ultralytics 8.0.191 fix yolo checks for missing packages (#5179)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Muhammad Rizwan Munawar <62513924+RizwanMunawar@users.noreply.github.com>

tests/conftest.py

@@ -5,10 +5,7 @@ from pathlib import Path
 
 import pytest
 
-from ultralytics.utils import ROOT
-from ultralytics.utils.torch_utils import init_seeds
-
-TMP = (ROOT / '../tests/tmp').resolve()  # temp directory for test files
+TMP = Path(__file__).resolve().parent / 'tmp'  # temp directory for test files
 
 
 def pytest_addoption(parser):
@@ -62,6 +59,8 @@ def pytest_sessionstart(session):
     Args:
         session (pytest.Session): The pytest session object.
     """
+    from ultralytics.utils.torch_utils import init_seeds
+
     init_seeds()
     shutil.rmtree(TMP, ignore_errors=True)  # delete any existing tests/tmp directory
     TMP.mkdir(parents=True, exist_ok=True)  # create a new empty directory
@@ -79,10 +78,14 @@ def pytest_terminal_summary(terminalreporter, exitstatus, config):
         exitstatus (int): The exit status of the test run.
         config (pytest.config.Config): The pytest config object.
     """
+    from ultralytics.utils import WEIGHTS_DIR
+
     # Remove files
-    for file in ['bus.jpg', 'decelera_landscape_min.mov']:
+    models = [path for x in ['*.onnx', '*.torchscript'] for path in WEIGHTS_DIR.rglob(x)]
+    for file in ['bus.jpg', 'yolov8n.onnx', 'yolov8n.torchscript'] + models:
         Path(file).unlink(missing_ok=True)
 
     # Remove directories
-    for directory in [ROOT / '../.pytest_cache', TMP]:
+    models = [path for x in ['*.mlpackage', '*_openvino_model'] for path in WEIGHTS_DIR.rglob(x)]
+    for directory in [TMP.parents[1] / '.pytest_cache', TMP] + models:
         shutil.rmtree(directory, ignore_errors=True)

tests/test_cli.py

@@ -1,16 +1,14 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
 import subprocess
-from pathlib import Path
 
 import pytest
 
-from ultralytics.utils import ASSETS, SETTINGS
+from ultralytics.utils import ASSETS, WEIGHTS_DIR
 from ultralytics.utils.checks import cuda_device_count, cuda_is_available
 
 CUDA_IS_AVAILABLE = cuda_is_available()
 CUDA_DEVICE_COUNT = cuda_device_count()
-WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
 TASK_ARGS = [
     ('detect', 'yolov8n', 'coco8.yaml'),
     ('segment', 'yolov8n-seg', 'coco8-seg.yaml'),

tests/test_cuda.py

@@ -1,19 +1,16 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 import contextlib
-from pathlib import Path
 
 import pytest
 import torch
 
 from ultralytics import YOLO, download
-from ultralytics.utils import ASSETS, SETTINGS
+from ultralytics.utils import ASSETS, DATASETS_DIR, WEIGHTS_DIR
 from ultralytics.utils.checks import cuda_device_count, cuda_is_available
 
 CUDA_IS_AVAILABLE = cuda_is_available()
 CUDA_DEVICE_COUNT = cuda_device_count()
 
-DATASETS_DIR = Path(SETTINGS['datasets_dir'])
-WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
 MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt'  # test spaces in path
 DATA = 'coco8.yaml'
 BUS = ASSETS / 'bus.jpg'
@@ -91,7 +88,7 @@ def test_predict_sam():
     model(ASSETS / 'zidane.jpg', points=[900, 370], labels=[1], device=0)
 
     # Create SAMPredictor
-    overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model='mobile_sam.pt')
+    overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model=WEIGHTS_DIR / 'mobile_sam.pt')
     predictor = SAMPredictor(overrides=overrides)
 
     # Set image

tests/test_engine.py

@@ -1,18 +1,16 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-from pathlib import Path
-
 from ultralytics import YOLO
 from ultralytics.cfg import get_cfg
 from ultralytics.engine.exporter import Exporter
 from ultralytics.models.yolo import classify, detect, segment
-from ultralytics.utils import ASSETS, DEFAULT_CFG, SETTINGS
+from ultralytics.utils import ASSETS, DEFAULT_CFG, WEIGHTS_DIR
 
