Refactor code for speed and clarity

README.md

@@ -5,7 +5,7 @@
 
 Welcome to the [THOP](https://github.com/ultralytics/thop) repository, your comprehensive solution for profiling PyTorch models by computing the number of Multiply-Accumulate Operations (MACs) and parameters. This tool is essential for deep learning practitioners to evaluate model efficiency and performance.
 
-[![GitHub Actions](https://github.com/ultralytics/thop/actions/workflows/format.yml/badge.svg)](https://github.com/ultralytics/thop/actions/workflows/main.yml) <a href="https://ultralytics.com/discord"><img alt="Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
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 ## 📄 Description
 

benchmark/evaluate_famous_models.py

@@ -1,7 +1,6 @@
 import torch
-from torchvision import models
-
 from thop.profile import profile
+from torchvision import models
 
 model_names = sorted(
     name

benchmark/evaluate_rnn_models.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-
 from thop.profile import profile
 
 input_size = 160

tests/test_conv2d.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-
 from thop import profile
 
 

tests/test_matmul.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-
 from thop import profile
 
 

tests/test_relu.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-
 from thop import profile