Transfomer added, many other changes.

3 files changed, 47 insertions, 11 deletions

diff --git a/src/training/trainer/callbacks/base.py b/src/training/trainer/callbacks/base.py
index f81fc1f..500b642 100644
--- a/src/training/trainer/callbacks/base.py
+++ b/src/training/trainer/callbacks/base.py
@@ -62,6 +62,14 @@ class Callback:
         """Called at the end of an epoch."""
         pass
 
+    def on_test_begin(self) -> None:
+        """Called at the beginning of test."""
+        pass
+
+    def on_test_end(self) -> None:
+        """Called at the end of test."""
+        pass
+
 
 class CallbackList:
     """Container for abstracting away callback calls."""
@@ -104,6 +112,16 @@ class CallbackList:
         for callback in self._callbacks:
             callback.on_fit_end()
 
+    def on_test_begin(self) -> None:
+        """Called when test begins."""
+        for callback in self._callbacks:
+            callback.on_test_begin()
+
+    def on_test_end(self) -> None:
+        """Called when test ends."""
+        for callback in self._callbacks:
+            callback.on_test_end()
+
     def on_epoch_begin(self, epoch: int, logs: Optional[Dict] = None) -> None:
         """Called at the beginning of an epoch."""
         for callback in self._callbacks:
diff --git a/src/training/trainer/callbacks/checkpoint.py b/src/training/trainer/callbacks/checkpoint.py
index 6fe06d3..a54e0a9 100644
--- a/src/training/trainer/callbacks/checkpoint.py
+++ b/src/training/trainer/callbacks/checkpoint.py
@@ -21,7 +21,7 @@ class Checkpoint(Callback):
 
     def __init__(
         self,
-        checkpoint_path: Path,
+        checkpoint_path: Union[str, Path],
         monitor: str = "accuracy",
         mode: str = "auto",
         min_delta: float = 0.0,
@@ -29,14 +29,14 @@ class Checkpoint(Callback):
         """Monitors a quantity that will allow us to determine the best model weights.
 
         Args:
-            checkpoint_path (Path): Path to the experiment with the checkpoint.
+            checkpoint_path (Union[str, Path]): Path to the experiment with the checkpoint.
             monitor (str): Name of the quantity to monitor. Defaults to "accuracy".
             mode (str): Description of parameter `mode`. Defaults to "auto".
             min_delta (float): Description of parameter `min_delta`. Defaults to 0.0.
 
         """
         super().__init__()
-        self.checkpoint_path = checkpoint_path
+        self.checkpoint_path = Path(checkpoint_path)
         self.monitor = monitor
         self.mode = mode
         self.min_delta = torch.tensor(min_delta)
diff --git a/src/training/trainer/callbacks/wandb_callbacks.py b/src/training/trainer/callbacks/wandb_callbacks.py
index d2df4d7..f24e5cc 100644
--- a/src/training/trainer/callbacks/wandb_callbacks.py
+++ b/src/training/trainer/callbacks/wandb_callbacks.py
@@ -64,37 +64,55 @@ class WandbImageLogger(Callback):
         """
 
         super().__init__()
+        self.caption = None
         self.example_indices = example_indices
+        self.test_sample_indices = None
         self.num_examples = num_examples
         self.transpose = Transpose() if use_transpose else None
 
     def set_model(self, model: Type[Model]) -> None:
         """Sets the model and extracts validation images from the dataset."""
         self.model = model
+        self.caption = "Validation Examples"
         if self.example_indices is None:
             self.example_indices = np.random.randint(
                 0, len(self.model.val_dataset), self.num_examples
             )
-        self.val_images = self.model.val_dataset.dataset.data[self.example_indices]
-        self.val_targets = self.model.val_dataset.dataset.targets[self.example_indices]
-        self.val_targets = self.val_targets.tolist()
+        self.images = self.model.val_dataset.dataset.data[self.example_indices]
+        self.targets = self.model.val_dataset.dataset.targets[self.example_indices]
+        self.targets = self.targets.tolist()
+
+    def on_test_begin(self) -> None:
+        """Get samples from test dataset."""
+        self.caption = "Test Examples"
+        if self.test_sample_indices is None:
+            self.test_sample_indices = np.random.randint(
+                0, len(self.model.test_dataset), self.num_examples
+            )
+        self.images = self.model.test_dataset.data[self.test_sample_indices]
+        self.targets = self.model.test_dataset.targets[self.test_sample_indices]
+        self.targets = self.targets.tolist()
+
+    def on_test_end(self) -> None:
+        """Log test images."""
+        self.on_epoch_end(0, {})
 
     def on_epoch_end(self, epoch: int, logs: Dict) -> None:
         """Get network predictions on validation images."""
         images = []
-        for i, image in enumerate(self.val_images):
+        for i, image in enumerate(self.images):
             image = self.transpose(image) if self.transpose is not None else image
             pred, conf = self.model.predict_on_image(image)
-            if isinstance(self.val_targets[i], list):
+            if isinstance(self.targets[i], list):
                 ground_truth = "".join(
                     [
                         self.model.mapper(int(target_index))
-                        for target_index in self.val_targets[i]
+                        for target_index in self.targets[i]
                     ]
                 ).rstrip("_")
             else:
-                ground_truth = self.val_targets[i]
+                ground_truth = self.targets[i]
             caption = f"Prediction: {pred} Confidence: {conf:.3f} Ground Truth: {ground_truth}"
             images.append(wandb.Image(image, caption=caption))
 
-        wandb.log({"examples": images}, commit=False)
+        wandb.log({f"{self.caption}": images}, commit=False)