1 files changed, 75 insertions, 0 deletions
diff --git a/text_recognizer/models/segmentation_model.py b/text_recognizer/models/segmentation_model.py
new file mode 100644
index 0000000..613108a
--- /dev/null
+++ b/text_recognizer/models/segmentation_model.py
@@ -0,0 +1,75 @@
+"""Segmentation model for detecting and segmenting lines."""
+from typing import Callable, Dict, Optional, Type, Union
+
+import numpy as np
+import torch
+from torch import nn
+from torch import Tensor
+from torch.utils.data import Dataset
+from torchvision.transforms import ToTensor
+
+from text_recognizer.models.base import Model
+
+
+class SegmentationModel(Model):
+    """Model for segmenting lines in an image."""
+
+    def __init__(
+        self,
+        network_fn: str,
+        dataset: str,
+        network_args: Optional[Dict] = None,
+        dataset_args: Optional[Dict] = None,
+        metrics: Optional[Dict] = None,
+        criterion: Optional[Callable] = None,
+        criterion_args: Optional[Dict] = None,
+        optimizer: Optional[Callable] = None,
+        optimizer_args: Optional[Dict] = None,
+        lr_scheduler: Optional[Callable] = None,
+        lr_scheduler_args: Optional[Dict] = None,
+        swa_args: Optional[Dict] = None,
+        device: Optional[str] = None,
+    ) -> None:
+        super().__init__(
+            network_fn,
+            dataset,
+            network_args,
+            dataset_args,
+            metrics,
+            criterion,
+            criterion_args,
+            optimizer,
+            optimizer_args,
+            lr_scheduler,
+            lr_scheduler_args,
+            swa_args,
+            device,
+        )
+        self.tensor_transform = ToTensor()
+        self.softmax = nn.Softmax(dim=2)
+
+    @torch.no_grad()
+    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tensor:
+        """Predict on a single input."""
+        self.eval()
+
+        if image.dtype is np.uint8:
+            # Converts an image with range [0, 255] with to PyTorch Tensor with range [0, 1].
+            image = self.tensor_transform(image)
+
+        # Rescale image between 0 and 1.
+        if image.dtype is torch.uint8 or image.dtype is torch.int64:
+            # If the image is an unscaled tensor.
+            image = image.type("torch.FloatTensor") / 255
+
+        if not torch.is_tensor(image):
+            image = Tensor(image)
+
+        # Put the image tensor on the device the model weights are on.
+        image = image.to(self.device)
+
+        logits = self.forward(image)
+
+        segmentation_mask = torch.argmax(logits, dim=1)
+
+        return segmentation_mask