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-rw-r--r--src/training/callbacks/__init__.py20
-rw-r--r--src/training/callbacks/base.py231
-rw-r--r--src/training/callbacks/early_stopping.py106
-rw-r--r--src/training/callbacks/lr_schedulers.py97
-rw-r--r--src/training/callbacks/wandb_callbacks.py93
5 files changed, 500 insertions, 47 deletions
diff --git a/src/training/callbacks/__init__.py b/src/training/callbacks/__init__.py
index 868d739..fbcc285 100644
--- a/src/training/callbacks/__init__.py
+++ b/src/training/callbacks/__init__.py
@@ -1 +1,19 @@
-"""TBC."""
+"""The callback modules used in the training script."""
+from .base import Callback, CallbackList, Checkpoint
+from .early_stopping import EarlyStopping
+from .lr_schedulers import CyclicLR, MultiStepLR, OneCycleLR, ReduceLROnPlateau, StepLR
+from .wandb_callbacks import WandbCallback, WandbImageLogger
+
+__all__ = [
+ "Callback",
+ "CallbackList",
+ "Checkpoint",
+ "EarlyStopping",
+ "WandbCallback",
+ "WandbImageLogger",
+ "CyclicLR",
+ "MultiStepLR",
+ "OneCycleLR",
+ "ReduceLROnPlateau",
+ "StepLR",
+]
diff --git a/src/training/callbacks/base.py b/src/training/callbacks/base.py
index d80a1e5..e0d91e6 100644
--- a/src/training/callbacks/base.py
+++ b/src/training/callbacks/base.py
@@ -1,12 +1,33 @@
"""Metaclass for callback functions."""
-from abc import ABC
-from typing import Callable, List, Type
+from enum import Enum
+from typing import Callable, Dict, List, Type, Union
+from loguru import logger
+import numpy as np
+import torch
-class Callback(ABC):
+from text_recognizer.models import Model
+
+
+class ModeKeys:
+ """Mode keys for CallbackList."""
+
+ TRAIN = "train"
+ VALIDATION = "validation"
+
+
+class Callback:
"""Metaclass for callbacks used in training."""
+ def __init__(self) -> None:
+ """Initializes the Callback instance."""
+ self.model = None
+
+ def set_model(self, model: Type[Model]) -> None:
+ """Set the model."""
+ self.model = model
+
def on_fit_begin(self) -> None:
"""Called when fit begins."""
pass
@@ -15,35 +36,27 @@ class Callback(ABC):
"""Called when fit ends."""
pass
- def on_train_epoch_begin(self) -> None:
- """Called at the beginning of an epoch."""
- pass
-
- def on_train_epoch_end(self) -> None:
- """Called at the end of an epoch."""
+ def on_epoch_begin(self, epoch: int, logs: Dict = {}) -> None:
+ """Called at the beginning of an epoch. Only used in training mode."""
pass
- def on_val_epoch_begin(self) -> None:
- """Called at the beginning of an epoch."""
+ def on_epoch_end(self, epoch: int, logs: Dict = {}) -> None:
+ """Called at the end of an epoch. Only used in training mode."""
pass
- def on_val_epoch_end(self) -> None:
- """Called at the end of an epoch."""
- pass
-
- def on_train_batch_begin(self) -> None:
+ def on_train_batch_begin(self, batch: int, logs: Dict = {}) -> None:
"""Called at the beginning of an epoch."""
pass
- def on_train_batch_end(self) -> None:
+ def on_train_batch_end(self, batch: int, logs: Dict = {}) -> None:
"""Called at the end of an epoch."""
pass
- def on_val_batch_begin(self) -> None:
+ def on_validation_batch_begin(self, batch: int, logs: Dict = {}) -> None:
"""Called at the beginning of an epoch."""
pass
- def on_val_batch_end(self) -> None:
+ def on_validation_batch_end(self, batch: int, logs: Dict = {}) -> None:
"""Called at the end of an epoch."""
pass
@@ -51,9 +64,29 @@ class Callback(ABC):
class CallbackList:
"""Container for abstracting away callback calls."""
- def __init__(self, callbacks: List[Callable] = None) -> None:
- """TBC."""
- self._callbacks = callbacks if callbacks is not None else []
+ mode_keys = ModeKeys()
+
+ def __init__(self, model: Type[Model], callbacks: List[Callback] = None) -> None:
+ """Container for `Callback` instances.
+
+ This object wraps a list of `Callback` instances and allows them all to be
+ called via a single end point.
+
+ Args:
+ model (Type[Model]): A `Model` instance.
+ callbacks (List[Callback]): List of `Callback` instances. Defaults to None.
