Add config.yaml for PVTV2 model

pretrained_models/.hydra/config.yaml

@@ -0,0 +1,143 @@
+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