 CFG_DET = 'yolov8n.yaml'
 CFG_SEG = 'yolov8n-seg.yaml'
 CFG_CLS = 'yolov8n-cls.yaml'  # or 'squeezenet1_0'
 CFG = get_cfg(DEFAULT_CFG)
-MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
+MODEL = WEIGHTS_DIR / 'yolov8n'
 
 
 def test_func(*args):  # noqa

tests/test_python.py

@@ -14,11 +14,11 @@ from torchvision.transforms import ToTensor
 from ultralytics import RTDETR, YOLO
 from ultralytics.cfg import TASK2DATA
 from ultralytics.data.build import load_inference_source
-from ultralytics.utils import ASSETS, DEFAULT_CFG, LINUX, MACOS, ONLINE, ROOT, SETTINGS, WINDOWS, is_dir_writeable
+from ultralytics.utils import (ASSETS, DEFAULT_CFG, DEFAULT_CFG_PATH, LINUX, MACOS, ONLINE, ROOT, WEIGHTS_DIR, WINDOWS,
+                               is_dir_writeable)
 from ultralytics.utils.downloads import download
 from ultralytics.utils.torch_utils import TORCH_1_9
 
-WEIGHTS_DIR = Path(SETTINGS['weights_dir'])
 MODEL = WEIGHTS_DIR / 'path with spaces' / 'yolov8n.pt'  # test spaces in path
 CFG = 'yolov8n.yaml'
 SOURCE = ASSETS / 'bus.jpg'
@@ -260,12 +260,12 @@ def test_predict_callback_and_setup():
 
 def test_results():
     for m in 'yolov8n-pose.pt', 'yolov8n-seg.pt', 'yolov8n.pt', 'yolov8n-cls.pt':
-        results = YOLO(m)([SOURCE, SOURCE], imgsz=160)
+        results = YOLO(WEIGHTS_DIR / m)([SOURCE, SOURCE], imgsz=160)
         for r in results:
             r = r.cpu().numpy()
             r = r.to(device='cpu', dtype=torch.float32)
-            r.save_txt(txt_file='runs/tests/label.txt', save_conf=True)
-            r.save_crop(save_dir='runs/tests/crops/')
+            r.save_txt(txt_file=TMP / 'runs/tests/label.txt', save_conf=True)
+            r.save_crop(save_dir=TMP / 'runs/tests/crops/')
             r.tojson(normalize=True)
             r.plot(pil=True)
             r.plot(conf=True, boxes=True)
@@ -299,14 +299,17 @@ def test_data_converter():
 
     file = 'instances_val2017.json'
     download(f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{file}', dir=TMP)
-    convert_coco(labels_dir=TMP, use_segments=True, use_keypoints=False, cls91to80=True)
+    convert_coco(labels_dir=TMP, save_dir=TMP / 'yolo_labels', use_segments=True, use_keypoints=False, cls91to80=True)
     coco80_to_coco91_class()
 
 
 def test_data_annotator():
     from ultralytics.data.annotator import auto_annotate
 
-    auto_annotate(ASSETS, det_model='yolov8n.pt', sam_model='mobile_sam.pt', output_dir=TMP / 'auto_annotate_labels')
+    auto_annotate(ASSETS,
+                  det_model=WEIGHTS_DIR / 'yolov8n.pt',
+                  sam_model=WEIGHTS_DIR / 'mobile_sam.pt',
+                  output_dir=TMP / 'auto_annotate_labels')
 
 
 def test_events():
@@ -326,6 +329,7 @@ def test_cfg_init():
     with contextlib.suppress(SyntaxError):
         check_dict_alignment({'a': 1}, {'b': 2})
     copy_default_cfg()
+    (Path.cwd() / DEFAULT_CFG_PATH.name.replace('.yaml', '_copy.yaml')).unlink(missing_ok=False)
     [smart_value(x) for x in ['none', 'true', 'false']]