Refactoring data with attrs and refactor conf for hydra

1 files changed, 16 insertions, 13 deletions

diff --git a/text_recognizer/data/base_data_module.py b/text_recognizer/data/base_data_module.py
index de5628f..18b1996 100644
--- a/text_recognizer/data/base_data_module.py
+++ b/text_recognizer/data/base_data_module.py
@@ -1,11 +1,13 @@
 """Base lightning DataModule class."""
 from pathlib import Path
-from typing import Dict
+from typing import Any, Dict, Tuple
 
 import attr
-import pytorch_lightning as LightningDataModule
+from pytorch_lightning import LightningDataModule
 from torch.utils.data import DataLoader
 
+from text_recognizer.data.base_dataset import BaseDataset
+
 
 def load_and_print_info(data_module_class: type) -> None:
     """Load dataset and print dataset information."""
@@ -19,17 +21,20 @@ def load_and_print_info(data_module_class: type) -> None:
 class BaseDataModule(LightningDataModule):
     """Base PyTorch Lightning DataModule."""
 
-    batch_size: int = attr.ib(default=16)
-    num_workers: int = attr.ib(default=0)
-
     def __attrs_pre_init__(self) -> None:
         super().__init__()
 
-    def __attrs_post_init__(self) -> None:
-        # Placeholders for subclasses.
-        self.dims = None
-        self.output_dims = None
-        self.mapping = None
+    batch_size: int = attr.ib(default=16)
+    num_workers: int = attr.ib(default=0)
+
+    # Placeholders
+    data_train: BaseDataset = attr.ib(init=False, default=None)
+    data_val: BaseDataset = attr.ib(init=False, default=None)
+    data_test: BaseDataset = attr.ib(init=False, default=None)
+    dims: Tuple[int, ...] = attr.ib(init=False, default=None)
+    output_dims: Tuple[int, ...] = attr.ib(init=False, default=None)
+    mapping: Any = attr.ib(init=False, default=None)
+    inverse_mapping: Dict[str, int] = attr.ib(init=False)
 
     @classmethod
     def data_dirname(cls) -> Path:
@@ -58,9 +63,7 @@ class BaseDataModule(LightningDataModule):
             stage (Any): Variable to set splits.
 
         """
-        self.data_train = None
-        self.data_val = None
-        self.data_test = None
+        pass
 
     def train_dataloader(self) -> DataLoader:
         """Retun DataLoader for train data."""