Update ultralytics 8.3.22 (#3038)

### Changes

- Update ultralytics 8.3.22

### Reason for changes

- Update ultralytics 8.3.22

### Related tickets

- Ref: 151072

### Tests

test_examples

examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py

@@ -42,7 +42,7 @@ def validate_ov_model(
 ) -> Tuple[Dict, int, int]:
     validator.seen = 0
     validator.jdict = []
-    validator.stats = []
+    validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
     validator.batch_i = 1
     validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
     compiled_model = ov.compile_model(ov_model, device_name="CPU")

examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/main.py

@@ -38,7 +38,7 @@ def validate(
 ) -> Tuple[Dict, int, int]:
     validator.seen = 0
     validator.jdict = []
-    validator.stats = []
+    validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
     validator.batch_i = 1
     validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
 
@@ -101,13 +101,12 @@ def print_statistics(stats: np.ndarray, total_images: int, total_objects: int) -
 
 
 def prepare_validation(model: YOLO, args: Any) -> Tuple[Validator, torch.utils.data.DataLoader]:
-    validator = model.smart_load("validator")(args)
+    validator = model.task_map[model.task]["validator"](args=args)
     validator.data = check_det_dataset(args.data)
+    validator.stride = 32
 
     data_loader = validator.get_dataloader(f"{DATASETS_DIR}/coco128-seg", 1)
 
-    validator = model.smart_load("validator")(args)
-
     validator.is_coco = True
     validator.class_map = coco80_to_coco91_class()
     validator.names = model.model.names
@@ -146,7 +145,7 @@ def validation_ac(
     ) -> float:
         validator.seen = 0
         validator.jdict = []
-        validator.stats = []
+        validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
         validator.batch_i = 1
         validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
 

examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt

@@ -1,3 +1,3 @@
-ultralytics==8.0.170
+ultralytics==8.3.22
 onnx==1.16.0
 openvino==2024.4

examples/post_training_quantization/openvino/yolov8/main.py

@@ -36,7 +36,7 @@ def validate(
 ) -> Tuple[Dict, int, int]:
     validator.seen = 0
     validator.jdict = []
-    validator.stats = []
+    validator.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
     validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
     model.reshape({0: [1, 3, -1, -1]})
     compiled_model = ov.compile_model(model, device_name="CPU")
@@ -66,8 +66,9 @@ def print_statistics(stats: np.ndarray, total_images: int, total_objects: int) -
 
 
 def prepare_validation(model: YOLO, args: Any) -> Tuple[Validator, torch.utils.data.DataLoader]:
-    validator = model.smart_load("validator")(args)
+    validator = model.task_map[model.task]["validator"](args=args)
     validator.data = check_det_dataset(args.data)
+    validator.stride = 32
     dataset = validator.data["val"]
     print(f"{dataset}")
 

examples/post_training_quantization/openvino/yolov8/requirements.txt

@@ -1,3 +1,3 @@
-ultralytics==8.0.170
+ultralytics==8.3.22
 onnx>=1.12.0,<1.16.2
 openvino==2024.4

examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py

@@ -38,7 +38,7 @@ def validate(
 ) -> Tuple[Dict, int, int]:
     validator.seen = 0
     validator.jdict = []
-    validator.stats = []
+    validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
     validator.batch_i = 1
     validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
     model.reshape({0: [1, 3, -1, -1]})
@@ -92,15 +92,14 @@ def print_statistics(stats: np.ndarray, total_images: int, total_objects: int) -
 
 
 def prepare_validation(model: YOLO, args: Any) -> Tuple[Validator, torch.utils.data.DataLoader]:
-    validator = model.smart_load("validator")(args)
+    validator = model.task_map[model.task]["validator"](args=args)
     validator.data = check_det_dataset(args.data)
+    validator.stride = 32
     dataset = validator.data["val"]
     print(f"{dataset}")
 
     data_loader = validator.get_dataloader(f"{DATASETS_DIR}/coco128-seg", 1)
 
-    validator = model.smart_load("validator")(args)
-
     validator.is_coco = True
     validator.class_map = coco80_to_coco91_class()
     validator.names = model.model.names
@@ -146,7 +145,7 @@ def validation_ac(
     ) -> float:
         validator.seen = 0
         validator.jdict = []
-        validator.stats = []
+        validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
         validator.batch_i = 1
         validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
         num_outputs = len(compiled_model.outputs)
@@ -211,6 +210,7 @@ def main():
     model = YOLO(f"{ROOT}/{MODEL_NAME}.pt")
     args = get_cfg(cfg=DEFAULT_CFG)
     args.data = "coco128-seg.yaml"
+    args.workers = 0
 
     # Prepare validation dataset and helper
     validator, data_loader = prepare_validation(model, args)

examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt

@@ -1,3 +1,3 @@
-ultralytics==8.0.170
+ultralytics==8.3.22
 onnx>=1.12.0,<1.16.2
 openvino==2024.4

tests/cross_fw/examples/test_examples.py

@@ -81,6 +81,7 @@ def test_examples(
     env["PYTHONPATH"] = str(PROJECT_ROOT)  # need this to be able to import from tests.* in run_example.py
     env["ONEDNN_MAX_CPU_ISA"] = "AVX2"  # Set ISA to AVX2 to get CPU independent results
     env["CUDA_VISIBLE_DEVICES"] = ""  # Disable GPU
+    env["YOLO_VERBOSE"] = "False"  # Set ultralytics to quiet mode
 
     metrics_file_path = tmp_path / "metrics.json"
     python_executable_with_venv = get_python_executable_with_venv(venv_path)