Apply ruff==0.9.0 formatting (#13486)

* Refactor code for speed and clarity

* Auto-format by https://ultralytics.com/actions

---------

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

utils/augmentations.py

@@ -197,15 +197,7 @@ def random_perspective(
         else:  # affine
             im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
 
-    # Visualize
-    # import matplotlib.pyplot as plt
-    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
-    # ax[0].imshow(im[:, :, ::-1])  # base
-    # ax[1].imshow(im2[:, :, ::-1])  # warped
-
-    # Transform label coordinates
-    n = len(targets)
-    if n:
+    if n := len(targets):
         use_segments = any(x.any() for x in segments) and len(segments) == n
         new = np.zeros((n, 4))
         if use_segments:  # warp segments

utils/dataloaders.py

@@ -356,8 +356,7 @@ class LoadImages:
         else:
             self.cap = None
         assert self.nf > 0, (
-            f"No images or videos found in {p}. "
-            f"Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}"
+            f"No images or videos found in {p}. Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}"
         )
 
     def __iter__(self):
@@ -713,8 +712,8 @@ class LoadImagesAndLabels(Dataset):
         cache = mem_required * (1 + safety_margin) < mem.available  # to cache or not to cache, that is the question
         if not cache:
             LOGGER.info(
-                f'{prefix}{mem_required / gb:.1f}GB RAM required, '
-                f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
+                f"{prefix}{mem_required / gb:.1f}GB RAM required, "
+                f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, "
                 f"{'caching images ✅' if cache else 'not caching images ⚠️'}"
             )
         return cache
@@ -774,8 +773,7 @@ class LoadImagesAndLabels(Dataset):
         index = self.indices[index]  # linear, shuffled, or image_weights
 
         hyp = self.hyp
-        mosaic = self.mosaic and random.random() < hyp["mosaic"]
-        if mosaic:
+        if mosaic := self.mosaic and random.random() < hyp["mosaic"]:
             # Load mosaic
             img, labels = self.load_mosaic(index)
             shapes = None
@@ -1162,8 +1160,7 @@ def verify_image_label(args):
                     segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
                     lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
                 lb = np.array(lb, dtype=np.float32)
-            nl = len(lb)
-            if nl:
+            if nl := len(lb):
                 assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
                 assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
                 assert (lb[:, 1:] <= 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"

utils/general.py

@@ -173,8 +173,7 @@ def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"):
     """Returns user configuration directory path, preferring environment variable `YOLOV5_CONFIG_DIR` if set, else OS-
     specific.
     """
-    env = os.getenv(env_var)
-    if env:
+    if env := os.getenv(env_var):
         path = Path(env)  # use environment variable
     else:
         cfg = {"Windows": "AppData/Roaming", "Linux": ".config", "Darwin": "Library/Application Support"}  # 3 OS dirs
@@ -496,9 +495,9 @@ def check_file(file, suffix=""):
             assert Path(file).exists() and Path(file).stat().st_size > 0, f"File download failed: {url}"  # check
         return file
     elif file.startswith("clearml://"):  # ClearML Dataset ID
-        assert (
-            "clearml" in sys.modules
-        ), "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'."
+        assert "clearml" in sys.modules, (
+            "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'."
+        )
         return file
     else:  # search
         files = []

utils/loggers/clearml/clearml_utils.py

@@ -41,11 +41,9 @@ def construct_dataset(clearml_info_string):
     with open(yaml_filenames[0]) as f:
         dataset_definition = yaml.safe_load(f)
 
-    assert set(
-        dataset_definition.keys()
-    ).issuperset(
-        {"train", "test", "val", "nc", "names"}
-    ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
+    assert set(dataset_definition.keys()).issuperset({"train", "test", "val", "nc", "names"}), (
+        "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
+    )
 
     data_dict = {
         "train": (

utils/loggers/comet/__init__.py

@@ -88,14 +88,12 @@ class CometLogger:
         self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET
         self.resume = self.opt.resume
 
