Fixing format

yolort/models/darknetv5.py

@@ -146,7 +146,7 @@ def _darknetv5(arch: str, pretrained: bool, progress: bool, *args: Any, **kwargs
     if pretrained:
         model_url = model_urls[arch]
         if model_url is None:
-            raise NotImplementedError("pretrained {} is not supported as of now".format(arch))
+            raise NotImplementedError(f"pretrained {arch} is not supported as of now")
         else:
             state_dict = load_state_dict_from_url(model_url, progress=progress)
             model.load_state_dict(state_dict)

yolort/models/darknetv6.py

@@ -57,7 +57,7 @@ def __init__(
     ) -> None:
         super().__init__()
 
-        assert version == "r4.0", ("Currently the module version used in DarkNetV6 is r4.0",)
+        assert version == "r4.0", "Currently the module version used in DarkNetV6 is r4.0."
 
         if block is None:
             block = C3
@@ -135,7 +135,7 @@ def _darknetv6(arch: str, pretrained: bool, progress: bool, *args: Any, **kwargs
     if pretrained:
         model_url = model_urls[arch]
         if model_url is None:
-            raise NotImplementedError("pretrained {} is not supported as of now".format(arch))
+            raise NotImplementedError(f"pretrained {arch} is not supported as of now")
         else:
             state_dict = load_state_dict_from_url(model_url, progress=progress)
             model.load_state_dict(state_dict)

yolort/models/transform.py

@@ -96,8 +96,8 @@ def forward(
 
             if image.dim() != 3:
                 raise ValueError(
-                    "images is expected to be a list of 3d tensors "
-                    "of shape [C, H, W], got {}".format(image.shape)
+                    "images is expected to be a list of 3d tensors of "
+                    f"shape [C, H, W], but got '{image.shape}'."
                 )
 
             image, target_index = self.resize(image, target_index)

yolort/v5/utils/autoanchor.py

@@ -64,7 +64,7 @@ def metric(k):  # compute metric
                 "to use these anchors in the future."
             )
         else:
-            print(f"{prefix}Original anchors better than new anchors. " "Proceeding with original anchors.")
+            print(f"{prefix}Original anchors better than new anchors. Proceeding with original anchors.")
     print("")  # newline
 
 
@@ -148,7 +148,7 @@ def print_results(k):
     s = wh.std(0)  # sigmas for whitening
     k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
     assert len(k) == n, (
-        f"{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points " f"but returned only {len(k)}"
+        f"{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}"
     )
     k *= s
     wh = torch.tensor(wh, dtype=torch.float32)  # filtered

yolort/v5/utils/datasets.py

@@ -363,7 +363,7 @@ def __init__(
             if cache["msgs"]:
                 logging.info("\n".join(cache["msgs"]))  # display warnings
         assert nf > 0 or not augment, (
-            f"{prefix}No labels in {cache_path}. " f"Can not train without labels. See {HELP_URL}"
+            f"{prefix}No labels in {cache_path}. Can not train without labels. See {HELP_URL}"
         )
 
         # Read cache

yolort/v5/utils/general.py

@@ -190,7 +190,7 @@ def check_img_size(imgsz, s=32, floor=0):
     else:  # list i.e. img_size=[640, 480]
         new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz]
     if new_size != imgsz:
-        print(f"WARNING: --img-size {imgsz} must be multiple of " f"max stride {s}, updating to {new_size}")
+        print(f"WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}")
     return new_size
 
 
@@ -516,7 +516,7 @@ def non_max_suppression(
 
     # Checks
     assert 0 <= conf_thres <= 1, (
-        f"Invalid Confidence threshold {conf_thres}, " "valid values are between 0.0 and 1.0"
+        f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0"
     )
     assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0"
 

yolort/v5/utils/torch_utils.py

@@ -94,7 +94,7 @@ def select_device(device="", batch_size=None):
         for i, d in enumerate(devices):
             p = torch.cuda.get_device_properties(i)
             # bytes to MB
-            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, " f"{p.total_memory / 1024 ** 2}MB)\n"
+            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n"
     else:
         s += "CPU\n"
 
@@ -309,7 +309,7 @@ def model_info(model, verbose=False, img_size=640):
         fs = ""
 
     LOGGER.info(
-        f"Model Summary: {len(list(model.modules()))} " f"layers, {n_p} parameters, {n_g} gradients{fs}"
+        f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}"
     )