Add IterationTimer callback

src/otx/algo/callbacks/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+"""Module for OTX custom callbacks."""

src/otx/algo/callbacks/iteration_timer.py

@@ -0,0 +1,137 @@
+# Copyright (C) 2023 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+"""Timer for logging iteration time for train, val, and test phases."""
+
+from __future__ import annotations
+
+from collections import defaultdict
+from time import time
+from typing import TYPE_CHECKING, Any
+
+from lightning import Callback
+
+if TYPE_CHECKING:
+    from lightning import LightningModule, Trainer
+    from lightning.pytorch.utilities.types import STEP_OUTPUT
+
+
+class IterationTimer(Callback):
+    """Timer for logging iteration time for train, val, and test phases."""
+
+    def __init__(
+        self,
+        prog_bar: bool = True,
+        on_step: bool = True,
+        on_epoch: bool = True,
+    ) -> None:
+        super().__init__()
+        self.prog_bar = prog_bar
+        self.on_step = on_step
+        self.on_epoch = on_epoch
+
+        self.start_time: dict[str, float] = defaultdict(float)
+        self.end_time: dict[str, float] = defaultdict(float)
+
+    def _on_batch_start(self, pl_module: LightningModule, phase: str) -> None:
+        self.start_time[phase] = time()
+
+        if not self.end_time[phase]:
+            return
+
+        name = f"{phase}/data_time"
+
+        data_time = self.start_time[phase] - self.end_time[phase]
+
+        pl_module.log(
+            name=name,
+            value=data_time,
+            prog_bar=self.prog_bar,
+            on_step=self.on_step,
+            on_epoch=self.on_epoch,
+        )
+
+    def _on_batch_end(self, pl_module: LightningModule, phase: str) -> None:
+        if not self.end_time[phase]:
+            self.end_time[phase] = time()
+            return
+
+        name = f"{phase}/iter_time"
+        curr_end_time = time()
+        iter_time = curr_end_time - self.end_time[phase]
+        self.end_time[phase] = curr_end_time
+
+        pl_module.log(
+            name=name,
+            value=iter_time,
+            prog_bar=self.prog_bar,
+            on_step=self.on_step,
+            on_epoch=self.on_epoch,
+        )
+
+    def on_train_batch_start(
+        self,
+        trainer: Trainer,
+        pl_module: LightningModule,
+        batch: Any,  # noqa: ANN401
+        batch_idx: int,
+    ) -> None:
+        """Log iteration data time on the training batch start."""
+        self._on_batch_start(pl_module=pl_module, phase="train")
+
+    def on_train_batch_end(
+        self,
+        trainer: Trainer,
+        pl_module: LightningModule,
+        outputs: STEP_OUTPUT,
+        batch: Any,  # noqa: ANN401
+        batch_idx: int,
+    ) -> None:
+        """Log iteration time on the training batch end."""
+        self._on_batch_end(pl_module=pl_module, phase="train")
+
+    def on_validation_batch_start(
+        self,
+        trainer: Trainer,
+        pl_module: LightningModule,
+        batch: Any,  # noqa: ANN401
+        batch_idx: int,
+        dataloader_idx: int = 0,
+    ) -> None:
+        """Log iteration data time on the validation batch start."""
+        self._on_batch_start(pl_module=pl_module, phase="validation")
+
+    def on_validation_batch_end(
+        self,
+        trainer: Trainer,
+        pl_module: LightningModule,
+        outputs: STEP_OUTPUT,
+        batch: Any,  # noqa: ANN401
+        batch_idx: int,
+        dataloader_idx: int = 0,
+    ) -> None:
+        """Log iteration time on the validation batch end."""
+        self._on_batch_end(pl_module=pl_module, phase="validation")
+
+    def on_test_batch_start(
+        self,
+        trainer: Trainer,
+        pl_module: LightningModule,
+        batch: Any,  # noqa: ANN401
+        batch_idx: int,
+        dataloader_idx: int = 0,
+    ) -> None:
+        """Log iteration data time on the test batch start."""
+        self._on_batch_start(pl_module=pl_module, phase="test")
+
+    def on_test_batch_end(
+        self,
+        trainer: Trainer,
+        pl_module: LightningModule,
+        outputs: STEP_OUTPUT,
+        batch: Any,  # noqa: ANN401
+        batch_idx: int,
+        dataloader_idx: int = 0,
+    ) -> None:
+        """Log iteration time on the test batch end."""
+        self._on_batch_end(pl_module=pl_module, phase="test")

src/otx/config/callbacks/default.yaml

@@ -6,7 +6,7 @@ defaults:
   - _self_
 
 model_checkpoint:
-  dirpath: ${paths.output_dir}/checkpoints
+  dirpath: ${base.output_dir}/checkpoints
   filename: "epoch_{epoch:03d}"
   monitor: "val/acc"
   mode: "max"

src/otx/config/callbacks/iteration_timer.yaml

@@ -0,0 +1,7 @@
+# https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.EarlyStopping.html
+
+iteration_timer:
+  _target_: otx.algo.callbacks.iteration_timer.IterationTimer
+  prog_bar: True
+  on_step: True
+  on_epoch: True

src/otx/config/train.yaml

@@ -1,6 +1,7 @@
 defaults:
   - base_config
   - base: default
+  - callbacks: iteration_timer
   - data: default
   - trainer: default
   - model: mmdet

src/otx/core/config/__init__.py

@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 """Config data type objects."""
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import Optional
 
 from .base import BaseConfig
@@ -16,14 +16,14 @@ class TrainConfig:
     """DTO for training."""
 
     base: BaseConfig
+    callbacks: dict
     data: DataModuleConfig
     trainer: TrainerConfig
     model: ModelConfig
     logger: dict
     recipe: Optional[str]  # noqa: FA100
     train: bool
     test: bool
-    callbacks: list = field(default_factory=list)
 
 
 def register_configs() -> None: