Bump ultralytics in examples (#3704)

### Changes

Bump ultralytics==8.3.221

### Reason for changes

To use torch==2.9.0 

### Tests

https://github.com/openvinotoolkit/nncf/actions/runs/18911508636

Author: AlexanderDokuchaev

.github/workflows/examples.yml

@@ -159,6 +159,8 @@ jobs:
     defaults:
       run:
         shell: bash
+    env:
+      PYTHONIOENCODING: utf-8
     steps:
       - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
         with:

examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/deploy.py

@@ -44,7 +44,7 @@ def validate_ov_model(
     validator.jdict = []
     validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
     validator.batch_i = 1
-    validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
+    validator.confusion_matrix = ConfusionMatrix(names=validator.names)
     compiled_model = ov.compile_model(ov_model, device_name="CPU")
     num_outputs = len(compiled_model.outputs)
     for batch_i, batch in enumerate(track(data_loader, description="Validating")):
@@ -62,7 +62,7 @@ def validate_ov_model(
         preds = validator.postprocess(preds)
         validator.update_metrics(preds, batch)
     stats = validator.get_stats()
-    return stats, validator.seen, validator.nt_per_class.sum()
+    return stats, validator.seen, validator.metrics.nt_per_class.sum()
 
 
 def run_benchmark(model_path: Path, config) -> float:

examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/main.py

@@ -34,11 +34,6 @@
 ROOT = Path(__file__).parent.resolve()
 
 
-# WA: to set default value for dynamo parameter to False
-# TODO(AlexanderDokuchaev): remove after update ultralytics
-torch.onnx.export = partial(torch.onnx.export, dynamo=False)
-
-
 def validate(
     model: onnx.ModelProto,
     data_loader: torch.utils.data.DataLoader,
@@ -49,7 +44,7 @@ def validate(
     validator.jdict = []
     validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
     validator.batch_i = 1
-    validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
+    validator.confusion_matrix = ConfusionMatrix(names=validator.names)
 
     input_name = model.graph.input[0].name
     serialized_model = model.SerializeToString()
@@ -77,7 +72,7 @@ def validate(
         validator.update_metrics(preds, batch)
     stats = validator.get_stats()
 
-    return stats, validator.seen, validator.nt_per_class.sum()
+    return stats, validator.seen, validator.metrics.nt_per_class.sum()
 
 
 def print_statistics(stats: dict[str, float], total_images: int, total_objects: int) -> None:
@@ -108,7 +103,7 @@ def print_statistics(stats: dict[str, float], total_images: int, total_objects:
 
 
 def prepare_validation(model: YOLO, args: Any) -> tuple[SegmentationValidator, torch.utils.data.DataLoader]:
-    validator: SegmentationValidator = model.task_map[model.task]["validator"](args=args)
+    validator = SegmentationValidator(args=args)
     validator.data = check_det_dataset(args.data)
     validator.stride = 32
     validator.is_coco = True
@@ -117,7 +112,8 @@ def prepare_validation(model: YOLO, args: Any) -> tuple[SegmentationValidator, t
     validator.metrics.names = validator.names
     validator.nc = model.model.model[-1].nc
     validator.process = ops.process_mask
-    validator.plot_masks = []
+    validator.device = torch.device("cpu")
+    validator.end2end = False
 
     coco_data_path = DATASETS_DIR / "coco128-seg"
     data_loader = validator.get_dataloader(coco_data_path.as_posix(), 1)
@@ -153,7 +149,7 @@ def validation_ac(
         validator.jdict = []
         validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
         validator.batch_i = 1
-        validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
+        validator.confusion_matrix = ConfusionMatrix(names=validator.names)
 
         rt_session_options = {"providers": ["CPUExecutionProvider"]}
         serialized_model = val_model.SerializeToString()
@@ -220,7 +216,7 @@ def run_example():
     model = YOLO(ROOT / f"{MODEL_NAME}.pt")
     args = get_cfg(cfg=DEFAULT_CFG)
     args.data = "coco128-seg.yaml"
-
+    args.plots = False
     validator, data_loader = prepare_validation(model, args)
 
     fp32_model, fp32_model_path = prepare_onnx_model(model, MODEL_NAME)

examples/post_training_quantization/onnx/yolov8_quantize_with_accuracy_control/requirements.txt

@@ -1,5 +1,5 @@
-ultralytics==8.3.22
+ultralytics==8.3.221
 onnx==1.17.0
 onnxruntime==1.21.1
 openvino==2025.3.0
-torch==2.8.0
+torch==2.9.0

examples/post_training_quantization/openvino/yolov8/main.py

@@ -10,7 +10,6 @@
 # limitations under the License.
 import re
 import subprocess
-from functools import partial
 from pathlib import Path
 from typing import Any
 
@@ -33,18 +32,13 @@
 ROOT = Path(__file__).parent.resolve()
 
