Metadata-Version: 2.1
Name: kindle
Version: 0.1.8
Summary: PyTorch no-code model builder.
Home-page: https://github.com/JeiKeiLim/kindle
Author: Jongkuk Lim
Author-email: lim.jeikei@gmail.com
License: MIT License  Copyright (c) 2021 Jongkuk Lim  Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:  The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 
Description: # Kindle - PyTorch no-code model builder
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/kindle)
        ![PyTorch Version](https://img.shields.io/badge/PyTorch-1.7.1-informational)
        ![GitHub Workflow Status](https://img.shields.io/github/workflow/status/JeiKeiLim/kindle/format-lint-test)
        ![PyPI](https://img.shields.io/pypi/v/kindle)
        ![LGTM Alerts](https://img.shields.io/lgtm/alerts/github/JeiKeiLim/kindle)
        
        |`Documentation`|
        |-------------|
        |[![API reference](https://img.shields.io/badge/api-reference-informational)](https://limjk.ai/kindle/)|
        
        Kindle is an easy model build package for [PyTorch](https://pytorch.org). 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](https://github.com/ultralytics/yolov5).
        
        # Contents
        - [Installation](#installation)
          - [Install with pip](#install-with-pip)
          - [Install from source](#install-from-source)
          - [For contributors](#for-contributors)
        - [AutoML with Kindle](#automl-with-kindle)
        - [Usage](#usage)
        - [Supported modules](#supported-modules)
        - [Custom module support](#custom-module-support)
          - [Custom module with yaml](#custom-module-with-yaml)
          - [Custom module from source](#custom-module-from-source)
        
        # Installation
        ## Install with pip
        **PyTorch** is required prior to install. Please visit [PyTorch installation guide](https://pytorch.org/get-started/locally/) to install.
        
        You can install `kindle` by pip.
        ```shell
        $ pip install kindle
        ```
        
        ## Install from source
        Please visit [Install from source wiki page](https://github.com/JeiKeiLim/kindle/wiki/Install-from-source)
        
        ## For contributors
        Please visit [For contributors wiki page](https://github.com/JeiKeiLim/kindle/wiki/For-contributors)
        
        # Usage
        ## Build a model
        
        1. Make model yaml file
          - Example model https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
        
        
        ```yaml
        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**
        
        ```python
        from kindle import Model
        
        model = Model("model.yaml"), verbose=True)
        ```
        
        ```shell
        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](https://github.com/JeiKeiLim/kindle) offers the easiest way to build your own deep learning architecture. Beyond building a model, AutoML became easier with [Kindle](https://github.com/JeiKeiLim/kindle) and [Optuna](https://optuna.org) or other optimization frameworks.
        * For further information, please refer to [here](https://github.com/JeiKeiLim/kindle/wiki/AutoML-with-kindle-and-optuna)
        
        # Supported modules
        * Detailed documents can be found [here](https://limjk.ai/kindle/api/kindle.modules/)
        
        |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|[]|
        |UpSample|UpSample|[]|
        
        # Custom module support
        ## Custom module with yaml
        You can make your own custom module with yaml file.
        
        **1. custom_module.yaml**
        ```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**
        ```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**
        ```python
        from kindle import Model
        
        model = Model("model_with_custom_module.yaml"), verbose=True)
        ```
        ```shell
        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**
        ```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
        ```python
        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**
        ```python
        from kindle import Model
        
        model = Model("custom_module_model.yaml"), verbose=True)
        ```
        ```shell
        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!
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
