Start Multi-OS CI (#172)

.github/workflows/ci.yaml

@@ -18,24 +18,18 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ ubuntu-latest ]
+        os: [ubuntu-latest, windows-latest, macos-latest]
         python-version: ['3.10']
         model: [yolov8n]
         torch: [latest]
-#        include:
+        include:
-#          - os: ubuntu-latest
+          - os: ubuntu-latest
-#            python-version: '3.7'  # '3.6.8' min
+            python-version: '3.7'  # '3.6.8' min
-#            model: yolov8n
+            model: yolov8n
-#          - os: ubuntu-latest
+          - os: ubuntu-latest
-#            python-version: '3.8'
+            python-version: '3.8'  # torch 1.7.0 requires python >=3.6, <=3.8
-#            model: yolov8n
+            model: yolov8n
-#          - os: ubuntu-latest
+            torch: '1.7.0'  # min torch version CI https://pypi.org/project/torchvision/
-#            python-version: '3.9'
-#            model: yolov8n
-#          - os: ubuntu-latest
-#            python-version: '3.8'  # torch 1.7.0 requires python >=3.6, <=3.8
-#            model: yolov8n
-#            torch: '1.7.0'  # min torch version CI https://pypi.org/project/torchvision/
     steps:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
@@ -92,13 +86,16 @@ jobs:
         run: |
           yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=1 imgsz=64
           yolo task=detect mode=val model=runs/detect/train/weights/last.pt imgsz=64
+          yolo task=detect mode=predict model=runs/detect/train/weights/last.pt imgsz=64 source=ultralytics/assets/bus.jpg
       - name: Test segmentation
         shell: bash  # for Windows compatibility
         run: |
           yolo task=segment mode=train model=yolov8n-seg.yaml data=coco128-seg.yaml epochs=1 imgsz=64
           yolo task=segment mode=val model=runs/segment/train/weights/last.pt data=coco128-seg.yaml imgsz=64
+          yolo task=segment mode=predict model=runs/segment/train/weights/last.pt imgsz=64 source=ultralytics/assets/bus.jpg
       - 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
+          yolo task=classify mode=val model=runs/classify/train/weights/last.pt data=mnist160 imgsz=32
+          yolo task=classify mode=predict model=runs/classify/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg

ultralytics/hub/utils.py

@@ -131,7 +131,7 @@ def smart_request(*args, retry=3, timeout=30, thread=True, code=-1, method="post
 
 
 @TryExcept()
-def sync_analytics(cfg, all_keys=False, enabled=True):
+def sync_analytics(cfg, all_keys=False, enabled=False):
     """
    Sync analytics data if enabled in the global settings
 

ultralytics/yolo/v8/classify/train.py

@@ -72,7 +72,8 @@ class ClassificationTrainer(BaseTrainer):
                                                imgsz=self.args.imgsz,
                                                batch_size=batch_size if mode == "train" else (batch_size * 2),
                                                augment=mode == "train",
-                                               rank=rank)
+                                               rank=rank,
+                                               workers=self.args.workers)
 
     def preprocess_batch(self, batch):
         batch["img"] = batch["img"].to(self.device)

ultralytics/yolo/v8/classify/val.py

@@ -36,7 +36,10 @@ class ClassificationValidator(BaseValidator):
         return self.metrics.results_dict
 
     def get_dataloader(self, dataset_path, batch_size):
-        return build_classification_dataloader(path=dataset_path, imgsz=self.args.imgsz, batch_size=batch_size)
+        return build_classification_dataloader(path=dataset_path,
+                                               imgsz=self.args.imgsz,
+                                               batch_size=batch_size,
+                                               workers=self.args.workers)
 
     def print_results(self):
         pf = '%22s' + '%11.3g' * len(self.metrics.keys)  # print format