yacrs 0.0.1

Yet Anothor Configuration and Registration System

YACRS: Yet Anothor Configuration and Registration System

Why YACRS?

There are hundreds of configuration system package for python. YACS (by @rbgirshick) is a great one especially for DeepLearnig projects. Many famous projects' configuration system is based on YACS. Such as Detectron2, fvcore, etc. However, even if yasc is a handy and reliable configuration system, it still has some inconvenience. YASC keep all information in CfgNode which leads to an unsolved issue, how to gracefully map these information to an existing code (funciton or class). For instance

import torch
from yacs.config import CfgNode


class Model(torch.nn.Module):
    def __init__(self, input_channels, output_channels):
        self.net = torch.nn.Linear(input_channels, output_channels)

    @staticmethod
    def from_config(cfg):
        return Model(
            input_channels=cfg.input_channels,
            output_channels=cfg.output_channels
        )

cfg = CfgNode({'input_channels': 3, 'output_channels': 32})

# Option 1, do arguments mapping in main loop
model = Model(
    input_channels=cfg.input_channels,
    output_channels=cfg.output_channels
)

# Option 2, do some extra coding in class defination. Define a staticmethod in class.
model = Model.from_config(cfg)

YACRS is offers a more convenient way

# Option 3, use yacr!
import torch
from yacrs import configurable, _C

# ----------------------------------------------------------------

_C.register('Model1')
_C.Model1.input_channels = 1
_C.Model1.output_channels = 2

# register module with specific scope

@configurable().register  # equal  @configurable(scope='Model1').register
class Model1(torch.nn.Module):
    def __init__(self, input_channels, output_channels):
        super().__init__()
        self.net = torch.nn.Linear(input_channels, output_channels)

model = Model1()

# ----------------------------------------------------------------

_C.register('l1')
_C.register('l2')
_C.l1.input_channels = 1
_C.l1.output_channels = 2
_C.l2.input_channels = 3
_C.l2.output_channels = 4

# register module with unbind scope

@configurable(configurable.UNBIND).register
class Model2(torch.nn.Module):
    def __init__(self, input_channels, output_channels=10):
        super().__init__()
        self.net = torch.nn.Linear(input_channels, output_channels)

# use configurable to bind scope 'l1' and 'l2' to Model2 class separately

model1 = configurable('l1')(Model2)()
model2 = configurable('l2')('Model2')() # both class and class name would work

# you can overwrite augments by passing args or kwargs
# augments priority args/kwargs > bind node > default value
model1 = configurable('l1')(Model2)(3, output_channels=1024)

Example

write a config.yaml for training.

train:
    epoch: 10
    lr: 0.01
    scheduler: 'ExponentialLR'
    optimizer: 'SGD'
    dataset: 'TrainDataset'
    model: 'torchvision.resnet50'

TrainDataset:
    data_files:
        - a.txt
        - b.txt
    mean: [0.5, 0.5, 0.5]
    std: [0.5, 0.5, 0.5]

torchvision:
    resnet50:
        num_classes:
        zero_init_residual: false


ExponentialLR:
    gamma: 0.9

SGD:
    momentum: 0.9

Training script

import torch
from torchvision.models import resnet50
from yacrs import configurable, _C, Node

# register module
configurable('torchvision.resnet50').register(resnet50)
configurable('ExponentialLR').register(torch.optim.lr_scheduler.ExponentialLR)
configurable('SGD').register(torch.optim.SGD)

@configurable().register
class TrainDataset(torch.utils.data.Dataset):
    def __init__(self, data_files, mean, std):
        super().__init__()
        pass


@configurable().register
def train(
    epoch,
    lr,
    scheduler,
    optimizer,
    dataset,
    model,
):
    dataset = configurable()(dataset)()
    model = configurable()(model)(num_classes=dataset.num_classes)
    opt = configurable()(optimizer)(model.parameters(), lr=lr)
    sch = configurable()(scheduler)(optimizer)

    for i in range(epoch):
        ...


if __name__ == '__main__':
    import yaml

    with open("config.yaml") as stream:
        cfg_json = yaml.safe_load(stream)
        cfg = Node(cfg_json)
    _C.update(cfg)

    # print global config
    print(_C.pprint())

    train()