Kindle is an easy model build package for PyTorch.

Related tags

Deep Learningmachine-learningdeep-learningpytorch
Overview

Kindle - PyTorch no-code model builder

PyPI - Python Version PyTorch Version GitHub Workflow Status PyPI LGTM Alerts

Documentation
API reference

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.

Contents

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 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:
    # [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]]
    ]
  1. 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

AutoML with Kindle

  • Kindle offers the easiest way to build your own deep learning architecture. Beyond building a model, AutoML became easier with Kindle and Optuna or other optimization frameworks.
  • For further information, please refer to here

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]
Add Add []

Custom module support

Custom module with yaml

You can make your own custom module with yaml file.

1. custom_module.yaml

args: [96, 32]

module:
    # [from, repeat, module, args]
    [
        [-1, 1, Conv, [arg0, 1, 1]],
        [0, 1, Conv, [arg1, 3, 1]],
        [0, 1, Conv, [arg1, 5, 1]],
        [0, 1, Conv, [arg1, 7, 1]],
        [[1, 2, 3], 1, Concat, [1]],
        [[0, 4], 1, Add, []],
    ]
  • Arguments of yaml module can be defined as arg0, arg1 ...

2. model_with_custom_module.yaml

input_size: [32, 32]
input_channel: 3

depth_multiple: 1.0
width_multiple: 1.0

backbone:
    [
        [-1, 1, Conv, [6, 5, 1, 0]],
        [-1, 1, MaxPool, [2]],
        [-1, 1, YamlModule, ["custom_module.yaml", 48, 16]],
        [-1, 1, MaxPool, [2]],
        [-1, 1, Flatten, []],
        [-1, 1, Linear, [120, ReLU]],
        [-1, 1, Linear, [84, ReLU]],
        [-1, 1, Linear, [10]]
    ]
  • Note that argument of yaml module can be provided.

3. Build model

from kindle import Model

model = Model("model_with_custom_module.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 |     10,832 |      YamlModule |    ['custom_module'] |                      [8 16 16] |    [24, 16, 16] |
  3 |         -1 |   1 |          0 |         MaxPool |                  [2] |                     [24 16 16] |      [24, 8, 8] |
  4 |         -1 |   1 |          0 |         Flatten |                   [] |                       [24 8 8] |          [1536] |
  5 |         -1 |   1 |    184,440 |          Linear |        [120, 'ReLU'] |                         [1536] |           [120] |
  6 |         -1 |   1 |     10,164 |          Linear |         [84, 'ReLU'] |                          [120] |            [84] |
  7 |         -1 |   1 |        850 |          Linear |                 [10] |                           [84] |            [10] |
Model Summary: 36 layers, 206,902 parameters, 206,902 gradients

Custom module from source

You can make your own custom module from the source.

1. custom_module_model.yaml

input_size: [32, 32]
input_channel: 3

depth_multiple: 1.0
width_multiple: 1.0

custom_module_paths: ["tests.test_custom_module"]  # Paths to the custom modules of the source

backbone:
    # [from, repeat, module, args]
    [
        [-1, 1, MyConv, [6, 5, 3]],
        [-1, 1, MaxPool, [2]],
        [-1, 1, MyConv, [16, 3, 5, SiLU]],
        [-1, 1, MaxPool, [2]],
        [-1, 1, Flatten, []],
        [-1, 1, Linear, [120, ReLU]],
        [-1, 1, Linear, [84, ReLU]],
        [-1, 1, Linear, [10]]
    ]

2. Write PyTorch module and ModuleGenerator

tests/test_custom_module.py

from typing import List, Union

import numpy as np
import torch
from torch import nn

from kindle.generator import GeneratorAbstract
from kindle.torch_utils import Activation, autopad


class MyConv(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int,
        n: int,
        activation: Union[str, None] = "ReLU",
    ) -> None:
        super().__init__()
        convs = []
        for i in range(n):
            convs.append(
                nn.Conv2d(
                    in_channels,
                    in_channels if (i + 1) != n else out_channels,
                    kernel_size,
                    padding=autopad(kernel_size),
                    bias=False,
                )
            )

        self.convs = nn.Sequential(*convs)
        self.batch_norm = nn.BatchNorm2d(out_channels)
        self.activation = Activation(activation)()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.activation(self.batch_norm(self.convs(x)))


class MyConvGenerator(GeneratorAbstract):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    @property
    def out_channel(self) -> int:
        return self._get_divisible_channel(self.args[0] * self.width_multiply)

    @property
    def in_channel(self) -> int:
        if isinstance(self.from_idx, list):
            raise Exception("from_idx can not be a list.")
        return self.in_channels[self.from_idx]

    @torch.no_grad()
    def compute_out_shape(self, size: np.ndarray, repeat: int = 1) -> List[int]:
        module = self(repeat=repeat)
        module.eval()
        module_out = module(torch.zeros([1, *list(size)]))
        return list(module_out.shape[-3:])

    def __call__(self, repeat: int = 1) -> nn.Module:
        args = [self.in_channel, self.out_channel, *self.args[1:]]
        if repeat > 1:
            module = [MyConv(*args) for _ in range(repeat)]
        else:
            module = MyConv(*args)

        return self._get_module(module)

