Kindle - Making a PyTorch model easier than ever!

Kindle is an easy model build package for PyTorch. Building a deep learning model became so simple that almost all model can be made by copy and paste from other existing model codes. So why code? when we can simply build a model with yaml markup file.

Kindle builds a model with yaml file which its method is inspired from YOLOv5.

Installation

Install with pip

PyTorch is required prior to install. Please visit PyTorch installation guide to install.

You can install kindle by pip.

$ pip install kindle

Install kindle for PyTorch under 1.7.1 (not tested)

pip install kindle --no-deps
pip install tqdm ptflops timm tabulate

Install from source

Please visit Install from source wiki page

For contributors

Please visit For contributors wiki page

Usage

Build a model

  1. Make model yaml file
input_size: [32, 32]
input_channel: 3

depth_multiple: 1.0
width_multiple: 1.0

backbone:
    [
        [-1, 1, Conv, [6, 5, 1, 0], {activation: LeakyReLU}],
        [-1, 1, MaxPool, [2]],
        [-1, 1, nn.Conv2d, [16, 5, 1, 2], {bias: False}],
        [-1, 1, nn.BatchNorm2d, []],
        [-1, 1, nn.ReLU, []],
        [-1, 1, MaxPool, [2]],
        [-1, 1, Flatten, []],
        [-1, 1, Linear, [120, ReLU]],
        [-1, 1, Linear, [84, ReLU]],
    ]

head:
  [
      [-1, 1, Linear, [10]]
  ]
  1. Build the model with kindle
from kindle import Model

model = Model("model.yaml"), verbose=True)

idx |       from |   n |   params |          module |                           arguments | in_channel | out_channel |        in shape |       out shape |
----------------------------------------------------------------------------------------------------------------------------------------------------------
  0 |         -1 |   1 |      616 |            Conv | [6, 5, 1, 0], activation: LeakyReLU |          3 |           8 |     [3, 32, 32] |     [8, 32, 32] |
  1 |         -1 |   1 |        0 |         MaxPool |                                 [2] |          8 |           8 |       [8 32 32] |     [8, 16, 16] |
  2 |         -1 |   1 |    3,200 |       nn.Conv2d |          [16, 5, 1, 2], bias: False |          8 |          16 |       [8 16 16] |    [16, 16, 16] |
  3 |         -1 |   1 |       32 |  nn.BatchNorm2d |                                  [] |         16 |          16 |      [16 16 16] |    [16, 16, 16] |
  4 |         -1 |   1 |        0 |         nn.ReLU |                                  [] |         16 |          16 |      [16 16 16] |    [16, 16, 16] |
  5 |         -1 |   1 |        0 |         MaxPool |                                 [2] |         16 |          16 |      [16 16 16] |      [16, 8, 8] |
  6 |         -1 |   1 |        0 |         Flatten |                                  [] |         -1 |        1024 |        [16 8 8] |          [1024] |
  7 |         -1 |   1 |  123,000 |          Linear |                       [120, 'ReLU'] |       1024 |         120 |          [1024] |           [120] |
  8 |         -1 |   1 |   10,164 |          Linear |                        [84, 'ReLU'] |        120 |          84 |           [120] |            [84] |
  9 |         -1 |   1 |      850 |          Linear |                                [10] |         84 |          10 |            [84] |            [10] |
Model Summary: 20 layers, 137,862 parameters, 137,862 gradients

GitHub

https://github.com/JeiKeiLim/kindle