Update numpy requirement from >=1.23.5 to >=2.2.6

models/common.py

@@ -160,8 +160,7 @@ def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcu
         self.add = shortcut and c1 == c2
 
     def forward(self, x):
-        """Executes forward pass, performing convolutional ops and optional shortcut addition; expects input tensor x.
-        """
+        """Executes forward pass, performing convolutional ops and optional shortcut addition; expects input tensor x."""
         return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
 
 
@@ -183,8 +182,7 @@ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, nu
         self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
 
     def forward(self, x):
-        """Processes input through layers, combining outputs with activation and normalization for feature extraction.
-        """
+        """Processes input through layers, combining outputs with activation and normalization for feature extraction."""
         y1 = self.cv3(self.m(self.cv1(x)))
         y2 = self.cv2(x)
         return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
@@ -212,8 +210,7 @@ class C3(nn.Module):
     """Implements a CSP Bottleneck with 3 convolutions, optional shortcuts, group convolutions, and expansion factor."""
 
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
-        """Initializes CSP Bottleneck with 3 convolutions, optional shortcuts, group convolutions, and expansion factor.
-        """
+        """Initializes CSP Bottleneck with 3 convolutions, optional shortcuts, group convolutions, and expansion factor."""
         super().__init__()
         c_ = int(c2 * e)  # hidden channels
         self.cv1 = Conv(c1, c_, 1, 1)

pyproject.toml

@@ -64,7 +64,7 @@ classifiers = [
 dependencies = [
     "gitpython>=3.1.30", # git repo interaction for training/versioning
     "matplotlib>=3.5.0",
-    "numpy>=1.23.5",
+    "numpy>=2.2.6",
     "opencv-python>=4.6.0",
     "pillow>=10.3.0",
     "pyyaml>=5.3.1",

requirements.txt

@@ -5,7 +5,7 @@
 # Base ------------------------------------------------------------------------
 gitpython>=3.1.30            # Git repo interaction for training/versioning
 matplotlib>=3.5.0            # Plotting results and graphs
-numpy>=1.23.5                # Fundamental for array/matrix operations
+numpy>=2.2.6                # Fundamental for array/matrix operations
 opencv-python>=4.6.0         # Image/video processing
 packaging>=20.0             # Version parsing for utils/general.py and loggers
 Pillow>=10.3.0               # Image reading/writing support

utils/activations.py

@@ -110,8 +110,7 @@ def __init__(self, c1, k=1, s=1, r=16):  # ch_in, kernel, stride, r
         # self.bn2 = nn.BatchNorm2d(c1)
 
     def forward(self, x):
-        """Apply the MetaAconC activation, generating beta from the input's spatial average via the bottleneck network.
-        """
+        """Apply the MetaAconC activation, generating beta from the input's spatial average via the bottleneck network."""
         y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
         # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
         # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y)))))  # bug/unstable

utils/augmentations.py

@@ -263,8 +263,7 @@ def cutout(im, labels, p=0.5):
 
 
 def mixup(im, labels, im2, labels2):
-    """Applies MixUp augmentation by blending images and labels; see https://arxiv.org/pdf/1710.09412.pdf for details.
-    """
+    """Applies MixUp augmentation by blending images and labels; see https://arxiv.org/pdf/1710.09412.pdf for details."""
     r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
     im = (im * r + im2 * (1 - r)).astype(np.uint8)
     labels = np.concatenate((labels, labels2), 0)

utils/dataloaders.py

@@ -481,8 +481,7 @@ def __len__(self):
 
 
 def img2label_paths(img_paths):
-    """Converts image paths to corresponding label paths by replacing `/images/` with `/labels/` and `.jpg` with `.txt`.
-    """
+    """Converts image paths to corresponding label paths by replacing `/images/` with `/labels/` and `.jpg` with `.txt`."""
     sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}"  # /images/, /labels/ substrings
     return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths]
 

utils/general.py

@@ -197,8 +197,7 @@ class Profile(contextlib.ContextDecorator):
     """Profiles code execution time, usable as a context manager or decorator for performance monitoring."""
 
     def __init__(self, t=0.0):
-        """Initializes a profiling context for YOLOv3 with optional timing threshold `t` and checks CUDA availability.
-        """
+        """Initializes a profiling context for YOLOv3 with optional timing threshold `t` and checks CUDA availability."""
         self.t = t
         self.cuda = torch.cuda.is_available()
 