+
+ """
+
+ self._callbacks = callbacks or []
+ if model:
+ self.set_model(model)
+
+ def set_model(self, model: Type[Model]) -> None:
+ """Set the model for all callbacks."""
+ self.model = model
+ for callback in self._callbacks:
+ callback.set_model(model=self.model)
def append(self, callback: Type[Callback]) -> None:
"""Append new callback to callback list."""
@@ -61,41 +94,147 @@ class CallbackList:
def on_fit_begin(self) -> None:
"""Called when fit begins."""
- for _ in self._callbacks:
- pass
+ for callback in self._callbacks:
+ callback.on_fit_begin()
def on_fit_end(self) -> None:
"""Called when fit ends."""
- pass
+ for callback in self._callbacks:
+ callback.on_fit_end()
- def on_train_epoch_begin(self) -> None:
+ def on_epoch_begin(self, epoch: int, logs: Dict = {}) -> None:
"""Called at the beginning of an epoch."""
- pass
+ for callback in self._callbacks:
+ callback.on_epoch_begin(epoch, logs)
- def on_train_epoch_end(self) -> None:
+ def on_epoch_end(self, epoch: int, logs: Dict = {}) -> None:
"""Called at the end of an epoch."""
- pass
-
- def on_val_epoch_begin(self) -> None:
+ for callback in self._callbacks:
+ callback.on_epoch_end(epoch, logs)
+
+ def _call_batch_hook(
+ self, mode: str, hook: str, batch: int, logs: Dict = {}
+ ) -> None:
+ """Helper function for all batch_{begin | end} methods."""
+ if hook == "begin":
+ self._call_batch_begin_hook(mode, batch, logs)
+ elif hook == "end":
+ self._call_batch_end_hook(mode, batch, logs)
+ else:
+ raise ValueError(f"Unrecognized hook {hook}.")
+
+ def _call_batch_begin_hook(self, mode: str, batch: int, logs: Dict = {}) -> None:
+ """Helper function for all `on_*_batch_begin` methods."""
+ hook_name = f"on_{mode}_batch_begin"
+ self._call_batch_hook_helper(hook_name, batch, logs)
+
+ def _call_batch_end_hook(self, mode: str, batch: int, logs: Dict = {}) -> None:
+ """Helper function for all `on_*_batch_end` methods."""
+ hook_name = f"on_{mode}_batch_end"
+ self._call_batch_hook_helper(hook_name, batch, logs)
+
+ def _call_batch_hook_helper(
+ self, hook_name: str, batch: int, logs: Dict = {}
+ ) -> None:
+ """Helper function for `on_*_batch_begin` methods."""
+ for callback in self._callbacks:
+ hook = getattr(callback, hook_name)
+ hook(batch, logs)
+
+ def on_train_batch_begin(self, batch: int, logs: Dict = {}) -> None:
"""Called at the beginning of an epoch."""
- pass
+ self._call_batch_hook(self.mode_keys.TRAIN, "begin", batch)
- def on_val_epoch_end(self) -> None:
+ def on_train_batch_end(self, batch: int, logs: Dict = {}) -> None:
"""Called at the end of an epoch."""
- pass
+ self._call_batch_hook(self.mode_keys.TRAIN, "end", batch)
- def on_train_batch_begin(self) -> None:
+ def on_validation_batch_begin(self, batch: int, logs: Dict = {}) -> None:
"""Called at the beginning of an epoch."""
- pass
-
- def on_train_batch_end(self) -> None:
- """Called at the end of an epoch."""
- pass
+ self._call_batch_hook(self.mode_keys.VALIDATION, "begin", batch)
- def on_val_batch_begin(self) -> None:
- """Called at the beginning of an epoch."""
- pass
-
- def on_val_batch_end(self) -> None:
+ def on_validation_batch_end(self, batch: int, logs: Dict = {}) -> None:
"""Called at the end of an epoch."""
- pass
+ self._call_batch_hook(self.mode_keys.VALIDATION, "end", batch)
+
+ def __iter__(self) -> iter:
+ """Iter function for callback list."""
+ return iter(self._callbacks)
+
+
+class Checkpoint(Callback):
+ """Saving model parameters at the end of each epoch."""
+
+ mode_dict = {
+ "min": torch.lt,
+ "max": torch.gt,
+ }
+
+ def __init__(
+ self, monitor: str = "accuracy", mode: str = "auto", min_delta: float = 0.0
+ ) -> None:
+ """Monitors a quantity that will allow us to determine the best model weights.
+
+ Args:
+ 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.monitor = monitor
+ self.mode = mode
+ self.min_delta = torch.tensor(min_delta)
+
+ if mode not in ["auto", "min", "max"]:
+ logger.warning(f"Checkpoint mode {mode} is unkown, fallback to auto mode.")
