Working training loop and testing of trained CharacterModel.

4 files changed, 69 insertions, 33 deletions

diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py
index d265dcf..ff10a07 100644
--- a/src/text_recognizer/models/__init__.py
+++ b/src/text_recognizer/models/__init__.py
@@ -1,2 +1,6 @@
 """Model modules."""
+from .base import Model
 from .character_model import CharacterModel
+from .metrics import accuracy
+
+__all__ = ["Model", "CharacterModel", "accuracy"]
diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py
index 0cc531a..b78eacb 100644
--- a/src/text_recognizer/models/base.py
+++ b/src/text_recognizer/models/base.py
@@ -1,9 +1,11 @@
 """Abstract Model class for PyTorch neural networks."""
 
 from abc import ABC, abstractmethod
+from glob import glob
 from pathlib import Path
+import re
 import shutil
-from typing import Callable, Dict, Optional, Tuple
+from typing import Callable, Dict, Optional, Tuple, Type
 
 from loguru import logger
 import torch
@@ -19,7 +21,7 @@ class Model(ABC):
 
     def __init__(
         self,
-        network_fn: Callable,
+        network_fn: Type[nn.Module],
         network_args: Dict,
         data_loader: Optional[Callable] = None,
         data_loader_args: Optional[Dict] = None,
@@ -35,7 +37,7 @@ class Model(ABC):
         """Base class, to be inherited by model for specific type of data.
 
         Args:
-            network_fn (Callable): The PyTorch network.
+            network_fn (Type[nn.Module]): The PyTorch network.
             network_args (Dict): Arguments for the network.
             data_loader (Optional[Callable]): A function that fetches train and val DataLoader.
             data_loader_args (Optional[Dict]):  Arguments for the DataLoader.
@@ -57,27 +59,29 @@ class Model(ABC):
             self._data_loaders = data_loader(**data_loader_args)
             dataset_name = self._data_loaders.__name__
         else:
-            dataset_name = ""
+            dataset_name = "*"
             self._data_loaders = None
 
-        self.name = f"{self.__class__.__name__}_{dataset_name}_{network_fn.__name__}"
+        self._name = f"{self.__class__.__name__}_{dataset_name}_{network_fn.__name__}"
 
         # Extract the input shape for the torchsummary.
-        self._input_shape = network_args.pop("input_shape")
+        if isinstance(network_args["input_size"], int):
+            self._input_shape = (1,) + tuple([network_args["input_size"]])
+        else:
+            self._input_shape = (1,) + tuple(network_args["input_size"])
 
         if metrics is not None:
             self._metrics = metrics
 
         # Set the device.
-        if self.device is None:
-            self._device = torch.device(
-                "cuda:0" if torch.cuda.is_available() else "cpu"
-            )
+        if device is None:
+            self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         else:
             self._device = device
 
         # Load network.
-        self._network = network_fn(**network_args)
+        self.network_args = network_args
+        self._network = network_fn(**self.network_args)
 
         # To device.
         self._network.to(self._device)
@@ -95,13 +99,29 @@ class Model(ABC):
         # Set learning rate scheduler.
         self._lr_scheduler = None
         if lr_scheduler is not None:
+            # OneCycleLR needs the number of steps in an epoch as an input argument.
+            if "OneCycleLR" in str(lr_scheduler):
+                lr_scheduler_args["steps_per_epoch"] = len(self._data_loaders("train"))
             self._lr_scheduler = lr_scheduler(self._optimizer, **lr_scheduler_args)
 
+        # Class mapping.
+        self._mapping = None
+
+    @property
+    def __name__(self) -> str:
+        """Returns the name of the model."""
+        return self._name
+
     @property
     def input_shape(self) -> Tuple[int, ...]:
         """The input shape."""
         return self._input_shape
 
+    @property
+    def mapping(self) -> Dict:
+        """Returns the class mapping."""
+        return self._mapping
+
     def eval(self) -> None:
         """Sets the network to evaluation mode."""
         self._network.eval()
@@ -149,13 +169,14 @@ class Model(ABC):
     def weights_filename(self) -> str:
         """Filepath to the network weights."""
         WEIGHT_DIRNAME.mkdir(parents=True, exist_ok=True)
-        return str(WEIGHT_DIRNAME / f"{self.name}_weights.pt")
+        return str(WEIGHT_DIRNAME / f"{self._name}_weights.pt")
 
     def summary(self) -> None:
         """Prints a summary of the network architecture."""
-        summary(self._network, self._input_shape, device=self.device)
+        device = re.sub("[^A-Za-z]+", "", self.device)
+        summary(self._network, self._input_shape, device=device)
 
-    def _get_state(self) -> Dict:
+    def _get_state_dict(self) -> Dict:
         """Get the state dict of the model."""
         state = {"model_state": self._network.state_dict()}
         if self._optimizer is not None:
@@ -172,6 +193,7 @@ class Model(ABC):
             epoch (int): The last epoch when the checkpoint was created.
 
