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@Asthestarsfalll Asthestarsfalll released this 29 Feb 06:56
· 23 commits to main since this release
0.1.1beta
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ExCore

ExCore is a Configuration/Registry System designed for deeplearning, with some utils.

✨ ExCore supports auto-completion, type-hinting, docstring and code navigation for config files

ExCore is still in an early development stage.

English | 中文

Features

Config System

Config system is the core of deeplearning projects which enable us to manage and adjust hyperparameters and expriments. There are some attempts of config system because the whole community has been suffering from the plain text config files for a long while.

Config System in ExCore is specifically designed for deeplearning training (generally refers to all similar part, e.g. testing, evaluating) procedure. The core premise is to categorize the objects to be created in the config into three classes - Primary, Intermediate, and Isolated objects

  1. Primary objects are those which are directly used in training, e.g. model, optimizer. ExCore will instantiate and return them.
  2. Intermediate objects are those which are indirectly used in training, e.g. backbone of the model, parameters of model that will pass to optimizer. ExCore will instantiate them, and pass them to target Primary objects as arguments according some rules.
  3. Isolated objects refer to python built-in objects which will be parsed when loading toml, e.g. int, string, list and dict.

ExCore extends the syntax of toml file, introducing some special prefix characters -- !, @, $ and '&' to simplify the config defination.

The config system has following features.

Get rid of `type` 
Model:
  type: ResNet # <----- ugly type
  layers: 50
  num_classes: 1

In order to get rid of type, ExCore regards all registered names as reserved words. The Primary module need to be defined like [PrimaryFields.ModuleName]. PrimaryFields are some pre-defined fields, e.g. Model, Optimizer. ModuleName are registered names.

[Model.FCN]
layers = 50
num_classes = 1
Eliminate modules nesting 
TrainData:
  type: Cityscapes
  dataset_root: data/cityscapes
  transforms:
   - type: ResizeStepScale
     min_scale_factor: 0.5
     max_scale_factor: 2.0
     scale_step_size: 0.25
   - type: RandomPaddingCrop
        crop_size: [1024, 512]
   - type: Normalize
  mode: train

ExCore use some special prefix characters to specify certain arguments are modules as well. More prefixes will be introduced later.

[TrainData.Cityscapes]
dataset_root = "data/cityscapes"
mode = 'train'
# use `!` to show this is a module, It's formal to use a quoted key "!transforms", but whatever
!transforms = ["ResizeStepScale", "RandomPaddingCrop", "Normalize"]

# `PrimaryFields` can be omitted in defination of `Intermediate` module
[ResizeStepScale]
min_scale_factor = 0.5
max_scale_factor = 2.0
scale_step_size = 0.25

# or explicitly specify ``PrimaryFields
[Transforms.RandomPaddingCrop]
crop_size = [1024, 512]

# It can even be undefined when there are no arguments
# [Normalize]
✨Auto-complement, type-hinting, docstring and code navigation for config files

The old-style design of plain text configs has been criticized for being difficult to write (without auto-completion) and not allowing navigation to the corresponding class. However, Language Server Protocol can be leveraged to support various code editing features, such as auto-completion, type-hinting, and code navigation. By utilizing lsp and json schema, it's able to provide the ability of auto-completion, some weak type-hinting (If code is well annotated, such as standard type hint in python, it will acheive more) and docstring of corresponding class.

config

ExCore dump the mappings of class name and it file location to support code navigation. Currently only support for neovim, see excore.nvim.

to_class

Config inheritance Use `__base__` to inherit from a toml file. Only dict can be updated locally, other types are overwritten directly. 
__base__ = ["xxx.toml", "xxxx.toml"]
`@`Reused module

ExCore use @ to mark the reused module, which is shared between different modules.

# FCN and SegNet will use the same ResNet object
[Model.FCN]
@backbone = "ResNet"

[Model.SegNet]
@backbone = "ResNet"

[ResNet]
layers = 50
in_channel = 3

equls to

resnet = ResNet(layers=50, in_channel=3)

FCN(backbone=resnet)
SegNet(backbone=resnet)

# If use `!`, it equls to

FCN(backbone=ResNet(layers=50, in_channel=3))
SegNet(backbone=ResNet(layers=50, in_channel=3))
`$`Refer Class and cross file

ExCore use $ to represents class itself, which will not be instantiated.

