def before_run(self, run_context):
""" Dumps graphs and loads checkpoint if there exits.
Called before each call to run().
Args:
run_context: A `SessionRunContext` object.
Returns: A `SessionRunArgs` object containing global_step.
"""
# We do write graph and saver_def at the first call of before_run.
# We cannot do this in begin, since we let other hooks to change graph and
# add variables in begin. Graph is finalized after all begin calls.
if self._is_chief and self._first_call:
training_util.write_graph(
ops.get_default_graph().as_graph_def(add_shapes=True),
self._checkpoint_dir,
"graph.pbtxt")
# dump model details "model_analysis.txt"
dump_model_analysis(self._checkpoint_dir) # dump model configs
graph = ops.get_default_graph()
meta_graph_def = meta_graph.create_meta_graph_def(
graph_def=graph.as_graph_def(add_shapes=True),
saver_def=self._saver.saver_def)
if self._summary_writer is not None:
self._summary_writer.add_graph(graph)
self._summary_writer.add_meta_graph(meta_graph_def)
tf.logging.info("CheckpointSaverHook (before_run): dump graph...")
self._first_call = False
return tf.train.SessionRunArgs(self._global_step)
def before_run(self, run_context):
""" Dumps graphs and loads checkpoint if there exits.
Called before each call to run().
Args:
run_context: A `SessionRunContext` object.
Returns: A `SessionRunArgs` object containing global_step.
"""
# We do write graph and saver_def at the first call of before_run.
# We cannot do this in begin, since we let other hooks to change graph and
# add variables in begin. Graph is finalized after all begin calls.
if self._is_chief and self._first_call:
training_util.write_graph(
ops.get_default_graph().as_graph_def(add_shapes=True),
self._checkpoint_dir, "graph.pbtxt")
# dump model details "model_analysis.txt"
dump_model_analysis(self._checkpoint_dir) # dump model configs
graph = ops.get_default_graph()
meta_graph_def = meta_graph.create_meta_graph_def(
graph_def=graph.as_graph_def(add_shapes=True),
saver_def=self._saver.saver_def)
if self._summary_writer is not None:
self._summary_writer.add_graph(graph)
self._summary_writer.add_meta_graph(meta_graph_def)
tf.logging.info("CheckpointSaverHook (before_run): dump graph...")
self._first_call = False
return tf.train.SessionRunArgs(self._global_step)
def before_run(self, run_context):
""" Dumps graphs and loads checkpoint if there exits.
Called before each call to run().
Args:
run_context: A `SessionRunContext` object.
Returns: A `SessionRunArgs` object containing global_step.
"""
# We do write graph and saver_def at the first call of before_run.
# We cannot do this in begin, since we let other hooks to change graph and
# add variables in begin. Graph is finalized after all begin calls.
if self._is_chief and self._first_call:
training_util.write_graph(
ops.get_default_graph().as_graph_def(add_shapes=True),
self._checkpoint_dir, "graph.pbtxt")
# dump model details "model_analysis.txt"
dump_model_analysis(self._checkpoint_dir) # dump model configs
graph = ops.get_default_graph()
meta_graph_def = meta_graph.create_meta_graph_def(
graph_def=graph.as_graph_def(add_shapes=True),
saver_def=self._saver.saver_def)
if self._summary_writer is not None:
self._summary_writer.add_graph(graph)
self._summary_writer.add_meta_graph(meta_graph_def)
tf.logging.info("CheckpointSaverHook (before_run): dump graph...")
checkpoint_path = saver_lib.latest_checkpoint(self._checkpoint_dir)
if self._first_call:
if checkpoint_path:
# reloading model
self._saver.restore(run_context.session, checkpoint_path)
gs = run_context.session.run(self._global_step)
tf.logging.info(
"CheckpointSaverHook (before_run): reloading models and reset global_step={}"
.format(gs))
StepTimer.reset_init_triggered_step(gs)
elif self._reload_var_ops:
tf.logging.info(
"Assign all variables with pretrained variables.")
run_context.session.run(self._reload_var_ops)
self._first_call = False
self._timer.register_before_run()
return tf.train.SessionRunArgs(self._global_step)