LAP/torchlap/configs/celeba_configs.py

from .base_config import BaseConfig, PhaseType
from typing import Union
from torch import nn
from torchvision import transforms
from ..data.content_loaders.celeba_loader import CelebALoader, CelebATag
from ..model_evaluation.binary_evaluator import BinaryEvaluator

class CelebAConfigs(BaseConfig):

    def __init__(self, 
        try_name: str, try_num: int, input_size, phase_type: PhaseType) -> None:
        super().__init__(try_name, try_num, 'default', input_size, phase_type, CelebALoader, BinaryEvaluator)
    
        # replaced configs!
        self.batch_size = 64
        self.iters_per_epoch = 200
        self.tags = [tag.name for tag in CelebATag if tag.name.endswith('Tag')]
        self.main_tag: Union[CelebATag, None] = None
        self.data_root: str = 'data/celeba'
        self.dataset_metadata: str = 'dataset_metadata/celeba.tsv'

        self.title_of_reference_metric_to_choose_best_epoch = 'AvgSS'
        self.operator_to_decide_on_improvement_of_val_reference_metric = '>='
        self.keep_best_and_last_epochs_only = True

        # augmentation
        self.augmentations_dict = {
            'x': nn.Sequential(
                transforms.RandomRotation(45),
                transforms.RandomAffine(0, shear=0.2, scale=(.8, 1.2)),
                transforms.RandomHorizontalFlip(),
                transforms.ColorJitter(brightness=.5, contrast=.5, saturation=.5),
                transforms.RandomPerspective())
        }