[Model.ResNet]
$block = "BasicBlock"
layers = 50
in_channel = 3

equls to

from xxx import ResNet, BasicBlock
ResNet(block=BasicBlock, layers=50, in_channel=3)

In order to refer module accross files, $ can be used before PrimaryFields. For example:

File A:

[Block.BasicBlock]

File B:

[Block.BottleneckBlock]

File C:

[Model.ResNet]
!block="$Block"

So we can combine file A and C or file B and C with a toml file

__base__ = ["A.toml", "C.toml"]
# or
__base__ = ["B.toml", "C.toml"]
`&`Variable reference

ExCore use & to refer a variable from the top-level of config.

Note: The value may be overwritten when inheriting, so the call it variable.

size = 224

[TrainData.ImageNet]
&train_size = "size"
!transforms = ['RandomResize', 'Pad']
data_path = 'xxx'

[Transform.Pad]
&pad_size = "size"

[TestData.ImageNet]
!transforms = ['Normalize']
&test_size = "size"
data_path = 'xxx'
✨Using python module in config file

The Registry in ExCore is able to register a module:

from excore import Registry
import torch

MODULE = Registry("module")
MODULE.register_module(torch)

Then you can use torch in config file:

[Model.ResNet]
$activation = "torch.nn.ReLU"
# or
!activation = "torch.nn.ReLU"
import torch
from xxx import ResNet

ResNet(torch.nn.ReLU)
# or

ResNet(torch.nn.ReLU())

Note: You shouldn't define arguments of a module.

✨Argument-level hook

ExCore provide a simple way to call argument-level hooks without arguments.

[Optimizer.AdamW]
@params = "$Model.parameters()"
weight_decay = 0.01

If you want to call a class or static method.

[Model.XXX]
$backbone = "A.from_pretained()"

Attributes can also be used.

[Model.XXX]
!channel = "$Block.out_channel"

It also can be chained invoke.

[Model.XXX]
!channel = "$Block.last_conv.out_channels"

This way requsts you to define such methods or attributes in target class and can not pass arguments. So ExCore provides ConfigArgumentHook.

class ConfigArgumentHook(node, enabled)

You need to implements your own class inherited from ConfigArgumentHook. For example:

from excore.engine.hook import ConfigArgumentHook

from . import HOOKS


@HOOKS.register()
class BnWeightDecayHook(ConfigArgumentHook):
    def __init__(self, node, enabled: bool, bn_weight_decay: bool, weight_decay: float):
        super().__init__(node, enabled)
        self.bn_weight_decay = bn_weight_decay
        self.weight_decay = weight_decay

    def hook(self):
        model = self.node()
        if self.bn_weight_decay:
            optim_params = model.parameters()
        else:
            p_bn = [p for n, p in model.named_parameters() if "bn" in n]
            p_non_bn = [p for n, p in model.named_parameters() if "bn" not in n]
            optim_params = [
                {"params": p_bn, "weight_decay": 0},
                {"params": p_non_bn, "weight_decay": self.weight_decay},
            ]
        return optim_params
[Optimizer.SGD]
@params = "$Model@BnWeightDecayHook"
lr = 0.05
momentum = 0.9
weight_decay = 0.0001

[ConfigHook.BnWeightDecayHook]
weight_decay = 0.0001
bn_weight_decay = false
enabled = true

Use @ to call user defined hooks.

Instance-level hook

If the logic of module building are too complicated, instance-level hook may be helpful.

TODO

✨Lazy Config with simple API The core conception of LazyConfig is 'Lazy', which represents a status of delay. Before instantiating, all the parameters will be stored in a special dict which additionally contains what the target class is. So It's easy to alter any parameters of the module and control which module should be instantiated and which module should not.

It's also used to address the defects of plain text configs through python lsp which is able to provide code navigation, auto-completion and more.

