Fix lr in callback

PiperOrigin-RevId: 304250237

Author: saberkun

official/vision/image_classification/callbacks.py

@@ -42,19 +42,22 @@ def get_callbacks(model_checkpoint: bool = True,
   callbacks = []
   if model_checkpoint:
     ckpt_full_path = os.path.join(model_dir, 'model.ckpt-{epoch:04d}')
-    callbacks.append(tf.keras.callbacks.ModelCheckpoint(
-        ckpt_full_path, save_weights_only=True, verbose=1))
+    callbacks.append(
+        tf.keras.callbacks.ModelCheckpoint(
+            ckpt_full_path, save_weights_only=True, verbose=1))
   if include_tensorboard:
-    callbacks.append(CustomTensorBoard(
-        log_dir=model_dir,
-        track_lr=track_lr,
-        initial_step=initial_step,
-        write_images=write_model_weights))
+    callbacks.append(
+        CustomTensorBoard(
+            log_dir=model_dir,
+            track_lr=track_lr,
+            initial_step=initial_step,
+            write_images=write_model_weights))
   if time_history:
-    callbacks.append(keras_utils.TimeHistory(
-        batch_size,
-        log_steps,
-        logdir=model_dir if include_tensorboard else None))
+    callbacks.append(
+        keras_utils.TimeHistory(
+            batch_size,
+            log_steps,
+            logdir=model_dir if include_tensorboard else None))
   return callbacks
 
 
@@ -74,13 +77,14 @@ class CustomTensorBoard(tf.keras.callbacks.TensorBoard):
   - Global learning rate
 
   Attributes:
-    log_dir: the path of the directory where to save the log files to be
-      parsed by TensorBoard.
+    log_dir: the path of the directory where to save the log files to be parsed
+      by TensorBoard.
     track_lr: `bool`, whether or not to track the global learning rate.
     initial_step: the initial step, used for preemption recovery.
-    **kwargs: Additional arguments for backwards compatibility. Possible key
-      is `period`.
+    **kwargs: Additional arguments for backwards compatibility. Possible key is
+      `period`.
   """
+
   # TODO(b/146499062): track params, flops, log lr, l2 loss,
   # classification loss
 
@@ -130,10 +134,8 @@ def _calculate_metrics(self) -> MutableMapping[str, Any]:
 
   def _calculate_lr(self) -> int:
     """Calculates the learning rate given the current step."""
-    lr = self._get_base_optimizer().lr
-    if callable(lr):
-      lr = lr(self.step)
-    return get_scalar_from_tensor(lr)
+    return get_scalar_from_tensor(
+        self._get_base_optimizer()._decayed_lr(var_dtype=tf.float32))
 
   def _get_base_optimizer(self) -> tf.keras.optimizers.Optimizer:
     """Get the base optimizer used by the current model."""