kindle 0.0.6

PyTorch no-code model builder.

Kindle - PyTorch no-code model builder

Documentation
Reference Document

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 no code but yaml file which its method is inspired from YOLOv5.

Working environment

• Other Python3 and PyTorch version should be working but we have not checked yet.

Python	PyTorch
3.8	1.7.1

Install

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

You can install kindle by pip.

$ pip install kindle

Install from source

Please visit Install from source wiki page

For contributors

Please visit For contributors wiki page

Usage

1. Make model yaml file

• Example model https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html

input_size: [32, 32]
input_channel: 3

depth_multiple: 1.0
width_multiple: 1.0

backbone:
    # [from, repeat, module, args]
    [
        [-1, 1, Conv, [6, 5, 1, 0]],
        [-1, 1, MaxPool, [2]],
        [-1, 1, Conv, [16, 5, 1, 0]],
        [-1, 1, MaxPool, [2]],
        [-1, 1, Flatten, []],
        [-1, 1, Linear, [120, ReLU]],
        [-1, 1, Linear, [84, ReLU]],
        [-1, 1, Linear, [10]]
    ]

2. Build the model with kindle

from kindle import Model

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

idx |       from |   n |     params |          module |            arguments |                       in shape |       out shape |
---------------------------------------------------------------------------------------------------------------------------------
  0 |         -1 |   1 |        616 |            Conv |         [6, 5, 1, 0] |                    [3, 32, 32] |     [8, 32, 32] |
  1 |         -1 |   1 |          0 |         MaxPool |                  [2] |                      [8 32 32] |     [8, 16, 16] |
  2 |         -1 |   1 |      3,232 |            Conv |        [16, 5, 1, 0] |                      [8 16 16] |    [16, 16, 16] |
  3 |         -1 |   1 |          0 |         MaxPool |                  [2] |                     [16 16 16] |      [16, 8, 8] |
  4 |         -1 |   1 |          0 |         Flatten |                   [] |                       [16 8 8] |          [1024] |
  5 |         -1 |   1 |    123,000 |          Linear |        [120, 'ReLU'] |                         [1024] |           [120] |
  6 |         -1 |   1 |     10,164 |          Linear |         [84, 'ReLU'] |                          [120] |            [84] |
  7 |         -1 |   1 |        850 |          Linear |                 [10] |                           [84] |            [10] |
Model Summary: 21 layers, 137,862 parameters, 137,862 gradients

Supported modules

• Detailed documents can be found here

Module	Components	Arguments
Conv	Conv -> BatchNorm -> Activation	[channel, kernel size, stride, padding, activation]
DWConv	DWConv -> BatchNorm -> Activation	[channel, kernel_size, stride, padding, activation]
Bottleneck	Expansion ConvBNAct -> ConvBNAct	[channel, shortcut, groups, expansion, activation]
AvgPool	Average pooling	[kernel_size, stride, padding]
MaxPool	Max pooling	[kernel_size, stride, padding]
GlobalAvgPool	Global Average Pooling	[]
Flatten	Flatten	[]
Concat	Concatenation	[dimension]
Linear	Linear	[channel, activation]

Planned features

• Custom module support
• Custom module with yaml support
• Use pre-trained model
• More modules!