ExCore implements some nodes - MoudleNode, InternNode, ReusedNode, ClassNode, ConfigHookNode, ChainedInvocationWrapper and VariableReference and a LazyConfig to manage all nodes.

ExCore provides only 2 simple API to build moduels -- 'load' and build_all.

Typically, we follow the following procedure.

from excore import config
layz_cfg = config.load('xxx.toml')
module_dict, run_info = config.build_all(layz_cfg)

The results of build_all are respectively Primary modules and Isolated objects.

If you only want to use a certain module.

from excore import config
layz_cfg = config.load('xxx.toml')
model = lazy_cfg.Model() # Model is one of `PrimaryFields`
# or
model = layz_cfg['Model']()

If you want to follow other logic to build modules, you can still use LazyConfig to adjust the arguments of nodes and more things.

from excore import config
layz_cfg = config.load('xxx.toml')
lazy_cfg.Model.add_params(pre_trained='./')

module_dict, run_info = config.build_all(layz_cfg)
Config print 
from excore import config
cfg = config.load_config('xx.toml')
print(cfg)

Result:

╒══════════════════════════╤══════════════════════════════════════════════════════════════════════╕
│ size                     │ 1024                                                                 │
├──────────────────────────┼──────────────────────────────────────────────────────────────────────┤
│ TrainData.CityScapes     │ ╒═════════════╤════════════════════════════════════════════════════╕ │
│                          │ │ &train_size │ size                                               │ │
│                          │ ├─────────────┼────────────────────────────────────────────────────┤ │
│                          │ │ !transforms │ ['RandomResize', 'RandomFlip', 'Normalize', 'Pad'] │ │
│                          │ ├─────────────┼────────────────────────────────────────────────────┤ │
│                          │ │ data_path   │ xxx                                                │ │
│                          │ ╘═════════════╧════════════════════════════════════════════════════╛ │
├──────────────────────────┼──────────────────────────────────────────────────────────────────────┤
│ Transform.RandomFlip     │ ╒══════╤═════╕                                                       │
│                          │ │ prob │ 0.5 │                                                       │
│                          │ ├──────┼─────┤                                                       │
│                          │ │ axis │ 0   │                                                       │
│                          │ ╘══════╧═════╛                                                       │
├──────────────────────────┼──────────────────────────────────────────────────────────────────────┤
│ Transform.Pad            │ ╒═══════════╤══════╕                                                 │
│                          │ │ &pad_size │ size │                                                 │
│                          │ ╘═══════════╧══════╛                                                 │
├──────────────────────────┼──────────────────────────────────────────────────────────────────────┤
│ Normalize.std            │ [0.5, 0.5, 0.5]                                                      │
├──────────────────────────┼──────────────────────────────────────────────────────────────────────┤
│ Normalize.mean           │ [0.5, 0.5, 0.5]                                                      │
├──────────────────────────┼──────────────────────────────────────────────────────────────────────┤
│ TestData.CityScapes      │ ╒═════════════╤═══════════════╕                                      │
│                          │ │ !transforms │ ['Normalize'] │                                      │
│                          │ ├─────────────┼───────────────┤                                      │
│                          │ │ &test_size  │ size          │                                      │
│                          │ ├─────────────┼───────────────┤                                      │
│                          │ │ data_path   │ xxx           │                                      │
│                          │ ╘═════════════╧═══════════════╛                                      │
├──────────────────────────┼──────────────────────────────────────────────────────────────────────┤
│ Model.FCN                │ ╒═══════════╤════════════╕                                           │
│                          │ │ @backbone │ ResNet     │                                           │
│                          │ ├───────────┼────────────┤                                           │
│                          │ │ @head     │ SimpleHead │                                           │
│                          │ ╘═══════════╧════════════╛                                           │
...

