Set workers=0 for MPS Train and Val modes (#4697)

.github/workflows/codeql.yaml

@@ -5,6 +5,7 @@ name: "CodeQL"
 on:
   schedule:
     - cron: '0 0 1 * *'
+  workflow_dispatch:
 
 jobs:
   analyze:

ultralytics/engine/trainer.py

@@ -107,7 +107,7 @@ class BaseTrainer:
             print_args(vars(self.args))
 
         # Device
-        if self.device.type == 'cpu':
+        if self.device.type in ('cpu', 'mps'):
             self.args.workers = 0  # faster CPU training as time dominated by inference, not dataloading
 
         # Model and Dataset

ultralytics/engine/validator.py

@@ -144,7 +144,7 @@ class BaseValidator:
             else:
                 raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' for task={self.args.task} not found ❌"))
 
-            if self.device.type == 'cpu':
+            if self.device.type in ('cpu', 'mps'):
                 self.args.workers = 0  # faster CPU val as time dominated by inference, not dataloading
             if not pt:
                 self.args.rect = False

ultralytics/utils/callbacks/base.py

@@ -196,17 +196,27 @@ def add_integration_callbacks(instance):
         instance (Trainer, Predictor, Validator, Exporter): An object with a 'callbacks' attribute that is a dictionary
             of callback lists.
     """
-    from .clearml import callbacks as clearml_cb
-    from .comet import callbacks as comet_cb
-    from .dvc import callbacks as dvc_cb
-    from .hub import callbacks as hub_cb
-    from .mlflow import callbacks as mlflow_cb
-    from .neptune import callbacks as neptune_cb
-    from .raytune import callbacks as tune_cb
-    from .tensorboard import callbacks as tensorboard_cb
-    from .wb import callbacks as wb_cb
 
-    for x in clearml_cb, comet_cb, hub_cb, mlflow_cb, neptune_cb, tune_cb, tensorboard_cb, wb_cb, dvc_cb:
+    # Load HUB callbacks
-        for k, v in x.items():
+    from .hub import callbacks
-            if v not in instance.callbacks[k]:  # prevent duplicate callbacks addition
+
-                instance.callbacks[k].append(v)  # callback[name].append(func)
+    # Load training callbacks
+    if 'Trainer' in instance.__class__.__name__:
+        from .clearml import callbacks as clear_cb
+        from .comet import callbacks as comet_cb
+        from .dvc import callbacks as dvc_cb
+        from .mlflow import callbacks as mlflow_cb
+        from .neptune import callbacks as neptune_cb
+        from .raytune import callbacks as tune_cb
+        from .tensorboard import callbacks as tb_cb
+        from .wb import callbacks as wb_cb
+        callbacks.update({**clear_cb, **comet_cb, **dvc_cb, **mlflow_cb, **neptune_cb, **tune_cb, **tb_cb, **wb_cb})
+
+    # Load export callbacks (patch to avoid CoreML protobuf error)
+    if 'Exporter' in instance.__class__.__name__:
+        from .tensorboard import callbacks as tb_cb
+        callbacks.update(tb_cb)
+
+    for k, v in callbacks.items():
+        if v not in instance.callbacks[k]:  # prevent duplicate callbacks addition
+            instance.callbacks[k].append(v)  # callback[name].append(func)

ultralytics/utils/callbacks/clearml.py

@@ -1,12 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-import re
-
-import matplotlib.image as mpimg
-import matplotlib.pyplot as plt
-
 from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING
-from ultralytics.utils.torch_utils import model_info_for_loggers
 
 try:
     assert not TESTS_RUNNING  # do not log pytest
@@ -15,8 +9,8 @@ try:
     from clearml import Task
     from clearml.binding.frameworks.pytorch_bind import PatchPyTorchModelIO
     from clearml.binding.matplotlib_bind import PatchedMatplotlib
-
     assert hasattr(clearml, '__version__')  # verify package is not directory
+
 except (ImportError, AssertionError):
     clearml = None
 
@@ -29,6 +23,8 @@ def _log_debug_samples(files, title='Debug Samples') -> None:
         files (list): A list of file paths in PosixPath format.
         title (str): A title that groups together images with the same values.
     """
+    import re
+
     if task := Task.current_task():
         for f in files:
             if f.exists():
@@ -48,6 +44,9 @@ def _log_plot(title, plot_path) -> None:
         title (str): The title of the plot.
         plot_path (str): The path to the saved image file.
     """
+    import matplotlib.image as mpimg
+    import matplotlib.pyplot as plt
+
     img = mpimg.imread(plot_path)
     fig = plt.figure()
     ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[])  # no ticks
@@ -103,6 +102,7 @@ def on_fit_epoch_end(trainer):
                                         value=trainer.epoch_time,
                                         iteration=trainer.epoch)
         if trainer.epoch == 0:
+            from ultralytics.utils.torch_utils import model_info_for_loggers
             for k, v in model_info_for_loggers(trainer).items():
                 task.get_logger().report_single_value(k, v)
 

