Ultralytics Refactor https://ultralytics.com/actions (#29)

Co-authored-by: UltralyticsAssistant <[email protected]>

README.md

@@ -1,5 +1,5 @@
 <br>
-<img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320">
+<a href="https://ultralytics.com" target="_blank"><img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320" alt="Ultralytics logo"></a>
 
 # 🚀 THOP: PyTorch-OpCounter
 
@@ -34,9 +34,10 @@ pip install --upgrade git+https://github.com/ultralytics/thop.git
 To profile a model, you can use the following example:
 
 ```python
+import torch
 from torchvision.models import resnet50
+
 from thop import profile
-import torch
 
 model = resnet50()
 input = torch.randn(1, 3, 224, 224)
@@ -154,7 +155,7 @@ For bugs or feature requests, please open an issue on [GitHub Issues](https://gi
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
   <a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
-  <a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
+  <a href="https://ultralytics.com/bilibili"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-bilibili.png" width="3%" alt="Ultralytics Instagram"></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
   <a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
 </div>

thop/vision/basic_hooks.py

@@ -21,7 +21,7 @@ def zero_ops(m, x, y):
 def count_convNd(m: _ConvNd, x, y: torch.Tensor):
     """Calculate and add the number of convolutional operations (FLOPs) to the model's total operations count."""
     x = x[0]
-    
+
     m.total_ops += calculate_conv2d_flops(
         input_size=list(x.shape),
         output_size=list(y.shape),