Testing the definition by interval using a dedicated yaml file

Signed-off-by: João Lucas de Sousa Almeida <[email protected]>

Author: Joao-L-S-Almeida

tests/manufactured-finetune_prithvi_swin_B_band_interval.yaml

@@ -0,0 +1,136 @@
+# lightning.pytorch==2.1.1
+seed_everything: 42
+trainer:
+  accelerator: auto
+  strategy: auto
+  devices: auto
+  num_nodes: 1
+  # precision: 16-mixed
+  logger:
+    class_path: TensorBoardLogger
+    init_args:
+      save_dir: tests/
+      name: all_ecos_random
+  callbacks:
+    - class_path: RichProgressBar
+    - class_path: LearningRateMonitor
+      init_args:
+        logging_interval: epoch
+    - class_path: EarlyStopping
+      init_args:
+        monitor: val/loss
+        patience: 100
+  max_epochs: 5
+  check_val_every_n_epoch: 1
+  log_every_n_steps: 20
+  enable_checkpointing: true
+  default_root_dir: tests/
+data:
+  class_path: GenericNonGeoPixelwiseRegressionDataModule
+  init_args:
+    batch_size: 2
+    num_workers: 4
+    train_transform:
+      - class_path: albumentations.HorizontalFlip
+        init_args:
+          p: 0.5      
+      - class_path: albumentations.Rotate
+        init_args:
+          limit: 30
+          border_mode: 0 # cv2.BORDER_CONSTANT
+          value: 0
+          # mask_value: 1
+          p: 0.5
+      - class_path: ToTensorV2
+    dataset_bands:
+      - [0, 11]
+    output_bands:
+      - [1, 3]
+      - [4, 6]
+    rgb_indices:
+      - 2
+      - 1
+      - 0
+    train_data_root: tests/
+    train_label_data_root: tests/
+    val_data_root: tests/
+    val_label_data_root: tests/
+    test_data_root: tests/
+    test_label_data_root: tests/ 
+    img_grep: "regression*input*.tif"
+    label_grep: "regression*label*.tif"
+    means:
+    - 547.36707
+    - 898.5121
+    - 1020.9082
+    - 2665.5352
+    - 2340.584
+    - 1610.1407
+    stds:
+    - 411.4701
+    - 558.54065
+    - 815.94025
+    - 812.4403
+    - 1113.7145
+    - 1067.641
+    no_label_replace: -1
+    no_data_replace: 0
+
+model:
+  class_path: terratorch.tasks.PixelwiseRegressionTask
+  init_args:
+    model_args:
+      decoder: UperNetDecoder
+      pretrained: true
+      backbone: prithvi_swin_B
+      backbone_pretrained_cfg_overlay:
+        file: tests/prithvi_swin_B.pt
+      backbone_drop_path_rate: 0.3
+      # backbone_window_size: 8
+      decoder_channels: 256
+      in_channels: 6
+      bands:
+        - BLUE
+        - GREEN
+        - RED
+        - NIR_NARROW
+        - SWIR_1
+        - SWIR_2
+      num_frames: 1
+      head_dropout: 0.5708022831486758
+      head_final_act: torch.nn.ReLU
+      head_learned_upscale_layers: 2
+    loss: rmse
+    #aux_heads:
+    #  - name: aux_head
+    #    decoder: IdentityDecoder
+    #    decoder_args:
+    #      decoder_out_index: 2
+    #      head_dropout: 0,5
+    #      head_channel_list:
+    #        - 64
+    #      head_final_act: torch.nn.ReLU
+    #aux_loss:
+    #  aux_head: 0.4
+    ignore_index: -1
+    freeze_backbone: true
+    freeze_decoder: false
+    model_factory: PrithviModelFactory
+
+    # uncomment this block for tiled inference
+    # tiled_inference_parameters:
+    #   h_crop: 224
+    #   h_stride: 192
+    #   w_crop: 224
+    #   w_stride: 192
+    #   average_patches: true
+optimizer:
+  class_path: torch.optim.AdamW
+  init_args:
+    lr: 0.00013524680528283027
+    weight_decay: 0.047782217873995426
+lr_scheduler:
+  class_path: ReduceLROnPlateau
+  init_args:
+    monitor: val/loss
+

tests/test_finetune.py

@@ -23,6 +23,19 @@ def test_finetune_multiple_backbones(model_name):
     command_list = ["fit", "-c", f"tests/manufactured-finetune_{model_name}.yaml"]
     _ = build_lightning_cli(command_list)
 
+@pytest.mark.parametrize("model_name", ["prithvi_swin_B"])
+def test_finetune_bands_intervals(model_name):
+
+    model_instance = timm.create_model(model_name)
+
+    state_dict = model_instance.state_dict()
+
+    torch.save(state_dict, os.path.join("tests/", model_name + ".pt"))
+
+    # Running the terratorch CLI
+    command_list = ["fit", "-c", f"tests/manufactured-finetune_{model_name}_band_interval.yaml"]
+    _ = build_lightning_cli(command_list)
+
 """
 @pytest.mark.parametrize("model_name", ["prithvi_swin_B", "prithvi_swin_L", "prithvi_vit_100", "prithvi_vit_300"])
 def test_finetune_multiple_backbones(model_name):