Metadata-Version: 2.1
Name: pytorch_modules
Version: 0.2.2
Summary: A neural network toolkit.
Home-page: https://github.com/woodsgao/pytorch_modules
Author: woodsgao
Author-email: woodsgao@outlook.com
License: MIT
Description: 
        # pytorch_modules
        
        ## Introduction
        
        A neural network toolkit built on pytorch/opencv/numpy that includes neural network layers, modules, loss functions, optimizers, data loaders, data augmentation, etc.
        
        ## Features
        
         - Advanced neural network modules, such as EfficientNet, ResNet, SENet, Xception, DenseNet, FocalLoss, AdaboundW
         - Ultra-efficient dataloader that allows you to take full advantage of GPU
         - High performance and multifunctional data augmentation(See [woodsgao/image_augments](https://github.com/woodsgao/image_augments))
        
        ## Installation
        
            sudo pip3 install pytorch_modules
        
        ## Usage
        
        ### pytorch_modules.nn
        
        This module contains a variety of neural network layers, modules and loss functions.
        
            import torch
            from pytorch_modules.nn import ResBlock
            
            # NCHW tensor
            inputs = torch.ones([8, 8, 224, 224])
            block = ResBlock(8, 16)
            outputs = block(inputs)
        
        ### pytorch_modules.augments
        
        See [woodsgao/image_augments](https://github.com/woodsgao/image_augments) for more details.
        
        ### pytorch_modules.backbones
        
        This module includes a series of modified backbone networks, such as EfficientNet, ResNet, SENet, Xception, DenseNet.
        
            import torch
            from pytorch_modules.backbones import ResNet
            
            # NCHW tensor
            inputs = torch.ones([8, 8, 224, 224])
            model = ResNet(32)
            outputs = model(inputs)
        
        ### pytorch_modules.datasets
        
        This module includes a series of dataset classes integrated from `pytorch_modules.datasets.BasicDataset` which is integrated from `torch.utils.data.Dataset` .
        The loading method of `pytorch_modules.datasets.BasicDataset` is modified to cache data with `LMDB` to speed up data loading. This allows your gpu to be fully used for model training without spending a lot of time on data loading and data augmentation. 
        Please see the corresponding repository for detailed usage.
        
         - `pytorch_modules.datasets.ClassificationDataset` > [woodsgao/pytorch_classification](https://github.com/woodsgao/pytorch_classification)
         - `pytorch_modules.datasets.SegmentationDataset` > [woodsgao/pytorch_segmentation](https://github.com/woodsgao/pytorch_segmentation)
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.5.2
Description-Content-Type: text/markdown
