Working Emnist lines dataset.

1 files changed, 93 insertions, 0 deletions

diff --git a/src/training/callbacks/wandb_callbacks.py b/src/training/callbacks/wandb_callbacks.py
new file mode 100644
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+++ b/src/training/callbacks/wandb_callbacks.py
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+"""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._mapping[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)