ultralytics 8.0.239 Ultralytics Actions and hub-sdk adoption (#7431)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>

ultralytics/data/build.py

@@ -9,8 +9,16 @@ import torch
 from PIL import Image
 from torch.utils.data import dataloader, distributed
 
-from ultralytics.data.loaders import (LOADERS, LoadImages, LoadPilAndNumpy, LoadScreenshots, LoadStreams, LoadTensor,
-                                      SourceTypes, autocast_list)
+from ultralytics.data.loaders import (
+    LOADERS,
+    LoadImages,
+    LoadPilAndNumpy,
+    LoadScreenshots,
+    LoadStreams,
+    LoadTensor,
+    SourceTypes,
+    autocast_list,
+)
 from ultralytics.data.utils import IMG_FORMATS, VID_FORMATS
 from ultralytics.utils import RANK, colorstr
 from ultralytics.utils.checks import check_file
@@ -29,7 +37,7 @@ class InfiniteDataLoader(dataloader.DataLoader):
     def __init__(self, *args, **kwargs):
         """Dataloader that infinitely recycles workers, inherits from DataLoader."""
         super().__init__(*args, **kwargs)
-        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
+        object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
         self.iterator = super().__iter__()
 
     def __len__(self):
@@ -70,29 +78,30 @@ class _RepeatSampler:
 
 def seed_worker(worker_id):  # noqa
     """Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader."""
-    worker_seed = torch.initial_seed() % 2 ** 32
+    worker_seed = torch.initial_seed() % 2**32
     np.random.seed(worker_seed)
     random.seed(worker_seed)
 
 
-def build_yolo_dataset(cfg, img_path, batch, data, mode='train', rect=False, stride=32):
+def build_yolo_dataset(cfg, img_path, batch, data, mode="train", rect=False, stride=32):
     """Build YOLO Dataset."""
     return YOLODataset(
         img_path=img_path,
         imgsz=cfg.imgsz,
         batch_size=batch,
-        augment=mode == 'train',  # augmentation
+        augment=mode == "train",  # augmentation
         hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
         rect=cfg.rect or rect,  # rectangular batches
         cache=cfg.cache or None,
         single_cls=cfg.single_cls or False,
         stride=int(stride),
-        pad=0.0 if mode == 'train' else 0.5,
-        prefix=colorstr(f'{mode}: '),
+        pad=0.0 if mode == "train" else 0.5,
+        prefix=colorstr(f"{mode}: "),
         task=cfg.task,
         classes=cfg.classes,
         data=data,
-        fraction=cfg.fraction if mode == 'train' else 1.0)
+        fraction=cfg.fraction if mode == "train" else 1.0,
+    )
 
 
 def build_dataloader(dataset, batch, workers, shuffle=True, rank=-1):
@@ -103,15 +112,17 @@ def build_dataloader(dataset, batch, workers, shuffle=True, rank=-1):
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
     generator = torch.Generator()
     generator.manual_seed(6148914691236517205 + RANK)
-    return InfiniteDataLoader(dataset=dataset,
-                              batch_size=batch,
-                              shuffle=shuffle and sampler is None,
-                              num_workers=nw,
-                              sampler=sampler,
-                              pin_memory=PIN_MEMORY,
-                              collate_fn=getattr(dataset, 'collate_fn', None),
-                              worker_init_fn=seed_worker,
-                              generator=generator)
+    return InfiniteDataLoader(
+        dataset=dataset,
+        batch_size=batch,
+        shuffle=shuffle and sampler is None,
+        num_workers=nw,
+        sampler=sampler,
+        pin_memory=PIN_MEMORY,
+        collate_fn=getattr(dataset, "collate_fn", None),
+        worker_init_fn=seed_worker,
+        generator=generator,
+    )
 
 
 def check_source(source):
@@ -120,9 +131,9 @@ def check_source(source):
     if isinstance(source, (str, int, Path)):  # int for local usb camera
         source = str(source)
         is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
-        is_url = source.lower().startswith(('https://', 'http://', 'rtsp://', 'rtmp://', 'tcp://'))
-        webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
-        screenshot = source.lower() == 'screen'
+        is_url = source.lower().startswith(("https://", "http://", "rtsp://", "rtmp://", "tcp://"))
+        webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
+        screenshot = source.lower() == "screen"
         if is_url and is_file:
             source = check_file(source)  # download
     elif isinstance(source, LOADERS):
@@ -135,7 +146,7 @@ def check_source(source):
     elif isinstance(source, torch.Tensor):
         tensor = True
     else:
-        raise TypeError('Unsupported image type. For supported types see https://docs.ultralytics.com/modes/predict')
+        raise TypeError("Unsupported image type. For supported types see https://docs.ultralytics.com/modes/predict")
 
     return source, webcam, screenshot, from_img, in_memory, tensor
 
@@ -171,6 +182,6 @@ def load_inference_source(source=None, imgsz=640, vid_stride=1, buffer=False):
         dataset = LoadImages(source, imgsz=imgsz, vid_stride=vid_stride)
 
     # Attach source types to the dataset
-    setattr(dataset, 'source_type', source_type)
+    setattr(dataset, "source_type", source_type)
 
     return dataset