Debug performance

synapse_net/inference/util.py

@@ -54,6 +54,7 @@ def __init__(self, scale, verbose):
             self.scale = scale
 
     def scale_input(self, input_volume, is_segmentation=False):
+        t0 = time.time()
         if self.scale is None:
             return input_volume
 
@@ -73,10 +74,11 @@ def scale_input(self, input_volume, is_segmentation=False):
             input_volume = rescale(input_volume, self.scale, preserve_range=True).astype(input_volume.dtype)
 
         if self.verbose:
-            print("Rescaled volume from", self._original_shape, "to", input_volume.shape)
+            print("Rescaled volume from", self._original_shape, "to", input_volume.shape, "in", time.time() - t0, "s")
         return input_volume
 
     def rescale_output(self, output, is_segmentation):
+        t0 = time.time()
         if self.scale is None:
             return output
 
@@ -91,6 +93,9 @@ def rescale_output(self, output, is_segmentation):
         else:
             output = resize(output, out_shape, preserve_range=True).astype(output.dtype)
 
+        if self.verbose:
+            print("Resized prediction back to original shape", output.shape, "in", time.time() - t0, "s")
+
         return output
 
 
@@ -463,7 +468,6 @@ def get_default_tiling(is_2d: bool = False) -> Dict[str, Dict[str, int]]:
         tiling = {"tile": tile, "halo": halo}
         print(f"Determined tile size for MPS: {tiling}")
 
-
     # I am not sure what is reasonable on a cpu. For now choosing very small tiling.
     # (This will not work well on a CPU in any case.)
     else:

synapse_net/tools/cli.py

@@ -146,6 +146,10 @@ def segmentation_cli():
         "By default, the scaling factor will be derived from the voxel size of the input data. "
         "If this parameter is given it will over-ride the default behavior. "
     )
+    parser.add_argument(
+        "--verbose", "-v", action="store_true",
+        help="Whether to print verbose information about the segmentation progress."
+    )
     args = parser.parse_args()
 
     if args.checkpoint is None:
@@ -169,7 +173,7 @@ def segmentation_cli():
         scale = (2 if is_2d else 3) * (args.scale,)
 
     segmentation_function = partial(
-        run_segmentation, model=model, model_type=args.model, verbose=False, tiling=tiling,
+        run_segmentation, model=model, model_type=args.model, verbose=args.verbose, tiling=tiling,
     )
     inference_helper(
         args.input_path, args.output_path, segmentation_function,