Refactor/resolve lint warnings

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "PyTorchYolo"
-version = "1.6.2"
+version = "1.6.3"
 readme = "README.md"
 repository = "https://github.com/eriklindernoren/PyTorch-YOLOv3"
 description = "Minimal PyTorch implementation of YOLO"
@@ -22,6 +22,7 @@ torchsummary = "^1.5.1"
 
 [tool.poetry.dev-dependencies]
 profilehooks = "^1.12.0"
+flake8 = "^4.0.1"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]

pytorchyolo/models.py

@@ -114,6 +114,7 @@ def forward(self, x):
         x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
         return x
 
+
 class Mish(nn.Module):
     """ The MISH activation function (https://github.com/digantamisra98/Mish) """
 
@@ -123,6 +124,7 @@ def __init__(self):
     def forward(self, x):
         return x * torch.tanh(F.softplus(x))
 
+
 class YOLOLayer(nn.Module):
     """Detection layer"""
 
@@ -152,7 +154,7 @@ def forward(self, x, img_size):
                 self.grid = self._make_grid(nx, ny).to(x.device)
 
             x[..., 0:2] = (x[..., 0:2].sigmoid() + self.grid) * stride  # xy
-            x[..., 2:4] = torch.exp(x[..., 2:4]) * self.anchor_grid # wh
+            x[..., 2:4] = torch.exp(x[..., 2:4]) * self.anchor_grid  # wh
             x[..., 4:] = x[..., 4:].sigmoid()
             x = x.view(bs, -1, self.no)
 
@@ -186,7 +188,7 @@ def forward(self, x):
                 combined_outputs = torch.cat([layer_outputs[int(layer_i)] for layer_i in module_def["layers"].split(",")], 1)
                 group_size = combined_outputs.shape[1] // int(module_def.get("groups", 1))
                 group_id = int(module_def.get("group_id", 0))
-                x = combined_outputs[:, group_size * group_id : group_size * (group_id + 1)] # Slice groupings used by yolo v4
+                x = combined_outputs[:, group_size * group_id:group_size * (group_id + 1)]  # Slice groupings used by yolo v4
             elif module_def["type"] == "shortcut":
                 layer_i = int(module_def["from"])
                 x = layer_outputs[-1] + layer_outputs[layer_i]

pytorchyolo/test.py

@@ -13,7 +13,7 @@
 from torch.autograd import Variable
 
 from pytorchyolo.models import load_model
-from pytorchyolo.utils.utils import load_classes, ap_per_class, get_batch_statistics, non_max_suppression, to_cpu, xywh2xyxy, print_environment_info
+from pytorchyolo.utils.utils import load_classes, ap_per_class, get_batch_statistics, non_max_suppression, xywh2xyxy, print_environment_info
 from pytorchyolo.utils.datasets import ListDataset
 from pytorchyolo.utils.transforms import DEFAULT_TRANSFORMS
 from pytorchyolo.utils.parse_config import parse_data_config

pytorchyolo/train.py

@@ -15,7 +15,7 @@
 from pytorchyolo.utils.utils import to_cpu, load_classes, print_environment_info, provide_determinism, worker_seed_set
 from pytorchyolo.utils.datasets import ListDataset
 from pytorchyolo.utils.augmentations import AUGMENTATION_TRANSFORMS
-#from pytorchyolo.utils.transforms import DEFAULT_TRANSFORMS
+# from pytorchyolo.utils.transforms import DEFAULT_TRANSFORMS
 from pytorchyolo.utils.parse_config import parse_data_config
 from pytorchyolo.utils.loss import compute_loss
 from pytorchyolo.test import _evaluate, _create_validation_data_loader

pytorchyolo/utils/loss.py

@@ -113,7 +113,7 @@ def compute_loss(predictions, targets, model):
 
         # Classification of the objectness the sequel
         # Calculate the BCE loss between the on the fly generated target and the network prediction
-        lobj += BCEobj(layer_predictions[..., 4], tobj) # obj loss
+        lobj += BCEobj(layer_predictions[..., 4], tobj)  # obj loss
 
     lbox *= 0.05
     lobj *= 1.0

pytorchyolo/utils/utils.py

@@ -20,6 +20,7 @@ def provide_determinism(seed=42):
     torch.backends.cudnn.benchmark = False
     torch.backends.cudnn.deterministic = True
 
+
 def worker_seed_set(worker_id):
     # See for details of numpy:
     # https://github.com/pytorch/pytorch/issues/5059#issuecomment-817392562
@@ -390,6 +391,7 @@ def print_environment_info():
 
     # Print commit hash if possible
     try:
-        print(f"Current Commit Hash: {subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], stderr=subprocess.DEVNULL).decode('ascii').strip()}")
+        commit_hash = subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], stderr=subprocess.DEVNULL).decode('ascii').strip()
+        print(f"Current Commit Hash: {commit_hash}")
     except (subprocess.CalledProcessError, FileNotFoundError):
         print("No git or repo found")