Add synthetic iam paragraphs dataset

1 files changed, 75 insertions, 0 deletions

diff --git a/text_recognizer/data/iam_extended_paragraphs.py b/text_recognizer/data/iam_extended_paragraphs.py
new file mode 100644
index 0000000..51050fc
--- /dev/null
+++ b/text_recognizer/data/iam_extended_paragraphs.py
@@ -0,0 +1,75 @@
+"""IAM original and sythetic dataset class."""
+from torch.utils.data import ConcatDataset
+
+from text_recognizer.data.base_dataset import BaseDataset
+from text_recognizer.data.base_data_module import BaseDataModule, load_and_print_info
+from text_recognizer.data.iam_paragraphs import IAMParagraphs
+from text_recognizer.data.iam_synthetic_paragraphs import IAMSyntheticParagraphs
+
+
+class IAMExtendedParagraphs(BaseDataModule):
+    def __init__(
+        self,
+        batch_size: int = 128,
+        num_workers: int = 0,
+        train_fraction: float = 0.8,
+        augment: bool = True,
+    ) -> None:
+        super().__init__(batch_size, num_workers)
+
+        self.iam_paragraphs = IAMParagraphs(
+            batch_size, num_workers, train_fraction, augment,
+        )
+        self.iam_synthetic_paragraphs = IAMSyntheticParagraphs(
+            batch_size, num_workers, train_fraction, augment,
+        )
+
+        self.dims = self.iam_paragraphs.dims
+        self.output_dims = self.iam_paragraphs.output_dims
+        self.mapping = self.iam_paragraphs.mapping
+        self.inverse_mapping = self.iam_paragraphs.inverse_mapping
+
+        self.data_train: BaseDataset = None
+        self.data_val: BaseDataset = None
+        self.data_test: BaseDataset = None
+
+    def prepare_data(self) -> None:
+        """Prepares the paragraphs data."""
+        self.iam_paragraphs.prepare_data()
+        self.iam_synthetic_paragraphs.prepare_data()
+
+    def setup(self, stage: str = None) -> None:
+        """Loads data for training/testing."""
+        self.iam_paragraphs.setup(stage)
+        self.iam_synthetic_paragraphs.setup(stage)
+
+        self.data_train = ConcatDataset(
+            [self.iam_paragraphs.data_train, self.iam_synthetic_paragraphs.data_train]
+        )
+        self.data_val = self.iam_paragraphs.data_val
+        self.data_test = self.iam_paragraphs.data_test
+
+    def __repr__(self) -> str:
+        """Returns info about the dataset."""
+        basic = (
+            "IAM Original and Synthetic Paragraphs Dataset\n"  # pylint: disable=no-member
+            f"Num classes: {len(self.mapping)}\n"
+            f"Dims: {self.dims}\n"
+            f"Output dims: {self.output_dims}\n"
+        )
+        if self.data_train is None and self.data_val is None and self.data_test is None:
+            return basic
+
+        x, y = next(iter(self.train_dataloader()))
+        xt, yt = next(iter(self.test_dataloader()))
+        data = (
+            f"Train/val/test sizes: {len(self.data_train)}, {len(self.data_val)}, {len(self.data_test)}\n"
+            f"Train Batch x stats: {(x.shape, x.dtype, x.min(), x.mean(), x.std(), x.max())}\n"
+            f"Train Batch y stats: {(y.shape, y.dtype, y.min(), y.max())}\n"
+            f"Test Batch x stats: {(xt.shape, xt.dtype, xt.min(), xt.mean(), xt.std(), xt.max())}\n"
+            f"Test Batch y stats: {(yt.shape, yt.dtype, yt.min(), yt.max())}\n"
+        )
+        return basic + data
+
+def show_dataset_info() -> None:
+    load_and_print_info(IAMExtendedParagraphs)