diff options
Diffstat (limited to 'src/training/trainer/callbacks')
| -rw-r--r-- | src/training/trainer/callbacks/base.py | 20 | ||||
| -rw-r--r-- | src/training/trainer/callbacks/checkpoint.py | 6 | ||||
| -rw-r--r-- | src/training/trainer/callbacks/lr_schedulers.py | 5 | ||||
| -rw-r--r-- | src/training/trainer/callbacks/wandb_callbacks.py | 34 |
4 files changed, 52 insertions, 13 deletions
diff --git a/src/training/trainer/callbacks/base.py b/src/training/trainer/callbacks/base.py index 8c7b085..500b642 100644 --- a/src/training/trainer/callbacks/base.py +++ b/src/training/trainer/callbacks/base.py @@ -62,6 +62,14 @@ class Callback: """Called at the end of an epoch.""" pass + def on_test_begin(self) -> None: + """Called at the beginning of test.""" + pass + + def on_test_end(self) -> None: + """Called at the end of test.""" + pass + class CallbackList: """Container for abstracting away callback calls.""" @@ -92,7 +100,7 @@ class CallbackList: def append(self, callback: Type[Callback]) -> None: """Append new callback to callback list.""" - self.callbacks.append(callback) + self._callbacks.append(callback) def on_fit_begin(self) -> None: """Called when fit begins.""" @@ -104,6 +112,16 @@ class CallbackList: for callback in self._callbacks: callback.on_fit_end() + def on_test_begin(self) -> None: + """Called when test begins.""" + for callback in self._callbacks: + callback.on_test_begin() + + def on_test_end(self) -> None: + """Called when test ends.""" + for callback in self._callbacks: + callback.on_test_end() + def on_epoch_begin(self, epoch: int, logs: Optional[Dict] = None) -> None: """Called at the beginning of an epoch.""" for callback in self._callbacks: diff --git a/src/training/trainer/callbacks/checkpoint.py b/src/training/trainer/callbacks/checkpoint.py index 6fe06d3..a54e0a9 100644 --- a/src/training/trainer/callbacks/checkpoint.py +++ b/src/training/trainer/callbacks/checkpoint.py @@ -21,7 +21,7 @@ class Checkpoint(Callback): def __init__( self, - checkpoint_path: Path, + checkpoint_path: Union[str, Path], monitor: str = "accuracy", mode: str = "auto", min_delta: float = 0.0, @@ -29,14 +29,14 @@ class Checkpoint(Callback): """Monitors a quantity that will allow us to determine the best model weights. Args: - checkpoint_path (Path): Path to the experiment with the checkpoint. + checkpoint_path (Union[str, Path]): Path to the experiment with the checkpoint. 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.checkpoint_path = checkpoint_path + self.checkpoint_path = Path(checkpoint_path) self.monitor = monitor self.mode = mode self.min_delta = torch.tensor(min_delta) diff --git a/src/training/trainer/callbacks/lr_schedulers.py b/src/training/trainer/callbacks/lr_schedulers.py index 907e292..630c434 100644 --- a/src/training/trainer/callbacks/lr_schedulers.py +++ b/src/training/trainer/callbacks/lr_schedulers.py @@ -22,7 +22,10 @@ class LRScheduler(Callback): def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None: """Takes a step at the end of every epoch.""" if self.interval == "epoch": - self.lr_scheduler.step() + if "ReduceLROnPlateau" in self.lr_scheduler.__class__.__name__: + self.lr_scheduler.step(logs["val_loss"]) + else: + self.lr_scheduler.step() def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None: """Takes a step at the end of every training batch.""" diff --git a/src/training/trainer/callbacks/wandb_callbacks.py b/src/training/trainer/callbacks/wandb_callbacks.py index d2df4d7..1627f17 100644 --- a/src/training/trainer/callbacks/wandb_callbacks.py +++ b/src/training/trainer/callbacks/wandb_callbacks.py @@ -64,37 +64,55 @@ class WandbImageLogger(Callback): """ super().__init__() + self.caption = None self.example_indices = example_indices + self.test_sample_indices = None self.num_examples = num_examples self.transpose = Transpose() if use_transpose else None 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.val_images = self.model.val_dataset.dataset.data[self.example_indices] - self.val_targets = self.model.val_dataset.dataset.targets[self.example_indices] - self.val_targets = self.val_targets.tolist() + 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.val_images): + for i, image in enumerate(self.images): image = self.transpose(image) if self.transpose is not None else image pred, conf = self.model.predict_on_image(image) - if isinstance(self.val_targets[i], list): + if isinstance(self.targets[i], list): ground_truth = "".join( [ self.model.mapper(int(target_index)) - for target_index in self.val_targets[i] + for target_index in self.targets[i] ] ).rstrip("_") else: - ground_truth = self.val_targets[i] + 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({"examples": images}, commit=False) + wandb.log({f"{self.caption}": images}, commit=False) |