Add timing outputs (#655)

micro_sam/sam_annotator/image_series_annotator.py

@@ -1,4 +1,5 @@
 import os
+import time
 
 from glob import glob
 from pathlib import Path
@@ -28,6 +29,8 @@ def _precompute(
     tile_shape, halo, precompute_amg_state,
     checkpoint_path, device, ndim, prefer_decoder,
 ):
+    t_start = time.time()
+
     device = util.get_device(device)
     predictor, state = util.get_sam_model(
         model_type=model_type, checkpoint_path=checkpoint_path, device=device, return_state=True
@@ -54,6 +57,11 @@ def _precompute(
             ]
         assert all(os.path.exists(emb_path) for emb_path in embedding_paths)
 
+    t_run = time.time() - t_start
+    minutes = int(t_run // 60)
+    seconds = int(round(t_run % 60, 0))
+    print("Precomputation took", t_run, f"seconds (= {minutes:02}:{seconds:02} minutes)")
+
     return predictor, decoder, embedding_paths
 
 

micro_sam/training/training.py

@@ -1,4 +1,5 @@
 import os
+import time
 from glob import glob
 from typing import Any, Dict, List, Optional, Tuple, Union
 
@@ -181,6 +182,8 @@ def train_sam(
         save_every_kth_epoch: Save checkpoints after every kth epoch separately.
         pbar_signals: Controls for napari progress bar.
     """
+    t_start = time.time()
+
     _check_loader(train_loader, with_segmentation_decoder)
     _check_loader(val_loader, with_segmentation_decoder)
 
@@ -281,6 +284,12 @@ def train_sam(
 
     trainer.fit(**trainer_fit_params)
 
+    t_run = time.time() - t_start
+    hours = int(t_run // 3600)
+    minutes = int(t_run // 60)
+    seconds = int(round(t_run % 60, 0))
+    print("Training took", t_run, f"seconds (= {hours:02}:{minutes:02}:{seconds:02} hours)")
+
 
 def _update_patch_shape(patch_shape, raw_paths, raw_key, with_channels):
     if not isinstance(raw_paths, (str, os.PathLike)):