Deprecate ModelCheckpoint.__init__'s load_weights_on_restart argument and provide a warning message if used.

PiperOrigin-RevId: 251711208

tensorflow/python/keras/callbacks.py

@@ -819,14 +819,6 @@ class ModelCheckpoint(Callback):
         monitored metric may potentially be less reliable (it could reflect as
         little as 1 batch, since the metrics get reset every epoch). Defaults to
         `'epoch'`
-      load_weights_on_restart: Whether the training should restore the model. If
-        True, the model will attempt to load the checkpoint file from `filepath`
-        at the start of `model.fit()`. This saves the need of manually calling
-        `model.load_weights()` before `model.fit(). In multi-worker distributed
-        training, this provides fault-tolerance and loads the model
-        automatically upon recovery of workers. The callback gives up loading if
-        the filepath does not exist, and raises ValueError if format does not
-        match. Defaults to False.
       **kwargs: Additional arguments for backwards compatibility. Possible key
         is `period`.
   """
@@ -839,7 +831,6 @@ class ModelCheckpoint(Callback):
                save_weights_only=False,
                mode='auto',
                save_freq='epoch',
-               load_weights_on_restart=False,
                **kwargs):
     super(ModelCheckpoint, self).__init__()
     self.monitor = monitor
@@ -848,10 +839,20 @@ class ModelCheckpoint(Callback):
     self.save_best_only = save_best_only
     self.save_weights_only = save_weights_only
     self.save_freq = save_freq
-    self.load_weights_on_restart = load_weights_on_restart
     self.epochs_since_last_save = 0
     self._samples_seen_since_last_saving = 0
 
+    # Deprecated field `load_weights_on_restart` is for loading the checkpoint
+    # file from `filepath` at the start of `model.fit()`
+    # TODO(rchao): Remove the arg during next breaking release.
+    if 'load_weights_on_restart' in kwargs:
+      self.load_weights_on_restart = kwargs['load_weights_on_restart']
+      logging.warning('`load_weights_on_restart` argument is deprecated. '
+                      'Please use `model.load_weights()` for loading weights '
+                      'before the start of `model.fit()`.')
+    else:
+      self.load_weights_on_restart = False
+
     # Deprecated field `period` is for the number of epochs between which
     # the model is saved.
     if 'period' in kwargs:
@@ -912,8 +913,6 @@ class ModelCheckpoint(Callback):
 
     # If this is not multi worker training, restoring is not needed, or
     # restoring failed, check if it should load weights on restart.
-    # TODO(rchao): Also restore the epoch in single-worker training when
-    # `self.load_weights_on_restart=True`.
     if self.load_weights_on_restart:
       # In multi worker training, it only should if `experimental_should_init`
       # is True.

tensorflow/tools/api/golden/v1/tensorflow.keras.callbacks.-model-checkpoint.pbtxt

@@ -5,7 +5,7 @@ tf_class {
   is_instance: "<type \'object\'>"
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'filepath\', \'monitor\', \'verbose\', \'save_best_only\', \'save_weights_only\', \'mode\', \'save_freq\', \'load_weights_on_restart\'], varargs=None, keywords=kwargs, defaults=[\'val_loss\', \'0\', \'False\', \'False\', \'auto\', \'epoch\', \'False\'], "
+    argspec: "args=[\'self\', \'filepath\', \'monitor\', \'verbose\', \'save_best_only\', \'save_weights_only\', \'mode\', \'save_freq\'], varargs=None, keywords=kwargs, defaults=[\'val_loss\', \'0\', \'False\', \'False\', \'auto\', \'epoch\'], "
   }
   member_method {
     name: "on_batch_begin"

tensorflow/tools/api/golden/v2/tensorflow.keras.callbacks.-model-checkpoint.pbtxt

@@ -5,7 +5,7 @@ tf_class {
   is_instance: "<type \'object\'>"
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'filepath\', \'monitor\', \'verbose\', \'save_best_only\', \'save_weights_only\', \'mode\', \'save_freq\', \'load_weights_on_restart\'], varargs=None, keywords=kwargs, defaults=[\'val_loss\', \'0\', \'False\', \'False\', \'auto\', \'epoch\', \'False\'], "
+    argspec: "args=[\'self\', \'filepath\', \'monitor\', \'verbose\', \'save_best_only\', \'save_weights_only\', \'mode\', \'save_freq\'], varargs=None, keywords=kwargs, defaults=[\'val_loss\', \'0\', \'False\', \'False\', \'auto\', \'epoch\'], "
   }
   member_method {
     name: "on_batch_begin"