CVX2

Name	CVX2 JSON
Version	0.3.1 JSON
	download
home_page	https://gitee.com/summry/cvx2
Summary	Tools of CV(Computer Vision)
upload_time	2025-02-10 09:41:23
maintainer	None
docs_url	None
author	summy
requires_python	>=3.6
license	None
keywords	cv computer vision machine learning deep learning torch
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            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)



            

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    "description": "Usage Sample\n''''''''''''\n\n.. code:: python\n\n        from torch import nn\n        from cvx2 import WidthBlock\n        from cvx2.wrapper import ImageClassifyModelWrapper\n\n        model = nn.Sequential(\n            WidthBlock(c1=1, c2=32),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            WidthBlock(c1=32, c2=64),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.Flatten(),\n            nn.Linear(in_features=64*49, out_features=1024),\n            nn.Dropout(0.2),\n            nn.SiLU(inplace=True),\n            nn.Linear(in_features=1024, out_features=2),\n        )\n\n        data_dir\n         |__train\n         |   |__class1\n         |   |   |__001.jpg\n         |   |   |__002.jpg\n         |   |__class2\n         |      |__001.jpg\n         |      |__002.jpg\n         |__test\n         |   |__class1\n         |   |   |__001.jpg\n         |   |   |__002.jpg\n         |   |__class2\n         |      |__001.jpg\n         |      |__002.jpg\n         |__val\n             |__class1\n             |   |__001.jpg\n             |   |__002.jpg\n             |__class2\n                |__001.jpg\n                |__002.jpg\n\n        model_wrapper = ImageClassifyModelWrapper(model)\n        model_wrapper.train(data='data_dir', imgsz=28)\n        result = model_wrapper.predict('data_dir/test/class1/001.jpg', imgsz=28)\n\n\n",
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