General ultralytics==8.0.6 updates (#351)

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

.github/workflows/ci.yaml

@@ -84,22 +84,22 @@ jobs:
       - name: Test detection
         shell: bash  # for Windows compatibility
         run: |
-          yolo task=detect mode=train model=yolov8n.yaml data=coco8.yaml epochs=1 imgsz=32
-          yolo task=detect mode=val model=runs/detect/train/weights/last.pt imgsz=32
+          yolo task=detect mode=train data=coco8.yaml model=yolov8n.yaml epochs=1 imgsz=32
+          yolo task=detect mode=val data=coco8.yaml model=runs/detect/train/weights/last.pt imgsz=32
           yolo task=detect mode=predict model=runs/detect/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
           yolo mode=export model=runs/detect/train/weights/last.pt imgsz=32 format=torchscript
       - name: Test segmentation
         shell: bash  # for Windows compatibility
         run: |
-          yolo task=segment mode=train model=yolov8n-seg.yaml data=coco8-seg.yaml epochs=1 imgsz=32
-          yolo task=segment mode=val model=runs/segment/train/weights/last.pt data=coco8-seg.yaml imgsz=32
+          yolo task=segment mode=train data=coco8-seg.yaml model=yolov8n-seg.yaml epochs=1 imgsz=32
+          yolo task=segment mode=val data=coco8-seg.yaml model=runs/segment/train/weights/last.pt imgsz=32
           yolo task=segment mode=predict model=runs/segment/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
           yolo mode=export model=runs/segment/train/weights/last.pt imgsz=32 format=torchscript
       - name: Test classification
         shell: bash  # for Windows compatibility
         run: |
-          yolo task=classify mode=train model=yolov8n-cls.yaml data=mnist160 epochs=1 imgsz=32
-          yolo task=classify mode=val model=runs/classify/train/weights/last.pt data=mnist160 imgsz=32
+          yolo task=classify mode=train data=mnist160 model=yolov8n-cls.yaml epochs=1 imgsz=32
+          yolo task=classify mode=val data=mnist160 model=runs/classify/train/weights/last.pt imgsz=32
           yolo task=classify mode=predict model=runs/classify/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
           yolo mode=export model=runs/classify/train/weights/last.pt imgsz=32 format=torchscript
       - name: Pytest tests

docker/Dockerfile

@@ -52,7 +52,7 @@ ENV OMP_NUM_THREADS=1
 # t=ultralytics/ultralytics:latest tnew=ultralytics/ultralytics:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
 
 # Clean up
-# docker system prune -a --volumes
+# sudo docker system prune -a --volumes
 
 # Update Ubuntu drivers
 # https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/

tests/test_engine.py

@@ -1,13 +1,16 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
+from pathlib import Path
+
 from ultralytics.yolo.configs import get_config
-from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT
+from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, SETTINGS
 from ultralytics.yolo.v8 import classify, detect, segment
 
 CFG_DET = 'yolov8n.yaml'
 CFG_SEG = 'yolov8n-seg.yaml'
 CFG_CLS = 'squeezenet1_0'
 CFG = get_config(DEFAULT_CONFIG)
+MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
 SOURCE = ROOT / "assets"
 
 
@@ -18,15 +21,14 @@ def test_detect():
     # Trainer
     trainer = detect.DetectionTrainer(overrides=overrides)
     trainer.train()
-    trained_model = trainer.best
 
     # Validator
     val = detect.DetectionValidator(args=CFG)
-    val(model=trained_model)
+    val(model=trainer.best)  # validate best.pt
 
     # Predictor
     pred = detect.DetectionPredictor(overrides={"imgsz": [64, 64]})
-    result = pred(source=SOURCE, model="yolov8n.pt", return_outputs=True)
+    result = pred(source=SOURCE, model=f"{MODEL}.pt", return_outputs=True)
     assert len(list(result)), "predictor test failed"
 
     overrides["resume"] = trainer.last
@@ -49,15 +51,14 @@ def test_segment():
     # trainer
     trainer = segment.SegmentationTrainer(overrides=overrides)
     trainer.train()
-    trained_model = trainer.best
 
