Apply ruff==0.9.0 formatting

README.md

@@ -207,7 +207,7 @@ YOLOv3 has been designed to be super easy to get started and simple to learn. We
   <summary>Figure Notes</summary>
 
 - **COCO AP val** denotes [email protected]:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
-- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
+- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p4/) V100 instance at batch-size 32.
 - **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
 - **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
 
@@ -234,7 +234,7 @@ YOLOv3 has been designed to be super easy to get started and simple to learn. We
 
 - All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
 - **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
+- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p4/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
 - **TTA** [Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
 
 </details>

README.zh-CN.md

@@ -207,7 +207,7 @@ YOLOv3 超级容易上手，简单易学。我们优先考虑现实世界的结
   <summary>图表笔记</summary>
 
 - **COCO AP val** 表示 [email protected]:0.95 指标，在 [COCO val2017](http://cocodataset.org) 数据集的 5000 张图像上测得， 图像包含 256 到 1536 各种推理大小。
-- **显卡推理速度** 为在 [COCO val2017](http://cocodataset.org) 数据集上的平均推理时间，使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例，batchsize 为 32 。
+- **显卡推理速度** 为在 [COCO val2017](http://cocodataset.org) 数据集上的平均推理时间，使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p4/) V100实例，batchsize 为 32 。
 - **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ， batchsize 为32。
 - **复现命令** 为 `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
 
@@ -234,7 +234,7 @@ YOLOv3 超级容易上手，简单易学。我们优先考虑现实世界的结
 
 - 所有模型都使用默认配置，训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ，其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。
 - \*\*mAP<sup>val</sup>\*\*在单模型单尺度上计算，数据集使用 [COCO val2017](http://cocodataset.org) 。<br>复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-- **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。<br>复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1`
+- **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p4/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。<br>复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1`
 - **TTA** [测试时数据增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation/) 包括反射和尺度变换。<br>复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
 
 </details>

classify/predict.py

@@ -192,7 +192,7 @@ def run(
                     vid_writer[i].write(im0)
 
         # Print time (inference-only)
-        LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms")
+        LOGGER.info(f"{s}{dt[1].dt * 1e3:.1f}ms")
 
     # Print results
     t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image

classify/train.py

@@ -203,10 +203,10 @@ def lf(x):
     scaler = amp.GradScaler(enabled=cuda)
     val = test_dir.stem  # 'val' or 'test'
     LOGGER.info(
-        f'Image sizes {imgsz} train, {imgsz} test\n'
-        f'Using {nw * WORLD_SIZE} dataloader workers\n'
+        f"Image sizes {imgsz} train, {imgsz} test\n"
+        f"Using {nw * WORLD_SIZE} dataloader workers\n"
         f"Logging results to {colorstr('bold', save_dir)}\n"
-        f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n'
+        f"Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n"
         f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}"
     )
     for epoch in range(epochs):  # loop over the dataset multiple times
@@ -292,13 +292,13 @@ def lf(x):
     # Train complete
     if RANK in {-1, 0} and final_epoch:
         LOGGER.info(
-            f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)'
+            f"\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)"
             f"\nResults saved to {colorstr('bold', save_dir)}"
-            f'\nPredict:         python classify/predict.py --weights {best} --source im.jpg'
-            f'\nValidate:        python classify/val.py --weights {best} --data {data_dir}'
-            f'\nExport:          python export.py --weights {best} --include onnx'
+            f"\nPredict:         python classify/predict.py --weights {best} --source im.jpg"
+            f"\nValidate:        python classify/val.py --weights {best} --data {data_dir}"
+            f"\nExport:          python export.py --weights {best} --include onnx"
             f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')"
-            f'\nVisualize:       https://netron.app\n'
+            f"\nVisualize:       https://netron.app\n"
         )
 
         # Plot examples

detect.py

@@ -273,7 +273,7 @@ def run(
                     vid_writer[i].write(im0)
 
         # Print time (inference-only)
-        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
+        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms")
 
     # Print results
     t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image

export.py

@@ -1498,12 +1498,12 @@ def run(
             else ""
         )
         LOGGER.info(
-            f'\nExport complete ({time.time() - t:.1f}s)'
+            f"\nExport complete ({time.time() - t:.1f}s)"
             f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
             f"\nDetect:          python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
             f"\nValidate:        python {dir / 'val.py'} --weights {f[-1]} {h}"
             f"\nPyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')  {s}"
-            f'\nVisualize:       https://netron.app'
+            f"\nVisualize:       https://netron.app"
         )
     return f  # return list of exported files/dirs
 

segment/predict.py

@@ -245,7 +245,7 @@ def run(
                     vid_writer[i].write(im0)
 
