498 Add logger_handler to LrScheduleHandler

monai/handlers/lr_schedule_handler.py

@@ -36,6 +36,7 @@ def __init__(
         name: Optional[str] = None,
         epoch_level: bool = True,
         step_transform: Callable[[Engine], Any] = lambda engine: (),
+        logger_handler: Optional[logging.Handler] = None,
     ) -> None:
         """
         Args:
@@ -47,6 +48,9 @@ def __init__(
                 `True` is epoch level, `False` is iteration level.
             step_transform: a callable that is used to transform the information from `engine`
                 to expected input data of lr_scheduler.step() function if necessary.
+            logger_handler: if `print_lr` is True, add additional handler to log the learning rate: save to file, etc.
+                all the existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html.
+                the handler should have a logging level of at least `INFO`.
 
         Raises:
             TypeError: When ``step_transform`` is not ``callable``.
@@ -59,6 +63,8 @@ def __init__(
         if not callable(step_transform):
             raise TypeError(f"step_transform must be callable but is {type(step_transform).__name__}.")
         self.step_transform = step_transform
+        if logger_handler is not None:
+            self.logger.addHandler(logger_handler)
 
         self._name = name
 

monai/handlers/stats_handler.py

@@ -82,7 +82,8 @@ def __init__(
                 tag_name: scalar_value to logger. Defaults to ``'Loss'``.
             key_var_format: a formatting string to control the output string format of key: value.
             logger_handler: add additional handler to handle the stats data: save to file, etc.
-                Add existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html
+                all the existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html.
+                the handler should have a logging level of at least `INFO`.
         """
 
         self.epoch_print_logger = epoch_print_logger

monai/transforms/utility/array.py

@@ -570,7 +570,7 @@ def __init__(
                 a typical example is to print some properties of Nifti image: affine, pixdim, etc.
             additional_info: user can define callable function to extract additional info from input data.
             logger_handler: add additional handler to output data: save to file, etc.
-                add existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html
+                all the existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html.
                 the handler should have a logging level of at least `INFO`.
 
         Raises:

monai/transforms/utility/dictionary.py

@@ -795,7 +795,7 @@ def __init__(
                 additional info from input data. it also can be a sequence of string, each element
                 corresponds to a key in ``keys``.
             logger_handler: add additional handler to output data: save to file, etc.
-                add existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html
+                all the existing python logging handlers: https://docs.python.org/3/library/logging.handlers.html.
                 the handler should have a logging level of at least `INFO`.
             allow_missing_keys: don't raise exception if key is missing.
 

tests/test_handler_lr_scheduler.py

@@ -10,7 +10,10 @@
 # limitations under the License.
 
 import logging
+import os
+import re
 import sys
+import tempfile
 import unittest
 
 import numpy as np
@@ -24,6 +27,8 @@ class TestHandlerLrSchedule(unittest.TestCase):
     def test_content(self):
         logging.basicConfig(stream=sys.stdout, level=logging.INFO)
         data = [0] * 8
+        test_lr = 0.1
+        gamma = 0.1
 
         # set up engine
         def _train_func(engine, batch):
@@ -41,24 +46,45 @@ def run_validation(engine):
         net = torch.nn.PReLU()
 
         def _reduce_lr_on_plateau():
-            optimizer = torch.optim.SGD(net.parameters(), 0.1)
+            optimizer = torch.optim.SGD(net.parameters(), test_lr)
             lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=1)
             handler = LrScheduleHandler(lr_scheduler, step_transform=lambda x: val_engine.state.metrics["val_loss"])
             handler.attach(train_engine)
-            return lr_scheduler
+            return handler
 
-        def _reduce_on_step():
-            optimizer = torch.optim.SGD(net.parameters(), 0.1)
-            lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=0.1)
-            handler = LrScheduleHandler(lr_scheduler)
-            handler.attach(train_engine)
-            return lr_scheduler
+        with tempfile.TemporaryDirectory() as tempdir:
+            key_to_handler = "test_log_lr"
+            key_to_print = "Current learning rate"
+            filename = os.path.join(tempdir, "test_lr.log")
+            # test with additional logging handler
+            file_saver = logging.FileHandler(filename, mode="w")
+            file_saver.setLevel(logging.INFO)
+
+            def _reduce_on_step():
+                optimizer = torch.optim.SGD(net.parameters(), test_lr)
+                lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=2, gamma=gamma)
+                handler = LrScheduleHandler(lr_scheduler, name=key_to_handler, logger_handler=file_saver)
+                handler.attach(train_engine)
+                handler.logger.setLevel(logging.INFO)
+                return handler
+
+            schedulers = _reduce_lr_on_plateau(), _reduce_on_step()
+
+            train_engine.run(data, max_epochs=5)
+            file_saver.close()
+            schedulers[1].logger.removeHandler(file_saver)
 
-        schedulers = _reduce_lr_on_plateau(), _reduce_on_step()
+            with open(filename) as f:
+                output_str = f.read()
+                has_key_word = re.compile(f".*{key_to_print}.*")
+                content_count = 0
+                for line in output_str.split("\n"):
+                    if has_key_word.match(line):
+                        content_count += 1
+                self.assertTrue(content_count > 0)
 
-        train_engine.run(data, max_epochs=5)
         for scheduler in schedulers:
-            np.testing.assert_allclose(scheduler._last_lr[0], 0.001)
+            np.testing.assert_allclose(scheduler.lr_scheduler._last_lr[0], 0.001)
 
 
 if __name__ == "__main__":

tests/test_handler_stats.py

@@ -136,18 +136,19 @@ def _train_func(engine, batch):
             # set up testing handler
             stats_handler = StatsHandler(name=key_to_handler, tag_name=key_to_print, logger_handler=handler)
             stats_handler.attach(engine)
+            stats_handler.logger.setLevel(logging.INFO)
 
             engine.run(range(3), max_epochs=2)
             handler.close()
             stats_handler.logger.removeHandler(handler)
             with open(filename) as f:
                 output_str = f.read()
-                grep = re.compile(f".*{key_to_handler}.*")
                 has_key_word = re.compile(f".*{key_to_print}.*")
-                for idx, line in enumerate(output_str.split("\n")):
-                    if grep.match(line):
-                        if idx in [1, 2, 3, 6, 7, 8]:
-                            self.assertTrue(has_key_word.match(line))
+                content_count = 0
+                for line in output_str.split("\n"):
+                    if has_key_word.match(line):
+                        content_count += 1
+                self.assertTrue(content_count > 0)
 
     def test_exception(self):
         # set up engine