From e1b504bca41a9793ed7e88ef14f2e2cbd85724f2 Mon Sep 17 00:00:00 2001
From: aktersnurra <gustaf.rydholm@gmail.com>
Date: Tue, 8 Sep 2020 23:14:23 +0200
Subject: IAM datasets implemented.

---
 src/text_recognizer/models/line_ctc_model.py | 105 +++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 src/text_recognizer/models/line_ctc_model.py

(limited to 'src/text_recognizer/models/line_ctc_model.py')

diff --git a/src/text_recognizer/models/line_ctc_model.py b/src/text_recognizer/models/line_ctc_model.py
new file mode 100644
index 0000000..97308a7
--- /dev/null
+++ b/src/text_recognizer/models/line_ctc_model.py
@@ -0,0 +1,105 @@
+"""Defines the LineCTCModel class."""
+from typing import Callable, Dict, Optional, Tuple, 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.datasets import EmnistMapper
+from text_recognizer.models.base import Model
+from text_recognizer.networks import greedy_decoder
+
+
+class LineCTCModel(Model):
+    """Model for predicting a sequence of characters from an image of a text line."""
+
+    def __init__(
+        self,
+        network_fn: Type[nn.Module],
+        dataset: Type[Dataset],
+        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,
+        )
+        if self._mapper is None:
+            self._mapper = EmnistMapper()
+        self.tensor_transform = ToTensor()
+
+    def loss_fn(self, output: Tensor, targets: Tensor) -> Tensor:
+        """Computes the CTC loss.
+
+        Args:
+            output (Tensor): Model predictions.
+            targets (Tensor): Correct output sequence.
+
+        Returns:
+            Tensor: The CTC loss.
+
+        """
+        input_lengths = torch.full(
+            size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long,
+        )
+        target_lengths = torch.full(
+            size=(output.shape[1],), fill_value=targets.shape[1], dtype=torch.long,
+        )
+        return self.criterion(output, targets, input_lengths, target_lengths)
+
+    @torch.no_grad()
+    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
+        """Predict on a single input."""
+        if image.dtype == 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 == torch.uint8:
+            # If the image is an unscaled tensor.
+            image = image.type("torch.FloatTensor") / 255
+
+        # Put the image tensor on the device the model weights are on.
+        image = image.to(self.device)
+        log_probs = (
+            self.swa_network(image)
+            if self.swa_network is not None
+            else self.network(image)
+        )
+
+        raw_pred, _ = greedy_decoder(
+            predictions=log_probs,
+            character_mapper=self.mapper,
+            blank_label=79,
+            collapse_repeated=True,
+        )
+
+        log_probs, _ = log_probs.max(dim=2)
+
+        predicted_characters = "".join(raw_pred[0])
+        confidence_of_prediction = torch.exp(log_probs.sum()).item()
+
+        return predicted_characters, confidence_of_prediction
-- 
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