         # Print time (inference-only)
-        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
+        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1e3:.1f}ms")
 
     # Print results
     t = tuple(x.t / seen * 1e3 for x in dt)  # speeds per image

segment/train.py

@@ -323,10 +323,10 @@ def lf(x):
     compute_loss = ComputeLoss(model, overlap=overlap)  # init loss class
     # callbacks.run('on_train_start')
     LOGGER.info(
-        f'Image sizes {imgsz} train, {imgsz} val\n'
-        f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
+        f"Image sizes {imgsz} train, {imgsz} val\n"
+        f"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n"
         f"Logging results to {colorstr('bold', save_dir)}\n"
-        f'Starting training for {epochs} epochs...'
+        f"Starting training for {epochs} epochs..."
     )
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
         # callbacks.run('on_train_epoch_start')
@@ -403,7 +403,7 @@ def lf(x):
             # Log
             if RANK in {-1, 0}:
                 mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
-                mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G"  # (GB)
+                mem = f"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G"  # (GB)
                 pbar.set_description(
                     ("%11s" * 2 + "%11.4g" * 6)
                     % (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1])
@@ -736,9 +736,9 @@ def main(opt, callbacks=Callbacks()):
         # Plot results
         plot_evolve(evolve_csv)
         LOGGER.info(
-            f'Hyperparameter evolution finished {opt.evolve} generations\n'
+            f"Hyperparameter evolution finished {opt.evolve} generations\n"
             f"Results saved to {colorstr('bold', save_dir)}\n"
-            f'Usage example: $ python train.py --hyp {evolve_yaml}'
+            f"Usage example: $ python train.py --hyp {evolve_yaml}"
         )
 
 

train.py

@@ -359,10 +359,10 @@ def lf(x):
     compute_loss = ComputeLoss(model)  # init loss class
     callbacks.run("on_train_start")
     LOGGER.info(
-        f'Image sizes {imgsz} train, {imgsz} val\n'
-        f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
+        f"Image sizes {imgsz} train, {imgsz} val\n"
+        f"Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n"
         f"Logging results to {colorstr('bold', save_dir)}\n"
-        f'Starting training for {epochs} epochs...'
+        f"Starting training for {epochs} epochs..."
     )
     for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
         callbacks.run("on_train_epoch_start")
@@ -436,7 +436,7 @@ def lf(x):
             # Log
             if RANK in {-1, 0}:
                 mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
-                mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G"  # (GB)
+                mem = f"{torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0:.3g}G"  # (GB)
                 pbar.set_description(
                     ("%11s" * 2 + "%11.4g" * 5)
                     % (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1])
@@ -805,9 +805,9 @@ def main(opt, callbacks=Callbacks()):
         # Plot results
         plot_evolve(evolve_csv)
         LOGGER.info(
-            f'Hyperparameter evolution finished {opt.evolve} generations\n'
+            f"Hyperparameter evolution finished {opt.evolve} generations\n"
             f"Results saved to {colorstr('bold', save_dir)}\n"
-            f'Usage example: $ python train.py --hyp {evolve_yaml}'
+            f"Usage example: $ python train.py --hyp {evolve_yaml}"
         )
 
 

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

@@ -317,8 +317,7 @@ def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vi
         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):
@@ -667,8 +666,8 @@ def check_cache_ram(self, safety_margin=0.1, prefix=""):
         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
@@ -730,8 +729,7 @@ def __getitem__(self, index):
         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
@@ -1109,8 +1107,7 @@ 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

@@ -161,8 +161,7 @@ def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"):
     """Returns user configuration directory path, prefers `env_var` if set, else uses OS-specific path, creates
     directory if needed.
     """
-    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
@@ -493,9 +492,9 @@ 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

@@ -39,11 +39,9 @@ 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

@@ -86,14 +86,12 @@ def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwar
         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 @@
 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

@@ -150,8 +150,7 @@ def __call__(self, p, targets):  # predictions, targets
             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
 
@@ -177,10 +176,6 @@ def __call__(self, p, targets):  # predictions, targets
                     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

@@ -67,16 +67,8 @@ 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):