-        # Default parameters to pass to Experiment objects
         self.default_experiment_kwargs = {
             "log_code": False,
             "log_env_gpu": True,
             "log_env_cpu": True,
             "project_name": COMET_PROJECT_NAME,
-        }
-        self.default_experiment_kwargs.update(experiment_kwargs)
+        } | experiment_kwargs
         self.experiment = self._get_experiment(self.comet_mode, run_id)
         self.experiment.set_name(self.opt.name)
 

utils/loggers/wandb/wandb_utils.py

@@ -18,7 +18,7 @@ if str(ROOT) not in sys.path:
 RANK = int(os.getenv("RANK", -1))
 DEPRECATION_WARNING = (
     f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. "
-    f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.'
+    f"See supported integrations at https://github.com/ultralytics/yolov5#integrations."
 )
 
 try:

utils/loss.py

@@ -148,8 +148,7 @@ class ComputeLoss:
             b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
             tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device)  # target obj
 
-            n = b.shape[0]  # number of targets
-            if n:
+            if n := b.shape[0]:
                 # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1)  # faster, requires torch 1.8.0
                 pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1)  # target-subset of predictions
 
@@ -175,10 +174,6 @@ class ComputeLoss:
                     t[range(n), tcls[i]] = self.cp
                     lcls += self.BCEcls(pcls, t)  # BCE
 
-                # Append targets to text file
-                # with open('targets.txt', 'a') as file:
-                #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
             obji = self.BCEobj(pi[..., 4], tobj)
             lobj += obji * self.balance[i]  # obj loss
             if self.autobalance:

utils/segment/augmentations.py

@@ -69,16 +69,8 @@ def random_perspective(
         else:  # affine
             im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
 
-    # Visualize
-    # import matplotlib.pyplot as plt
-    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
-    # ax[0].imshow(im[:, :, ::-1])  # base
-    # ax[1].imshow(im2[:, :, ::-1])  # warped
-
-    # Transform label coordinates
-    n = len(targets)
     new_segments = []
-    if n:
+    if n := len(targets):
         new = np.zeros((n, 4))
         segments = resample_segments(segments)  # upsample
         for i, segment in enumerate(segments):

utils/segment/dataloaders.py

@@ -132,9 +132,7 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
         index = self.indices[index]  # linear, shuffled, or image_weights
 
         hyp = self.hyp
-        mosaic = self.mosaic and random.random() < hyp["mosaic"]
-        masks = []
-        if mosaic:
+        if mosaic := self.mosaic and random.random() < hyp["mosaic"]:
             # Load mosaic
             img, labels, segments = self.load_mosaic(index)
             shapes = None
@@ -180,6 +178,7 @@ class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels):  # for training/testing
                 )
 
         nl = len(labels)  # number of labels
+        masks = []
         if nl:
             labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)
             if self.overlap:

utils/segment/loss.py

@@ -61,8 +61,7 @@ class ComputeLoss:
             b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
             tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device)  # target obj
 
-            n = b.shape[0]  # number of targets
-            if n:
+            if n := b.shape[0]:
                 pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1)  # subset of predictions
 
                 # Box regression

utils/torch_utils.py

@@ -121,9 +121,9 @@ def select_device(device="", batch_size=0, newline=True):
         os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # force torch.cuda.is_available() = False
     elif device:  # non-cpu device requested
         os.environ["CUDA_VISIBLE_DEVICES"] = device  # set environment variable - must be before assert is_available()
-        assert torch.cuda.is_available() and torch.cuda.device_count() >= len(
-            device.replace(",", "")
-        ), f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
+        assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(",", "")), (
+            f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
+        )
 
     if not cpu and not mps and torch.cuda.is_available():  # prefer GPU if available
         devices = device.split(",") if device else "0"  # range(torch.cuda.device_count())  # i.e. 0,1,6,7
@@ -380,7 +380,7 @@ def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e-5):
     optimizer.add_param_group({"params": g[1], "weight_decay": 0.0})  # add g1 (BatchNorm2d weights)
     LOGGER.info(
         f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
-        f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias'
+        f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias"
     )
     return optimizer