Enables timer callback and disables checkpoint saving in retinanet benchmark test.

PiperOrigin-RevId: 275080469

official/benchmark/retinanet_benchmark.py

@@ -95,10 +95,8 @@ class DetectionBenchmarkBase(tf.test.Benchmark):
     }]
     if self.timer_callback:
       metrics.append({
-          'name':
-              'exp_per_second',
-          'value':
-              self.timer_callback.get_examples_per_sec(FLAGS.train_batch_size)
+          'name': 'exp_per_second',
+          'value': self.timer_callback.get_examples_per_sec(train_batch_size)
       })
     else:
       metrics.append({
@@ -134,7 +132,7 @@ class RetinanetBenchmarkBase(DetectionBenchmarkBase):
 
   def _run_detection_main(self):
     """Starts detection job."""
-    return detection.main('unused_argv')
+    return detection.run(callbacks=[self.timer_callback])
 
 
 class RetinanetAccuracy(RetinanetBenchmarkBase):
@@ -166,7 +164,8 @@ class RetinanetAccuracy(RetinanetBenchmarkBase):
         stats=summary,
         wall_time_sec=wall_time_sec,
         min_ap=min_ap,
-        max_ap=max_ap)
+        max_ap=max_ap,
+        train_batch_size=self.params_override['train']['batch_size'])
 
   def _setup(self):
     super(RetinanetAccuracy, self)._setup()
@@ -228,6 +227,8 @@ class RetinanetBenchmarkReal(RetinanetAccuracy):
     params['eval']['eval_samples'] = 8
     FLAGS.params_override = json.dumps(params)
     FLAGS.model_dir = self._get_model_dir('real_benchmark_8_gpu_coco')
+    # Use negative value to avoid saving checkpoints.
+    FLAGS.save_checkpoint_freq = -1
     if self.timer_callback is None:
       logging.error('Cannot measure performance without timer callback')
     else:

official/modeling/training/distributed_executor.py

@@ -103,6 +103,8 @@ def initialize_common_flags():
   flags.DEFINE_integer(
       'task_index', 0,
       'If multi-worker training, the task_index of this worker.')
+  flags.DEFINE_integer('save_checkpoint_freq', None,
+                       'Number of steps to save checkpoint.')
 
 
 def strategy_flags_dict():
@@ -447,6 +449,12 @@ class DistributedExecutor(object):
     if save_config:
       self._save_config(model_dir)
 
+    if FLAGS.save_checkpoint_freq:
+      save_freq = FLAGS.save_checkpoint_freq
+    else:
+      save_freq = iterations_per_loop
+    last_save_checkpoint_step = 0
+
     params = self._params
     strategy = self._strategy
     # To reduce unnecessary send/receive input pipeline operation, we place
@@ -540,9 +548,11 @@ class DistributedExecutor(object):
       # iterations_per_loop steps.
       # To avoid repeated model saving, we do not save after the last
       # step of training.
-      if current_step < total_steps:
+      if save_freq > 0 and current_step < total_steps and (
+          current_step - last_save_checkpoint_step) >= save_freq:
         _save_checkpoint(checkpoint, model_dir,
                          checkpoint_name.format(step=current_step))
+        last_save_checkpoint_step = current_step
 
       if test_step:
         eval_iterator = self._get_input_iterator(eval_input_fn, strategy)

official/vision/detection/main.py

@@ -55,7 +55,10 @@ flags.DEFINE_string('eval_file_pattern', None, 'Location of ther eval data')
 FLAGS = flags.FLAGS
 
 
-def run_executor(params, train_input_fn=None, eval_input_fn=None):
+def run_executor(params,
+                 train_input_fn=None,
+                 eval_input_fn=None,
+                 callbacks=None):
   """Runs Retinanet model on distribution strategy defined by the user."""
 
   model_builder = model_factory.model_generator(params)
@@ -92,6 +95,7 @@ def run_executor(params, train_input_fn=None, eval_input_fn=None):
         iterations_per_loop=params.train.iterations_per_loop,
         total_steps=params.train.total_steps,
         init_checkpoint=model_builder.make_restore_checkpoint_fn(),
+        custom_callbacks=callbacks,
         save_config=True)
   elif FLAGS.mode == 'eval':
 
@@ -124,9 +128,7 @@ def run_executor(params, train_input_fn=None, eval_input_fn=None):
     raise ValueError('Mode not found: %s.' % FLAGS.mode)
 
 
-def main(argv):
-  del argv  # Unused.
-
+def run(callbacks=None):
   params = config_factory.config_generator(FLAGS.model)
 
   params = params_dict.override_params_dict(
@@ -171,7 +173,16 @@ def main(argv):
         batch_size=params.eval.batch_size,
         num_examples=params.eval.eval_samples)
   return run_executor(
-      params, train_input_fn=train_input_fn, eval_input_fn=eval_input_fn)
+      params,
+      train_input_fn=train_input_fn,
+      eval_input_fn=eval_input_fn,
+      callbacks=callbacks)
+
+
+def main(argv):
+  del argv  # Unused.
+
+  return run()
 
 
 if __name__ == '__main__':