`ultralytics 8.0.29` DDP-cls and default arg fixes (#813)

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.28"
+__version__ = "8.0.29"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils import ops

ultralytics/yolo/cfg/__init__.py

@@ -262,8 +262,8 @@ def entrypoint(debug=''):
             LOGGER.warning(f"WARNING ⚠️ 'format=' is missing. Using default 'format={overrides['format']}'.")
 
     # Run command in python
-    cfg = get_cfg(overrides=overrides)
+    # getattr(model, mode)(**vars(get_cfg(overrides=overrides)))  # default args using default.yaml
-    getattr(model, mode)(**vars(cfg))
+    getattr(model, mode)(**overrides)  # default args from model
 
 
 # Special modes --------------------------------------------------------------------------------------------------------

ultralytics/yolo/engine/exporter.py

@@ -184,9 +184,6 @@ class Exporter:
             y = model(im)  # dry runs
         if self.args.half and not coreml and not xml:
             im, model = im.half(), model.half()  # to FP16
-        shape = tuple((y[0] if isinstance(y, tuple) else y).shape)  # model output shape
-        LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} and "
-                    f"output shape {shape} ({file_size(file):.1f} MB)")
 
         # Warnings
         warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)  # suppress TracerWarning
@@ -207,6 +204,9 @@ class Exporter:
             'stride': int(max(model.stride)),
             'names': model.names}  # model metadata
 
+        LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with input shape {tuple(im.shape)} and "
+                    f"output shape {self.output_shape} ({file_size(file):.1f} MB)")
+
         # Exports
         f = [''] * len(fmts)  # exported filenames
         if jit:  # TorchScript
@@ -220,9 +220,8 @@ class Exporter:
         if coreml:  # CoreML
             f[4], _ = self._export_coreml()
         if any((saved_model, pb, tflite, edgetpu, tfjs)):  # TensorFlow formats
-            raise NotImplementedError('YOLOv8 TensorFlow export support is still under development. '
+            LOGGER.warning('WARNING ⚠️ YOLOv8 TensorFlow export support is still under development. '
-                                      'Please consider contributing to the effort if you have TF expertise. Thank you!')
+                           'Please consider contributing to the effort if you have TF expertise. Thank you!')
-            assert not isinstance(model, ClassificationModel), 'ClassificationModel TF exports not yet supported.'
             nms = False
             f[5], s_model = self._export_saved_model(nms=nms or self.args.agnostic_nms or tfjs,
                                                      agnostic_nms=self.args.agnostic_nms or tfjs)
@@ -236,7 +235,7 @@ class Exporter:
                                               agnostic_nms=self.args.agnostic_nms)
                 if edgetpu:
                     f[8], _ = self._export_edgetpu()
-                self._add_tflite_metadata(f[8] or f[7], num_outputs=len(s_model.outputs))
+                self._add_tflite_metadata(f[8] or f[7], num_outputs=len(self.output_shape))
             if tfjs:
                 f[9], _ = self._export_tfjs()
         if paddle:  # PaddlePaddle
@@ -552,13 +551,13 @@ class Exporter:
         return f, keras_model
 
     @try_export
-    def _export_pb(self, keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
+    def _export_pb(self, keras_model, prefix=colorstr('TensorFlow GraphDef:')):
         # YOLOv8 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
         import tensorflow as tf  # noqa
         from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2  # noqa
 
         LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
-        f = file.with_suffix('.pb')
+        f = self.file.with_suffix('.pb')
 
         m = tf.function(lambda x: keras_model(x))  # full model
         m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))

ultralytics/yolo/engine/model.py

@@ -119,7 +119,6 @@ class YOLO:
     def fuse(self):
         self.model.fuse()
 
-    @smart_inference_mode()
     def predict(self, source=None, stream=False, **kwargs):
         """
         Perform prediction using the YOLO model.
@@ -258,8 +257,6 @@ class YOLO:
 
     @staticmethod
     def _reset_ckpt_args(args):
-        for arg in 'verbose', 'project', 'name', 'exist_ok', 'resume', 'batch', 'epochs', 'cache', 'save_json', \
+        for arg in 'augment', 'verbose', 'project', 'name', 'exist_ok', 'resume', 'batch', 'epochs', 'cache', \
-                'half', 'v5loader':
+                'save_json', 'half', 'v5loader', 'device', 'cfg', 'save', 'rect', 'plots':
             args.pop(arg, None)
-
-        args["device"] = ''  # set device to '' to prevent auto-DDP usage

ultralytics/yolo/engine/trainer.py

@@ -457,7 +457,7 @@ class BaseTrainer:
     def get_validator(self):
         raise NotImplementedError("get_validator function not implemented in trainer")
 
-    def get_dataloader(self, dataset_path, batch_size=16, rank=0):
+    def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode="train"):
         """
         Returns dataloader derived from torch.data.Dataloader.
         """

ultralytics/yolo/utils/__init__.py

@@ -485,18 +485,20 @@ def set_sentry():
 
     if SETTINGS['sync'] and \
             RANK in {-1, 0} and \
+            sys.argv[0].endswith('yolo') and \
             not is_pytest_running() and \
             not is_github_actions_ci() and \
             ((is_pip_package() and not is_git_dir()) or
              (get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git" and get_git_branch() == "main")):
+
         import sentry_sdk  # noqa
+        from ultralytics import __version__
 
