Metadata-Version: 2.1
Name: CVX2
Version: 0.5.1
Summary: Tools of CV(Computer Vision)
Home-page: https://gitee.com/summry/cvx2
Author: summy
Author-email: fkfkfk2024@2925.com
License: UNKNOWN
Keywords: CV,Computer Vision,Machine learning,Deep learning,torch
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

Usage Sample
''''''''''''

.. code:: python

        from torch import nn
        from cvx2 import WidthBlock
        from cvx2.wrapper import ImageClassifyModelWrapper

        model = nn.Sequential(
            WidthBlock(c1=1, c2=32),
            nn.MaxPool2d(kernel_size=2, stride=2),
            WidthBlock(c1=32, c2=64),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Flatten(),
            nn.Linear(in_features=64*49, out_features=1024),
            nn.Dropout(0.2),
            nn.SiLU(inplace=True),
            nn.Linear(in_features=1024, out_features=2),
        )

        data_dir
         |__train
         |   |__class1
         |   |   |__001.jpg
         |   |   |__002.jpg
         |   |__class2
         |      |__001.jpg
         |      |__002.jpg
         |__test
         |   |__class1
         |   |   |__001.jpg
         |   |   |__002.jpg
         |   |__class2
         |      |__001.jpg
         |      |__002.jpg
         |__val
             |__class1
             |   |__001.jpg
             |   |__002.jpg
             |__class2
                |__001.jpg
                |__002.jpg

        model_wrapper = ImageClassifyModelWrapper(model)
        model_wrapper.train(data='data_dir', imgsz=28)
        result = model_wrapper.predict('data_dir/test/class1/001.jpg', imgsz=28)


