ultralytics 8.0.46 TFLite and Benchmarks updates (#1141)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/engine/exporter.py

@@ -294,7 +294,7 @@ class Exporter:
         # YOLOv8 ONNX export
         requirements = ['onnx>=1.12.0']
         if self.args.simplify:
-            requirements += ['onnxsim', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime']
+            requirements += ['onnxsim>=0.4.17', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime']
         check_requirements(requirements)
         import onnx  # noqa
 
@@ -513,8 +513,8 @@ class Exporter:
             cuda = torch.cuda.is_available()
             check_requirements(f"tensorflow{'-macos' if MACOS else '-aarch64' if ARM64 else '' if cuda else '-cpu'}")
             import tensorflow as tf  # noqa
-        check_requirements(('onnx', 'onnx2tf', 'sng4onnx', 'onnxsim', 'onnx_graphsurgeon', 'tflite_support',
-                            'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime'),
+        check_requirements(('onnx', 'onnx2tf>=1.7.7', 'sng4onnx>=1.0.1', 'onnxsim>=0.4.17', 'onnx_graphsurgeon>=0.3.26',
+                            'tflite_support', 'onnxruntime-gpu' if torch.cuda.is_available() else 'onnxruntime'),
                            cmds='--extra-index-url https://pypi.ngc.nvidia.com')
 
         LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
@@ -529,7 +529,7 @@ class Exporter:
 
         # Export to TF
         int8 = '-oiqt -qt per-tensor' if self.args.int8 else ''
-        cmd = f'onnx2tf -i {f_onnx} -o {f} --non_verbose {int8}'
+        cmd = f'onnx2tf -i {f_onnx} -o {f} -nuo --non_verbose {int8}'
         LOGGER.info(f'\n{prefix} running {cmd}')
         subprocess.run(cmd, shell=True)
         yaml_save(f / 'metadata.yaml', self.metadata)  # add metadata.yaml

ultralytics/yolo/engine/model.py

@@ -9,8 +9,9 @@ from ultralytics.nn.tasks import (ClassificationModel, DetectionModel, Segmentat
                                   guess_model_task, nn)
 from ultralytics.yolo.cfg import get_cfg
 from ultralytics.yolo.engine.exporter import Exporter
-from ultralytics.yolo.utils import DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, callbacks, yaml_load
-from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_yaml
+from ultralytics.yolo.utils import (DEFAULT_CFG, DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, ROOT, callbacks,
+                                    is_git_dir, is_pip_package, yaml_load)
+from ultralytics.yolo.utils.checks import check_file, check_imgsz, check_pip_update, check_yaml
 from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
 from ultralytics.yolo.utils.torch_utils import smart_inference_mode
 
@@ -150,6 +151,13 @@ class YOLO:
                             f"'yolo export model=yolov8n.pt', but exported formats like ONNX, TensorRT etc. only "
                             f"support 'predict' and 'val' modes, i.e. 'yolo predict model=yolov8n.onnx'.")
 
+    def _check_pip_update(self):
+        """
+        Inform user of ultralytics package update availability
+        """
+        if is_pip_package():
+            check_pip_update()
+
     def reset(self):
         """
         Resets the model modules.
@@ -189,6 +197,10 @@ class YOLO:
         Returns:
             (List[ultralytics.yolo.engine.results.Results]): The prediction results.
         """
+        if source is None:
+            source = ROOT / 'assets' if is_git_dir() else 'https://ultralytics.com/images/bus.jpg'
+            LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using 'source={source}'.")
+
         overrides = self.overrides.copy()
         overrides['conf'] = 0.25
         overrides.update(kwargs)  # prefer kwargs
@@ -251,11 +263,12 @@ class YOLO:
         Args:
             **kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
         """
-        from ultralytics.yolo.utils.benchmarks import run_benchmarks
+        self._check_is_pytorch_model()
+        from ultralytics.yolo.utils.benchmarks import benchmark
         overrides = self.model.args.copy()
         overrides.update(kwargs)
         overrides = {**DEFAULT_CFG_DICT, **overrides}  # fill in missing overrides keys with defaults
-        return run_benchmarks(model=self, imgsz=overrides['imgsz'], half=overrides['half'], device=overrides['device'])
+        return benchmark(model=self, imgsz=overrides['imgsz'], half=overrides['half'], device=overrides['device'])
 
     def export(self, **kwargs):
         """
@@ -283,6 +296,7 @@ class YOLO:
             **kwargs (Any): Any number of arguments representing the training configuration.
         """
         self._check_is_pytorch_model()
+        self._check_pip_update()
         overrides = self.overrides.copy()
         overrides.update(kwargs)
         if kwargs.get('cfg'):

ultralytics/yolo/engine/predictor.py

@@ -178,7 +178,12 @@ class BasePredictor:
             self.run_callbacks('on_predict_postprocess_end')
 
             # visualize, save, write results
-            for i in range(len(im)):
+            n = len(im)
+            for i in range(n):
+                self.results[i].speed = {
+                    'preprocess': self.dt[0].dt * 1E3 / n,
+                    'inference': self.dt[1].dt * 1E3 / n,
+                    'postprocess': self.dt[2].dt * 1E3 / n}
                 p, im0 = (path[i], im0s[i].copy()) if self.source_type.webcam or self.source_type.from_img \
                     else (path, im0s.copy())
                 p = Path(p)