     # Validator
     val = segment.SegmentationValidator(args=CFG)
-    val(model=trained_model)
+    val(model=trainer.best)  # validate best.pt
 
     # Predictor
     pred = segment.SegmentationPredictor(overrides={"imgsz": [64, 64]})
-    result = pred(source=SOURCE, model="yolov8n-seg.pt", return_outputs=True)
+    result = pred(source=SOURCE, model=f"{MODEL}-seg.pt", return_outputs=True)
     assert len(list(result)) == 2, "predictor test failed"
 
     # Test resume
@@ -82,13 +83,12 @@ def test_classify():
     # Trainer
     trainer = classify.ClassificationTrainer(overrides=overrides)
     trainer.train()
-    trained_model = trainer.best
 
     # Validator
     val = classify.ClassificationValidator(args=CFG)
-    val(model=trained_model)
+    val(model=trainer.best)
 
     # Predictor
     pred = classify.ClassificationPredictor(overrides={"imgsz": [64, 64]})
-    result = pred(source=SOURCE, model=trained_model, return_outputs=True)
+    result = pred(source=SOURCE, model=trainer.best, return_outputs=True)
     assert len(list(result)) == 2, "predictor test failed"

ultralytics/hub/session.py

@@ -1,7 +1,5 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-import signal
-import sys
 from pathlib import Path
 from time import sleep
 
@@ -15,19 +13,21 @@ AGENT_NAME = f'python-{__version__}-colab' if is_colab() else f'python-{__versio
 
 session = None
 
-
-def signal_handler(signum, frame):
-    """ Confirm exit """
-    global hub_logger
-    LOGGER.info(f'Signal received. {signum} {frame}')
-    if isinstance(session, HubTrainingSession):
-        hub_logger.alive = False
-        del hub_logger
-    sys.exit(signum)
-
-
-signal.signal(signal.SIGTERM, signal_handler)
-signal.signal(signal.SIGINT, signal_handler)
+# Causing problems in tests (non-authenticated)
+# import signal
+# import sys
+# def signal_handler(signum, frame):
+#     """ Confirm exit """
+#     global hub_logger
+#     LOGGER.info(f'Signal received. {signum} {frame}')
+#     if isinstance(session, HubTrainingSession):
+#         hub_logger.alive = False
+#         del hub_logger
+#     sys.exit(signum)
+#
+#
+# signal.signal(signal.SIGTERM, signal_handler)
+# signal.signal(signal.SIGINT, signal_handler)
 
 
 class HubTrainingSession:

ultralytics/yolo/configs/__init__.py

@@ -8,13 +8,13 @@ from omegaconf import DictConfig, OmegaConf
 from ultralytics.yolo.configs.hydra_patch import check_config_mismatch
 
 
-def get_config(config: Union[str, DictConfig], overrides: Union[str, Dict] = None):
+def get_config(config: Union[str, Path, DictConfig], overrides: Union[str, Dict] = None):
     """
     Load and merge configuration data from a file or dictionary.
 
     Args:
-        config (Union[str, DictConfig]): Configuration data in the form of a file name or a DictConfig object.
-        overrides (Union[str, Dict], optional): Overrides in the form of a file name or a dictionary. Default is None.
+        config (str) or (Path) or (DictConfig): Configuration data in the form of a file name or a DictConfig object.
+        overrides (str) or(Dict), optional: Overrides in the form of a file name or a dictionary. Default is None.
 