+
+ self.mode = "auto"
+
+ if self.mode == "auto":
+ if "accuracy" in self.monitor:
+ self.mode = "max"
+ else:
+ self.mode = "min"
+ logger.debug(
+ f"Checkpoint mode set to {self.mode} for monitoring {self.monitor}."
+ )
+
+ torch_inf = torch.tensor(np.inf)
+ self.min_delta *= 1 if self.monitor_op == torch.gt else -1
+ self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf
+
+ @property
+ def monitor_op(self) -> float:
+ """Returns the comparison method."""
+ return self.mode_dict[self.mode]
+
+ def on_epoch_end(self, epoch: int, logs: Dict) -> None:
+ """Saves a checkpoint for the network parameters.
+
+ Args:
+ epoch (int): The current epoch.
+ logs (Dict): The log containing the monitored metrics.
+
+ """
+ current = self.get_monitor_value(logs)
+ if current is None:
+ return
+ if self.monitor_op(current - self.min_delta, self.best_score):
+ self.best_score = current
+ is_best = True
+ else:
+ is_best = False
+
+ self.model.save_checkpoint(is_best, epoch, self.monitor)
+
+ def get_monitor_value(self, logs: Dict) -> Union[float, None]:
+ """Extracts the monitored value."""
+ monitor_value = logs.get(self.monitor)
+ if monitor_value is None:
+ logger.warning(
+ f"Checkpoint is conditioned on metric {self.monitor} which is not available. Available"
+ + f"metrics are: {','.join(list(logs.keys()))}"
+ )
+ return None
+ return monitor_value
diff --git a/src/training/callbacks/early_stopping.py b/src/training/callbacks/early_stopping.py
index 4da0e85..c9b7907 100644
--- a/src/training/callbacks/early_stopping.py
+++ b/src/training/callbacks/early_stopping.py
@@ -1 +1,107 @@
"""Implements Early stopping for PyTorch model."""
+from typing import Dict, Union
+
+from loguru import logger
+import numpy as np
+import torch
+from training.callbacks import Callback
+
+
+class EarlyStopping(Callback):
+ """Stops training when a monitored metric stops improving."""
+
+ mode_dict = {
+ "min": torch.lt,
+ "max": torch.gt,
+ }
+
+ def __init__(
+ self,
+ monitor: str = "val_loss",
+ min_delta: float = 0.0,
+ patience: int = 3,
+ mode: str = "auto",
+ ) -> None:
+ """Initializes the EarlyStopping callback.
+
+ Args:
+ monitor (str): Description of parameter `monitor`. Defaults to "val_loss".
+ min_delta (float): Description of parameter `min_delta`. Defaults to 0.0.
+ patience (int): Description of parameter `patience`. Defaults to 3.
+ mode (str): Description of parameter `mode`. Defaults to "auto".
+
+ """
+ super().__init__()
+ self.monitor = monitor
+ self.patience = patience
+ self.min_delta = torch.tensor(min_delta)
+ self.mode = mode
+ self.wait_count = 0
+ self.stopped_epoch = 0
+
+ if mode not in ["auto", "min", "max"]:
+ logger.warning(
+ f"EarlyStopping mode {mode} is unkown, fallback to auto mode."
+ )
+
+ self.mode = "auto"
+
+ if self.mode == "auto":
+ if "accuracy" in self.monitor:
+ self.mode = "max"
+ else:
+ self.mode = "min"
+ logger.debug(
+ f"EarlyStopping mode set to {self.mode} for monitoring {self.monitor}."
+ )
+
+ self.torch_inf = torch.tensor(np.inf)
+ self.min_delta *= 1 if self.monitor_op == torch.gt else -1
+ self.best_score = (
+ self.torch_inf if self.monitor_op == torch.lt else -self.torch_inf
+ )
+
+ @property
+ def monitor_op(self) -> float:
+ """Returns the comparison method."""
+ return self.mode_dict[self.mode]
+
+ def on_fit_begin(self) -> Union[torch.lt, torch.gt]:
+ """Reset the early stopping variables for reuse."""
+ self.wait_count = 0
+ self.stopped_epoch = 0
+ self.best_score = (
+ self.torch_inf if self.monitor_op == torch.lt else -self.torch_inf
+ )
+
+ def on_epoch_end(self, epoch: int, logs: Dict) -> None:
+ """Computes the early stop criterion."""
+ current = self.get_monitor_value(logs)
+ if current is None:
+ return
+ if self.monitor_op(current - self.min_delta, self.best_score):
+ self.best_score = current
+ self.wait_count = 0
+ else:
+ self.wait_count += 1
+ if self.wait_count >= self.patience:
+ self.stopped_epoch = epoch
+ self.model.stop_training = True
+
+ def on_fit_end(self) -> None:
+ """Logs if early stopping was used."""
