refactor detection and add sem segm

src/functions.py

@@ -9,6 +9,7 @@
 geometry_to_task_type = {
     TaskType.OBJECT_DETECTION: [sly.Rectangle, sly.AnyGeometry],
     TaskType.INSTANCE_SEGMENTATION: [sly.Bitmap, sly.Polygon, sly.AnyGeometry],
+    TaskType.SEMANTIC_SEGMENTATION: [sly.Bitmap, sly.Polygon, sly.AnyGeometry],
 }
 
 

src/ui/evaluation.py

@@ -7,21 +7,8 @@
 import src.workflow as w
 import supervisely as sly
 import supervisely.app.widgets as widgets
-from supervisely._utils import rand_str
-from supervisely.nn import TaskType
-from supervisely.nn.benchmark import (
-    InstanceSegmentationBenchmark,
-    ObjectDetectionBenchmark,
-)
-from supervisely.nn.benchmark.evaluation.instance_segmentation_evaluator import (
-    InstanceSegmentationEvaluator,
-)
-from supervisely.nn.benchmark.evaluation.object_detection_evaluator import (
-    ObjectDetectionEvaluator,
-)
 from supervisely.nn.inference.session import SessionJSON
 
-
 no_classes_label = widgets.Text(
     "Not found any classes in the project that are present in the model", status="error"
 )
@@ -83,7 +70,7 @@ def run_evaluation(
     project_id: Optional[int] = None,
     params: Optional[Union[str, Dict]] = None,
 ):
-    work_dir = g.STORAGE_DIR + "/benchmark_" + rand_str(6)
+    work_dir = g.STORAGE_DIR + "/benchmark_" + sly.rand_str(6)
 
     if session_id is not None:
         g.session_id = session_id
@@ -102,7 +89,6 @@ def run_evaluation(
         if len(dataset_ids) == 0:
             raise ValueError("No datasets selected")
 
-
     # ==================== Workflow input ====================
     w.workflow_input(g.api, project, g.session_id)
     # =======================================================
@@ -116,38 +102,52 @@ def run_evaluation(
     eval_pbar.show()
     sec_eval_pbar.show()
 
-    evaluation_params = eval_params.get_value() or params
-    if isinstance(evaluation_params, str):
-        evaluation_params = yaml.safe_load(evaluation_params)
+    params = eval_params.get_value() or params
+    if isinstance(params, str):
+        params = yaml.safe_load(params)
 
-    if task_type == TaskType.OBJECT_DETECTION:
-        if evaluation_params is None:
-            evaluation_params = ObjectDetectionEvaluator.load_yaml_evaluation_params()
-            evaluation_params = yaml.safe_load(evaluation_params)
-        bm = ObjectDetectionBenchmark(
+    if task_type == sly.nn.TaskType.OBJECT_DETECTION:
+        if params is None:
+            params = sly.nn.benchmark.ObjectDetectionEvaluator.load_yaml_evaluation_params()
+            params = yaml.safe_load(params)
+        bm = sly.nn.benchmark.ObjectDetectionBenchmark(
             g.api,
             project.id,
+            gt_dataset_ids=dataset_ids,
             output_dir=work_dir,
+            progress=eval_pbar,
+            progress_secondary=sec_eval_pbar,
+            classes_whitelist=g.selected_classes,
+            evaluation_params=params,
+        )
+    elif task_type == sly.nn.TaskType.INSTANCE_SEGMENTATION:
+        if params is None:
+            params = sly.nn.benchmark.InstanceSegmentationEvaluator.load_yaml_evaluation_params()
+            params = yaml.safe_load(params)
+        bm = sly.nn.benchmark.InstanceSegmentationBenchmark(
+            g.api,
+            project.id,
             gt_dataset_ids=dataset_ids,
+            output_dir=work_dir,
             progress=eval_pbar,
             progress_secondary=sec_eval_pbar,
             classes_whitelist=g.selected_classes,
-            evaluation_params=evaluation_params,
+            evaluation_params=params,
         )
-    elif task_type == TaskType.INSTANCE_SEGMENTATION:
-        if evaluation_params is None:
-            evaluation_params = InstanceSegmentationEvaluator.load_yaml_evaluation_params()
-            evaluation_params = yaml.safe_load(evaluation_params)
-        bm = InstanceSegmentationBenchmark(
+    elif task_type == sly.nn.TaskType.SEMANTIC_SEGMENTATION:
+        params = sly.nn.benchmark.SemanticSegmentationEvaluator.load_yaml_evaluation_params()
+        bm = sly.nn.benchmark.SemanticSegmentationBenchmark(
             g.api,
             project.id,
             gt_dataset_ids=dataset_ids,
             output_dir=work_dir,
             progress=eval_pbar,
             progress_secondary=sec_eval_pbar,
             classes_whitelist=g.selected_classes,
-            evaluation_params=evaluation_params,
+            evaluation_params=params,
         )
+
+    bm.evaluator_app_info = g.api.task.get_info_by_id(g.task_id)
     sly.logger.info(f"{g.session_id = }")
 
     task_info = g.api.task.get_info_by_id(g.session_id)
@@ -201,9 +201,9 @@ def run_evaluation(
         f"Predictions project: "
         f"  name {bm.dt_project_info.name}, "
         f"  workspace_id {bm.dt_project_info.workspace_id}. "
-        f"Differences project: "
-        f"  name {bm.diff_project_info.name}, "
-        f"  workspace_id {bm.diff_project_info.workspace_id}"
+        # f"Differences project: "
+        # f"  name {bm.diff_project_info.name}, "
+        # f"  workspace_id {bm.diff_project_info.workspace_id}"
     )
 
     eval_button.loading = False
@@ -233,10 +233,12 @@ def update_eval_params():
     if g.session is None:
         g.session = SessionJSON(g.api, g.session_id)
     task_type = g.session.get_deploy_info()["task_type"]
-    if task_type == TaskType.OBJECT_DETECTION:
-        params = ObjectDetectionEvaluator.load_yaml_evaluation_params()
-    elif task_type == TaskType.INSTANCE_SEGMENTATION:
-        params = InstanceSegmentationEvaluator.load_yaml_evaluation_params()
+    if task_type == sly.nn.TaskType.OBJECT_DETECTION:
+        params = sly.nn.benchmark.ObjectDetectionEvaluator.load_yaml_evaluation_params()
+    elif task_type == sly.nn.TaskType.INSTANCE_SEGMENTATION:
+        params = sly.nn.benchmark.InstanceSegmentationEvaluator.load_yaml_evaluation_params()
+    elif task_type == sly.nn.TaskType.SEMANTIC_SEGMENTATION:
+        params = "# Semantic Segmentation evaluation parameters are not available yet."
     eval_params.set_text(params, language_mode="yaml")
     eval_params_card.uncollapse()
 
@@ -269,9 +271,10 @@ def handle_sel_app_session(session_id: Optional[int]):
     if g.session_id:
         update_eval_params()
 
+
 @all_datasets_checkbox.value_changed
 def handle_all_datasets_checkbox(checked: bool):
     if checked:
         sel_dataset.hide()
     else:
-        sel_dataset.show()
+        sel_dataset.show()