Metadata-Version: 2.1
Name: visualizer-torch
Version: 0.0.1
Summary: UNKNOWN
Home-page: https://github.com/bironsecret/pytorch-model-visualizer
Author: neonsecret
License: UNKNOWN
Download-URL: https://github.com/bironsecret/pytorch-model-visualizer/archive/refs/tags/v0.0.1.tar.gz
Description: This module is designed to help you visualize pytorch model
        
        Example usage:
            from Visualizer import Visualizer
            from ExampleNet import ExampleNet
        
            vis = Visualizer()
        
            model = ExampleNet()  # the model you want to visualize
        
            params = vis._get_learnable_parts(model)
            print(params)
            # Out[]: <generator object Visualizer._get_learnable_parts at 0x00000262852F3740>
        
            for param, name in params:
                print(param, "\t", name)
        
            # Out[]:
            # Conv2d(1, 8, kernel_size=(5, 5), stride=(1, 1))                            ExampleNet.0.Conv2d.seq_block.Conv2d
            # MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ExampleNet.1.MaxPool2d.seq_block.MaxPool2d
            # ReLU(inplace=True)                                                         ExampleNet.2.ReLU.seq_block.ReLU
            # Conv2d(8, 16, kernel_size=(5, 5), stride=(1, 1))                           ExampleNet.3.Conv2d.seq_block.Conv2d
            # MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ExampleNet.4.MaxPool2d.seq_block.MaxPool2d
            # ReLU(inplace=True)                                                         ExampleNet.5.ReLU.seq_block.ReLU
            # Linear(in_features=144, out_features=72, bias=True)                        ExampleNet.0.Linear.fc.Linear
            # Linear(in_features=72, out_features=10, bias=True)                         ExampleNet.1.Linear.fc.Linear
        
        Example usage in a loop:
            from Visualizer import Visualizer
            from ExampleNet import ExampleNet
        
            vis = Visualizer()
        
            model = ExampleNet()  # the model you want to visualize
        
            (...)
        
            for ep in range(epochs):
                (...)
                vis.visualize_loop(ep, model, total_loss=total_loss, other_params={"Some param": ["some_value"]})
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
