Kindle - PyTorch no-code model builder
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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
- 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]]
]
- 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 supportCustom module with yaml support- Use pre-trained model
- More modules!
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