Merge pull request #10259 from AUTOMATIC1111/ruff

Ruff

3 files changed, 7 insertions, 8 deletions

diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
index e8783bca..55dd94ab 100644
--- a/extensions-builtin/SwinIR/scripts/swinir_model.py
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -1,4 +1,3 @@
-import contextlib
 import os
 
 import numpy as np
@@ -8,7 +7,7 @@ from basicsr.utils.download_util import load_file_from_url
 from tqdm import tqdm
 
 from modules import modelloader, devices, script_callbacks, shared
-from modules.shared import cmd_opts, opts, state
+from modules.shared import opts, state
 from swinir_model_arch import SwinIR as net
 from swinir_model_arch_v2 import Swin2SR as net2
 from modules.upscaler import Upscaler, UpscalerData
@@ -45,7 +44,7 @@ class UpscalerSwinIR(Upscaler):
         img = upscale(img, model)
         try:
             torch.cuda.empty_cache()
-        except:
+        except Exception:
             pass
         return img
 
diff --git a/extensions-builtin/SwinIR/swinir_model_arch.py b/extensions-builtin/SwinIR/swinir_model_arch.py
index 863f42db..73e37cfa 100644
--- a/extensions-builtin/SwinIR/swinir_model_arch.py
+++ b/extensions-builtin/SwinIR/swinir_model_arch.py
@@ -644,7 +644,7 @@ class SwinIR(nn.Module):
     """
 
     def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
+                 embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
                  window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
                  norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
@@ -844,7 +844,7 @@ class SwinIR(nn.Module):
         H, W = self.patches_resolution
         flops += H * W * 3 * self.embed_dim * 9
         flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
+        for layer in self.layers:
             flops += layer.flops()
         flops += H * W * 3 * self.embed_dim * self.embed_dim
         flops += self.upsample.flops()
diff --git a/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/extensions-builtin/SwinIR/swinir_model_arch_v2.py
index 0e28ae6e..3ca9be78 100644
--- a/extensions-builtin/SwinIR/swinir_model_arch_v2.py
+++ b/extensions-builtin/SwinIR/swinir_model_arch_v2.py
@@ -74,7 +74,7 @@ class WindowAttention(nn.Module):
     """

 

     def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,

-                 pretrained_window_size=[0, 0]):

+                 pretrained_window_size=(0, 0)):

 

         super().__init__()

         self.dim = dim

@@ -698,7 +698,7 @@ class Swin2SR(nn.Module):
     """

 

     def __init__(self, img_size=64, patch_size=1, in_chans=3,

-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],

+                 embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),

                  window_size=7, mlp_ratio=4., qkv_bias=True, 

                  drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,

                  norm_layer=nn.LayerNorm, ape=False, patch_norm=True,

@@ -994,7 +994,7 @@ class Swin2SR(nn.Module):
         H, W = self.patches_resolution

         flops += H * W * 3 * self.embed_dim * 9

         flops += self.patch_embed.flops()

-        for i, layer in enumerate(self.layers):

+        for layer in self.layers:

             flops += layer.flops()

         flops += H * W * 3 * self.embed_dim * self.embed_dim

         flops += self.upsample.flops()