Add metadata

1 files changed, 14 insertions, 24 deletions

diff --git a/text_recognizer/data/iam_paragraphs.py b/text_recognizer/data/iam_paragraphs.py
index c7d5229..eec1b1f 100644
--- a/text_recognizer/data/iam_paragraphs.py
+++ b/text_recognizer/data/iam_paragraphs.py
@@ -18,17 +18,7 @@ from text_recognizer.data.base_dataset import (
 from text_recognizer.data.iam import IAM
 from text_recognizer.data.mappings import EmnistMapping
 from text_recognizer.data.transforms.load_transform import load_transform_from_file
-
-PROCESSED_DATA_DIRNAME = BaseDataModule.data_dirname() / "processed" / "iam_paragraphs"
-
-NEW_LINE_TOKEN = "\n"
-
-SEED = 4711
-IMAGE_SCALE_FACTOR = 2
-IMAGE_HEIGHT = 1152 // IMAGE_SCALE_FACTOR
-IMAGE_WIDTH = 1280 // IMAGE_SCALE_FACTOR
-MAX_LABEL_LENGTH = 682
-MAX_WORD_PIECE_LENGTH = 451
+from text_recognizer.metadata import iam_paragraphs as metadata
 
 
 class IAMParagraphs(BaseDataModule):
@@ -55,17 +45,17 @@ class IAMParagraphs(BaseDataModule):
             num_workers,
             pin_memory,
         )
-        self.dims = (1, IMAGE_HEIGHT, IMAGE_WIDTH)
-        self.output_dims = (MAX_LABEL_LENGTH, 1)
+        self.dims = (1, metadata.IMAGE_HEIGHT, metadata.IMAGE_WIDTH)
+        self.output_dims = (metadata.MAX_LABEL_LENGTH, 1)
 
     def prepare_data(self) -> None:
         """Create data for training/testing."""
-        if PROCESSED_DATA_DIRNAME.exists():
+        if metadata.PROCESSED_DATA_DIRNAME.exists():
             return
 
         log.info("Cropping IAM paragraph regions and saving them along with labels...")
 
-        iam = IAM(mapping=EmnistMapping(extra_symbols={NEW_LINE_TOKEN}))
+        iam = IAM(mapping=EmnistMapping(extra_symbols={metadata.NEW_LINE_TOKEN}))
         iam.prepare_data()
 
         properties = {}
@@ -84,7 +74,7 @@ class IAMParagraphs(BaseDataModule):
                 }
             )
 
-        with (PROCESSED_DATA_DIRNAME / "_properties.json").open("w") as f:
+        with (metadata.PROCESSED_DATA_DIRNAME / "_properties.json").open("w") as f:
             json.dump(properties, f, indent=4)
 
     def setup(self, stage: str = None) -> None:
@@ -94,7 +84,7 @@ class IAMParagraphs(BaseDataModule):
             split: str, transform: T.Compose, target_transform: T.Compose
         ) -> BaseDataset:
             crops, labels = _load_processed_crops_and_labels(split)
-            data = [resize_image(crop, IMAGE_SCALE_FACTOR) for crop in crops]
+            data = [resize_image(crop, metadata.IMAGE_SCALE_FACTOR) for crop in crops]
             targets = convert_strings_to_labels(
                 strings=labels,
                 mapping=self.mapping.inverse_mapping,
@@ -117,7 +107,7 @@ class IAMParagraphs(BaseDataModule):
                 target_transform=self.target_transform,
             )
             self.data_train, self.data_val = split_dataset(
-                dataset=data_train, fraction=self.train_fraction, seed=SEED
+                dataset=data_train, fraction=self.train_fraction, seed=metadata.SEED
             )
 
         if stage == "test" or stage is None:
@@ -162,7 +152,7 @@ class IAMParagraphs(BaseDataModule):
 
 def get_dataset_properties() -> Dict:
     """Return properties describing the overall dataset."""
-    with (PROCESSED_DATA_DIRNAME / "_properties.json").open("r") as f:
+    with (metadata.PROCESSED_DATA_DIRNAME / "_properties.json").open("r") as f:
         properties = json.load(f)
 
     def _get_property_values(key: str) -> List:
@@ -193,7 +183,7 @@ def _validate_data_dims(
     """Validates input and output dimensions against the properties of the dataset."""
     properties = get_dataset_properties()
 
-    max_image_shape = properties["crop_shape"]["max"] / IMAGE_SCALE_FACTOR
+    max_image_shape = properties["crop_shape"]["max"] / metadata.IMAGE_SCALE_FACTOR
     if (
         input_dims is not None
         and input_dims[1] < max_image_shape[0]
@@ -246,7 +236,7 @@ def _get_paragraph_crops_and_labels(
         lines = iam.line_strings_by_id[id_]
 
         crops[id_] = image.crop(paragraph_box)
-        labels[id_] = NEW_LINE_TOKEN.join(lines)
+        labels[id_] = metadata.NEW_LINE_TOKEN.join(lines)
 
     if len(crops) != len(labels):
         raise ValueError(f"Crops ({len(crops)}) does not match labels ({len(labels)})")
@@ -258,7 +248,7 @@ def _save_crops_and_labels(
     crops: Dict[str, Image.Image], labels: Dict[str, str], split: str
 ) -> None:
     """Save crops, labels, and shapes of crops of a split."""
-    (PROCESSED_DATA_DIRNAME / split).mkdir(parents=True, exist_ok=True)
+    (metadata.PROCESSED_DATA_DIRNAME / split).mkdir(parents=True, exist_ok=True)
 
     with _labels_filename(split).open("w") as f:
         json.dump(labels, f, indent=4)
@@ -289,12 +279,12 @@ def _load_processed_crops_and_labels(
 
 def _labels_filename(split: str) -> Path:
     """Return filename of processed labels."""
-    return PROCESSED_DATA_DIRNAME / split / "_labels.json"
+    return metadata.PROCESSED_DATA_DIRNAME / split / "_labels.json"
 
 
 def _crop_filename(id: str, split: str) -> Path:
     """Return filename of processed crop."""
-    return PROCESSED_DATA_DIRNAME / split / f"{id}.png"
+    return metadata.PROCESSED_DATA_DIRNAME / split / f"{id}.png"
 
 
 def _num_lines(label: str) -> int: