Usage

Installation

To use torchdistill, first install it using pip:

$ pip install torchdistill

Examples

The official repository (https://github.com/yoshitomo-matsubara/torchdistill) offers many example scripts, configs, and checkpoints of trained models in torchdistill.

Currently, example scripts cover the following tasks:

• Image classification (ILSVRC 2012, CIFAR-10/100)

• Object detection (COCO 2017)

• Semantic segmentation (PASCAL VOC 2012, COCO 2017)

• Text classification (GLUE tasks)

How to Add Your Modules

Step 1: Define your own module

Step 2: Register the module e.g., add a registry function to the module as a Python decorator

Step 3: Run your script with a yaml file containing the module name (key) and parameters, call the Python decorator, and then your module is available in the registry

Steps 1 and 2: Create a Python file (e.g., my_module.py) containing your own module (e.g., “MyNewCoolModel”) with a Python decorator “register_model”

 from torch import nn
 from torchdistill.models.registry import register_model

 @register_model
 class MyNewCoolModel(nn.Module):
     def __init__(self, some_value, some_list, some_dict):
         super().__init__()
         print('some_value: ', some_value)
         print('some_list: ', some_list)
         print('some_dict: ', some_dict)
     ...

Step 3: Run your script (e.g., example/torchvision/image_classification.py) with a yaml containing the registered module name (“MyNewCoolModel”) and parameters (“some_value”, “some_list”, “some_dict”)

 dependencies:
   - name: 'my_module'
 ...
 models:
   model:
     key: 'MyNewCoolModel'
     kwargs:
       some_value: 777
       some_list: ['this', 'is', 'some_list']
       some_dict:
         some_key: 'some_value'
         test: 0.123
     src_ckpt:
 ...