Fix `LoadStreams` final frame bug (#4387)

Co-authored-by: Nadim Bou Alwan <64587372+nadinator@users.noreply.github.com>

docs/guides/kfold-cross-validation.md

@@ -246,7 +246,7 @@ fold_lbl_distrb.to_csv(save_path / "kfold_label_distribution.csv")
     results = {}
     for k in range(ksplit):
         dataset_yaml = ds_yamls[k]
-        results = model.train(data=dataset_yaml, *args, **kwargs)  # Include any training arguments
+        model.train(data=dataset_yaml, *args, **kwargs)  # Include any training arguments
         results[k] = model.metrics  # save output metrics for further analysis
     ```
 

setup.cfg

@@ -13,7 +13,7 @@ norecursedirs =
     build
 addopts =
     --doctest-modules
-    --durations=25
+    --durations=30
     --color=yes
 
 [coverage:run]

tests/conftest.py

@@ -1,7 +1,14 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
+import shutil
+from pathlib import Path
+
 import pytest
 
+from ultralytics.utils import ROOT
+
+TMP = (ROOT / '../tests/tmp').resolve()  # temp directory for test files
+
 
 def pytest_addoption(parser):
     parser.addoption('--runslow', action='store_true', default=False, help='run slow tests')
@@ -19,3 +26,21 @@ def pytest_collection_modifyitems(config, items):
     for item in items:
         if 'slow' in item.keywords:
             item.add_marker(skip_slow)
+
+
+def pytest_sessionstart(session):
+    """
+    Called after the 'Session' object has been created and before performing test collection.
+    """
+    shutil.rmtree(TMP, ignore_errors=True)  # delete any existing tests/tmp directory
+    TMP.mkdir(parents=True, exist_ok=True)  # create a new empty directory
+
+
+def pytest_terminal_summary(terminalreporter, exitstatus, config):
+    # Remove files
+    for file in ['bus.jpg', 'decelera_landscape_min.mov']:
+        Path(file).unlink(missing_ok=True)
+
+    # Remove directories
+    for directory in ['.pytest_cache/', TMP]:
+        shutil.rmtree(directory, ignore_errors=True)

tests/test_cli.py

@@ -5,7 +5,7 @@ from pathlib import Path
 
 import pytest
 
-from ultralytics.utils import ONLINE, ROOT, SETTINGS
+from ultralytics.utils import ROOT, SETTINGS
 
 WEIGHT_DIR = Path(SETTINGS['weights_dir'])
 TASK_ARGS = [
@@ -30,7 +30,6 @@ def test_special_modes():
     run('yolo checks')
     run('yolo version')
     run('yolo settings reset')
-    run('yolo copy-cfg')
     run('yolo cfg')
 
 
@@ -49,28 +48,14 @@ def test_predict(task, model, data):
     run(f"yolo predict model={WEIGHT_DIR / model}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt")
 
 
-@pytest.mark.skipif(not ONLINE, reason='environment is offline')
-@pytest.mark.parametrize('task,model,data', TASK_ARGS)
-def test_predict_online(task, model, data):
-    mode = 'track' if task in ('detect', 'segment', 'pose') else 'predict'  # mode for video inference
-    model = WEIGHT_DIR / model
-    run(f'yolo predict model={model}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
-    run(f'yolo {mode} model={model}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=96')
-
-    # Run Python YouTube tracking because CLI is broken. TODO: fix CLI YouTube
-    # run(f'yolo {mode} model={model}.pt source=https://youtu.be/G17sBkb38XQ imgsz=32 tracker=bytetrack.yaml')
-
-
 @pytest.mark.parametrize('model,format', EXPORT_ARGS)
 def test_export(model, format):
     run(f'yolo export model={WEIGHT_DIR / model}.pt format={format} imgsz=32')
 
 
-# Test SAM, RTDETR Models
 def test_rtdetr(task='detect', model='yolov8n-rtdetr.yaml', data='coco8.yaml'):
     # Warning: MUST use imgsz=640
     run(f'yolo train {task} model={model} data={data} imgsz=640 epochs=1 cache=disk')
-    run(f'yolo val {task} model={model} data={data} imgsz=640')
     run(f"yolo predict {task} model={model} source={ROOT / 'assets/bus.jpg'} imgsz=640 save save_crop save_txt")
 
 

tests/test_python.py

@@ -1,17 +1,20 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
 import shutil
+from copy import copy
 from pathlib import Path
 
 import cv2
 import numpy as np
+import pytest
 import torch
 from PIL import Image
 from torchvision.transforms import ToTensor
 
 from ultralytics import RTDETR, YOLO
 from ultralytics.data.build import load_inference_source
-from ultralytics.utils import LINUX, MACOS, ONLINE, ROOT, SETTINGS
+from ultralytics.utils import DEFAULT_CFG, LINUX, ONLINE, ROOT, SETTINGS
+from ultralytics.utils.downloads import download
 from ultralytics.utils.torch_utils import TORCH_1_9
 
 WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
@@ -19,13 +22,6 @@ MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt'  # test spaces in path
 CFG = 'yolov8n.yaml'
 SOURCE = ROOT / 'assets/bus.jpg'
 TMP = (ROOT / '../tests/tmp').resolve()  # temp directory for test files
-SOURCE_GREYSCALE = Path(f'{SOURCE.parent / SOURCE.stem}_greyscale.jpg')
-SOURCE_RGBA = Path(f'{SOURCE.parent / SOURCE.stem}_4ch.png')
-
-# Convert SOURCE to greyscale and 4-ch
-im = Image.open(SOURCE)
-im.convert('L').save(SOURCE_GREYSCALE)  # greyscale
-im.convert('RGBA').save(SOURCE_RGBA)  # 4-ch PNG with alpha
 
 
 def test_model_forward():
@@ -84,16 +80,32 @@ def test_predict_img():
 
 
 def test_predict_grey_and_4ch():
+    # Convert SOURCE to greyscale and 4-ch
+    im = Image.open(SOURCE)
+    source_greyscale = Path(f'{SOURCE.parent / SOURCE.stem}_greyscale.jpg')
+    source_rgba = Path(f'{SOURCE.parent / SOURCE.stem}_4ch.png')
+    im.convert('L').save(source_greyscale)  # greyscale
+    im.convert('RGBA').save(source_rgba)  # 4-ch PNG with alpha
+
+    # Inference
     model = YOLO(MODEL)
-    for f in SOURCE_RGBA, SOURCE_GREYSCALE:
+    for f in source_rgba, source_greyscale:
         for source in Image.open(f), cv2.imread(str(f)), f:
             model(source, save=True, verbose=True, imgsz=32)
 
+    # Cleanup
+    source_greyscale.unlink()
+    source_rgba.unlink()
+
 
+@pytest.mark.skipif(not ONLINE, reason='environment is offline')
 def test_track_stream():
     # Test YouTube streaming inference (short 10 frame video) with non-default ByteTrack tracker
+    # imgsz=160 required for tracking for higher confidence and better matches
     model = YOLO(MODEL)
-    model.track('https://youtu.be/G17sBkb38XQ', imgsz=96, tracker='bytetrack.yaml')
+    model.predict('https://youtu.be/G17sBkb38XQ', imgsz=96)
+    model.track('https://ultralytics.com/assets/decelera_portrait_min.mov', imgsz=160, tracker='bytetrack.yaml')
+    model.track('https://ultralytics.com/assets/decelera_portrait_min.mov', imgsz=160, tracker='botsort.yaml')
 
 
 def test_val():
@@ -101,13 +113,6 @@ def test_val():
     model.val(data='coco8.yaml', imgsz=32)
 
 
-def test_amp():
-    if torch.cuda.is_available():
-        from ultralytics.utils.checks import check_amp
-        model = YOLO(MODEL).model.cuda()
-        assert check_amp(model)
-
-
 def test_train_scratch():
     model = YOLO(CFG)
     model.train(data='coco8.yaml', epochs=1, imgsz=32, cache='disk', batch=-1)  # test disk caching with AutoBatch
@@ -133,7 +138,6 @@ def test_export_onnx():
 
 
 def test_export_openvino():
-    if not MACOS:
     model = YOLO(MODEL)
     f = model.export(format='openvino')
     YOLO(f)(SOURCE)  # exported model inference
@@ -173,7 +177,7 @@ def test_all_model_yamls():
     for m in (ROOT / 'cfg' / 'models').rglob('*.yaml'):
         if 'rtdetr' in m.name:
             if TORCH_1_9:  # torch<=1.8 issue - TypeError: __init__() got an unexpected keyword argument 'batch_first'
-                RTDETR(m.name)
+                RTDETR(m.name)(SOURCE, imgsz=640)
         else:
             YOLO(m.name)
 
@@ -225,17 +229,14 @@ def test_results():
                 print(getattr(r, k))
 
 
+@pytest.mark.skipif(not ONLINE, reason='environment is offline')
 def test_data_utils():
     # Test functions in ultralytics/data/utils.py
     from ultralytics.data.utils import HUBDatasetStats, autosplit, zip_directory
-    from ultralytics.utils.downloads import download
 