3. Build a model

from kindle import Model

model = Model("custom_module_model.yaml"), verbose=True)
idx |       from |   n |     params |          module |            arguments |                       in shape |       out shape |
---------------------------------------------------------------------------------------------------------------------------------
  0 |         -1 |   1 |      1,066 |          MyConv |            [6, 5, 3] |                    [3, 32, 32] |     [8, 32, 32] |
  1 |         -1 |   1 |          0 |         MaxPool |                  [2] |                      [8 32 32] |     [8, 16, 16] |
  2 |         -1 |   1 |      3,488 |          MyConv |   [16, 3, 5, 'SiLU'] |                      [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: 29 layers, 138,568 parameters, 138,568 gradients

Planned features

  • Custom module support
  • Custom module with yaml support
  • Use pre-trained model
  • More modules!
Owner
Jongkuk Lim
Deep Learning, Machine Learning, Data Science, Edge Computing, Fitness Enthusiast
Jongkuk Lim
GitHub Repository
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation (ICCV 2021)

A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation (ICCV 2021) This repository contains the official implemen

81 Dec 14, 2022
Leaf: Multiple-Choice Question Generation

Leaf: Multiple-Choice Question Generation Easy to use and understand multiple-choice question generation algorithm using T5 Transformers. The applicat

Kristiyan Vachev 62 Dec 20, 2022
Fashion Entity Classification

Fashion-Entity-Classification - Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grays

ADITYA SHAH 1 Jan 04, 2022
The official PyTorch code for NeurIPS 2021 ML4AD Paper, "Does Thermal data make the detection systems more reliable?"

MultiModal-Collaborative (MMC) Learning Framework for integrating RGB and Thermal spectral modalities This is the official code for NeurIPS 2021 Machi

NeurAI 12 Nov 02, 2022
Codecov coverage standard for Python

Python-Standard Last Updated: 01/07/22 00:09:25 What is this? This is a Python application, with basic unit tests, for which coverage is uploaded to C

Codecov 10 Nov 04, 2022
Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow.

custom-cnn-fashion-mnist Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow. The following

Danielle Almeida 1 Mar 05, 2022
RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth, in ICCV 2021 (oral)

RINDNet RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth Mengyang Pu, Yaping Huang, Qingji Guan and Haibin Lin

Mengyang Pu 75 Dec 15, 2022
v objective diffusion inference code for PyTorch.

v-diffusion-pytorch v objective diffusion inference code for PyTorch, by Katherine Crowson (@RiversHaveWings) and Chainbreakers AI (@jd_pressman). The

Katherine Crowson 635 Dec 30, 2022
Just Randoms Cats with python

Random-Cat Just Randoms Cats with python.

OriCode 2 Dec 21, 2021
Load What You Need: Smaller Multilingual Transformers for Pytorch and TensorFlow 2.0.

Smaller Multilingual Transformers This repository shares smaller versions of multilingual transformers that keep the same representations offered by t

Geotrend 79 Dec 28, 2022
Speed-Test - You can check your intenet speed using this tool

Speed-Test Tool By Hez_X AVAILABLE ON : Termux & Kali linux & Ubuntu (Linux E

Hez-X 3 Feb 17, 2022
MLP-Like Vision Permutator for Visual Recognition (PyTorch)

Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition (arxiv) This is a Pytorch implementation of our paper. We present Vision

Qibin (Andrew) Hou 162 Nov 28, 2022
PyTorch implementation of MLP-Mixer

PyTorch implementation of MLP-Mixer MLP-Mixer: an all-MLP architecture composed of alternate token-mixing and channel-mixing operations. The token-mix

Duo Li 33 Nov 27, 2022
Official source code of Fast Point Transformer, CVPR 2022

Fast Point Transformer Project Page | Paper This repository contains the official source code and data for our paper: Fast Point Transformer Chunghyun

182 Dec 23, 2022
Official repo of the paper "Surface Form Competition: Why the Highest Probability Answer Isn't Always Right"

Surface Form Competition This is the official repo of the paper "Surface Form Competition: Why the Highest Probability Answer Isn't Always Right" We p

Peter West 46 Dec 23, 2022
State of the art Semantic Sentence Embeddings

Contrastive Tension State of the art Semantic Sentence Embeddings Published Paper · Huggingface Models · Report Bug Overview This is the official code

Fredrik Carlsson 88 Dec 30, 2022
Official NumPy Implementation of Deep Networks from the Principle of Rate Reduction (2021)

Deep Networks from the Principle of Rate Reduction This repository is the official NumPy implementation of the paper Deep Networks from the Principle

Ryan Chan 49 Dec 16, 2022
Self-Supervised Learning

Self-Supervised Learning Features self_supervised offers features like modular framework support for multi-gpu training using PyTorch Lightning easy t

Robin 1 Dec 14, 2021
A highly modular PyTorch framework with a focus on Neural Architecture Search (NAS).

UniNAS A highly modular PyTorch framework with a focus on Neural Architecture Search (NAS). under development (which happens mostly on our internal Gi

Cognitive Systems Research Group 19 Nov 23, 2022
[CVPR'21 Oral] Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning

Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning [CVPR'21, Oral] By Zhicheng Huang*, Zhaoyang Zeng*, Yupan H

Multimedia Research 196 Dec 13, 2022