Add python-level structured config

[deepkyu]

Description

Please include a summary in English, of the changes in this pull request. If it closes an issue, please mention it here.

Closes: #203

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Change(s)

• Add python-level structured config which is 100% compatible with the current yaml configuration
  • User can train the model manipulating with pythonic configuration
  • Backward-support: netspresso-train **yaml_files on CLI interface also works
  • Pythonic configuration can be also exported to yaml files when saving experiment log

netspresso-trainer/train.py

Lines 4 to 38 in 717b6ff

	from netspresso_trainer.cfg import ClassificationResNetModelConfig, ExampleBeansDataset
	"""
	Declare dataset config (Use an example dataset provided from the module)
	"""
	example_dataset = ExampleBeansDataset
	example_dataset.metadata.custom_cache_dir = "./data/huggingface"
	"""
	Declare model config
	"""
	example_model = ClassificationResNetModelConfig()
	# ### If you try to train torch.fx model from PyNetsPresso, use this block instead
	# example_model = ClassificationResNetModelConfig(checkpoint=None)
	# example_model.fx_model_checkpoint = "classification_resnet50_best_fx.pt"
	# ###
	"""
	Declare trainer config
	"""
	cfg = TrainerConfig(
	task='classification',
	auto=True,
	data=ExampleBeansDataset,
	model=example_model
	)
	"""
	Update major field values considering the spec of training machine
	"""
	cfg.batch_size = 64
	cfg.epochs = 5
	train(cfg, log_level='INFO')

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- Added a new feature by `@myusername` in [PR 2023](https://github.com/Nota-NetsPresso/netspresso-trainer/pull/2023)

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[illian01]

Thank you for your great works. 👍
I found minor mismatch. Please check the comment.

[illian01]

LGTM!