Registry

✨LazyRegistry To reduce the unnecessary imports, `ExCore` provides `LazyRegistry`, which store the mappings of class/function name to its `qualname` and dump the mappings to local. When config parsing, the necessary modules will be imported.
Extra information 
from excore import Registry

Models = Registry("Model", extra_field="is_backbone")


@Models.register(is_backbone=True)
class ResNet:
    pass
Modules classification and fuzzy search 
from excore import Registry

Models = Registry("Model", extra_field="is_backbone")


@Models.register(is_backbone=True)
class ResNet:
    pass

@Models.register(is_backbone=True)
class ResNet50:
    pass

@Models.register(is_backbone=True)
class ResNet101:
    pass

@Models.register(is_backbone=False)
class head:
    pass


print(Models.module_table(select_info='is_backbone'))

print(Models.module_table(filter='**Res**'))

results:

  ╒═══════════╤═══════════════╕
  │ Model     │ is_backbone   │
  ╞═══════════╪═══════════════╡
  │ ResNet    │ True          │
  ├───────────┼───────────────┤
  │ ResNet101 │ True          │
  ├───────────┼───────────────┤
  │ ResNet50  │ True          │
  ├───────────┼───────────────┤
  │ head      │ False         │
  ╘═══════════╧═══════════════╛

  ╒═══════════╕
  │ Model     │
  ╞═══════════╡
  │ ResNet    │
  ├───────────┤
  │ ResNet101 │
  ├───────────┤
  │ ResNet50  │
  ╘═══════════╛
Register all 
from torch import optim
from excore import Registry

OPTIM = Registry("Optimizer")


def _get_modules(name: str, module) -> bool:
    if name[0].isupper():
        return True
    return False


OPTIM.match(optim, _get_modules)
print(OPTIM)

results:

╒════════════╤════════════════════════════════════╕
│ NAEM       │ DIR                                │
╞════════════╪════════════════════════════════════╡
│ Adadelta   │ torch.optim.adadelta.Adadelta      │
├────────────┼────────────────────────────────────┤
│ Adagrad    │ torch.optim.adagrad.Adagrad        │
├────────────┼────────────────────────────────────┤
│ Adam       │ torch.optim.adam.Adam              │
├────────────┼────────────────────────────────────┤
│ AdamW      │ torch.optim.adamw.AdamW            │
├────────────┼────────────────────────────────────┤
│ SparseAdam │ torch.optim.sparse_adam.SparseAdam │
├────────────┼────────────────────────────────────┤
│ Adamax     │ torch.optim.adamax.Adamax          │
├────────────┼────────────────────────────────────┤
│ ASGD       │ torch.optim.asgd.ASGD              │
├────────────┼────────────────────────────────────┤
│ SGD        │ torch.optim.sgd.SGD                │
├────────────┼────────────────────────────────────┤
│ RAdam      │ torch.optim.radam.RAdam            │
├────────────┼────────────────────────────────────┤
│ Rprop      │ torch.optim.rprop.Rprop            │
├────────────┼────────────────────────────────────┤
│ RMSprop    │ torch.optim.rmsprop.RMSprop        │
├────────────┼────────────────────────────────────┤
│ Optimizer  │ torch.optim.optimizer.Optimizer    │
├────────────┼────────────────────────────────────┤
│ NAdam      │ torch.optim.nadam.NAdam            │
├────────────┼────────────────────────────────────┤
│ LBFGS      │ torch.optim.lbfgs.LBFGS            │
╘════════════╧════════════════════════════════════╛
All in one

Through Registry to find all registries. Make registries into a global one.

from excore import Registry

MODEL = Registry.get_registry("Model")

G = Registry.make_global()
✨Register module

Registry is able to not only register class or function, but also a python module, for example:

from excore import Registry
import torch

MODULE = Registry("module")
MODULE.register_module(torch)

Then you can use torch in config file:

[Model.ResNet]
$activation = "torch.nn.ReLU"
# or
!activation = "torch.nn.ReLU"

equls to

import torch
from xxx import ResNet

ResNet(torch.nn.ReLU)
# or
ResNet(torch.nn.ReLU())

RoadMap

For more features you may refer to Roadmap of ExCore

What's Changed

Full Changelog: https://github.com/Asthestarsfalll/ExCore/commits/0.1.1beta

Contributors

Asthestarsfalll
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