Update evaluation functions for optional verbose (#585)

micro_sam/evaluation/evaluation.py

@@ -115,14 +115,11 @@ def run_evaluation_for_iterative_prompting(
     list_of_results = []
     prediction_folders = sorted(glob(os.path.join(prediction_root, "iteration*")))
     for pred_folder in prediction_folders:
-        print("Evaluating", pred_folder)
+        print("Evaluating", os.path.split(pred_folder)[-1])
         pred_paths = sorted(glob(os.path.join(pred_folder, "*")))
         result = run_evaluation(gt_paths=gt_paths, prediction_paths=pred_paths, save_path=None)
         list_of_results.append(result)
         print(result)
 
     res_df = pd.concat(list_of_results, ignore_index=True)
     res_df.to_csv(csv_path)
-
-
-# TODO function to evaluate full experiment and resave in one table

micro_sam/evaluation/inference.py

@@ -393,6 +393,8 @@ def _run_inference_with_iterative_prompting_for_image(
     prediction_paths,
     use_masks=False
 ) -> None:
+    verbose_embeddings = False
+
     prompt_generator = IterativePromptGenerator()
 
     gt_ids = np.unique(gt)[1:]
@@ -426,10 +428,17 @@ def _run_inference_with_iterative_prompting_for_image(
                 logits_masks = None
 
         batched_outputs = batched_inference(
-            predictor, image, batch_size,
-            boxes=boxes, points=points, point_labels=point_labels,
-            multimasking=multimasking, embedding_path=embedding_path,
-            return_instance_segmentation=False, logits_masks=logits_masks
+            predictor=predictor,
+            image=image,
+            batch_size=batch_size,
+            boxes=boxes,
+            points=points,
+            point_labels=point_labels,
+            multimasking=multimasking,
+            embedding_path=embedding_path,
+            return_instance_segmentation=False,
+            logits_masks=logits_masks,
+            verbose_embeddings=verbose_embeddings,
         )
 
         # switching off multimasking after first iter, as next iters (with multiple prompts) don't expect multimasking
@@ -484,7 +493,7 @@ def run_inference_with_iterative_prompting(
             around which points will not be sampled.
         batch_size: The batch size used for batched predictions.
         n_iterations: The number of iterations for iterative prompting.
-        use_masks: Whether to make use of logits from previous prompt-based segmentation
+        use_masks: Whether to make use of logits from previous prompt-based segmentation.
     """
     if len(image_paths) != len(gt_paths):
         raise ValueError(f"Expect same number of images and gt images, got {len(image_paths)}, {len(gt_paths)}")
@@ -497,7 +506,9 @@ def run_inference_with_iterative_prompting(
         print("The iterative prompting will make use of logits masks from previous iterations.")
 
     for image_path, gt_path in tqdm(
-        zip(image_paths, gt_paths), total=len(image_paths), desc="Run inference with iterative prompting for all images"
+        zip(image_paths, gt_paths),
+        total=len(image_paths),
+        desc="Run inference with iterative prompting for all images",
     ):
         image_name = os.path.basename(image_path)
 

micro_sam/evaluation/instance_segmentation.py

@@ -181,13 +181,15 @@ def run_instance_segmentation_grid_search(
         result_dir: Folder to cache the evaluation results per image.
         embedding_dir: Folder to cache the image embeddings.
         fixed_generate_kwargs: Fixed keyword arguments for the `generate` method of the segmenter.
-        verbose_gs: Whether to run the gridsearch for individual images in a verbose mode.
+        verbose_gs: Whether to run the grid-search for individual images in a verbose mode.
         image_key: Key for loading the image data from a more complex file format like HDF5.
             If not given a simple image format like tif is assumed.
         gt_key: Key for loading the ground-truth data from a more complex file format like HDF5.
             If not given a simple image format like tif is assumed.
         rois: Region of interests to resetrict the evaluation to.
     """
+    verbose_embeddings = False
+
     assert len(image_paths) == len(gt_paths)
     fixed_generate_kwargs = {} if fixed_generate_kwargs is None else fixed_generate_kwargs
 
@@ -229,7 +231,9 @@ def run_instance_segmentation_grid_search(
         else:
             assert predictor is not None
             embedding_path = os.path.join(embedding_dir, f"{os.path.splitext(image_name)[0]}.zarr")
-            image_embeddings = util.precompute_image_embeddings(predictor, image, embedding_path, ndim=2)
+            image_embeddings = util.precompute_image_embeddings(
+                predictor, image, embedding_path, ndim=2, verbose=verbose_embeddings
+            )
             segmenter.initialize(image, image_embeddings)
 
         _grid_search_iteration(
@@ -255,6 +259,8 @@ def run_instance_segmentation_inference(
         generate_kwargs: The keyword arguments for the `generate` method of the segmenter.
     """
 
+    verbose_embeddings = False
+
     generate_kwargs = {} if generate_kwargs is None else generate_kwargs
     predictor = segmenter._predictor
     min_object_size = generate_kwargs.get("min_mask_region_area", 0)
@@ -271,7 +277,9 @@ def run_instance_segmentation_inference(
         image = imageio.imread(image_path)
 
         embedding_path = os.path.join(embedding_dir, f"{os.path.splitext(image_name)[0]}.zarr")
-        image_embeddings = util.precompute_image_embeddings(predictor, image, embedding_path, ndim=2)
+        image_embeddings = util.precompute_image_embeddings(
+            predictor, image, embedding_path, ndim=2, verbose=verbose_embeddings
+        )
 
         segmenter.initialize(image, image_embeddings)
         masks = segmenter.generate(**generate_kwargs)
@@ -384,6 +392,7 @@ def run_instance_segmentation_grid_search_and_inference(
     best_kwargs, best_msa = evaluate_instance_segmentation_grid_search(result_dir, list(grid_search_values.keys()))
     best_param_str = ", ".join(f"{k} = {v}" for k, v in best_kwargs.items())
     print("Best grid-search result:", best_msa, "with parmeters:\n", best_param_str)
+    print()
 
     save_grid_search_best_params(best_kwargs, best_msa, Path(embedding_dir).parent)
 

micro_sam/inference.py

@@ -26,7 +26,8 @@ def batched_inference(
     return_instance_segmentation: bool = True,
     segmentation_ids: Optional[list] = None,
     reduce_multimasking: bool = True,
-    logits_masks: Optional[torch.Tensor] = None
+    logits_masks: Optional[torch.Tensor] = None,
+    verbose_embeddings: bool = True,
 ):
     """Run batched inference for input prompts.
 
@@ -51,6 +52,7 @@ def batched_inference(
             highest ious from multimasking
         logits_masks: The logits masks. Array of shape N_PROMPTS x 1 x 256 x 256.
             Whether to use the logits masks from previous segmentation.
+        verbose_embeddings: Whether to show progress outputs of computing image embeddings.
 
     Returns:
         The predicted segmentation masks.
@@ -103,7 +105,9 @@ def batched_inference(
         )
 
     # Compute the image embeddings.
-    image_embeddings = util.precompute_image_embeddings(predictor, image, embedding_path, ndim=2)
+    image_embeddings = util.precompute_image_embeddings(
+        predictor, image, embedding_path, ndim=2, verbose=verbose_embeddings
+    )
     util.set_precomputed(predictor, image_embeddings)
 
     # Determine the number of batches.