Explicitly import estimator from tensorflow as a separate import instead of...

Explicitly import estimator from tensorflow as a separate import instead of accessing it via tf.estimator and depend on the tensorflow estimator target.

PiperOrigin-RevId: 437338390

research/object_detection/inputs.py

@@ -21,6 +21,7 @@ from __future__ import print_function
 import functools
 
 import tensorflow.compat.v1 as tf
+from tensorflow.compat.v1 import estimator as tf_estimator
 from object_detection.builders import dataset_builder
 from object_detection.builders import image_resizer_builder
 from object_detection.builders import model_builder
@@ -1114,7 +1115,7 @@ def create_predict_input_fn(model_config, predict_input_config):
     true_image_shape = tf.expand_dims(
         input_dict[fields.InputDataFields.true_image_shape], axis=0)
 
-    return tf.estimator.export.ServingInputReceiver(
+    return tf_estimator.export.ServingInputReceiver(
         features={
             fields.InputDataFields.image: images,
             fields.InputDataFields.true_image_shape: true_image_shape},

research/object_detection/model_lib.py

@@ -23,6 +23,7 @@ import functools
 import os
 
 import tensorflow.compat.v1 as tf
+from tensorflow.compat.v1 import estimator as tf_estimator
 import tensorflow.compat.v2 as tf2
 import tf_slim as slim
 
@@ -465,7 +466,7 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,
     """
     params = params or {}
     total_loss, train_op, detections, export_outputs = None, None, None, None
-    is_training = mode == tf.estimator.ModeKeys.TRAIN
+    is_training = mode == tf_estimator.ModeKeys.TRAIN
 
     # Make sure to set the Keras learning phase. True during training,
     # False for inference.
@@ -479,11 +480,11 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,
         is_training=is_training, add_summaries=(not use_tpu))
     scaffold_fn = None
 
-    if mode == tf.estimator.ModeKeys.TRAIN:
+    if mode == tf_estimator.ModeKeys.TRAIN:
       labels = unstack_batch(
           labels,
           unpad_groundtruth_tensors=train_config.unpad_groundtruth_tensors)
-    elif mode == tf.estimator.ModeKeys.EVAL:
+    elif mode == tf_estimator.ModeKeys.EVAL:
       # For evaling on train data, it is necessary to check whether groundtruth
       # must be unpadded.
       boxes_shape = (
@@ -493,7 +494,7 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,
       labels = unstack_batch(
           labels, unpad_groundtruth_tensors=unpad_groundtruth_tensors)
 
-    if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL):
+    if mode in (tf_estimator.ModeKeys.TRAIN, tf_estimator.ModeKeys.EVAL):
       provide_groundtruth(detection_model, labels)
 
     preprocessed_images = features[fields.InputDataFields.image]
@@ -514,7 +515,7 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,
     def postprocess_wrapper(args):
       return detection_model.postprocess(args[0], args[1])
 
-    if mode in (tf.estimator.ModeKeys.EVAL, tf.estimator.ModeKeys.PREDICT):
+    if mode in (tf_estimator.ModeKeys.EVAL, tf_estimator.ModeKeys.PREDICT):
       if use_tpu and postprocess_on_cpu:
         detections = tf.tpu.outside_compilation(
             postprocess_wrapper,
@@ -525,7 +526,7 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,
             prediction_dict,
             features[fields.InputDataFields.true_image_shape]))
 
-    if mode == tf.estimator.ModeKeys.TRAIN:
+    if mode == tf_estimator.ModeKeys.TRAIN:
       load_pretrained = hparams.load_pretrained if hparams else False
       if train_config.fine_tune_checkpoint and load_pretrained:
         if not train_config.fine_tune_checkpoint_type:
@@ -557,8 +558,8 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,
           tf.train.init_from_checkpoint(train_config.fine_tune_checkpoint,
                                         available_var_map)
 
-    if mode in (tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL):
-      if (mode == tf.estimator.ModeKeys.EVAL and
+    if mode in (tf_estimator.ModeKeys.TRAIN, tf_estimator.ModeKeys.EVAL):
+      if (mode == tf_estimator.ModeKeys.EVAL and
           eval_config.use_dummy_loss_in_eval):
         total_loss = tf.constant(1.0)
         losses_dict = {'Loss/total_loss': total_loss}
@@ -590,7 +591,7 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,
       training_optimizer, optimizer_summary_vars = optimizer_builder.build(
           train_config.optimizer)
 
-    if mode == tf.estimator.ModeKeys.TRAIN:
+    if mode == tf_estimator.ModeKeys.TRAIN:
       if use_tpu:
         training_optimizer = tf.tpu.CrossShardOptimizer(training_optimizer)
 
@@ -628,16 +629,16 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,
           summaries=summaries,
           name='')  # Preventing scope prefix on all variables.
 