         """
+        logger.debug("Loading checkpoint...")
         if not path.exists():
             logger.debug("File does not exist {str(path)}")
 
@@ -200,6 +222,7 @@ class Model(ABC):
         state = self._get_state_dict()
         state["is_best"] = is_best
         state["epoch"] = epoch
+        state["network_args"] = self.network_args
 
         path.mkdir(parents=True, exist_ok=True)
 
@@ -216,15 +239,18 @@ class Model(ABC):
     def load_weights(self) -> None:
         """Load the network weights."""
         logger.debug("Loading network weights.")
-        weights = torch.load(self.weights_filename)["model_state"]
+        filename = glob(self.weights_filename)[0]
+        weights = torch.load(filename, map_location=torch.device(self._device))[
+            "model_state"
+        ]
         self._network.load_state_dict(weights)
 
-    def save_weights(self) -> None:
+    def save_weights(self, path: Path) -> None:
         """Save the network weights."""
-        logger.debug("Saving network weights.")
-        torch.save({"model_state": self._network.state_dict()}, self.weights_filename)
+        logger.debug("Saving the best network weights.")
+        shutil.copyfile(str(path / "best.pt"), self.weights_filename)
 
     @abstractmethod
-    def mapping(self) -> Dict:
-        """Mapping from network output to class."""
+    def load_mapping(self) -> None:
+        """Loads class mapping from network output to character."""
         ...
diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
index fd69bf2..527fc7d 100644
--- a/src/text_recognizer/models/character_model.py
+++ b/src/text_recognizer/models/character_model.py
@@ -1,5 +1,5 @@
 """Defines the CharacterModel class."""
-from typing import Callable, Dict, Optional, Tuple
+from typing import Callable, Dict, Optional, Tuple, Type
 
 import numpy as np
 import torch
@@ -8,7 +8,6 @@ from torchvision.transforms import ToTensor
 
 from text_recognizer.datasets.emnist_dataset import load_emnist_mapping
 from text_recognizer.models.base import Model
-from text_recognizer.networks.mlp import mlp
 
 
 class CharacterModel(Model):
@@ -16,8 +15,9 @@ class CharacterModel(Model):
 
     def __init__(
         self,
-        network_fn: Callable,
+        network_fn: Type[nn.Module],
         network_args: Dict,
+        data_loader: Optional[Callable] = None,
         data_loader_args: Optional[Dict] = None,
         metrics: Optional[Dict] = None,
         criterion: Optional[Callable] = None,
@@ -33,6 +33,7 @@ class CharacterModel(Model):
         super().__init__(
             network_fn,
             network_args,
+            data_loader,
             data_loader_args,
             metrics,
             criterion,
@@ -43,13 +44,13 @@ class CharacterModel(Model):
             lr_scheduler_args,
             device,
         )
-        self.emnist_mapping = self.mapping()
-        self.eval()
+        self.load_mapping()
+        self.tensor_transform = ToTensor()
+        self.softmax = nn.Softmax(dim=0)
 
-    def mapping(self) -> Dict[int, str]:
+    def load_mapping(self) -> None:
         """Mapping between integers and classes."""
-        mapping = load_emnist_mapping()
-        return mapping
+        self._mapping = load_emnist_mapping()
 
     def predict_on_image(self, image: np.ndarray) -> Tuple[str, float]:
         """Character prediction on an image.
@@ -61,15 +62,20 @@ class CharacterModel(Model):
             Tuple[str, float]: The predicted character and the confidence in the prediction.
 
         """
+
         if image.dtype == np.uint8:
             image = (image / 255).astype(np.float32)
 
         # Conver to Pytorch Tensor.
-        image = ToTensor(image)
+        image = self.tensor_transform(image)
+
+        with torch.no_grad():
+            logits = self.network(image)
+
+        prediction = self.softmax(logits.data.squeeze())
 
-        prediction = self.network(image)
-        index = torch.argmax(prediction, dim=1)
+        index = int(torch.argmax(prediction, dim=0))
         confidence_of_prediction = prediction[index]
-        predicted_character = self.emnist_mapping[index]
+        predicted_character = self._mapping[index]
 
         return predicted_character, confidence_of_prediction
diff --git a/src/text_recognizer/models/metrics.py b/src/text_recognizer/models/metrics.py
index e2a30a9..ac8d68e 100644
--- a/src/text_recognizer/models/metrics.py
+++ b/src/text_recognizer/models/metrics.py
@@ -3,7 +3,7 @@
 import torch
 
 
-def accuracy(outputs: torch.Tensor, labels: torch.Tensro) -> float:
+def accuracy(outputs: torch.Tensor, labels: torch.Tensor) -> float:
     """Computes the accuracy.
 
     Args: