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Registry System

LazyRegistry​

The blog of ppwwyyxx inspired 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.

Rather than calling it a registry, it's more like providing a tagging feature. With the feature, ExCore can find all class/function and statically analysis them, then dump the results in local to support some editing features to config files, see config extention.

Features​

Extra information​

from excore import Registry

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


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

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"