ultralytics/utils/callbacks/comet.py

@@ -1,10 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-import os
-from pathlib import Path
-
 from ultralytics.utils import LOGGER, RANK, SETTINGS, TESTS_RUNNING, ops
-from ultralytics.utils.torch_utils import model_info_for_loggers
 
 try:
     assert not TESTS_RUNNING  # do not log pytest
@@ -12,18 +8,22 @@ try:
     import comet_ml
 
     assert hasattr(comet_ml, '__version__')  # verify package is not directory
+
+    import os
+    from pathlib import Path
+
+    # Ensures certain logging functions only run for supported tasks
+    COMET_SUPPORTED_TASKS = ['detect']
+
+    # Names of plots created by YOLOv8 that are logged to Comet
+    EVALUATION_PLOT_NAMES = 'F1_curve', 'P_curve', 'R_curve', 'PR_curve', 'confusion_matrix'
+    LABEL_PLOT_NAMES = 'labels', 'labels_correlogram'
+
+    _comet_image_prediction_count = 0
+
 except (ImportError, AssertionError):
     comet_ml = None
 
-# Ensures certain logging functions only run for supported tasks
-COMET_SUPPORTED_TASKS = ['detect']
-
-# Names of plots created by YOLOv8 that are logged to Comet
-EVALUATION_PLOT_NAMES = 'F1_curve', 'P_curve', 'R_curve', 'PR_curve', 'confusion_matrix'
-LABEL_PLOT_NAMES = 'labels', 'labels_correlogram'
-
-_comet_image_prediction_count = 0
-
 
 def _get_comet_mode():
     return os.getenv('COMET_MODE', 'online')
@@ -327,6 +327,7 @@ def on_fit_epoch_end(trainer):
     experiment.log_metrics(trainer.metrics, step=curr_step, epoch=curr_epoch)
     experiment.log_metrics(trainer.lr, step=curr_step, epoch=curr_epoch)
     if curr_epoch == 1:
+        from ultralytics.utils.torch_utils import model_info_for_loggers
         experiment.log_metrics(model_info_for_loggers(trainer), step=curr_step, epoch=curr_epoch)
 
     if not save_assets:

ultralytics/utils/callbacks/dvc.py

@@ -1,37 +1,37 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-import os
-import re
-from pathlib import Path
-
-import pkg_resources as pkg
-
 from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING
-from ultralytics.utils.torch_utils import model_info_for_loggers
 
 try:
     assert not TESTS_RUNNING  # do not log pytest
     assert SETTINGS['dvc'] is True  # verify integration is enabled
-    from importlib.metadata import version
-
     import dvclive
 
+    assert hasattr(dvclive, '__version__')  # verify package is not directory
+
+    import os
+    import re
+    from importlib.metadata import version
+    from pathlib import Path
+
+    import pkg_resources as pkg
+
     ver = version('dvclive')
     if pkg.parse_version(ver) < pkg.parse_version('2.11.0'):
         LOGGER.debug(f'DVCLive is detected but version {ver} is incompatible (>=2.11 required).')
         dvclive = None  # noqa: F811
+
+    # DVCLive logger instance
+    live = None
+    _processed_plots = {}
+
+    # `on_fit_epoch_end` is called on final validation (probably need to be fixed) for now this is the way we
+    # distinguish final evaluation of the best model vs last epoch validation
+    _training_epoch = False
+
 except (ImportError, AssertionError, TypeError):
     dvclive = None
 
-# DVCLive logger instance
-live = None
-_processed_plots = {}
-
-# `on_fit_epoch_end` is called on final validation (probably need to be fixed)
-# for now this is the way we distinguish final evaluation of the best model vs
-# last epoch validation
-_training_epoch = False
-
 
 def _log_images(path, prefix=''):
     if live:
@@ -103,6 +103,7 @@ def on_fit_epoch_end(trainer):
             live.log_metric(metric, value)
 
         if trainer.epoch == 0:
+            from ultralytics.utils.torch_utils import model_info_for_loggers
             for metric, value in model_info_for_loggers(trainer).items():
                 live.log_metric(metric, value, plot=False)
 

ultralytics/utils/callbacks/hub.py

@@ -5,7 +5,6 @@ from time import time
 
 from ultralytics.hub.utils import HUB_WEB_ROOT, PREFIX, events
 from ultralytics.utils import LOGGER, SETTINGS
-from ultralytics.utils.torch_utils import model_info_for_loggers
 
 
 def on_pretrain_routine_end(trainer):
@@ -24,6 +23,7 @@ def on_fit_epoch_end(trainer):
         # Upload metrics after val end
         all_plots = {**trainer.label_loss_items(trainer.tloss, prefix='train'), **trainer.metrics}
         if trainer.epoch == 0:
+            from ultralytics.utils.torch_utils import model_info_for_loggers
             all_plots = {**all_plots, **model_info_for_loggers(trainer)}
         session.metrics_queue[trainer.epoch] = json.dumps(all_plots)
         if time() - session.timers['metrics'] > session.rate_limits['metrics']:

