task_name: predict
tags:
- dev
train: true
test: true
ckpt_path: null
seed: 42
float32_matmul_precision: high
clean_pred: true
data:
  _target_: src.data.canopy_datamodule.GEODataModule
  geometry_path: ${paths.data_dir}canopy_height/geometries.geojson
  imageside: 336
  imagesize: 224
  mean:
  - 124
  - 124
  - 124
  - 124
  std:
  - 124
  - 124
  - 124
  - 124
  mean_type: global
  iinter: 1
  batch_size: 64
  pin_memory: true
  num_workers: 0
  sample_multiplier: 1
  tsize_base: null
  tsize_enum_sizes:
  - 1
  tsize_enum_probs:
  - 1
  tsize_range_frac: 0.5
  tsize_range_sizes:
  - 0.5
  - 2
  trot_prob: 0.5
  trot_angle: 90
  min_overlap: 0.2
  test_overlap: 0.5
model:
  _target_: src.models.regression_module.RegressionModule
  optimizer:
    _target_: torch.optim.Adam
    _partial_: true
    lr: 0.001
    weight_decay: 0.0
  scheduler:
    _target_: torch.optim.lr_scheduler.ReduceLROnPlateau
    _partial_: true
    mode: min
    factor: 0.5
    patience: 1
    threshold: 0.01
    threshold_mode: rel
  metric_monitored: val/RMSE
  warmup_scheduler:
    _target_: src.models.components.utils.WarmupScheduler
    _partial_: true
    min_lr: 1.0e-05
    max_lr: ${model.optimizer.lr}
    fract: 0.04
  net:
    _target_: src.models.components.timmNet.timmNet
    img_size: ${data.imagesize}
    num_channels: 4
    num_classes: 1
    backbone: pvt_v2_b3.in1k
    pretrained: false
    pretrained_path: datasets/Models/pvt_v2_b3.in1k.bin
    segmentation_head:
      _partial_: true
      _target_: src.models.components.utils.SimpleSegmentationHead
      decoder_stride: 32
  save_eval_only: true
  save_freq: 1000
  test_overlap: ${data.test_overlap}
  compile: false
  num_classes: ${model.net.num_classes}
  aux_loss_factor: 0.0
  loss: l1
  activation: none
callbacks:
  model_checkpoint:
    _target_: lightning.pytorch.callbacks.ModelCheckpoint
    dirpath: ${paths.output_dir}/checkpoints
    filename: epoch_{epoch:03d}
    monitor: val/RMSE
    verbose: false
    save_last: true
    save_top_k: 1
    mode: min
    auto_insert_metric_name: false
    save_weights_only: false
    every_n_train_steps: null
    train_time_interval: null
    every_n_epochs: null
    save_on_train_epoch_end: null
  early_stopping:
    _target_: lightning.pytorch.callbacks.EarlyStopping
    monitor: ${callbacks.model_checkpoint.monitor}
    min_delta: 0.0
    patience: 3
    verbose: false
    mode: min
    strict: true
    check_finite: true
    stopping_threshold: null
    divergence_threshold: null
    check_on_train_epoch_end: null
  model_summary:
    _target_: lightning.pytorch.callbacks.RichModelSummary
    max_depth: -1
  rich_progress_bar:
    _target_: lightning.pytorch.callbacks.RichProgressBar
  learning_rate_monitor:
    _target_: lightning.pytorch.callbacks.LearningRateMonitor
    logging_interval: step
logger: null
trainer:
  _target_: lightning.pytorch.trainer.Trainer
  default_root_dir: ${paths.output_dir}
  min_epochs: 10
  max_epochs: 25
  accelerator: gpu
  devices: 1
  reload_dataloaders_every_n_epochs: 1
  check_val_every_n_epoch: 1
  log_every_n_steps: 20
  deterministic: false
paths:
  root_dir: ${oc.env:PROJECT_ROOT}
  data_dir: ${paths.root_dir}/datasets/
  log_dir: ${paths.root_dir}/logs/
  output_dir: ${hydra:runtime.output_dir}
  work_dir: ${hydra:runtime.cwd}
extras:
  ignore_warnings: false
  enforce_tags: true
  print_config: true