     Returns:
         OmegaConf.Namespace: Training arguments namespace.

ultralytics/yolo/data/utils.py

@@ -14,12 +14,11 @@ import numpy as np
 import torch
 from PIL import ExifTags, Image, ImageOps
 
-from ultralytics.yolo.utils import LOGGER, ROOT, colorstr, yaml_load
+from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, ROOT, colorstr, yaml_load
 from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
 from ultralytics.yolo.utils.downloads import download
 from ultralytics.yolo.utils.files import unzip_file
-
-from ..utils.ops import segments2boxes
+from ultralytics.yolo.utils.ops import segments2boxes
 
 HELP_URL = "See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data"
 IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm"  # include image suffixes
@@ -173,12 +172,7 @@ def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
     areas = []
     ms = []
     for si in range(len(segments)):
-        mask = polygon2mask(
-            imgsz,
-            [segments[si].reshape(-1)],
-            downsample_ratio=downsample_ratio,
-            color=1,
-        )
+        mask = polygon2mask(imgsz, [segments[si].reshape(-1)], downsample_ratio=downsample_ratio, color=1)
         ms.append(mask)
         areas.append(mask.sum())
     areas = np.asarray(areas)
@@ -194,13 +188,14 @@ def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
 def check_dataset_yaml(data, autodownload=True):
     # Download, check and/or unzip dataset if not found locally
     data = check_file(data)
-    DATASETS_DIR = (Path.cwd() / "../datasets").resolve()  # TODO: handle global dataset dir
+
     # Download (optional)
     extract_dir = ''
     if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
         download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1)
         data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
         extract_dir, autodownload = data.parent, False
+
     # Read yaml (optional)
     if isinstance(data, (str, Path)):
         data = yaml_load(data, append_filename=True)  # dictionary
@@ -215,7 +210,7 @@ def check_dataset_yaml(data, autodownload=True):
     # Resolve paths
     path = Path(extract_dir or data.get('path') or '')  # optional 'path' default to '.'
     if not path.is_absolute():
-        path = (Path.cwd() / path).resolve()
+        path = (DATASETS_DIR / path).resolve()
         data['path'] = path  # download scripts
     for k in 'train', 'val', 'test':
         if data.get(k):  # prepend path
@@ -253,6 +248,7 @@ def check_dataset_yaml(data, autodownload=True):
             s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌"
             LOGGER.info(f"Dataset download {s}")
     check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True)  # download fonts
+
     return data  # dictionary
 
 
@@ -274,12 +270,12 @@ def check_dataset(dataset: str):
             'nc': Number of classes in the dataset
             'names': List of class names in the dataset
     """
-    data_dir = (Path.cwd() / "datasets" / dataset).resolve()
+    data_dir = (DATASETS_DIR / dataset).resolve()
     if not data_dir.is_dir():
         LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
         t = time.time()
         if dataset == 'imagenet':
-            subprocess.run(f"bash {ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
+            subprocess.run(f"bash {ROOT / 'yolo/data/scripts/get_imagenet.sh'}", shell=True, check=True)
         else:
             url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
             download(url, dir=data_dir.parent)

ultralytics/yolo/engine/predictor.py

@@ -240,7 +240,7 @@ class BasePredictor:
                 if isinstance(self.vid_writer[idx], cv2.VideoWriter):
                     self.vid_writer[idx].release()  # release previous video writer
                 if vid_cap:  # video
-                    fps = vid_cap.get(cv2.CAP_PROP_FPS)
+                    fps = int(vid_cap.get(cv2.CAP_PROP_FPS))  # integer required, floats produce error in MP4 codec
                     w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                     h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                 else:  # stream

ultralytics/yolo/engine/trainer.py

@@ -506,10 +506,12 @@ class BaseTrainer:
     def check_resume(self):
         resume = self.args.resume
         if resume:
-            last = Path(check_file(resume) if isinstance(resume, str) else get_latest_run())
+            last = Path(check_file(resume) if isinstance(resume, (str, Path)) else get_latest_run())
             args_yaml = last.parent.parent / 'args.yaml'  # train options yaml
-            if args_yaml.is_file():
-                args = get_config(args_yaml)  # replace
+            assert args_yaml.is_file(), \
+                FileNotFoundError('Resume checkpoint f{last} not found. '
+                                  'Please pass a valid checkpoint to resume from, i.e. yolo resume=path/to/last.pt')
+            args = get_config(args_yaml)  # replace
             args.model, resume = str(last), True  # reinstate
             self.args = args
         self.resume = resume

ultralytics/yolo/utils/__init__.py

@@ -187,7 +187,7 @@ def get_git_root_dir():
     """
     try:
         output = subprocess.run(["git", "rev-parse", "--git-dir"], capture_output=True, check=True)
-        return Path(output.stdout.strip().decode('utf-8')).parent  # parent/.git
+        return Path(output.stdout.strip().decode('utf-8')).parent.resolve()  # parent/.git
     except subprocess.CalledProcessError:
         return None
 
@@ -348,16 +348,18 @@ def yaml_load(file='data.yaml', append_filename=False):
         return {**yaml.safe_load(f), 'yaml_file': str(file)} if append_filename else yaml.safe_load(f)
 
 
-def get_settings(file=USER_CONFIG_DIR / 'settings.yaml'):
+def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.0'):
     """
-    Loads a global settings YAML file or creates one with default values if it does not exist.
+    Loads a global Ultralytics settings YAML file or creates one with default values if it does not exist.
 
     Args:
-        file (Path): Path to the settings YAML file. Defaults to 'settings.yaml' in the USER_CONFIG_DIR.
+        file (Path): Path to the Ultralytics settings YAML file. Defaults to 'settings.yaml' in the USER_CONFIG_DIR.
+        version (str): Settings version. If min settings version not met, new default settings will be saved.
 
     Returns:
         dict: Dictionary of settings key-value pairs.
     """
+    from ultralytics.yolo.utils.checks import check_version
     from ultralytics.yolo.utils.torch_utils import torch_distributed_zero_first
 
     root = get_git_root_dir() or Path('')  # not is_pip_package()
@@ -366,7 +368,8 @@ def get_settings(file=USER_CONFIG_DIR / 'settings.yaml'):
         'weights_dir': str(root / 'weights'),  # default weights directory.
         'runs_dir': str(root / 'runs'),  # default runs directory.
         'sync': True,  # sync analytics to help with YOLO development
-        'uuid': uuid.getnode()}  # device UUID to align analytics
+        'uuid': uuid.getnode(),  # device UUID to align analytics
+        'settings_version': version}  # Ultralytics settings version
 
     with torch_distributed_zero_first(RANK):
         if not file.exists():
@@ -375,12 +378,14 @@ def get_settings(file=USER_CONFIG_DIR / 'settings.yaml'):
         settings = yaml_load(file)
 
         # Check that settings keys and types match defaults
-        correct = settings.keys() == defaults.keys() and \
-                  all(type(a) == type(b) for a, b in zip(settings.values(), defaults.values()))
+        correct = settings.keys() == defaults.keys() \
+                  and all(type(a) == type(b) for a, b in zip(settings.values(), defaults.values())) \
+                  and check_version(settings['settings_version'], version)
         if not correct:
-            LOGGER.warning('WARNING ⚠️ Different global settings detected, resetting to defaults. '
-                           'This may be due to an ultralytics package update. '
-                           f'View and update your global settings directly in {file}')
+            LOGGER.warning('WARNING ⚠️ Ultralytics settings reset to defaults. '
+                           '\nThis is normal and may be due to a recent ultralytics package update, '
+                           'but may have overwritten previous settings. '
+                           f"\nYou may view and update settings directly in '{file}'")
             settings = defaults  # merge **defaults with **settings (prefer **settings)
             yaml_save(file, settings)  # save updated defaults
 

ultralytics/yolo/utils/callbacks/hub.py

@@ -3,8 +3,6 @@
 import json
 from time import time
 
-import torch
-
 from ultralytics.hub.utils import PREFIX, sync_analytics
 from ultralytics.yolo.utils import LOGGER
 

