A lot of stuff working :D. ResNet implemented!

2 files changed, 28 insertions, 25 deletions

diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py
index 6d40b49..74fd223 100644
--- a/src/text_recognizer/models/base.py
+++ b/src/text_recognizer/models/base.py
@@ -53,8 +53,8 @@ class Model(ABC):
 
         """
 
-        # Fetch data loaders and dataset info.
-        dataset_name, self._data_loaders, self._mapper = self._load_data_loader(
+        # Configure data loaders and dataset info.
+        dataset_name, self._data_loaders, self._mapper = self._configure_data_loader(
             data_loader_args
         )
         self._input_shape = self._mapper.input_shape
@@ -70,16 +70,19 @@ class Model(ABC):
         else:
             self._device = device
 
-        # Load network.
-        self._network, self._network_args = self._load_network(network_fn, network_args)
+        # Configure network.
+        self._network, self._network_args = self._configure_network(
+            network_fn, network_args
+        )
 
         # To device.
         self._network.to(self._device)
 
-        # Set training objects.
-        self._criterion = self._load_criterion(criterion, criterion_args)
-        self._optimizer = self._load_optimizer(optimizer, optimizer_args)
-        self._lr_scheduler = self._load_lr_scheduler(lr_scheduler, lr_scheduler_args)
+        # Configure training objects.
+        self._criterion = self._configure_criterion(criterion, criterion_args)
+        self._optimizer, self._lr_scheduler = self._configure_optimizers(
+            optimizer, optimizer_args, lr_scheduler, lr_scheduler_args
+        )
 
         # Experiment directory.
         self.model_dir = None
@@ -87,7 +90,7 @@ class Model(ABC):
         # Flag for stopping training.
         self.stop_training = False
 
-    def _load_data_loader(
+    def _configure_data_loader(
         self, data_loader_args: Optional[Dict]
     ) -> Tuple[str, Dict, EmnistMapper]:
         """Loads data loader, dataset name, and dataset mapper."""
@@ -102,7 +105,7 @@ class Model(ABC):
             data_loaders = None
         return dataset_name, data_loaders, mapper
 
-    def _load_network(
+    def _configure_network(
         self, network_fn: Type[nn.Module], network_args: Optional[Dict]
     ) -> Tuple[Type[nn.Module], Dict]:
         """Loads the network."""
@@ -113,7 +116,7 @@ class Model(ABC):
             network = network_fn(**network_args)
         return network, network_args
 
-    def _load_criterion(
+    def _configure_criterion(
         self, criterion: Optional[Callable], criterion_args: Optional[Dict]
     ) -> Optional[Callable]:
         """Loads the criterion."""
@@ -123,27 +126,27 @@ class Model(ABC):
             _criterion = None
         return _criterion
 
-    def _load_optimizer(
-        self, optimizer: Optional[Callable], optimizer_args: Optional[Dict]
-    ) -> Optional[Callable]:
-        """Loads the optimizer."""
+    def _configure_optimizers(
+        self,
+        optimizer: Optional[Callable],
+        optimizer_args: Optional[Dict],
+        lr_scheduler: Optional[Callable],
+        lr_scheduler_args: Optional[Dict],
+    ) -> Tuple[Optional[Callable], Optional[Callable]]:
+        """Loads the optimizers."""
         if optimizer is not None:
             _optimizer = optimizer(self._network.parameters(), **optimizer_args)
         else:
             _optimizer = None
-        return _optimizer
 
-    def _load_lr_scheduler(
-        self, lr_scheduler: Optional[Callable], lr_scheduler_args: Optional[Dict]
-    ) -> Optional[Callable]:
-        """Loads learning rate scheduler."""
         if self._optimizer and lr_scheduler is not None:
             if "OneCycleLR" in str(lr_scheduler):
                 lr_scheduler_args["steps_per_epoch"] = len(self._data_loaders["train"])
             _lr_scheduler = lr_scheduler(self._optimizer, **lr_scheduler_args)
         else:
             _lr_scheduler = None
-        return _lr_scheduler
+
+        return _optimizer, _lr_scheduler
 
     @property
     def __name__(self) -> str:
diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
index 0a0ab2d..0fd7afd 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/src/text_recognizer/models/character_model.py
@@ -44,6 +44,7 @@ class CharacterModel(Model):
         self.tensor_transform = ToTensor()
         self.softmax = nn.Softmax(dim=0)
 
+    @torch.no_grad()
     def predict_on_image(
         self, image: Union[np.ndarray, torch.Tensor]
     ) -> Tuple[str, float]:
@@ -64,10 +65,9 @@ class CharacterModel(Model):
             # If the image is an unscaled tensor.
             image = image.type("torch.FloatTensor") / 255
 
-        with torch.no_grad():
-            # Put the image tensor on the device the model weights are on.
-            image = image.to(self.device)
-            logits = self.network(image)
+        # Put the image tensor on the device the model weights are on.
+        image = image.to(self.device)
+        logits = self.network(image)
 
         prediction = self.softmax(logits.data.squeeze())