     # from ultralytics.utils.files import WorkingDirectory
     # with WorkingDirectory(ROOT.parent / 'tests'):
 
-    shutil.rmtree(TMP, ignore_errors=True)
-    TMP.mkdir(parents=True)
-
     download('https://github.com/ultralytics/hub/raw/master/example_datasets/coco8.zip', unzip=False)
     shutil.move('coco8.zip', TMP)
     stats = HUBDatasetStats(TMP / 'coco8.zip', task='detect')
@@ -244,4 +245,25 @@ def test_data_utils():
 
     autosplit(TMP / 'coco8')
     zip_directory(TMP / 'coco8/images/val')  # zip
-    shutil.rmtree(TMP)
+
+
+@pytest.mark.skipif(not ONLINE, reason='environment is offline')
+def test_data_converter():
+    # Test dataset converters
+    from ultralytics.data.converter import convert_coco
+
+    file = 'instances_val2017.json'
+    download(f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{file}')
+    shutil.move(file, TMP)
+    convert_coco(labels_dir=TMP, use_segments=True, use_keypoints=False, cls91to80=True)
+
+
+def test_events():
+    # Test event sending
+    from ultralytics.hub.utils import Events
+
+    events = Events()
+    events.enabled = True
+    cfg = copy(DEFAULT_CFG)  # does not require deepcopy
+    cfg.mode = 'test'
+    events(cfg)

ultralytics/cfg/__init__.py

@@ -442,5 +442,5 @@ def copy_default_cfg():
 
 
 if __name__ == '__main__':
-    # Example Usage: entrypoint(debug='yolo predict model=yolov8n.pt')
+    # Example: entrypoint(debug='yolo predict model=yolov8n.pt')
     entrypoint(debug='')

ultralytics/data/converter.py

@@ -36,11 +36,12 @@ def convert_coco(labels_dir='../coco/annotations/', use_segments=False, use_keyp
         use_keypoints (bool, optional): Whether to include keypoint annotations in the output.
         cls91to80 (bool, optional): Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs.
 
-    Raises:
-        FileNotFoundError: If the labels_dir path does not exist.
+    Example:
+        ```python
+        from ultralytics.data.converter import convert_coco
 
-    Example Usage:
-        convert_coco(labels_dir='../coco/annotations/', use_segments=True, use_keypoints=True, cls91to80=True)
+        convert_coco('../datasets/coco/annotations/', use_segments=True, use_keypoints=False, cls91to80=True)
+        ```
 
     Output:
         Generates output files in the specified output directory.

ultralytics/data/loaders.py

@@ -79,19 +79,18 @@ class LoadStreams:
     def update(self, i, cap, stream):
         """Read stream `i` frames in daemon thread."""
         n, f = 0, self.frames[i]  # frame number, frame array
-        while self.running and cap.isOpened() and n < f:
+        while self.running and cap.isOpened() and n < (f - 1):
             # Only read a new frame if the buffer is empty
             if not self.imgs[i]:
                 n += 1
                 cap.grab()  # .read() = .grab() followed by .retrieve()
                 if n % self.vid_stride == 0:
                     success, im = cap.retrieve()
-                    if success:
-                        self.imgs[i].append(im)  # add image to buffer
-                    else:
+                    if not success:
+                        im = np.zeros(self.shape[i], dtype=np.uint8)
                         LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.')
-                        self.imgs[i].append(np.zeros(self.shape[i]))
                         cap.open(stream)  # re-open stream if signal was lost
+                    self.imgs[i].append(im)  # add image to buffer
             else:
                 time.sleep(0.01)  # wait until the buffer is empty
 

ultralytics/engine/exporter.py

@@ -463,6 +463,7 @@ class Exporter:
         yaml_save(f / 'metadata.yaml', self.metadata)  # add metadata.yaml
         return str(f), None
 
+    @try_export
     def export_coreml(self, prefix=colorstr('CoreML:')):
         """YOLOv8 CoreML export."""
         mlmodel = self.args.format.lower() == 'mlmodel'  # legacy *.mlmodel export format requested

ultralytics/nn/modules/conv.py

@@ -175,15 +175,6 @@ class RepConv(nn.Module):
         kernelid, biasid = self._fuse_bn_tensor(self.bn)
         return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
 
-    def _avg_to_3x3_tensor(self, avgp):
-        channels = self.c1
-        groups = self.g
-        kernel_size = avgp.kernel_size
-        input_dim = channels // groups
-        k = torch.zeros((channels, input_dim, kernel_size, kernel_size))
-        k[np.arange(channels), np.tile(np.arange(input_dim), groups), :, :] = 1.0 / kernel_size ** 2
-        return k
-
     def _pad_1x1_to_3x3_tensor(self, kernel1x1):
         if kernel1x1 is None:
             return 0

ultralytics/utils/checks.py

@@ -437,11 +437,17 @@ def check_amp(model):
     Args:
         model (nn.Module): A YOLOv8 model instance.
 
+    Example:
+        ```python
+        from ultralytics import YOLO
+        from ultralytics.utils.checks import check_amp
+
+        model = YOLO('yolov8n.pt').model.cuda()
+        check_amp(model)
+        ```
+
     Returns:
         (bool): Returns True if the AMP functionality works correctly with YOLOv8 model, else False.
-
-    Raises:
-        AssertionError: If the AMP checks fail, indicating anomalies with the AMP functionality on the system.
     """
     device = next(model.parameters()).device  # get model device
     if device.type in ('cpu', 'mps'):