IAM datasets implemented.

1 files changed, 22 insertions, 16 deletions

diff --git a/src/text_recognizer/datasets/emnist_lines_dataset.py b/src/text_recognizer/datasets/emnist_lines_dataset.py
index b0617f5..656131a 100644
--- a/src/text_recognizer/datasets/emnist_lines_dataset.py
+++ b/src/text_recognizer/datasets/emnist_lines_dataset.py
@@ -9,8 +9,8 @@ from loguru import logger
 import numpy as np
 import torch
 from torch import Tensor
-from torch.utils.data import DataLoader, Dataset
-from torchvision.transforms import Compose, Normalize, ToTensor
+from torch.utils.data import Dataset
+from torchvision.transforms import ToTensor
 
 from text_recognizer.datasets import (
     DATA_DIRNAME,
@@ -20,6 +20,7 @@ from text_recognizer.datasets import (
 )
 from text_recognizer.datasets.sentence_generator import SentenceGenerator
 from text_recognizer.datasets.util import Transpose
+from text_recognizer.networks import sliding_window
 
 DATA_DIRNAME = DATA_DIRNAME / "processed" / "emnist_lines"
 
@@ -55,7 +56,7 @@ class EmnistLinesDataset(Dataset):
 
         self.transform = transform
         if self.transform is None:
-            self.transform = Compose([ToTensor()])
+            self.transform = ToTensor()
 
         self.target_transform = target_transform
         if self.target_transform is None:
@@ -63,14 +64,14 @@ class EmnistLinesDataset(Dataset):
 
         # Extract dataset information.
         self._mapper = EmnistMapper()
-        self.input_shape = self._mapper.input_shape
+        self._input_shape = self._mapper.input_shape
         self.num_classes = self._mapper.num_classes
 
         self.max_length = max_length
         self.min_overlap = min_overlap
         self.max_overlap = max_overlap
         self.num_samples = num_samples
-        self.input_shape = (
+        self._input_shape = (
             self.input_shape[0],
             self.input_shape[1] * self.max_length,
         )
@@ -84,6 +85,11 @@ class EmnistLinesDataset(Dataset):
         # Load dataset.
         self._load_or_generate_data()
 
+    @property
+    def input_shape(self) -> Tuple:
+        """Input shape of the data."""
+        return self._input_shape
+
     def __len__(self) -> int:
         """Returns the length of the dataset."""
         return len(self.data)
@@ -112,11 +118,6 @@ 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 (
@@ -136,13 +137,18 @@ class EmnistLinesDataset(Dataset):
         return self._mapper
 
     @property
+    def mapping(self) -> Dict:
+        """Return EMNIST mapping from index to character."""
+        return self._mapper.mapping
+
+    @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"
         if self.train:
             filename = "train_" + filename
         else:
-            filename = "val_" + filename
+            filename = "test_" + filename
         return DATA_DIRNAME / filename
 
     def _load_or_generate_data(self) -> None:
@@ -184,7 +190,7 @@ class EmnistLinesDataset(Dataset):
             )
 
             targets = convert_strings_to_categorical_labels(
-                targets, self.emnist.inverse_mapping
+                targets, emnist.inverse_mapping
             )
 
             f.create_dataset("data", data=data, dtype="u1", compression="lzf")
@@ -322,13 +328,13 @@ def create_datasets(
     min_overlap: float = 0,
     max_overlap: float = 0.33,
     num_train: int = 10000,
-    num_val: int = 1000,
+    num_test: int = 1000,
 ) -> None:
     """Creates a training an validation dataset of Emnist lines."""
     emnist_train = EmnistDataset(train=True, sample_to_balance=True)
-    emnist_val = EmnistDataset(train=False, sample_to_balance=True)
-    datasets = [emnist_train, emnist_val]
-    num_samples = [num_train, num_val]
+    emnist_test = EmnistDataset(train=False, sample_to_balance=True)
+    datasets = [emnist_train, emnist_test]
+    num_samples = [num_train, num_test]
     for num, train, dataset in zip(num_samples, [True, False], datasets):
         emnist_lines = EmnistLinesDataset(
             train=train,