ultralytics 8.0.37 add TFLite metadata in AutoBackend (#953)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Yonghye Kwon <developer.0hye@gmail.com>
Co-authored-by: Aarni Koskela <akx@iki.fi>

ultralytics/yolo/__init__.py

@@ -1,3 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
 from . import v8
+
+__all__ = ["v8"]

ultralytics/yolo/cfg/__init__.py

@@ -142,7 +142,7 @@ def check_cfg_mismatch(base: Dict, custom: Dict, e=None):
         string = ''
         for x in mismatched:
             matches = get_close_matches(x, base)  # key list
-            matches = [f"{k}={DEFAULT_CFG_DICT[k]}" if DEFAULT_CFG_DICT[k] is not None else k for k in matches]  # k=v
+            matches = [f"{k}={DEFAULT_CFG_DICT[k]}" if DEFAULT_CFG_DICT.get(k) is not None else k for k in matches]
             match_str = f"Similar arguments are i.e. {matches}." if matches else ''
             string += f"'{colorstr('red', 'bold', x)}' is not a valid YOLO argument. {match_str}\n"
         raise SyntaxError(string + CLI_HELP_MSG) from e

ultralytics/yolo/data/__init__.py

@@ -4,3 +4,13 @@ from .base import BaseDataset
 from .build import build_classification_dataloader, build_dataloader, load_inference_source
 from .dataset import ClassificationDataset, SemanticDataset, YOLODataset
 from .dataset_wrappers import MixAndRectDataset
+
+__all__ = [
+    "BaseDataset",
+    "ClassificationDataset",
+    "MixAndRectDataset",
+    "SemanticDataset",
+    "YOLODataset",
+    "build_classification_dataloader",
+    "build_dataloader",
+    "load_inference_source",]

ultralytics/yolo/engine/exporter.py

@@ -73,7 +73,7 @@ from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, __version__, callbacks,
 from ultralytics.yolo.utils.checks import check_imgsz, check_requirements, check_version, check_yaml
 from ultralytics.yolo.utils.files import file_size
 from ultralytics.yolo.utils.ops import Profile
-from ultralytics.yolo.utils.torch_utils import select_device, smart_inference_mode, get_latest_opset
+from ultralytics.yolo.utils.torch_utils import get_latest_opset, select_device, smart_inference_mode
 
 MACOS = platform.system() == 'Darwin'  # macOS environment
 
@@ -508,7 +508,7 @@ class Exporter:
         onnx = self.file.with_suffix('.onnx')
 
         # Export to TF SavedModel
-        subprocess.run(f'onnx2tf -i {onnx} --output_signaturedefs -o {f}', shell=True)
+        subprocess.run(f'onnx2tf -i {onnx} -o {f} --non_verbose', shell=True)
 
         # Add TFLite metadata
         for tflite_file in Path(f).rglob('*.tflite'):

ultralytics/yolo/engine/model.py

@@ -108,8 +108,8 @@ class YOLO:
         Raises TypeError is model is not a PyTorch model
         """
         if not isinstance(self.model, nn.Module):
-            raise TypeError(f"model='{self.model}' must be a PyTorch model, but is a different type. PyTorch models "
-                            f"can be used to train, val, predict and export, i.e. "
+            raise TypeError(f"model='{self.model}' must be a *.pt PyTorch model, but is a different type. "
+                            f"PyTorch models can be used to train, val, predict and export, i.e. "
                             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'.")
 
@@ -240,7 +240,7 @@ class YOLO:
         if RANK in {0, -1}:
             self.model, _ = attempt_load_one_weight(str(self.trainer.best))
             self.overrides = self.model.args
-        self.metrics_data = self.trainer.validator.metrics
+            self.metrics_data = self.trainer.validator.metrics
 
     def to(self, device):
         """

ultralytics/yolo/utils/__init__.py

@@ -221,11 +221,10 @@ def is_jupyter():
     Returns:
         bool: True if running inside a Jupyter Notebook, False otherwise.
     """
-    try:
+    with contextlib.suppress(Exception):
         from IPython import get_ipython
         return get_ipython() is not None
-    except ImportError:
-        return False
+    return False
 
 
 def is_docker() -> bool:
@@ -287,11 +286,9 @@ def is_pytest_running():
     Returns:
         (bool): True if pytest is running, False otherwise.
     """
-    try:
-        import sys
+    with contextlib.suppress(Exception):
         return "pytest" in sys.modules
-    except ImportError:
-        return False
+    return False
 
 
 def is_github_actions_ci() -> bool:

ultralytics/yolo/utils/callbacks/__init__.py

@@ -1 +1,5 @@
 from .base import add_integration_callbacks, default_callbacks
+
+__all__ = [
+    'add_integration_callbacks',
+    'default_callbacks',]

ultralytics/yolo/utils/checks.py

@@ -17,7 +17,6 @@ import numpy as np
 import pkg_resources as pkg
 import psutil
 import torch
-from IPython import display
 from matplotlib import font_manager
 
 from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads, emojis,
@@ -292,8 +291,10 @@ def check_yolo(verbose=True):
         gib = 1 << 30  # bytes per GiB
         ram = psutil.virtual_memory().total
         total, used, free = shutil.disk_usage("/")
-        display.clear_output()
         s = f'({os.cpu_count()} CPUs, {ram / gib:.1f} GB RAM, {(total - free) / gib:.1f}/{total / gib:.1f} GB disk)'
+        with contextlib.suppress(Exception):  # clear display if ipython is installed
+            from IPython import display
+            display.clear_output()
     else:
         s = ''
 

ultralytics/yolo/v8/classify/__init__.py

@@ -3,3 +3,5 @@
 from ultralytics.yolo.v8.classify.predict import ClassificationPredictor, predict
 from ultralytics.yolo.v8.classify.train import ClassificationTrainer, train
 from ultralytics.yolo.v8.classify.val import ClassificationValidator, val
+
+__all__ = ["ClassificationPredictor", "predict", "ClassificationTrainer", "train", "ClassificationValidator", "val"]

ultralytics/yolo/v8/detect/__init__.py

@@ -3,3 +3,5 @@
 from .predict import DetectionPredictor, predict
 from .train import DetectionTrainer, train
 from .val import DetectionValidator, val
+
+__all__ = ["DetectionPredictor", "predict", "DetectionTrainer", "train", "DetectionValidator", "val"]

ultralytics/yolo/v8/segment/__init__.py

@@ -3,3 +3,5 @@
 from .predict import SegmentationPredictor, predict
 from .train import SegmentationTrainer, train
 from .val import SegmentationValidator, val
+
+__all__ = ["SegmentationPredictor", "predict", "SegmentationTrainer", "train", "SegmentationValidator", "val"]