-    if mode == tf.estimator.ModeKeys.PREDICT:
+    if mode == tf_estimator.ModeKeys.PREDICT:
       exported_output = exporter_lib.add_output_tensor_nodes(detections)
       export_outputs = {
           tf.saved_model.signature_constants.PREDICT_METHOD_NAME:
-              tf.estimator.export.PredictOutput(exported_output)
+              tf_estimator.export.PredictOutput(exported_output)
       }
 
     eval_metric_ops = None
     scaffold = None
-    if mode == tf.estimator.ModeKeys.EVAL:
+    if mode == tf_estimator.ModeKeys.EVAL:
       class_agnostic = (
           fields.DetectionResultFields.detection_classes not in detections)
       groundtruth = _prepare_groundtruth_for_eval(
@@ -711,8 +712,8 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,
         scaffold = tf.train.Scaffold(saver=saver)
 
     # EVAL executes on CPU, so use regular non-TPU EstimatorSpec.
-    if use_tpu and mode != tf.estimator.ModeKeys.EVAL:
-      return tf.estimator.tpu.TPUEstimatorSpec(
+    if use_tpu and mode != tf_estimator.ModeKeys.EVAL:
+      return tf_estimator.tpu.TPUEstimatorSpec(
           mode=mode,
           scaffold_fn=scaffold_fn,
           predictions=detections,
@@ -730,7 +731,7 @@ def create_model_fn(detection_model_fn, configs, hparams=None, use_tpu=False,
             save_relative_paths=True)
         tf.add_to_collection(tf.GraphKeys.SAVERS, saver)
         scaffold = tf.train.Scaffold(saver=saver)
-      return tf.estimator.EstimatorSpec(
+      return tf_estimator.EstimatorSpec(
           mode=mode,
           predictions=detections,
           loss=total_loss,
@@ -895,7 +896,7 @@ def create_estimator_and_inputs(run_config,
   model_fn = model_fn_creator(detection_model_fn, configs, hparams, use_tpu,
                               postprocess_on_cpu)
   if use_tpu_estimator:
-    estimator = tf.estimator.tpu.TPUEstimator(
+    estimator = tf_estimator.tpu.TPUEstimator(
         model_fn=model_fn,
         train_batch_size=train_config.batch_size,
         # For each core, only batch size 1 is supported for eval.
@@ -906,7 +907,7 @@ def create_estimator_and_inputs(run_config,
         eval_on_tpu=False,  # Eval runs on CPU, so disable eval on TPU
         params=params if params else {})
   else:
-    estimator = tf.estimator.Estimator(model_fn=model_fn, config=run_config)
+    estimator = tf_estimator.Estimator(model_fn=model_fn, config=run_config)
 
   # Write the as-run pipeline config to disk.
   if run_config.is_chief and save_final_config:
@@ -951,7 +952,7 @@ def create_train_and_eval_specs(train_input_fn,
     True, the last `EvalSpec` in the list will correspond to training data. The
     rest EvalSpecs in the list are evaluation datas.
   """
-  train_spec = tf.estimator.TrainSpec(
+  train_spec = tf_estimator.TrainSpec(
       input_fn=train_input_fn, max_steps=train_steps)
 
   if eval_spec_names is None:
@@ -966,10 +967,10 @@ def create_train_and_eval_specs(train_input_fn,
       exporter_name = final_exporter_name
     else:
       exporter_name = '{}_{}'.format(final_exporter_name, eval_spec_name)
-    exporter = tf.estimator.FinalExporter(
+    exporter = tf_estimator.FinalExporter(
         name=exporter_name, serving_input_receiver_fn=predict_input_fn)
     eval_specs.append(
-        tf.estimator.EvalSpec(
+        tf_estimator.EvalSpec(
             name=eval_spec_name,
             input_fn=eval_input_fn,
             steps=None,
@@ -977,7 +978,7 @@ def create_train_and_eval_specs(train_input_fn,
 
   if eval_on_train_data:
     eval_specs.append(
-        tf.estimator.EvalSpec(
+        tf_estimator.EvalSpec(
             name='eval_on_train', input_fn=eval_on_train_input_fn, steps=None))
 
