Add flag to do test time augmentation to improve prediction robustness

docs/cli.md

@@ -39,6 +39,7 @@ $ kom find-kelp --help
   --rgbi           --rgb                  Use RGB and NIR bands for classification. Assumes RGBI ordering. [default: rgb]
                -b                INTEGER  GDAL-style band re-ordering flag. Defaults to RGB or RGBI order. To e.g., reorder a BGRI image at runtime, pass flags `-b 3 -b 2 -b 1 -b 4`. [default: None]
   --gpu            --no-gpu               Enable or disable GPU, if available. [default: gpu]
+  --tta            --no-tta               Use test time augmentation to improve accuracy at the cost of processing time. [default: no-tta]
   --help       -h                         Show this message and exit.
 ```
 
@@ -110,6 +111,7 @@ $ kom find-mussels --help
   --crop-size                  INTEGER  The data window size to run through the segmentation model. [default: 1024]
                -b              INTEGER  GDAL-style band re-ordering flag. Defaults to RGB order. To e.g., reorder a BGR image at runtime, pass flags `-b 3 -b 2 -b 1`. [default: None]
   --gpu            --no-gpu             Enable or disable GPU, if available. [default: gpu]
+  --tta            --no-tta             Use test time augmentation to improve accuracy at the cost of processing time. [default: no-tta]
   --help       -h                       Show this message and exit.
 ```
 

kelp_o_matic/cli.py

@@ -39,12 +39,18 @@ def find_kelp(
     use_gpu: bool = typer.Option(
         True, "--gpu/--no-gpu", help="Enable or disable GPU, if available."
     ),
+    use_tta: bool = typer.Option(
+        False,
+        "--tta/--no-tta",
+        help="Use test time augmentation to improve accuracy at the cost of "
+        "processing time.",
+    ),
 ):
     """
     Detect kelp in image at path SOURCE and output the resulting classification raster
     to file at path DEST.
     """
-    find_kelp_(source, dest, species, crop_size, use_nir, band_order, use_gpu)
+    find_kelp_(source, dest, species, crop_size, use_nir, band_order, use_gpu, use_tta)
 
 
 @cli.command()
@@ -64,12 +70,18 @@ def find_mussels(
     use_gpu: bool = typer.Option(
         True, "--gpu/--no-gpu", help="Enable or disable GPU, if available."
     ),
+    use_tta: bool = typer.Option(
+        False,
+        "--tta/--no-tta",
+        help="Use test time augmentation to improve accuracy at the cost of "
+        "processing time.",
+    ),
 ):
     """
     Detect mussels in image at path SOURCE and output the resulting classification
     raster to file at path DEST.
     """
-    find_mussels_(source, dest, crop_size, band_order, use_gpu)
+    find_mussels_(source, dest, crop_size, band_order, use_gpu, use_tta)
 
 
 def version_callback(value: bool) -> None:

kelp_o_matic/lib.py

@@ -72,6 +72,7 @@ def find_kelp(
     use_nir: bool = False,
     band_order: Optional[list[int]] = None,
     use_gpu: bool = True,
+    test_time_augmentation: bool = False,
 ):
     """
     Detect kelp in image at path `source` and output the resulting classification raster
@@ -86,6 +87,7 @@ def find_kelp(
         band_order: GDAL-style band re-ordering. Defaults to RGB or RGBI order.
             e.g. to reorder a BGRI image at runtime, pass `[3,2,1,4]`.
         use_gpu: Disable Cuda GPU usage and run on CPU only.
+        test_time_augmentation: Use test time augmentation to improve model accuracy.
     """
     if not band_order:
         band_order = [1, 2, 3]
@@ -104,7 +106,12 @@ def find_kelp(
     else:
         model = KelpRGBPresenceSegmentationModel(use_gpu=use_gpu)
     RichSegmentationManager(
-        model, Path(source), Path(dest), band_order=band_order, crop_size=crop_size
+        model,
+        Path(source),
+        Path(dest),
+        band_order=band_order,
+        crop_size=crop_size,
+        test_time_augmentation=test_time_augmentation,
     )()
 
 
@@ -114,6 +121,7 @@ def find_mussels(
     crop_size: int = 1024,
     band_order: Optional[list[int]] = None,
     use_gpu: bool = True,
+    test_time_augmentation: bool = False,
 ):
     """
     Detect mussels in image at path `source` and output the resulting classification
@@ -126,6 +134,7 @@ def find_mussels(
         band_order: GDAL-style band re-ordering flag. Defaults to RGB order.
             e.g. to reorder a BGR image at runtime, pass `[3,2,1]`.
         use_gpu: Disable Cuda GPU usage and run on CPU only.
+        test_time_augmentation: Use test time augmentation to improve model accuracy.
     """
     if not band_order:
         band_order = [1, 2, 3]
@@ -134,5 +143,10 @@ def find_mussels(
     _validate_paths(Path(source), Path(dest))
     model = MusselRGBPresenceSegmentationModel(use_gpu=use_gpu)
     RichSegmentationManager(
-        model, Path(source), Path(dest), band_order=band_order, crop_size=crop_size
+        model,
+        Path(source),
+        Path(dest),
+        band_order=band_order,
+        crop_size=crop_size,
+        test_time_augmentation=test_time_augmentation,
     )()

kelp_o_matic/managers.py

@@ -23,6 +23,7 @@ def __init__(
         output_path: Union[str, Path],
         band_order: tuple[int] = (1, 2, 3),
         crop_size: int = 1024,
+        test_time_augmentation: bool = False,
     ):
         """Create the segmentation object.
 
@@ -35,10 +36,12 @@ def __init__(
                 Defaults to [1, 2, 3] for RGB ordering.
             crop_size: The size of image crop to classify iteratively until the entire
                 image is classified.
+            test_time_augmentation: Use test time augmentation to improve accuracy.
         """
         self.model = model
         self.band_order = band_order
         self.crop_size = crop_size
+        self.tta = test_time_augmentation
         self.input_path = str(Path(input_path).expanduser().resolve())
         self.output_path = str(Path(output_path).expanduser().resolve())
 
@@ -84,6 +87,18 @@ def __call__(self):
                     )
                     logits = self.model(crop.unsqueeze(0))[0]
 
+                    if self.tta:
+                        for k in range(1, 4):
+                            aug_crop = torch.rot90(crop, k=k, dims=(1, 2))
+                            aug_logits = self.model(aug_crop.unsqueeze(0))[0]
+                            unaug_logits = torch.rot90(aug_logits, k=-k, dims=(1, 2))
+                            logits = torch.maximum(logits, unaug_logits)
+                        for d in [1, 2]:
+                            aug_crop = torch.flip(crop, dims=(d,))
+                            aug_logits = self.model(aug_crop.unsqueeze(0))[0]
+                            unaug_logits = torch.flip(aug_logits, dims=(d,))
+                            logits = torch.maximum(logits, unaug_logits)
+
                 logits = self.kernel(
                     logits,
                     top=self.reader.is_top_window(read_window),