summaryrefslogtreecommitdiff
path: root/training/trainer/callbacks/wandb_callbacks.py
diff options
context:
space:
mode:
Diffstat (limited to 'training/trainer/callbacks/wandb_callbacks.py')
-rw-r--r--training/trainer/callbacks/wandb_callbacks.py261
1 files changed, 0 insertions, 261 deletions
diff --git a/training/trainer/callbacks/wandb_callbacks.py b/training/trainer/callbacks/wandb_callbacks.py
deleted file mode 100644
index 552a4f4..0000000
--- a/training/trainer/callbacks/wandb_callbacks.py
+++ /dev/null
@@ -1,261 +0,0 @@
-"""Callback for W&B."""
-from typing import Callable, Dict, List, Optional, Type
-
-import numpy as np
-from training.trainer.callbacks import Callback
-import wandb
-
-import text_recognizer.datasets.transforms as transforms
-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: Optional[Dict] = None) -> None:
- """Logs training metrics."""
- if logs is not None:
- logs["lr"] = self.model.optimizer.param_groups[0]["lr"]
- self._on_batch_end(batch, logs)
-
- def on_validation_batch_end(self, batch: int, logs: Optional[Dict] = None) -> 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,
- transform: Optional[bool] = 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.
- transform (Optional[Dict]): Use transform on image or not. Defaults to None.
-
- """
-
- super().__init__()
- self.caption = None
- self.example_indices = example_indices
- self.test_sample_indices = None
- self.num_examples = num_examples
- self.transform = (
- self._configure_transform(transform) if transform is not None else None
- )
-
- def _configure_transform(self, transform: Dict) -> Callable:
- args = transform["args"] or {}
- return getattr(transforms, transform["type"])(**args)
-
- 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.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.images):
- image = self.transform(image) if self.transform is not None else image
- pred, conf = self.model.predict_on_image(image)
- if isinstance(self.targets[i], list):
- ground_truth = "".join(
- [
- self.model.mapper(int(target_index) - 26)
- if target_index > 35
- else self.model.mapper(int(target_index))
- for target_index in self.targets[i]
- ]
- ).rstrip("_")
- else:
- ground_truth = self.model.mapper(int(self.targets[i]))
- caption = f"Prediction: {pred} Confidence: {conf:.3f} Ground Truth: {ground_truth}"
- images.append(wandb.Image(image, caption=caption))
-
- wandb.log({f"{self.caption}": images}, commit=False)
-
-
-class WandbSegmentationLogger(Callback):
- """Custom W&B callback for image logging."""
-
- def __init__(
- self,
- class_labels: Dict,
- example_indices: Optional[List] = None,
- num_examples: int = 4,
- ) -> None:
- """Initializes the WandbImageLogger with the model to train.
-
- Args:
- class_labels (Dict): A dict with int as key and class string as value.
- 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.
-
- """
-
- super().__init__()
- self.caption = None
- self.class_labels = {int(k): v for k, v in class_labels.items()}
- self.example_indices = example_indices
- self.test_sample_indices = None
- self.num_examples = num_examples
-
- def set_model(self, model: Type[Model]) -> None:
- """Sets the model and extracts validation images from the dataset."""
- self.model = model
- self.caption = "Validation Segmentation Examples"
- if self.example_indices is None:
- self.example_indices = np.random.randint(
- 0, len(self.model.val_dataset), self.num_examples
- )
- 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 Segmentation 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.images):
- pred_mask = (
- self.model.predict_on_image(image).detach().squeeze(0).cpu().numpy()
- )
- gt_mask = np.array(self.targets[i])
- images.append(
- wandb.Image(
- image,
- masks={
- "predictions": {
- "mask_data": pred_mask,
- "class_labels": self.class_labels,
- },
- "ground_truth": {
- "mask_data": gt_mask,
- "class_labels": self.class_labels,
- },
- },
- )
- )
-
- wandb.log({f"{self.caption}": images}, commit=False)
-
-
-class WandbReconstructionLogger(Callback):
- """Custom W&B callback for image reconstructions logging."""
-
- def __init__(
- self, example_indices: Optional[List] = None, num_examples: int = 4,
- ) -> 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.
-
- """
-
- super().__init__()
- self.caption = None
- self.example_indices = example_indices
- self.test_sample_indices = None
- self.num_examples = num_examples
-
- def set_model(self, model: Type[Model]) -> None:
- """Sets the model and extracts validation images from the dataset."""
- self.model = model
- self.caption = "Validation Reconstructions Examples"
- if self.example_indices is None:
- self.example_indices = np.random.randint(
- 0, len(self.model.val_dataset), self.num_examples
- )
- self.images = self.model.val_dataset.dataset.data[self.example_indices]
-
- def on_test_begin(self) -> None:
- """Get samples from test dataset."""
- self.caption = "Test Reconstructions 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]
-
- 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 image in self.images:
- reconstructed_image = (
- self.model.predict_on_image(image).detach().squeeze(0).cpu().numpy()
- )
- images.append(image)
- images.append(reconstructed_image)
-
- wandb.log(
- {f"{self.caption}": [wandb.Image(image) for image in images]}, commit=False,
- )
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-11-10 10:02:37 +0000