+ if self.stopped_epoch > 0:
+ logger.info(
+ f"Stopped training at epoch {self.stopped_epoch + 1} with early stopping."
+ )
+
+ def get_monitor_value(self, logs: Dict) -> Union[torch.Tensor, None]:
+ """Extracts the monitor value."""
+ monitor_value = logs.get(self.monitor)
+ if monitor_value is None:
+ logger.warning(
+ f"Early stopping is conditioned on metric {self.monitor} which is not available. Available"
+ + f"metrics are: {','.join(list(logs.keys()))}"
+ )
+ return None
+ return torch.tensor(monitor_value)
diff --git a/src/training/callbacks/lr_schedulers.py b/src/training/callbacks/lr_schedulers.py
new file mode 100644
index 0000000..00c7e9b
--- /dev/null
+++ b/src/training/callbacks/lr_schedulers.py
@@ -0,0 +1,97 @@
+"""Callbacks for learning rate schedulers."""
+from typing import Callable, Dict, List, Optional, Type
+
+from training.callbacks import Callback
+
+from text_recognizer.models import Model
+
+
+class StepLR(Callback):
+ """Callback for StepLR."""
+
+ def __init__(self) -> None:
+ """Initializes the callback."""
+ super().__init__()
+ self.lr_scheduler = None
+
+ def set_model(self, model: Type[Model]) -> None:
+ """Sets the model and lr scheduler."""
+ self.model = model
+ self.lr_scheduler = self.model.lr_scheduler
+
+ def on_epoch_end(self, epoch: int, logs: Dict = {}) -> None:
+ """Takes a step at the end of every epoch."""
+ self.lr_scheduler.step()
+
+
+class MultiStepLR(Callback):
+ """Callback for MultiStepLR."""
+
+ def __init__(self) -> None:
+ """Initializes the callback."""
+ super().__init__()
+ self.lr_scheduler = None
+
+ def set_model(self, model: Type[Model]) -> None:
+ """Sets the model and lr scheduler."""
+ self.model = model
+ self.lr_scheduler = self.model.lr_scheduler
+
+ def on_epoch_end(self, epoch: int, logs: Dict = {}) -> None:
+ """Takes a step at the end of every epoch."""
+ self.lr_scheduler.step()
+
+
+class ReduceLROnPlateau(Callback):
+ """Callback for ReduceLROnPlateau."""
+
+ def __init__(self) -> None:
+ """Initializes the callback."""
+ super().__init__()
+ self.lr_scheduler = None
+
+ def set_model(self, model: Type[Model]) -> None:
+ """Sets the model and lr scheduler."""
+ self.model = model
+ self.lr_scheduler = self.model.lr_scheduler
+
+ def on_epoch_end(self, epoch: int, logs: Dict = {}) -> None:
+ """Takes a step at the end of every epoch."""
+ val_loss = logs["val_loss"]
+ self.lr_scheduler.step(val_loss)
+
+
+class CyclicLR(Callback):
+ """Callback for CyclicLR."""
+
+ def __init__(self) -> None:
+ """Initializes the callback."""
+ super().__init__()
+ self.lr_scheduler = None
+
+ def set_model(self, model: Type[Model]) -> None:
+ """Sets the model and lr scheduler."""
+ self.model = model
+ self.lr_scheduler = self.model.lr_scheduler
+
+ def on_train_batch_end(self, batch: int, logs: Dict = {}) -> None:
+ """Takes a step at the end of every training batch."""
+ self.lr_scheduler.step()
+
+
+class OneCycleLR(Callback):
+ """Callback for OneCycleLR."""
+
+ def __init__(self) -> None:
+ """Initializes the callback."""
+ super().__init__()
+ self.lr_scheduler = None
+
+ def set_model(self, model: Type[Model]) -> None:
+ """Sets the model and lr scheduler."""
+ self.model = model
+ self.lr_scheduler = self.model.lr_scheduler
+
+ def on_train_batch_end(self, batch: int, logs: Dict = {}) -> None:
+ """Takes a step at the end of every training batch."""
+ self.lr_scheduler.step()
diff --git a/src/training/callbacks/wandb_callbacks.py b/src/training/callbacks/wandb_callbacks.py
new file mode 100644
index 0000000..f64cbe1
--- /dev/null
+++ b/src/training/callbacks/wandb_callbacks.py
@@ -0,0 +1,93 @@
+"""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)
generated by cgit v1.2.3-70-g09d2 (git 2.46.0) at 2024-12-26 20:43:16 +0000