Refactor model_tpu_main.py files and move continuous eval loop into model_lib.py

PiperOrigin-RevId: 192512429

research/object_detection/model_lib.py

@@ -19,6 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import functools
+import os
 
 import tensorflow as tf
 
@@ -574,6 +575,48 @@ def create_train_and_eval_specs(train_input_fn,
   return train_spec, eval_specs
 
 
+def continuous_eval(estimator, model_dir, input_fn, eval_steps, train_steps,
+                    name):
+  """Perform continuous evaluation on checkpoints written to a model directory.
+
+  Args:
+    estimator: Estimator object to use for evaluation.
+    model_dir: Model directory to read checkpoints for continuous evaluation.
+    input_fn: Input function to use for evaluation.
+    eval_steps: Number of steps to run during each evaluation.
+    train_steps: Number of training steps. This is used to infer the last
+      checkpoint and stop evaluation loop.
+    name: Namescope for eval summary.
+  """
+  def terminate_eval():
+    tf.logging.info('Terminating eval after 180 seconds of no checkpoints')
+    return True
+
+  for ckpt in tf.contrib.training.checkpoints_iterator(
+      model_dir, min_interval_secs=180, timeout=None,
+      timeout_fn=terminate_eval):
+
+    tf.logging.info('Starting Evaluation.')
+    try:
+      eval_results = estimator.evaluate(
+          input_fn=input_fn,
+          steps=eval_steps,
+          checkpoint_path=ckpt,
+          name=name)
+      tf.logging.info('Eval results: %s' % eval_results)
+
+      # Terminate eval job when final checkpoint is reached
+      current_step = int(os.path.basename(ckpt).split('-')[1])
+      if current_step >= train_steps:
+        tf.logging.info(
+            'Evaluation finished after training step %d' % current_step)
+        break
+
+    except tf.errors.NotFoundError:
+      tf.logging.info(
+          'Checkpoint %s no longer exists, skipping checkpoint' % ckpt)
+
+
 def populate_experiment(run_config,
                         hparams,
                         pipeline_config_path,

research/object_detection/model_tpu_main.py

@@ -22,12 +22,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import os
 from absl import flags
 import tensorflow as tf
 
 from tensorflow.contrib.tpu.python.tpu import tpu_config
-from tensorflow.contrib.training.python.training import evaluation
 
 from object_detection import model_hparams
 from object_detection import model_lib
@@ -48,14 +46,7 @@ flags.DEFINE_string(
 flags.DEFINE_string(
     'tpu_name',
     default=None,
-    help='Name of the Cloud TPU for Cluster Resolvers. You must specify either '
-    'this flag or --master.')
-
-flags.DEFINE_string(
-    'master',
-    default=None,
-    help='GRPC URL of the master (e.g. grpc://ip.address.of.tpu:8470). You '
-    'must specify either this flag or --tpu_name.')
+    help='Name of the Cloud TPU for Cluster Resolvers.')
 
 flags.DEFINE_integer('num_shards', 8, 'Number of shards (TPU cores).')
 flags.DEFINE_integer('iterations_per_loop', 100,
@@ -63,16 +54,10 @@ flags.DEFINE_integer('iterations_per_loop', 100,
 # For mode=train_and_eval, evaluation occurs after training is finished.
 # Note: independently of steps_per_checkpoint, estimator will save the most
 # recent checkpoint every 10 minutes by default for train_and_eval
-flags.DEFINE_string('mode', 'train_and_eval',
-                    'Mode to run: train, eval, train_and_eval')
+flags.DEFINE_string('mode', 'train',
+                    'Mode to run: train, eval')
 flags.DEFINE_integer('train_batch_size', 32 * 8, 'Batch size for training.')
 
-# For EVAL.
-flags.DEFINE_integer('min_eval_interval_secs', 180,
-                     'Minimum seconds between evaluations.')
-flags.DEFINE_integer(
-    'eval_timeout_secs', None,
-    'Maximum seconds between checkpoints before evaluation terminates.')
 flags.DEFINE_string(
     'hparams_overrides', None, 'Comma-separated list of '
     'hyperparameters to override defaults.')
@@ -93,15 +78,6 @@ def main(unused_argv):
   flags.mark_flag_as_required('model_dir')
   flags.mark_flag_as_required('pipeline_config_path')
 
-  if FLAGS.master is None and FLAGS.tpu_name is None:
-    raise RuntimeError('You must specify either --master or --tpu_name.')
-
-  if FLAGS.master is not None:
-    if FLAGS.tpu_name is not None:
-      tf.logging.warn('Both --master and --tpu_name are set. Ignoring '
-                      '--tpu_name and using --master.')
-    tpu_grpc_url = FLAGS.master
-  else:
   tpu_cluster_resolver = (
       tf.contrib.cluster_resolver.python.training.TPUClusterResolver(
           tpu_names=[FLAGS.tpu_name],
@@ -134,53 +110,19 @@ def main(unused_argv):
   train_steps = train_and_eval_dict['train_steps']
   eval_steps = train_and_eval_dict['eval_steps']
 
-  if FLAGS.mode in ['train', 'train_and_eval']:
+  if FLAGS.mode == 'train':
     estimator.train(input_fn=train_input_fn, max_steps=train_steps)
 
-  if FLAGS.mode == 'train_and_eval':
-    # Eval one time.
-    eval_results = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
-    tf.logging.info('Eval results: %s' % eval_results)
-
   # Continuously evaluating.
   if FLAGS.mode == 'eval':
-    def terminate_eval():
-      tf.logging.info('Terminating eval after %d seconds of no checkpoints' %
-                      FLAGS.eval_timeout_secs)
-      return True
-
-    # Run evaluation when there's a new checkpoint.
-    for ckpt in evaluation.checkpoints_iterator(
-        FLAGS.model_dir,
-        min_interval_secs=FLAGS.min_eval_interval_secs,
-        timeout=FLAGS.eval_timeout_secs,
-        timeout_fn=terminate_eval):
-
-      tf.logging.info('Starting to evaluate.')
     if FLAGS.eval_training_data:
       name = 'training_data'
       input_fn = eval_on_train_input_fn
     else:
       name = 'validation_data'
       input_fn = eval_input_fn
-      try:
-        eval_results = estimator.evaluate(
-            input_fn=input_fn,
-            steps=eval_steps,
-            checkpoint_path=ckpt,
-            name=name)
-        tf.logging.info('Eval results: %s' % eval_results)
-
-        # Terminate eval job when final checkpoint is reached
-        current_step = int(os.path.basename(ckpt).split('-')[1])
-        if current_step >= train_steps:
-          tf.logging.info(
-              'Evaluation finished after training step %d' % current_step)
-          break
-
-      except tf.errors.NotFoundError:
-        tf.logging.info(
-            'Checkpoint %s no longer exists, skipping checkpoint' % ckpt)
+    model_lib.continuous_eval(estimator, FLAGS.model_dir, input_fn, eval_steps,
+                              train_steps, name)
 
 
 if __name__ == '__main__':