@@ -225,8 +224,7 @@ class Timeout(contextlib.ContextDecorator):
     """Enforces a timeout on code execution, raising TimeoutError on expiry."""
 
     def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True):
-        """Initializes a timeout context/decorator with specified duration, custom message, and error handling option.
-        """
+        """Initializes a timeout context/decorator with specified duration, custom message, and error handling option."""
         self.seconds = int(seconds)
         self.timeout_message = timeout_msg
         self.suppress = bool(suppress_timeout_errors)
@@ -311,15 +309,13 @@ def get_default_args(func):
 
 
 def get_latest_run(search_dir="."):
-    """Returns path to the most recent 'last.pt' file within 'search_dir' for resuming, or an empty string if not found.
-    """
+    """Returns path to the most recent 'last.pt' file within 'search_dir' for resuming, or an empty string if not found."""
     last_list = glob.glob(f"{search_dir}/**/last*.pt", recursive=True)
     return max(last_list, key=os.path.getctime) if last_list else ""
 
 
 def check_online():
-    """Checks internet connectivity by attempting to connect to "1.1.1.1" on port 443 twice; returns True if successful.
-    """
+    """Checks internet connectivity by attempting to connect to "1.1.1.1" on port 443 twice; returns True if successful."""
     import socket
 
     def run_once():
@@ -614,12 +610,10 @@ def url2file(url):
 
 
 def download(url, dir=".", unzip=True, delete=True, curl=False, threads=1, retry=3):
-    """Downloads files from URLs into a specified directory, optionally unzips, and supports multithreading and retries.
-    """
+    """Downloads files from URLs into a specified directory, optionally unzips, and supports multithreading and retries."""
 
     def download_one(url, dir):
-        """Downloads a file from a URL into the specified directory, supporting retries and using curl or torch methods.
-        """
+        """Downloads a file from a URL into the specified directory, supporting retries and using curl or torch methods."""
         success = True
         if os.path.isfile(url):
             f = Path(url)  # filename

utils/loss.py

@@ -83,8 +83,7 @@ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
         self.loss_fcn.reduction = "none"  # required to apply FL to each element
 
     def forward(self, pred, true):
-        """Computes focal loss between predictions and true labels using configured loss function, `gamma`, and `alpha`.
-        """
+        """Computes focal loss between predictions and true labels using configured loss function, `gamma`, and `alpha`."""
         loss = self.loss_fcn(pred, true)
 
         pred_prob = torch.sigmoid(pred)  # prob from logits
@@ -187,8 +186,7 @@ def __call__(self, p, targets):  # predictions, targets
         return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
 
     def build_targets(self, p, targets):
-        """Match `targets` (image, class, x, y, w, h) to anchors per layer, returning tcls, tbox, indices, and anchors.
-        """
+        """Match `targets` (image, class, x, y, w, h) to anchors per layer, returning tcls, tbox, indices, and anchors."""
         na, nt = self.na, targets.shape[0]  # number of anchors, targets
         tcls, tbox, indices, anch = [], [], [], []
         gain = torch.ones(7, device=self.device)  # normalized to gridspace gain

utils/torch_utils.py

@@ -184,8 +184,7 @@ def de_parallel(model):
 
 
 def sparsity(model):
-    """Calculates and returns the global sparsity of a model as the ratio of zero-valued parameters to total parameters.
-    """
+    """Calculates and returns the global sparsity of a model as the ratio of zero-valued parameters to total parameters."""
     a, b = 0, 0
     for p in model.parameters():
         a += p.numel()

utils/triton.py

@@ -71,8 +71,7 @@ def __call__(self, *args, **kwargs) -> torch.Tensor | tuple[torch.Tensor, ...]:
         return result[0] if len(result) == 1 else result
 
     def _create_inputs(self, *args, **kwargs):
-        """Generates model inputs from args or kwargs, not allowing both; raises error if neither or both are provided.
-        """
+        """Generates model inputs from args or kwargs, not allowing both; raises error if neither or both are provided."""
         args_len, kwargs_len = len(args), len(kwargs)
         if not args_len and not kwargs_len:
             raise RuntimeError("No inputs provided.")