easy-module-attribute-getter 0.9.11

Select module classes and functions using yaml, without any if-statements.

easy_module_attribute_getter

Installation:

pip install easy_module_attribute_getter

Simple example, using PytorchGetter which extends EasyModuleAttributeGetter:

1. Specify class names and arguments in your yaml config file:

models:
  modelA:
    densenet121:
      pretrained: True
      memory_efficient: True
  modelB:
    resnext50_32x4d:
      pretrained: True
losses:
  lossA:
    CrossEntropyLoss:
  lossB:
    L1Loss:

2. Read yaml file, and get objects from modules:

from easy_module_attribute_getter import YamlReader, PytorchGetter
yaml_reader = YamlReader()
args, _, _ = yaml_reader.load_yamls(['example.yaml'])
pytorch_getter = PytorchGetter()
models = pytorch_getter.get_multiple("model", args.models)
losses = pytorch_getter.get_multiple("loss", args.losses)
# "models" is a dictionary with keys "modelA" and "modelB" as specified
# in the yaml file. The values are the loaded objects (in this case
# pytorch models).
# The same is true for "losses".

Easily register your own modules into an existing getter.

from pytorch_metric_learning import losses, miners, samplers 
pytorch_getter = PytorchGetter()
# The 'loss' key already exists, so the 'losses' module will be appended to the existing module.
pytorch_getter.register('loss', losses) 
pytorch_getter.register('miner', miners)
pytorch_getter.register('sampler', samplers)

# Both modules will be searched when get() or get_multiple() is used.
# The first loss comes from the module that was just registered.
# The second loss comes from the Pytorch library that is registered by default.
metric_loss = pytorch_getter.get('loss', class_name='TripletMarginLoss', return_uninitialized=True)
kl_div_loss = pytorch_getter.get('loss', class_name='KLDivLoss', return_uninitialized=True)