Allowing the segmentation task to output multiple class labels

terratorch/cli_tools.py

@@ -81,6 +81,11 @@ def is_one_band(img):
 
 def write_tiff(img_wrt, filename, metadata):
 
+    # Adapting the number of bands to be compatible with the 
+    # output dimensions.
+    count = img_wrt.shape[0]
+    metadata['count'] = count
+
     with rasterio.open(filename, "w", **metadata) as dest:
         if is_one_band(img_wrt):
             img_wrt = img_wrt[None]

terratorch/tasks/segmentation_tasks.py

@@ -65,6 +65,7 @@ def __init__(
         tiled_inference_parameters: TiledInferenceParameters = None,
         test_dataloaders_names: list[str] | None = None,
         lr_overrides: dict[str, float] | None = None,
+        output_most_probable: bool = True,
     ) -> None:
         """Constructor
 
@@ -110,6 +111,8 @@ def __init__(
             lr_overrides (dict[str, float] | None, optional): Dictionary to override the default lr in specific
                 parameters. The key should be a substring of the parameter names (it will check the substring is
                 contained in the parameter name)and the value should be the new lr. Defaults to None.
+            output_most_probable (bool): A boolean to define if the output during the inference will be just
+                for the most probable class or if it will include all of them. 
         """
         self.tiled_inference_parameters = tiled_inference_parameters
         self.aux_loss = aux_loss
@@ -136,6 +139,12 @@ def __init__(
         self.val_loss_handler = LossHandler(self.val_metrics.prefix)
         self.monitor = f"{self.val_metrics.prefix}loss"
         self.plot_on_val = int(plot_on_val)
+        self.output_most_probable = output_most_probable
+
+        if output_most_probable:
+            self.select_classes = lambda y: y.argmax(dim=1) 
+        else:
+            self.select_classes = lambda y: y
 
     def configure_losses(self) -> None:
         """Initialize the loss criterion.
@@ -349,5 +358,7 @@ def model_forward(x):
             )
         else:
             y_hat: Tensor = self(x, **rest).output
-        y_hat = y_hat.argmax(dim=1)
+
+        y_hat = self.select_classes(y_hat)
+
         return y_hat, file_names

tests/resources/configs/manufactured-finetune_prithvi_swin_B_segmentation.yaml

@@ -51,12 +51,12 @@ data:
       - 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/ 
+    train_data_root: tests/resources/inputs
+    train_label_data_root: tests/resources/inputs
+    val_data_root: tests/resources/inputs
+    val_label_data_root: tests/resources/inputs
+    test_data_root: tests/resources/inputs
+    test_label_data_root: tests/resources/inputs 
     img_grep: "segmentation*input*.tif"
     label_grep: "segmentation*label*.tif"
     means:
@@ -83,8 +83,8 @@ model:
       decoder: UperNetDecoder
       pretrained: true
       backbone: prithvi_swin_B
-      backbone_pretrained_cfg_overlay:
-        file: tests/prithvi_swin_B.pt
+        #backbone_pretrained_cfg_overlay:
+        #file: tests/prithvi_swin_B.pt
       backbone_drop_path_rate: 0.3
       # backbone_window_size: 8
       decoder_channels: 256
@@ -99,6 +99,7 @@ model:
       num_frames: 1
       num_classes: 2
       head_dropout: 0.5708022831486758
+    output_most_probable: false
     loss: ce
     #aux_heads:
     #  - name: aux_head