Working bash scripts etc.

1 files changed, 23 insertions, 106 deletions

diff --git a/src/text_recognizer/datasets/emnist_lines_dataset.py b/src/text_recognizer/datasets/emnist_lines_dataset.py
index 1c6e959..d64a991 100644
--- a/src/text_recognizer/datasets/emnist_lines_dataset.py
+++ b/src/text_recognizer/datasets/emnist_lines_dataset.py
@@ -12,10 +12,9 @@ from torch.utils.data import DataLoader, Dataset
 from torchvision.transforms import Compose, Normalize, ToTensor
 
 from text_recognizer.datasets import (
-    _augment_emnist_mapping,
-    _load_emnist_essentials,
     DATA_DIRNAME,
     EmnistDataset,
+    EmnistMapper,
     ESSENTIALS_FILENAME,
 )
 from text_recognizer.datasets.sentence_generator import SentenceGenerator
@@ -30,7 +29,6 @@ class EmnistLinesDataset(Dataset):
     def __init__(
         self,
         train: bool = False,
-        emnist: Optional[EmnistDataset] = None,
         transform: Optional[Callable] = None,
         target_transform: Optional[Callable] = None,
         max_length: int = 34,
@@ -39,10 +37,9 @@ class EmnistLinesDataset(Dataset):
         num_samples: int = 10000,
         seed: int = 4711,
     ) -> None:
-        """Short summary.
+        """Set attributes and loads the dataset.
 
         Args:
-            emnist (EmnistDataset): A EmnistDataset object.
             train (bool): Flag for the filename. Defaults to False. Defaults to None.
             transform (Optional[Callable]): The transform of the data. Defaults to None.
             target_transform (Optional[Callable]): The transform of the target. Defaults to None.
@@ -54,7 +51,6 @@ class EmnistLinesDataset(Dataset):
 
         """
         self.train = train
-        self.emnist = emnist
 
         self.transform = transform
         if self.transform is None:
@@ -64,11 +60,10 @@ class EmnistLinesDataset(Dataset):
         if self.target_transform is None:
             self.target_transform = torch.tensor
 
-        # Load emnist dataset infromation.
-        essentials = _load_emnist_essentials()
-        self.mapping = _augment_emnist_mapping(dict(essentials["mapping"]))
-        self.num_classes = len(self.mapping)
-        self.input_shape = essentials["input_shape"]
+        # Extract dataset information.
+        self._mapper = EmnistMapper()
+        self.input_shape = self._mapper.input_shape
+        self.num_classes = self._mapper.num_classes
 
         self.max_length = max_length
         self.min_overlap = min_overlap
@@ -81,10 +76,13 @@ class EmnistLinesDataset(Dataset):
         self.output_shape = (self.max_length, self.num_classes)
         self.seed = seed
 
-        # Placeholders for the generated dataset.
+        # Placeholders for the dataset.
         self.data = None
         self.target = None
 
+        # Load dataset.
+        self._load_or_generate_data()
+
     def __len__(self) -> int:
         """Returns the length of the dataset."""
         return len(self.data)
@@ -104,7 +102,6 @@ class EmnistLinesDataset(Dataset):
         if torch.is_tensor(index):
             index = index.tolist()
 
-        # data = np.array([self.data[index]])
         data = self.data[index]
         targets = self.targets[index]
 
@@ -116,6 +113,11 @@ class EmnistLinesDataset(Dataset):
 
         return data, targets
 
+    @property
+    def __name__(self) -> str:
+        """Returns the name of the dataset."""
+        return "EmnistLinesDataset"
+
     def __repr__(self) -> str:
         """Returns information about the dataset."""
         return (
@@ -130,6 +132,11 @@ class EmnistLinesDataset(Dataset):
         )
 
     @property
+    def mapper(self) -> EmnistMapper:
+        """Returns the EmnistMapper."""
+        return self._mapper
+
+    @property
     def data_filename(self) -> Path:
         """Path to the h5 file."""
         filename = f"ml_{self.max_length}_o{self.min_overlap}_{self.max_overlap}_n{self.num_samples}.pt"
@@ -161,9 +168,10 @@ class EmnistLinesDataset(Dataset):
         sentence_generator = SentenceGenerator(self.max_length)
 
         # Load emnist dataset.
-        self.emnist.load_emnist_dataset()
+        emnist = EmnistDataset(train=self.train, sample_to_balance=True)
+
         samples_by_character = get_samples_by_character(
-            self.emnist.data.numpy(), self.emnist.targets.numpy(), self.emnist.mapping,
+            emnist.data.numpy(), emnist.targets.numpy(), self.mapper.mapping,
         )
 
         DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
@@ -332,94 +340,3 @@ def create_datasets(
             num_samples=num,
         )
         emnist_lines._load_or_generate_data()
-
-
-class EmnistLinesDataLoaders:
-    """Wrapper for a PyTorch Data loaders for the EMNIST lines dataset."""
-
-    def __init__(
-        self,
-        splits: List[str],
-        max_length: int = 34,
-        min_overlap: float = 0,
-        max_overlap: float = 0.33,
-        num_samples: int = 10000,
-        transform: Optional[Callable] = None,
-        target_transform: Optional[Callable] = None,
-        batch_size: int = 128,
-        shuffle: bool = False,
-        num_workers: int = 0,
-        cuda: bool = True,
-        seed: int = 4711,
-    ) -> None:
-        """Sets the data loader arguments."""
-        self.splits = splits
-        self.dataset_args = {
-            "max_length": max_length,
-            "min_overlap": min_overlap,
-            "max_overlap": max_overlap,
-            "num_samples": num_samples,
-            "transform": transform,
-            "target_transform": target_transform,
-            "seed": seed,
-        }
-        self.batch_size = batch_size
-        self.shuffle = shuffle
-        self.num_workers = num_workers
-        self.cuda = cuda
-        self._data_loaders = self._load_data_loaders()
-
-    def __repr__(self) -> str:
-        """Returns information about the dataset."""
-        return self._data_loaders[self.splits[0]].dataset.__repr__()
-
-    @property
-    def __name__(self) -> str:
-        """Returns the name of the dataset."""
-        return "EmnistLines"
-
-    def __call__(self, split: str) -> DataLoader:
-        """Returns the `split` DataLoader.
-
-        Args:
-            split (str): The dataset split, i.e. train or val.
-
-        Returns:
-            DataLoader: A PyTorch DataLoader.
-
-        Raises:
-            ValueError: If the split does not exist.
-
-        """
-        try:
-            return self._data_loaders[split]
-        except KeyError:
-            raise ValueError(f"Split {split} does not exist.")
-
-    def _load_data_loaders(self) -> Dict[str, DataLoader]:
-        """Fetches the EMNIST Lines dataset and return a Dict of PyTorch DataLoaders."""
-        data_loaders = {}
-
-        for split in ["train", "val"]:
-            if split in self.splits:
-
-                if split == "train":
-                    self.dataset_args["train"] = True
-                else:
-                    self.dataset_args["train"] = False
-
-                emnist_lines_dataset = EmnistLinesDataset(**self.dataset_args)
-
-                emnist_lines_dataset._load_or_generate_data()
-
-                data_loader = DataLoader(
-                    dataset=emnist_lines_dataset,
-                    batch_size=self.batch_size,
-                    shuffle=self.shuffle,
-                    num_workers=self.num_workers,
-                    pin_memory=self.cuda,
-                )
-
-                data_loaders[split] = data_loader
-
-        return data_loaders