lint fixes

modules/errors.py

@@ -71,18 +71,25 @@ def profile(profiler, msg: str):
     # p.print_callers(10)
     profiler = None
     lines = stream.getvalue().split('\n')
-    lines = [l for l in lines if '<frozen' not in l and '{built-in' not in l and '/logging' not in l and 'Ordered by' not in l and 'List reduced' not in l and '_lsprof' not in l and '/profiler' not in l and 'rich' not in l and l.strip() != '']
+    lines = [x for x in lines if '<frozen' not in x
+             and '{built-in' not in x
+             and '/logging' not in x
+             and 'Ordered by' not in x
+             and 'List reduced' not in x
+             and '_lsprof' not in x
+             and '/profiler' not in x
+             and 'rich' not in x
+             and x.strip() != ''
+            ]
     txt = '\n'.join(lines[:min(5, len(lines))])
     log.debug(f'Profile {msg}: {txt}')
 
 
 def profile_torch(profiler, msg: str):
     profiler.stop()
-    from rich import print # pylint: disable=redefined-builtin
-    # lines = profiler.key_averages().table(sort_by="self_cuda_time_total", row_limit=6)
     lines = profiler.key_averages().table(sort_by="self_cpu_time_total", row_limit=12)
     lines = lines.split('\n')
-    lines = [l for l in lines if '/profiler' not in l and '---' not in l]
+    lines = [x for x in lines if '/profiler' not in x and '---' not in x]
     txt = '\n'.join(lines)
     # print(f'Torch {msg}:', txt)
     log.debug(f'Torch profile {msg}: \n{txt}')

modules/loader.py

@@ -53,5 +53,5 @@ try:
         torch.set_num_threads(math.floor(affinity / 2))
         threads = torch.get_num_threads()
         errors.log.debug(f'Detected: cores={cores} affinity={affinity} set threads={threads}')
-except Exception as e:
+except Exception:
     pass

modules/merging/merge_PermSpec.py

@@ -37,7 +37,7 @@ def sdunet_permutation_spec() -> PermutationSpec:
     }
 
     # VAE blocks - Unused
-    easyblock2 = lambda name, p: {  # pylint: disable=unnecessary-lambda-assignment
+    easyblock2 = lambda name, p: {  # pylint: disable=unnecessary-lambda-assignment, unused-variable # noqa: F841
         **norm(f"{name}.norm1", p),
         **conv(f"{name}.conv1", p, f"P_{name}_inner"),
         **norm(f"{name}.norm2", f"P_{name}_inner"),
@@ -45,7 +45,7 @@ def sdunet_permutation_spec() -> PermutationSpec:
     }
 
     # This is for blocks that use a residual connection, but change the number of channels via a Conv.
-    shortcutblock = lambda name, p_in, p_out: {  # pylint: disable=unnecessary-lambda-assignment
+    shortcutblock = lambda name, p_in, p_out: {  # pylint: disable=unnecessary-lambda-assignment, , unused-variable # noqa: F841
         **norm(f"{name}.norm1", p_in),
         **conv(f"{name}.conv1", p_in, f"P_{name}_inner"),
         **norm(f"{name}.norm2", f"P_{name}_inner"),

modules/processing_correction.py

@@ -28,7 +28,7 @@ def soft_clamp_tensor(input_tensor, threshold=0.8, boundary=4):
     return input_tensor
 
 
-def center_tensor(input_tensor, channel_shift=1.0, full_shift=1.0, channels=[0, 1, 2, 3]):
+def center_tensor(input_tensor, channel_shift=1.0, full_shift=1.0, channels=[0, 1, 2, 3]): # pylint: disable=dangerous-default-value # noqa: B006
     if channel_shift == 0 and full_shift == 0:
         return input_tensor
     means = []
@@ -40,7 +40,7 @@ def center_tensor(input_tensor, channel_shift=1.0, full_shift=1.0, channels=[0,
     return input_tensor
 
 
-def maximize_tensor(input_tensor, boundary=1.0, channels=[0, 1, 2]):
+def maximize_tensor(input_tensor, boundary=1.0, channels=[0, 1, 2]): # pylint: disable=dangerous-default-value # noqa: B006
     if boundary == 1.0:
         return input_tensor
     boundary *= 4

modules/rife/model_ifnet.py

@@ -82,7 +82,7 @@ class IFNet(nn.Module):
         # self.contextnet = Contextnet()
         # self.unet = Unet()
 
-    def forward( self, x, timestep=0.5, scale_list=[8, 4, 2, 1], training=False, fastmode=True, ensemble=False):
+    def forward( self, x, timestep=0.5, scale_list=[8, 4, 2, 1], training=False, fastmode=True, ensemble=False): # pylint: disable=dangerous-default-value # noqa: B006
         if training is False:
             channel = x.shape[1] // 2
             img0 = x[:, :channel]