Metadata-Version: 2.1
Name: model-loads
Version: 0.2.4
Summary: Loads GPU or CPU pytorch models
Home-page: https://github.com/cwh94/model_loads
Author: wangchao
Author-email: chaowanghs@gmail.com
License: UNKNOWN
Description: **model_loads** is an open-source Python package for pytorch load models easy.
        
        [PyTorch](http://pytorch.org/) is a Python package that provides two high-level features:
        
        - Tensor computation (like NumPy) with strong GPU acceleration
        - Deep neural networks built on a tape-based autograd system
        
        It's annoying to load cpu model to gpu devices or load multi-gpus trained model to single gpu devices sometimes, And this package try to simplify it.
        
        
        ## Table of Contents
        
        - [Table of Contents](#table-of-contents) 
        - [Installation](#installation)
        - [Getting Started](#getting-started)
        - [TODO list](#todo-list)
        
        ## Installation
        
        To install load_models, you can do as follow:
        
        ```
            pip install model-loads
        ```
        
        Or from source
        
        ```
            git clone https://github.com/cwh94/model_loads.git
            cd load_models
            python setup.py bdist_egg
            python setup.py install
        ```
        
        ## Getting Started
        
        
        1. load pth model to GPU device
        ```
        import model_loads as lo
        import torchvision.models as models
        
        model = models.MobileNetV2()
        model_path = "../examples/models/pth/mobilenet_v2-b0353104.pth"
        model, _ = lo.load_models(model_path, model, use_gpu=True)
        print(model)
        print(type(model))
        ```
        
        2. load tar model(which contains state_dict and optimization info or accuracy) to CPU device
        
        ```
        from models.tar.mobilenet_v2 import MobileNetV2
        
        model = MobileNetV2()
        model_path = "models/tar/checkpoint.pth.tar"
        
        model, other_param = lo.load_models(model_path, model)
        print(model)
        print(other_param)
        
        ```
        
        4. load model to CPU device
        ```
        import os
        
        os.environ["CUDA_VISIBLE_DEVICES"] = ""
        
        model = models.MobileNetV2()
        model_path = "models/pth/mobilenet_v2-b0353104.pth"
        model, _ = lo.load_models(model_path, model, use_gpu=True)
        print(model)
        print(type(model))
        ```
        
        
        
        
        Done:
        
        load model which save model by 
        
        ```
        torch.save(model, "path/to/model")
        ```
        
        ## Todo list
        
        DATA PARALLELISM
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
