IAM datasets implemented.

1 files changed, 19 insertions, 13 deletions

diff --git a/src/training/trainer/callbacks/wandb_callbacks.py b/src/training/trainer/callbacks/wandb_callbacks.py
index e44c745..6643a44 100644
--- a/src/training/trainer/callbacks/wandb_callbacks.py
+++ b/src/training/trainer/callbacks/wandb_callbacks.py
@@ -2,7 +2,8 @@
 from typing import Callable, Dict, List, Optional, Type
 
 import numpy as np
-from torchvision.transforms import Compose, ToTensor
+import torch
+from torchvision.transforms import ToTensor
 from training.trainer.callbacks import Callback
 import wandb
 
@@ -50,43 +51,48 @@ class WandbImageLogger(Callback):
         self,
         example_indices: Optional[List] = None,
         num_examples: int = 4,
-        transfroms: Optional[Callable] = None,
+        use_transpose: Optional[bool] = False,
     ) -> None:
         """Initializes the WandbImageLogger with the model to train.
 
         Args:
             example_indices (Optional[List]): Indices for validation images. Defaults to None.
             num_examples (int): Number of random samples to take if example_indices are not specified. Defaults to 4.
-            transfroms (Optional[Callable]): Transforms to use on the validation images, e.g. transpose. Defaults to
-                None.
+            use_transpose (Optional[bool]): Use transpose on image or not. Defaults to False.
 
         """
 
         super().__init__()
         self.example_indices = example_indices
         self.num_examples = num_examples
-        self.transfroms = transfroms
-        if self.transfroms is None:
-            self.transforms = Compose([Transpose()])
+        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
-        data_loader = self.model.data_loaders["val"]
         if self.example_indices is None:
             self.example_indices = np.random.randint(
-                0, len(data_loader.dataset.data), self.num_examples
+                0, len(self.model.val_dataset), self.num_examples
             )
-        self.val_images = data_loader.dataset.data[self.example_indices]
-        self.val_targets = data_loader.dataset.targets[self.example_indices].numpy()
+        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()
 
     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):
-            image = self.transforms(image)
+            image = self.transpose(image) if self.transpose is not None else image
             pred, conf = self.model.predict_on_image(image)
-            ground_truth = self.model.mapper(int(self.val_targets[i]))
+            if isinstance(self.val_targets[i], list):
+                ground_truth = "".join(
+                    [
+                        self.model.mapper(int(target_index))
+                        for target_index in self.val_targets[i]
+                    ]
+                ).rstrip("_")
+            else:
+                ground_truth = self.val_targets[i]
             caption = f"Prediction: {pred} Confidence: {conf:.3f} Ground Truth: {ground_truth}"
             images.append(wandb.Image(image, caption=caption))