Add base class for TensorBoard handlers

docs/source/handlers.rst

@@ -85,6 +85,9 @@ Training stats handler
 
 Tensorboard handlers
 --------------------
+.. autoclass:: TensorBoardHandler
+    :members:
+
 .. autoclass:: TensorBoardStatsHandler
     :members:
 

monai/handlers/__init__.py

@@ -24,7 +24,7 @@
 from .smartcache_handler import SmartCacheHandler
 from .stats_handler import StatsHandler
 from .surface_distance import SurfaceDistance
-from .tensorboard_handlers import TensorBoardImageHandler, TensorBoardStatsHandler
+from .tensorboard_handlers import TensorBoardHandler, TensorBoardImageHandler, TensorBoardStatsHandler
 from .utils import (
     evenly_divisible_all_gather,
     stopping_fn_from_loss,

monai/handlers/metrics_saver.py

@@ -34,20 +34,22 @@ class MetricsSaver:
             "*" - save all the existing metrics in `engine.state.metrics` dict into separate files.
             list of strings - specify the expected metrics to save.
             default to "*" to save all the metrics into `metrics.csv`.
-        metric_details: expected metric details to save into files, for example: mean dice
-            of every channel of every image in the validation dataset.
-            the data in `engine.state.metric_details` must contain at least 2 dims: (batch, classes, ...),
+        metric_details: expected metric details to save into files, the data comes from
+            `engine.state.metric_details`, which should be provided by different `Metrics`,
+            typically, it's some intermediate values in metric computation.
+            for example: mean dice of every channel of every image in the validation dataset.
+            it must contain at least 2 dims: (batch, classes, ...),
             if not, will unsequeeze to 2 dims.
             this arg can be: None, "*" or list of strings.
-            None - don't save any metrics into files.
-            "*" - save all the existing metrics in `engine.state.metric_details` dict into separate files.
-            list of strings - specify the expected metrics to save.
-            if not None, every metric will save a separate `{metric name}_raw.csv` file.
+            None - don't save any metric_details into files.
+            "*" - save all the existing metric_details in `engine.state.metric_details` dict into separate files.
+            list of strings - specify the metric_details of expected metrics to save.
+            if not None, every metric_details array will save a separate `{metric name}_raw.csv` file.
         batch_transform: callable function to extract the meta_dict from input batch data if saving metric details.
             used to extract filenames from input dict data.
-        summary_ops: expected computation operations to generate the summary report.
+        summary_ops: expected computation operations to generate the summary report based on specified metric_details.
             it can be: None, "*" or list of strings.
-            None - don't generate summary report for every expected metric_details
+            None - don't generate summary report for every specified metric_details
             "*" - generate summary report for every metric_details with all the supported operations.
             list of strings - generate summary report for every metric_details with specified operations, they
             should be within this list: [`mean`, `median`, `max`, `min`, `90percent`, `std`].

monai/handlers/tensorboard_handlers.py

@@ -29,7 +29,38 @@
 DEFAULT_TAG = "Loss"
 
 
-class TensorBoardStatsHandler:
+class TensorBoardHandler:
+    """
+    Base class for the handlers to write data into TensorBoard.
+
+    Args:
+        summary_writer: user can specify TensorBoard SummaryWriter,
+            default to create a new writer.
+        log_dir: if using default SummaryWriter, write logs to this directory, default is `./runs`.
+
+    """
+
+    def __init__(self, summary_writer: Optional[SummaryWriter] = None, log_dir: str = "./runs"):
+        if summary_writer is None:
+            self._writer = SummaryWriter(log_dir=log_dir)
+            self.internal_writer = True
+        else:
+            self._writer = summary_writer
+            self.internal_writer = False
+
+    def attach(self, engine: Engine) -> None:
+        raise NotImplementedError(f"Subclass {self.__class__.__name__} must implement this method.")
+
+    def close(self):
+        """
+        Close the summary writer if created in this TensorBoard handler.
+
+        """
+        if self.internal_writer:
+            self._writer.close()
+
+
+class TensorBoardStatsHandler(TensorBoardHandler):
     """
     TensorBoardStatsHandler defines a set of Ignite Event-handlers for all the TensorBoard logics.
     It's can be used for any Ignite Engine(trainer, validator and evaluator).
@@ -71,7 +102,7 @@ def __init__(
                 when plotting epoch vs metric curves.
             tag_name: when iteration output is a scalar, tag_name is used to plot, defaults to ``'Loss'``.
         """
-        self._writer = SummaryWriter(log_dir=log_dir) if summary_writer is None else summary_writer
+        super().__init__(summary_writer=summary_writer, log_dir=log_dir)
         self.epoch_event_writer = epoch_event_writer
         self.iteration_event_writer = iteration_event_writer
         self.output_transform = output_transform
@@ -176,7 +207,7 @@ def _default_iteration_writer(self, engine: Engine, writer: SummaryWriter) -> No
         writer.flush()
 
 
-class TensorBoardImageHandler:
+class TensorBoardImageHandler(TensorBoardHandler):
     """
     TensorBoardImageHandler is an Ignite Event handler that can visualize images, labels and outputs as 2D/3D images.
     2D output (shape in Batch, channel, H, W) will be shown as simple image using the first element in the batch,
@@ -229,7 +260,7 @@ def __init__(
             max_channels: number of channels to plot.
             max_frames: number of frames for 2D-t plot.
         """
-        self._writer = SummaryWriter(log_dir=log_dir) if summary_writer is None else summary_writer
+        super().__init__(summary_writer=summary_writer, log_dir=log_dir)
         self.interval = interval
         self.epoch_level = epoch_level
         self.batch_transform = batch_transform

monai/handlers/utils.py

@@ -128,7 +128,7 @@ def write_metrics_reports(
         images: name or path of every input image corresponding to the metric_details data.
             if None, will use index number as the filename of every input image.
         metrics: a dictionary of (metric name, metric value) pairs.
-        metric_details: a dictionary of (metric name, metric raw values) pairs,
+        metric_details: a dictionary of (metric name, metric raw values) pairs, usually, it comes from metrics computation,
             for example, the raw value can be the mean_dice of every channel of every input image.
         summary_ops: expected computation operations to generate the summary report.
             it can be: None, "*" or list of strings.

tests/test_handler_tb_image.py

@@ -40,6 +40,7 @@ def _train_func(engine, batch):
 
             data = zip(np.random.normal(size=(10, 4, *shape)), np.random.normal(size=(10, 4, *shape)))
             engine.run(data, epoch_length=10, max_epochs=1)
+            stats_handler.close()
 
             self.assertTrue(len(glob.glob(tempdir)) > 0)
 

tests/test_handler_tb_stats.py

@@ -39,6 +39,7 @@ def _update_metric(engine):
             stats_handler = TensorBoardStatsHandler(log_dir=tempdir)
             stats_handler.attach(engine)
             engine.run(range(3), max_epochs=2)
+            stats_handler.close()
             # check logging output
             self.assertTrue(len(glob.glob(tempdir)) > 0)
 
@@ -64,6 +65,7 @@ def _update_metric(engine):
             )
             stats_handler.attach(engine)
             engine.run(range(3), max_epochs=2)
+            writer.close()
             # check logging output
             self.assertTrue(len(glob.glob(tempdir)) > 0)
 

tests/test_integration_workflows.py

@@ -22,6 +22,7 @@
 import numpy as np
 import torch
 from ignite.metrics import Accuracy
+from torch.utils.tensorboard import SummaryWriter
 
 import monai
 from monai.data import create_test_image_3d
@@ -105,6 +106,7 @@ def run_training_test(root_dir, device="cuda:0", amp=False, num_workers=4):
     loss = monai.losses.DiceLoss(sigmoid=True)
     opt = torch.optim.Adam(net.parameters(), 1e-3)
     lr_scheduler = torch.optim.lr_scheduler.StepLR(opt, step_size=2, gamma=0.1)
+    summary_writer = SummaryWriter(log_dir=root_dir)
 
     val_post_transforms = Compose(
         [
@@ -123,7 +125,7 @@ def _forward_completed(self, engine):
 
     val_handlers = [
         StatsHandler(output_transform=lambda x: None),
-        TensorBoardStatsHandler(log_dir=root_dir, output_transform=lambda x: None),
+        TensorBoardStatsHandler(summary_writer=summary_writer, output_transform=lambda x: None),
         TensorBoardImageHandler(
             log_dir=root_dir, batch_transform=lambda x: (x["image"], x["label"]), output_transform=lambda x: x["pred"]
         ),
@@ -176,7 +178,9 @@ def _optimizer_completed(self, engine):
         LrScheduleHandler(lr_scheduler=lr_scheduler, print_lr=True),
         ValidationHandler(validator=evaluator, interval=2, epoch_level=True),
         StatsHandler(tag_name="train_loss", output_transform=lambda x: x["loss"]),
-        TensorBoardStatsHandler(log_dir=root_dir, tag_name="train_loss", output_transform=lambda x: x["loss"]),
+        TensorBoardStatsHandler(
+            summary_writer=summary_writer, tag_name="train_loss", output_transform=lambda x: x["loss"]
+        ),
         CheckpointSaver(save_dir=root_dir, save_dict={"net": net, "opt": opt}, save_interval=2, epoch_level=True),
         _TestTrainIterEvents(),
     ]