1 files changed, 0 insertions, 93 deletions
diff --git a/src/training/callbacks/wandb_callbacks.py b/src/training/callbacks/wandb_callbacks.py
deleted file mode 100644
index 6ada6df..0000000
--- a/src/training/callbacks/wandb_callbacks.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""Callbacks using wandb."""
-from typing import Callable, Dict, List, Optional, Type
-
-import numpy as np
-from torchvision.transforms import Compose, ToTensor
-from training.callbacks import Callback
-import wandb
-
-from text_recognizer.datasets import Transpose
-from text_recognizer.models.base import Model
-
-
-class WandbCallback(Callback):
-    """A custom W&B metric logger for the trainer."""
-
-    def __init__(self, log_batch_frequency: int = None) -> None:
-        """Short summary.
-
-        Args:
-            log_batch_frequency (int): If None, metrics will be logged every epoch.
-                If set to an integer, callback will log every metrics every log_batch_frequency.
-
-        """
-        super().__init__()
-        self.log_batch_frequency = log_batch_frequency
-
-    def _on_batch_end(self, batch: int, logs: Dict) -> None:
-        if self.log_batch_frequency and batch % self.log_batch_frequency == 0:
-            wandb.log(logs, commit=True)
-
-    def on_train_batch_end(self, batch: int, logs: Dict = {}) -> None:
-        """Logs training metrics."""
-        if logs is not None:
-            self._on_batch_end(batch, logs)
-
-    def on_validation_batch_end(self, batch: int, logs: Dict = {}) -> None:
-        """Logs validation metrics."""
-        if logs is not None:
-            self._on_batch_end(batch, logs)
-
-    def on_epoch_end(self, epoch: int, logs: Dict) -> None:
-        """Logs at epoch end."""
-        wandb.log(logs, commit=True)
-
-
-class WandbImageLogger(Callback):
-    """Custom W&B callback for image logging."""
-
-    def __init__(
-        self,
-        example_indices: Optional[List] = None,
-        num_examples: int = 4,
-        transfroms: Optional[Callable] = None,
-    ) -> 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.
-
-        """
-
-        super().__init__()
-        self.example_indices = example_indices
-        self.num_examples = num_examples
-        self.transfroms = transfroms
-        if self.transfroms is None:
-            self.transforms = Compose([Transpose()])
-
-    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
-            )
-        self.val_images = data_loader.dataset.data[self.example_indices]
-        self.val_targets = data_loader.dataset.targets[self.example_indices].numpy()
-
-    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)
-            pred, conf = self.model.predict_on_image(image)
-            ground_truth = self.model.mapper(int(self.val_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)