dataloaders_every_n_epochs: 0 default_root_dir: /dccstor/geofm-finetuning/benchmark-geo-bench-paolo/ model: class_path: terratorch.tasks.SemanticSegmentationTask init_args: model_args: backbone_pretrained: true backbone: prithvi_eo_v2_300_tl decoder: UperNetDecoder decoder_channels: 256 decoder_scale_modules: true num_classes: 2 rescale: true backbone_bands: - BLUE - GREEN - RED - NIR_NARROW - SWIR_1 - SWIR_2 head_dropout: 0.1 necks: - name: SelectIndices indices: - 5 - 11 - 17 - 23 - name: ReshapeTokensToImage model_factory: EncoderDecoderFactory loss: ce ignore_index: -1 lr: 0.001 freeze_backbone: false freeze_decoder: false plot_on_val: 10 data: class_path: terratorch.datamodules.Sen1Floods11NonGeoDataModule init_args: data_root: /dccstor/geofm-finetuning/datasets/sen1floods11 batch_size: 16 num_workers: 8 bands: - BLUE - GREEN - RED - NIR_NARROW - SWIR_1 - SWIR_2 train_transform: - class_path: albumentations.Resize init_args: height: 448 width: 448 interpolation: 1 always_apply: false p: 1 - class_path: albumentations.RandomCrop init_args: height: 224 width: 224 always_apply: false p: 1.0 - class_path: albumentations.HorizontalFlip init_args: always_apply: false p: 0.5 - class_path: albumentations.VerticalFlip init_args: always_apply: false p: 0.5 - class_path: albumentations.pytorch.ToTensorV2 init_args: transpose_mask: false always_apply: true p: 1.0 val_transform: - class_path: albumentations.Resize init_args: height: 448 width: 448 interpolation: 1 always_apply: false p: 1 - class_path: albumentations.pytorch.ToTensorV2 init_args: transpose_mask: false always_apply: true p: 1.0 test_transform: - class_path: albumentations.Resize init_args: height: 448 width: 448 interpolation: 1 always_apply: false p: 1 - class_path: albumentations.pytorch.ToTensorV2 init_args: transpose_mask: false always_apply: true p: 1.0 drop_last: true constant_scale: 0.0001 no_data_replace: 0.0 no_label_replace: -1 use_metadata: false out_dtype: int16 deploy_config_file: true ModelCheckpoint: filename: '{epoch}' monitor: val/loss verbose: false save_top_k: 1 mode: min save_weights_only: false auto_insert_metric_name: true enable_version_counter: true StateDictModelCheckpoint: filename: '{epoch}_state_dict' monitor: val/loss verbose: false save_top_k: 1 mode: min save_weights_only: true auto_insert_metric_name: true enable_version_counter: true optimizer: class_path: torch.optim.AdamW init_args: lr: 5.0e-05 betas: - 0.9 - 0.999 eps: 1.0e-08 weight_decay: 0.05 amsgrad: false maximize: false capturable: false differentiable: false lr_scheduler: class_path: torch.optim.lr_scheduler.CosineAnnealingLR init_args: T_max: 50 eta_min: 0 last_epoch: -1 verbose: deprecated