 
-# WA: to set default value for dynamo parameter to False
-# TODO(AlexanderDokuchaev): remove after update ultralytics
-torch.onnx.export = partial(torch.onnx.export, dynamo=False)
-
-
 def validate(
     model: ov.Model, data_loader: torch.utils.data.DataLoader, validator: DetectionValidator, num_samples: int = None
 ) -> tuple[dict, int, int]:
     validator.seen = 0
     validator.jdict = []
     validator.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
-    validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
+    validator.confusion_matrix = ConfusionMatrix(names=validator.names)
     model.reshape({0: [1, 3, -1, -1]})
     compiled_model = ov.compile_model(model, device_name="CPU")
     output_layer = compiled_model.output(0)
@@ -56,7 +50,7 @@ def validate(
         preds = validator.postprocess(preds)
         validator.update_metrics(preds, batch)
     stats = validator.get_stats()
-    return stats, validator.seen, validator.nt_per_class.sum()
+    return stats, validator.seen, validator.metrics.nt_per_class.sum()
 
 
 def print_statistics(stats: dict[str, float], total_images: int, total_objects: int) -> None:
@@ -73,14 +67,15 @@ def print_statistics(stats: dict[str, float], total_images: int, total_objects:
 
 
 def prepare_validation(model: YOLO, args: Any) -> tuple[DetectionValidator, torch.utils.data.DataLoader]:
-    validator: DetectionValidator = model.task_map[model.task]["validator"](args=args)
+    validator = DetectionValidator(args=args)
     validator.data = check_det_dataset(args.data)
     validator.stride = 32
     validator.is_coco = True
     validator.class_map = coco80_to_coco91_class()
     validator.names = model.model.names
     validator.metrics.names = validator.names
-    validator.nc = model.model.model[-1].nc
+    validator.device = torch.device("cpu")
+    validator.end2end = False
 
     coco_data_path = DATASETS_DIR / "coco128"
     data_loader = validator.get_dataloader(coco_data_path.as_posix(), 1)
@@ -150,6 +145,7 @@ def main():
     model = YOLO(ROOT / f"{MODEL_NAME}.pt")
     args = get_cfg(cfg=DEFAULT_CFG)
     args.data = "coco128.yaml"
+    args.plots = False
 
     # Prepare validation dataset and helper
     validator, data_loader = prepare_validation(model, args)

examples/post_training_quantization/openvino/yolov8/requirements.txt

@@ -1,4 +1,4 @@
-ultralytics==8.3.22
+ultralytics==8.3.221
 onnx==1.17.0
 openvino==2025.3.0
 torch==2.9.0

examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py

@@ -33,10 +33,6 @@
 
 ROOT = Path(__file__).parent.resolve()
 
-# WA: to set default value for dynamo parameter to False
-# TODO(AlexanderDokuchaev): remove after update ultralytics
-torch.onnx.export = partial(torch.onnx.export, dynamo=False)
-
 
 def validate(
     model: ov.Model, data_loader: torch.utils.data.DataLoader, validator: SegmentationValidator, num_samples: int = None
@@ -45,7 +41,7 @@ def validate(
     validator.jdict = []
     validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
     validator.batch_i = 1
-    validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
+    validator.confusion_matrix = ConfusionMatrix(names=validator.names)
     model.reshape({0: [1, 3, -1, -1]})
     compiled_model = ov.compile_model(model, device_name="CPU")
     num_outputs = len(model.outputs)
@@ -64,7 +60,7 @@ def validate(
         preds = validator.postprocess(preds)
         validator.update_metrics(preds, batch)
     stats = validator.get_stats()
-    return stats, validator.seen, validator.nt_per_class.sum()
+    return stats, validator.seen, validator.metrics.nt_per_class.sum()
 
 
 def print_statistics(stats: dict[str, float], total_images: int, total_objects: int) -> None:
@@ -95,7 +91,7 @@ def print_statistics(stats: dict[str, float], total_images: int, total_objects:
 
 
 def prepare_validation(model: YOLO, args: Any) -> tuple[SegmentationValidator, torch.utils.data.DataLoader]:
-    validator: SegmentationValidator = model.task_map[model.task]["validator"](args=args)
+    validator = SegmentationValidator(args=args)
     validator.data = check_det_dataset(args.data)
     validator.stride = 32
     validator.is_coco = True
@@ -104,7 +100,8 @@ def prepare_validation(model: YOLO, args: Any) -> tuple[SegmentationValidator, t
     validator.metrics.names = validator.names
     validator.nc = model.model.model[-1].nc
     validator.process = ops.process_mask
-    validator.plot_masks = []
+    validator.device = torch.device("cpu")
+    validator.end2end = False
 
     coco_data_path = DATASETS_DIR / "coco128-seg"
     data_loader = validator.get_dataloader(coco_data_path.as_posix(), 1)
@@ -154,7 +151,7 @@ def validation_ac(
         validator.jdict = []
         validator.stats = dict(tp_m=[], tp=[], conf=[], pred_cls=[], target_cls=[], target_img=[])
         validator.batch_i = 1
-        validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
+        validator.confusion_matrix = ConfusionMatrix(names=validator.names)
         num_outputs = len(compiled_model.outputs)
 
         for batch_i, batch in enumerate(validation_loader):
@@ -213,6 +210,7 @@ def main():
     args = get_cfg(cfg=DEFAULT_CFG)
     args.data = "coco128-seg.yaml"
     args.workers = 0
+    args.plots = False
 
     # Prepare validation dataset and helper
     validator, data_loader = prepare_validation(model, args)

examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/requirements.txt

@@ -1,4 +1,4 @@
-ultralytics==8.3.22
+ultralytics==8.3.221
 onnx==1.17.0
 openvino==2025.3.0
 torch==2.9.0