ultralytics/utils/callbacks/mlflow.py

@@ -1,17 +1,16 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-import os
+from ultralytics.utils import LOGGER, ROOT, SETTINGS, TESTS_RUNNING, colorstr
-import re
-from pathlib import Path
-
-from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, colorstr
 
 try:
     assert not TESTS_RUNNING  # do not log pytest
     assert SETTINGS['mlflow'] is True  # verify integration is enabled
     import mlflow
-
     assert hasattr(mlflow, '__version__')  # verify package is not directory
+
+    import os
+    import re
+
 except (ImportError, AssertionError):
     mlflow = None
 
@@ -56,11 +55,10 @@ def on_fit_epoch_end(trainer):
 def on_train_end(trainer):
     """Called at end of train loop to log model artifact info."""
     if mlflow:
-        root_dir = Path(__file__).resolve().parents[3]
         run.log_artifact(trainer.last)
         run.log_artifact(trainer.best)
         run.pyfunc.log_model(artifact_path=experiment_name,
-                             code_path=[str(root_dir)],
+                             code_path=[str(ROOT.parent)],
                              artifacts={'model_path': str(trainer.save_dir)},
                              python_model=run.pyfunc.PythonModel())
 

ultralytics/utils/callbacks/neptune.py

@@ -1,10 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-import matplotlib.image as mpimg
-import matplotlib.pyplot as plt
-
 from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING
-from ultralytics.utils.torch_utils import model_info_for_loggers
 
 try:
     assert not TESTS_RUNNING  # do not log pytest
@@ -13,11 +9,12 @@ try:
     from neptune.types import File
 
     assert hasattr(neptune, '__version__')
+
+    run = None  # NeptuneAI experiment logger instance
+
 except (ImportError, AssertionError):
     neptune = None
 
-run = None  # NeptuneAI experiment logger instance
-
 
 def _log_scalars(scalars, step=0):
     """Log scalars to the NeptuneAI experiment logger."""
@@ -42,6 +39,9 @@ def _log_plot(title, plot_path):
         title (str) Title of the plot
         plot_path (PosixPath or str) Path to the saved image file
         """
+    import matplotlib.image as mpimg
+    import matplotlib.pyplot as plt
+
     img = mpimg.imread(plot_path)
     fig = plt.figure()
     ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect='auto', xticks=[], yticks=[])  # no ticks
@@ -70,6 +70,7 @@ def on_train_epoch_end(trainer):
 def on_fit_epoch_end(trainer):
     """Callback function called at end of each fit (train+val) epoch."""
     if run and trainer.epoch == 0:
+        from ultralytics.utils.torch_utils import model_info_for_loggers
         run['Configuration/Model'] = model_info_for_loggers(trainer)
     _log_scalars(trainer.metrics, trainer.epoch + 1)
 

ultralytics/utils/callbacks/raytune.py

@@ -7,6 +7,7 @@ try:
     import ray
     from ray import tune
     from ray.air import session
+
 except (ImportError, AssertionError):
     tune = None
 

ultralytics/utils/callbacks/tensorboard.py

@@ -8,12 +8,12 @@ try:
 
     assert not TESTS_RUNNING  # do not log pytest
     assert SETTINGS['tensorboard'] is True  # verify integration is enabled
+    WRITER = None  # TensorBoard SummaryWriter instance
+
 except (ImportError, AssertionError, TypeError):
     # TypeError for handling 'Descriptors cannot not be created directly.' protobuf errors in Windows
     SummaryWriter = None
 
-WRITER = None  # TensorBoard SummaryWriter instance
-
 
 def _log_scalars(scalars, step=0):
     """Logs scalar values to TensorBoard."""
@@ -72,4 +72,4 @@ callbacks = {
     'on_pretrain_routine_start': on_pretrain_routine_start,
     'on_train_start': on_train_start,
     'on_fit_epoch_end': on_fit_epoch_end,
-    'on_batch_end': on_batch_end}
+    'on_batch_end': on_batch_end} if SummaryWriter else {}

ultralytics/utils/callbacks/wb.py

@@ -9,11 +9,12 @@ try:
     import wandb as wb
 
     assert hasattr(wb, '__version__')
+
+    _processed_plots = {}
+
 except (ImportError, AssertionError):
     wb = None
 
-_processed_plots = {}
-
 
 def _log_plots(plots, step):
     for name, params in plots.items():

ultralytics/utils/downloads.py

@@ -273,7 +273,7 @@ def safe_download(url,
     """
 
     # Check if the URL is a Google Drive link
-    gdrive = 'drive.google.com' in url
+    gdrive = url.startswith('https://drive.google.com/')
     if gdrive:
         url, file = get_google_drive_file_info(url)