ultralytics/yolo/utils/metrics.py

@@ -252,7 +252,7 @@ class ConfusionMatrix:
                        vmin=0.0,
                        xticklabels=ticklabels,
                        yticklabels=ticklabels).set_facecolor((1, 1, 1))
-        ax.set_ylabel('True')
+        ax.set_xlabel('True')
         ax.set_ylabel('Predicted')
         ax.set_title('Confusion Matrix')
         fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)

ultralytics/yolo/v8/classify/train.py

@@ -113,11 +113,10 @@ class ClassificationTrainer(BaseTrainer):
         """
         # Not needed for classification but necessary for segmentation & detection
         keys = [f"{prefix}/{x}" for x in self.loss_names]
-        if loss_items is not None:
-            loss_items = [round(float(loss_items), 5)]
-            return dict(zip(keys, loss_items))
-        else:
+        if loss_items is None:
             return keys
+        loss_items = [round(float(loss_items), 5)]
+        return dict(zip(keys, loss_items))
 
     def resume_training(self, ckpt):
         pass

ultralytics/yolo/v8/detect/train.py

@@ -48,14 +48,14 @@ class DetectionTrainer(BaseTrainer):
         return batch
 
     def set_model_attributes(self):
-        nl = de_parallel(self.model).model[-1].nl  # number of detection layers (to scale hyps)
-        self.args.box *= 3 / nl  # scale to layers
+        # nl = de_parallel(self.model).model[-1].nl  # number of detection layers (to scale hyps)
+        # self.args.box *= 3 / nl  # scale to layers
         # self.args.cls *= self.data["nc"] / 80 * 3 / nl  # scale to classes and layers
-        self.args.cls *= (self.args.imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers
+        # self.args.cls *= (self.args.imgsz / 640) ** 2 * 3 / nl  # scale to image size and layers
         self.model.nc = self.data["nc"]  # attach number of classes to model
+        self.model.names = self.data["names"]  # attach class names to model
         self.model.args = self.args  # attach hyperparameters to model
         # TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
-        self.model.names = self.data["names"]
 
     def get_model(self, cfg=None, weights=None, verbose=True):
         model = DetectionModel(cfg, ch=3, nc=self.data["nc"], verbose=verbose)

ultralytics/yolo/v8/segment/predict.py

@@ -6,8 +6,7 @@ import torch
 from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, ops
 from ultralytics.yolo.utils.checks import check_imgsz
 from ultralytics.yolo.utils.plotting import colors, save_one_box
-
-from ..detect.predict import DetectionPredictor
+from ultralytics.yolo.v8.detect.predict import DetectionPredictor
 
 
 class SegmentationPredictor(DetectionPredictor):

ultralytics/yolo/v8/segment/train.py

@@ -13,14 +13,15 @@ from ultralytics.yolo.utils.ops import crop_mask, xyxy2xywh
 from ultralytics.yolo.utils.plotting import plot_images, plot_results
 from ultralytics.yolo.utils.tal import make_anchors
 from ultralytics.yolo.utils.torch_utils import de_parallel
-
-from ..detect.train import Loss
+from ultralytics.yolo.v8.detect.train import Loss
 
 
 # BaseTrainer python usage
 class SegmentationTrainer(v8.detect.DetectionTrainer):
 
-    def __init__(self, config=DEFAULT_CONFIG, overrides={}):
+    def __init__(self, config=DEFAULT_CONFIG, overrides=None):
+        if overrides is None:
+            overrides = {}
         overrides["task"] = "segment"
         super().__init__(config, overrides)
 

ultralytics/yolo/v8/segment/val.py

@@ -13,8 +13,7 @@ from ultralytics.yolo.utils import DEFAULT_CONFIG, NUM_THREADS, ops
 from ultralytics.yolo.utils.checks import check_requirements
 from ultralytics.yolo.utils.metrics import ConfusionMatrix, SegmentMetrics, box_iou, mask_iou
 from ultralytics.yolo.utils.plotting import output_to_target, plot_images
-
-from ..detect import DetectionValidator
+from ultralytics.yolo.v8.detect import DetectionValidator
 
 
 class SegmentationValidator(DetectionValidator):