   return train_spec, eval_specs

research/object_detection/model_lib_tf1_test.py

@@ -23,6 +23,7 @@ import os
 import unittest
 import numpy as np
 import tensorflow.compat.v1 as tf
+from tensorflow.compat.v1 import estimator as tf_estimator
 
 from object_detection import inputs
 from object_detection import model_hparams
@@ -137,21 +138,21 @@ class ModelLibTest(tf.test.TestCase):
             inputs.create_train_input_fn(configs['train_config'],
                                          configs['train_input_config'],
                                          configs['model'])()).get_next()
-        model_mode = tf.estimator.ModeKeys.TRAIN
+        model_mode = tf_estimator.ModeKeys.TRAIN
         batch_size = train_config.batch_size
       elif mode == 'eval':
         features, labels = _make_initializable_iterator(
             inputs.create_eval_input_fn(configs['eval_config'],
                                         configs['eval_input_config'],
                                         configs['model'])()).get_next()
-        model_mode = tf.estimator.ModeKeys.EVAL
+        model_mode = tf_estimator.ModeKeys.EVAL
         batch_size = 1
       elif mode == 'eval_on_train':
         features, labels = _make_initializable_iterator(
             inputs.create_eval_input_fn(configs['eval_config'],
                                         configs['train_input_config'],
                                         configs['model'])()).get_next()
-        model_mode = tf.estimator.ModeKeys.EVAL
+        model_mode = tf_estimator.ModeKeys.EVAL
         batch_size = 1
 
       detection_model_fn = functools.partial(
@@ -183,7 +184,7 @@ class ModelLibTest(tf.test.TestCase):
         if mode == 'eval':
           self.assertIn('Detections_Left_Groundtruth_Right/0',
                         estimator_spec.eval_metric_ops)
-      if model_mode == tf.estimator.ModeKeys.TRAIN:
+      if model_mode == tf_estimator.ModeKeys.TRAIN:
         self.assertIsNotNone(estimator_spec.train_op)
       return estimator_spec
 
@@ -202,7 +203,7 @@ class ModelLibTest(tf.test.TestCase):
           hparams_overrides='load_pretrained=false')
 
       model_fn = model_lib.create_model_fn(detection_model_fn, configs, hparams)
-      estimator_spec = model_fn(features, None, tf.estimator.ModeKeys.PREDICT)
+      estimator_spec = model_fn(features, None, tf_estimator.ModeKeys.PREDICT)
 
       self.assertIsNone(estimator_spec.loss)
       self.assertIsNone(estimator_spec.train_op)
@@ -279,7 +280,7 @@ class ModelLibTest(tf.test.TestCase):
 
   def test_create_estimator_and_inputs(self):
     """Tests that Estimator and input function are constructed correctly."""
-    run_config = tf.estimator.RunConfig()
+    run_config = tf_estimator.RunConfig()
     hparams = model_hparams.create_hparams(
         hparams_overrides='load_pretrained=false')
     pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
@@ -291,7 +292,7 @@ class ModelLibTest(tf.test.TestCase):
         train_steps=train_steps)
     estimator = train_and_eval_dict['estimator']
     train_steps = train_and_eval_dict['train_steps']
-    self.assertIsInstance(estimator, tf.estimator.Estimator)
+    self.assertIsInstance(estimator, tf_estimator.Estimator)
     self.assertEqual(20, train_steps)
     self.assertIn('train_input_fn', train_and_eval_dict)
     self.assertIn('eval_input_fns', train_and_eval_dict)
@@ -299,7 +300,7 @@ class ModelLibTest(tf.test.TestCase):
 
   def test_create_estimator_and_inputs_sequence_example(self):
     """Tests that Estimator and input function are constructed correctly."""
-    run_config = tf.estimator.RunConfig()
+    run_config = tf_estimator.RunConfig()
     hparams = model_hparams.create_hparams(
         hparams_overrides='load_pretrained=false')
     pipeline_config_path = get_pipeline_config_path(
@@ -312,7 +313,7 @@ class ModelLibTest(tf.test.TestCase):
         train_steps=train_steps)
     estimator = train_and_eval_dict['estimator']
     train_steps = train_and_eval_dict['train_steps']
-    self.assertIsInstance(estimator, tf.estimator.Estimator)
+    self.assertIsInstance(estimator, tf_estimator.Estimator)
     self.assertEqual(20, train_steps)
     self.assertIn('train_input_fn', train_and_eval_dict)
     self.assertIn('eval_input_fns', train_and_eval_dict)
@@ -320,7 +321,7 @@ class ModelLibTest(tf.test.TestCase):
 
