Working Emnist lines dataset.

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)