romeokienzler
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Create config.yaml
Browse files- config.yaml +174 -0
config.yaml
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1 |
+
# lightning.pytorch==2.1.1
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seed_everything: 0
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### Trainer configuration
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trainer:
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accelerator: auto
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strategy: auto
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devices: auto
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num_nodes: 1
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# precision: 16-mixed
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logger:
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# You can swtich to TensorBoard for logging by uncommenting the below line and commenting out the procedding line
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#class_path: TensorBoardLogger
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class_path: lightning.pytorch.loggers.csv_logs.CSVLogger
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init_args:
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save_dir: ./experiments
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name: fine_tune_suhi
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callbacks:
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- class_path: RichProgressBar
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- class_path: LearningRateMonitor
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init_args:
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logging_interval: epoch
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- class_path: EarlyStopping
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init_args:
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monitor: val/loss
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patience: 600
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max_epochs: 600
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check_val_every_n_epoch: 1
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log_every_n_steps: 10
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enable_checkpointing: true
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default_root_dir: ./experiments
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out_dtype: float32
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### Data configuration
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data:
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class_path: GenericNonGeoPixelwiseRegressionDataModule
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init_args:
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batch_size: 64
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num_workers: 8
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train_transform:
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- class_path: albumentations.HorizontalFlip
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init_args:
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p: 0.5
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- class_path: albumentations.Rotate
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init_args:
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limit: 30
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border_mode: 0 # cv2.BORDER_CONSTANT
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value: 0
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mask_value: 1
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p: 0.5
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- class_path: ToTensorV2
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# Specify all bands which are in the input data.
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dataset_bands:
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# 6 HLS bands
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- BLUE
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- GREEN
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- RED
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- NIR_NARROW
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- SWIR_1
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- SWIR_2
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# ERA5-Land t2m_spatial_avg
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- 7
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# ERA5-Land t2m_sunrise_avg
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- 8
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# ERA5-Land t2m_midnight_avg
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- 9
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# ERA5-Land t2m_delta_avg
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- 10
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# cos_tod
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- 11
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# sin_tod
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- 12
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# cos_doy
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- 13
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# sin_doy
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- 14
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# Specify the bands which are used from the input data.
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# Bands 8 - 14 were discarded in the final model
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output_bands:
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- BLUE
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- GREEN
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- RED
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- NIR_NARROW
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- SWIR_1
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- SWIR_2
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- 7
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rgb_indices:
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- 2
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- 1
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- 0
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# Directory roots to training, validation and test datasplits:
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train_data_root: train/inputs
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train_label_data_root: train/targets
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val_data_root: val/inputs
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val_label_data_root: val/targets
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test_data_root: test/inputs
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test_label_data_root: test/targets
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img_grep: "*.inputs.tif"
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label_grep: "*.lst.tif"
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# Nodata value in the input data
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no_data_replace: 0
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# Nodata value in label (target) data
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no_label_replace: -9999
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# Mean value of the training dataset per band
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means:
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- 702.4754028320312
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- 1023.23291015625
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- 1118.8924560546875
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- 2440.750732421875
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- 2052.705810546875
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- 1514.15087890625
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- 21.031919479370117
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# Standard deviation of the training dataset per band
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stds:
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- 554.8255615234375
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- 613.5565185546875
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- 745.929443359375
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- 715.0111083984375
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- 761.47607421875
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- 734.991943359375
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- 8.66781997680664
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### Model configuration
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model:
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class_path: terratorch.tasks.PixelwiseRegressionTask
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init_args:
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model_args:
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decoder: UperNetDecoder
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pretrained: false
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backbone: prithvi_swin_L
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img_size: 224
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backbone_drop_path_rate: 0.3
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decoder_channels: 256
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in_channels: 7
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bands:
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- BLUE
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- GREEN
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- RED
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- NIR_NARROW
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- SWIR_1
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- SWIR_2
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- 7
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num_frames: 1
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loss: rmse
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aux_heads:
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- name: aux_head
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decoder: IdentityDecoder
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decoder_args:
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head_dropout: 0.5
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head_channel_list:
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- 1
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head_final_act: torch.nn.LazyLinear
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aux_loss:
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aux_head: 0.4
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ignore_index: -9999
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freeze_backbone: false
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freeze_decoder: false
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model_factory: PrithviModelFactory
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# uncomment this block for tiled inference
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tiled_inference_parameters:
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h_crop: 224
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h_stride: 224
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w_crop: 224
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w_stride: 224
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average_patches: true
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optimizer:
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class_path: torch.optim.AdamW
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init_args:
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lr: 0.0001
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weight_decay: 0.05
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lr_scheduler:
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class_path: ReduceLROnPlateau
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init_args:
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monitor: val/loss
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