-        import ultralytics
         sentry_sdk.init(
-            dsn="https://1f331c322109416595df20a91f4005d3@o4504521589325824.ingest.sentry.io/4504521592406016",
+            dsn="https://f805855f03bb4363bc1e16cb7d87b654@o4504521589325824.ingest.sentry.io/4504521592406016",
             debug=False,
             traces_sample_rate=1.0,
-            release=ultralytics.__version__,
+            release=__version__,
             environment='production',  # 'dev' or 'production'
             before_send=before_send,
             ignore_errors=[KeyboardInterrupt, FileNotFoundError])

ultralytics/yolo/utils/checks.py

@@ -1,5 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
-
+import contextlib
 import glob
 import inspect
 import math
@@ -7,9 +7,9 @@ import os
 import platform
 import re
 import shutil
+import subprocess
 import urllib
 from pathlib import Path
-from subprocess import check_output
 from typing import Optional
 
 import cv2
@@ -155,12 +155,11 @@ def check_online() -> bool:
         bool: True if connection is successful, False otherwise.
     """
     import socket
-    try:
+    with contextlib.suppress(subprocess.CalledProcessError):
-        # Check host accessibility by attempting to establish a connection
+        host = socket.gethostbyname("www.github.com")
-        socket.create_connection(("1.1.1.1", 443), timeout=5)
+        socket.create_connection((host, 80), timeout=2)
         return True
-    except OSError:
+    return False
-        return False
 
 
 def check_python(minimum: str = '3.7.0') -> bool:
@@ -181,6 +180,7 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
     # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages or single package str)
     prefix = colorstr('red', 'bold', 'requirements:')
     check_python()  # check python version
+    file = None
     if isinstance(requirements, Path):  # requirements.txt file
         file = requirements.resolve()
         assert file.exists(), f"{prefix} {file} not found, check failed."
@@ -202,9 +202,8 @@ def check_requirements(requirements=ROOT.parent / 'requirements.txt', exclude=()
         LOGGER.info(f"{prefix} YOLOv8 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...")
         try:
             assert check_online(), "AutoUpdate skipped (offline)"
-            LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode())
+            LOGGER.info(subprocess.check_output(f'pip install {s} {cmds}', shell=True).decode())
-            source = file if 'file' in locals() else requirements
+            s = f"{prefix} {n} package{'s' * (n > 1)} updated per {file or requirements}\n" \
-            s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
                 f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
             LOGGER.info(s)
         except Exception as e:
@@ -306,7 +305,7 @@ def git_describe(path=ROOT):  # path must be a directory
     # Return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
     try:
         assert (Path(path) / '.git').is_dir()
-        return check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
+        return subprocess.check_output(f'git -C {path} describe --tags --long --always', shell=True).decode()[:-1]
     except AssertionError:
         return ''
 

ultralytics/yolo/utils/torch_utils.py

@@ -246,7 +246,7 @@ def intersect_dicts(da, db, exclude=()):
 
 def is_parallel(model):
     # Returns True if model is of type DP or DDP
-    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
+    return isinstance(model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel))
 
 
 def de_parallel(model):

ultralytics/yolo/v8/classify/train.py

@@ -1,5 +1,4 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
-import sys
 
 import torch
 import torchvision
@@ -9,7 +8,7 @@ from ultralytics.yolo import v8
 from ultralytics.yolo.data import build_classification_dataloader
 from ultralytics.yolo.engine.trainer import BaseTrainer
 from ultralytics.yolo.utils import DEFAULT_CFG
-from ultralytics.yolo.utils.torch_utils import strip_optimizer
+from ultralytics.yolo.utils.torch_utils import strip_optimizer, is_parallel
 
 
 class ClassificationTrainer(BaseTrainer):
@@ -56,7 +55,7 @@ class ClassificationTrainer(BaseTrainer):
         # Load a YOLO model locally, from torchvision, or from Ultralytics assets
         if model.endswith(".pt"):
             self.model, _ = attempt_load_one_weight(model, device='cpu')
-            for p in model.parameters():
+            for p in self.model.parameters():
                 p.requires_grad = True  # for training
         elif model.endswith(".yaml"):
             self.model = self.get_model(cfg=model)
@@ -75,8 +74,12 @@ class ClassificationTrainer(BaseTrainer):
                                                  augment=mode == "train",
                                                  rank=rank,
                                                  workers=self.args.workers)
+        # Attach inference transforms
         if mode != "train":
-            self.model.transforms = loader.dataset.torch_transforms  # attach inference transforms
+            if is_parallel(self.model):
+                self.model.module.transforms = loader.dataset.torch_transforms
+            else:
+                self.model.transforms = loader.dataset.torch_transforms
         return loader
 
     def preprocess_batch(self, batch):