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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

@@ -201,10 +201,10 @@ 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
@@ -290,13 +290,13 @@ 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

@@ -309,7 +309,7 @@ def write_to_csv(image_name, prediction, confidence):
                     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

@@ -1471,12 +1471,12 @@ 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

@@ -325,10 +325,10 @@ 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')
@@ -405,7 +405,7 @@ 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])
@@ -740,9 +740,9 @@ 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

@@ -357,10 +357,10 @@ 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")
@@ -434,7 +434,7 @@ 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])
@@ -880,9 +880,9 @@ 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/dataloaders.py

@@ -356,8 +356,7 @@ 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):
@@ -713,8 +712,8 @@ 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

utils/general.py

@@ -495,9 +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/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

@@ -136,7 +136,7 @@ def __init__(self, model, autobalance=False):
         self.anchors = m.anchors
         self.device = device
 
-    def __call__(self, p, targets):    # predictions, targets
+    def __call__(self, p, targets):  # predictions, targets
         """Performs forward pass, calculating class, box, and object loss for given predictions and targets."""
         lcls = torch.zeros(1, device=self.device)  # class loss
         lbox = torch.zeros(1, device=self.device)  # box loss

utils/segment/loss.py

@@ -46,7 +46,7 @@ def __init__(self, model, autobalance=False, overlap=False):
         self.anchors = m.anchors
         self.device = device
 
-    def __call__(self, preds, targets, masks):    # predictions, targets, model
+    def __call__(self, preds, targets, masks):  # predictions, targets, model
         """Evaluates YOLOv5 model's loss for given predictions, targets, and masks; returns total loss components."""
         p, proto = preds
         bs, nm, mask_h, mask_w = proto.shape  # batch size, number of masks, mask height, mask width

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