Metadata-Version: 2.1
Name: thop
Version: 0.0.30-190806
Summary: A tool to count the FLOPs of PyTorch model.
Home-page: https://github.com/Lyken17/pytorch-OpCounter/
Author: Ligeng Zhu
Author-email: lykensyu+github@gmail.com
License: MIT
Description: # THOP: PyTorch-OpCounter
        
        ## How to install 
        * Through PyPi
            
            `pip install thop`
            
        * Using GitHub (always latest)
            
            `pip install --upgrade git+https://github.com/Lyken17/pytorch-OpCounter.git`
            
        ## How to use 
        * Basic usage 
            ```python
            from torchvision.models import resnet50
            from thop import profile
            model = resnet50()
            input = torch.randn(1, 3, 224, 224)
            flops, params = profile(model, inputs=(input, ))
            ```    
        
        * Define the rule for 3rd party module.
            ```python
            class YourModule(nn.Module):
                # your definition
            def count_your_model(model, x, y):
                # your rule here
            
            input = torch.randn(1, 3, 224, 224)
            flops, params = profile(model, inputs=(input, ), 
                                    custom_ops={YourModule: count_your_model})
            ```
            
        * Improve the output readability
        
            Call `thop.clever_format` to give a better format of the output.
            ```python
            from thop import clever_format
            flops, params = clever_format([flops, params], "%.3f")
            ```    
            
        ## Results on Recent Models
        
        <p align="center">
        <table>
        <tr>
        <td>
        
        Model | Params(M) | FLOPs(G)
        ---|---|---
        alexnet | 58.27 | 0.72
        densenet121 | 7.61 | 2.70
        densenet161 | 27.35 | 7.31
        densenet169 | 13.49 | 3.20
        densenet201 | 19.09 | 4.09
        resnet18 | 11.15 | 1.70
        resnet34 | 20.79 | 3.43
        resnet50 | 24.37 | 3.85
        resnet101 | 42.49 | 7.33
        resnet152 | 57.40 | 10.81
        
        </td>
        <td>
        
        Model | Params(M) | FLOPs(G)
        ---|---|---
        squeezenet1_0 | 1.19 | 0.77
        squeezenet1_1 | 1.18 | 0.33
        vgg11 | 126.71 | 7.21
        vgg11_bn | 126.71 | 7.24
        vgg13 | 126.88 | 10.66
        vgg13_bn | 126.89 | 10.70
        vgg16 | 131.95 | 14.54
        vgg16_bn | 131.96 | 14.59
        vgg19 | 137.01 | 18.41
        vgg19_bn | 137.02 | 18.47
        
        </td>
        </tr>
        </p>
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
