IAM datasets implemented.

1 files changed, 16 insertions, 4 deletions

diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
index 0fd7afd..64ba693 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/src/text_recognizer/models/character_model.py
@@ -4,8 +4,10 @@ from typing import Callable, Dict, Optional, Tuple, Type, Union
 import numpy as np
 import torch
 from torch import nn
+from torch.utils.data import Dataset
 from torchvision.transforms import ToTensor
 
+from text_recognizer.datasets import EmnistMapper
 from text_recognizer.models.base import Model
 
 
@@ -15,8 +17,9 @@ class CharacterModel(Model):
     def __init__(
         self,
         network_fn: Type[nn.Module],
+        dataset: Type[Dataset],
         network_args: Optional[Dict] = None,
-        data_loader_args: Optional[Dict] = None,
+        dataset_args: Optional[Dict] = None,
         metrics: Optional[Dict] = None,
         criterion: Optional[Callable] = None,
         criterion_args: Optional[Dict] = None,
@@ -24,14 +27,16 @@ class CharacterModel(Model):
         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:
         """Initializes the CharacterModel."""
 
         super().__init__(
             network_fn,
+            dataset,
             network_args,
-            data_loader_args,
+            dataset_args,
             metrics,
             criterion,
             criterion_args,
@@ -39,8 +44,11 @@ class CharacterModel(Model):
             optimizer_args,
             lr_scheduler,
             lr_scheduler_args,
+            swa_args,
             device,
         )
+        if self._mapper is None:
+            self._mapper = EmnistMapper()
         self.tensor_transform = ToTensor()
         self.softmax = nn.Softmax(dim=0)
 
@@ -67,9 +75,13 @@ class CharacterModel(Model):
 
         # Put the image tensor on the device the model weights are on.
         image = image.to(self.device)
-        logits = self.network(image)
+        logits = (
+            self.swa_network(image)
+            if self.swa_network is not None
+            else self.network(image)
+        )
 
-        prediction = self.softmax(logits.data.squeeze())
+        prediction = self.softmax(logits.squeeze(0))
 
         index = int(torch.argmax(prediction, dim=0))
         confidence_of_prediction = prediction[index]