   def test_create_estimator_with_default_train_eval_steps(self):
     """Tests that number of train/eval defaults to config values."""
-    run_config = tf.estimator.RunConfig()
+    run_config = tf_estimator.RunConfig()
     hparams = model_hparams.create_hparams(
         hparams_overrides='load_pretrained=false')
     pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
@@ -331,12 +332,12 @@ class ModelLibTest(tf.test.TestCase):
     estimator = train_and_eval_dict['estimator']
     train_steps = train_and_eval_dict['train_steps']
 
-    self.assertIsInstance(estimator, tf.estimator.Estimator)
+    self.assertIsInstance(estimator, tf_estimator.Estimator)
     self.assertEqual(config_train_steps, train_steps)
 
   def test_create_tpu_estimator_and_inputs(self):
     """Tests that number of train/eval defaults to config values."""
-    run_config = tf.estimator.tpu.RunConfig()
+    run_config = tf_estimator.tpu.RunConfig()
     hparams = model_hparams.create_hparams(
         hparams_overrides='load_pretrained=false')
     pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
@@ -350,12 +351,12 @@ class ModelLibTest(tf.test.TestCase):
     estimator = train_and_eval_dict['estimator']
     train_steps = train_and_eval_dict['train_steps']
 
-    self.assertIsInstance(estimator, tf.estimator.tpu.TPUEstimator)
+    self.assertIsInstance(estimator, tf_estimator.tpu.TPUEstimator)
     self.assertEqual(20, train_steps)
 
   def test_create_train_and_eval_specs(self):
     """Tests that `TrainSpec` and `EvalSpec` is created correctly."""
-    run_config = tf.estimator.RunConfig()
+    run_config = tf_estimator.RunConfig()
     hparams = model_hparams.create_hparams(
         hparams_overrides='load_pretrained=false')
     pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
@@ -390,7 +391,7 @@ class ModelLibTest(tf.test.TestCase):
 
   def test_experiment(self):
     """Tests that the `Experiment` object is constructed correctly."""
-    run_config = tf.estimator.RunConfig()
+    run_config = tf_estimator.RunConfig()
     hparams = model_hparams.create_hparams(
         hparams_overrides='load_pretrained=false')
     pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)

research/object_detection/model_main.py

@@ -21,6 +21,7 @@ from __future__ import print_function
 from absl import flags
 
 import tensorflow.compat.v1 as tf
+from tensorflow.compat.v1 import estimator as tf_estimator
 
 from object_detection import model_lib
 
@@ -59,7 +60,7 @@ FLAGS = flags.FLAGS
 def main(unused_argv):
   flags.mark_flag_as_required('model_dir')
   flags.mark_flag_as_required('pipeline_config_path')
-  config = tf.estimator.RunConfig(model_dir=FLAGS.model_dir)
+  config = tf_estimator.RunConfig(model_dir=FLAGS.model_dir)
 
   train_and_eval_dict = model_lib.create_estimator_and_inputs(
       run_config=config,
@@ -101,7 +102,7 @@ def main(unused_argv):
         eval_on_train_data=False)
 
     # Currently only a single Eval Spec is allowed.
-    tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
+    tf_estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
 
 
 if __name__ == '__main__':

research/object_detection/model_tpu_main.py

@@ -24,6 +24,7 @@ from __future__ import print_function
 
 from absl import flags
 import tensorflow.compat.v1 as tf
+from tensorflow.compat.v1 import estimator as tf_estimator
 
 
 from object_detection import model_lib
@@ -89,11 +90,11 @@ def main(unused_argv):
           tpu=[FLAGS.tpu_name], zone=FLAGS.tpu_zone, project=FLAGS.gcp_project))
   tpu_grpc_url = tpu_cluster_resolver.get_master()
 
-  config = tf.estimator.tpu.RunConfig(
+  config = tf_estimator.tpu.RunConfig(
       master=tpu_grpc_url,
       evaluation_master=tpu_grpc_url,
       model_dir=FLAGS.model_dir,
-      tpu_config=tf.estimator.tpu.TPUConfig(
+      tpu_config=tf_estimator.tpu.TPUConfig(
           iterations_per_loop=FLAGS.iterations_per_loop,
           num_shards=FLAGS.num_shards))