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Commit b33d067

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hertschuhalbert-copybara
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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: 441225284
1 parent 932b41f commit b33d067

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6 files changed

+23
-17
lines changed

6 files changed

+23
-17
lines changed

classifier_utils.py

Lines changed: 4 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -25,6 +25,7 @@
2525
from albert import optimization
2626
from albert import tokenization
2727
import tensorflow.compat.v1 as tf
28+
from tensorflow.compat.v1 import estimator as tf_estimator
2829
from tensorflow.contrib import data as contrib_data
2930
from tensorflow.contrib import metrics as contrib_metrics
3031
from tensorflow.contrib import tpu as contrib_tpu
@@ -835,7 +836,7 @@ def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
835836
else:
836837
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
837838

838-
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
839+
is_training = (mode == tf_estimator.ModeKeys.TRAIN)
839840

840841
(total_loss, per_example_loss, probabilities, logits, predictions) = \
841842
create_model(albert_config, is_training, input_ids, input_mask,
@@ -867,7 +868,7 @@ def tpu_scaffold():
867868
init_string)
868869

869870
output_spec = None
870-
if mode == tf.estimator.ModeKeys.TRAIN:
871+
if mode == tf_estimator.ModeKeys.TRAIN:
871872

872873
train_op = optimization.create_optimizer(
873874
total_loss, learning_rate, num_train_steps, num_warmup_steps,
@@ -878,7 +879,7 @@ def tpu_scaffold():
878879
loss=total_loss,
879880
train_op=train_op,
880881
scaffold_fn=scaffold_fn)
881-
elif mode == tf.estimator.ModeKeys.EVAL:
882+
elif mode == tf_estimator.ModeKeys.EVAL:
882883
if task_name not in ["sts-b", "cola"]:
883884
def metric_fn(per_example_loss, label_ids, logits, is_real_example):
884885
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)

race_utils.py

Lines changed: 4 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -27,6 +27,7 @@
2727
from albert import optimization
2828
from albert import tokenization
2929
import tensorflow.compat.v1 as tf
30+
from tensorflow.compat.v1 import estimator as tf_estimator
3031
from tensorflow.contrib import tpu as contrib_tpu
3132

3233

@@ -356,7 +357,7 @@ def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
356357
else:
357358
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
358359

359-
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
360+
is_training = (mode == tf_estimator.ModeKeys.TRAIN)
360361

361362
(total_loss, per_example_loss, probabilities, logits, predictions) = \
362363
create_model(albert_config, is_training, input_ids, input_mask,
@@ -389,7 +390,7 @@ def tpu_scaffold():
389390
init_string)
390391

391392
output_spec = None
392-
if mode == tf.estimator.ModeKeys.TRAIN:
393+
if mode == tf_estimator.ModeKeys.TRAIN:
393394

394395
train_op = optimization.create_optimizer(
395396
total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
@@ -399,7 +400,7 @@ def tpu_scaffold():
399400
loss=total_loss,
400401
train_op=train_op,
401402
scaffold_fn=scaffold_fn)
402-
elif mode == tf.estimator.ModeKeys.EVAL:
403+
elif mode == tf_estimator.ModeKeys.EVAL:
403404
def metric_fn(per_example_loss, label_ids, logits, is_real_example):
404405
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
405406
accuracy = tf.metrics.accuracy(

run_classifier.py

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -25,6 +25,7 @@
2525
from albert import fine_tuning_utils
2626
from albert import modeling
2727
import tensorflow.compat.v1 as tf
28+
from tensorflow.compat.v1 import estimator as tf_estimator
2829
from tensorflow.contrib import cluster_resolver as contrib_cluster_resolver
2930
from tensorflow.contrib import tpu as contrib_tpu
3031

@@ -177,7 +178,7 @@ def _serving_input_receiver_fn():
177178
t = tf.to_int32(t)
178179
feature_map[name] = t
179180

180-
return tf.estimator.export.ServingInputReceiver(
181+
return tf_estimator.export.ServingInputReceiver(
181182
features=feature_map, receiver_tensors=serialized_example)
182183

183184

run_pretraining.py

Lines changed: 4 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -24,6 +24,7 @@
2424
from albert import optimization
2525
from six.moves import range
2626
import tensorflow.compat.v1 as tf
27+
from tensorflow.compat.v1 import estimator as tf_estimator
2728
from tensorflow.contrib import cluster_resolver as contrib_cluster_resolver
2829
from tensorflow.contrib import data as contrib_data
2930
from tensorflow.contrib import tpu as contrib_tpu
@@ -153,7 +154,7 @@ def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
153154
# it does represent sentence_order_labels.
154155
sentence_order_labels = features["next_sentence_labels"]
155156

156-
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
157+
is_training = (mode == tf_estimator.ModeKeys.TRAIN)
157158

158159
model = modeling.AlbertModel(
159160
config=albert_config,
@@ -217,7 +218,7 @@ def tpu_scaffold():
217218
init_string)
218219

219220
output_spec = None
220-
if mode == tf.estimator.ModeKeys.TRAIN:
221+
if mode == tf_estimator.ModeKeys.TRAIN:
221222
train_op = optimization.create_optimizer(
222223
total_loss, learning_rate, num_train_steps, num_warmup_steps,
223224
use_tpu, optimizer, poly_power, start_warmup_step)
@@ -227,7 +228,7 @@ def tpu_scaffold():
227228
loss=total_loss,
228229
train_op=train_op,
229230
scaffold_fn=scaffold_fn)
230-
elif mode == tf.estimator.ModeKeys.EVAL:
231+
elif mode == tf_estimator.ModeKeys.EVAL:
231232

232233
def metric_fn(*args):
233234
"""Computes the loss and accuracy of the model."""

run_squad_v1.py

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -29,6 +29,7 @@
2929
from albert import squad_utils
3030
import six
3131
import tensorflow.compat.v1 as tf
32+
from tensorflow.compat.v1 import estimator as tf_estimator
3233

3334
from tensorflow.contrib import cluster_resolver as contrib_cluster_resolver
3435
from tensorflow.contrib import tpu as contrib_tpu
@@ -236,7 +237,7 @@ def _seq_serving_input_fn():
236237
"input_mask": input_mask,
237238
"segment_ids": segment_ids
238239
}
239-
return tf.estimator.export.ServingInputReceiver(features=inputs,
240+
return tf_estimator.export.ServingInputReceiver(features=inputs,
240241
receiver_tensors=inputs)
241242

242243
return _seq_serving_input_fn

squad_utils.py

Lines changed: 7 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -33,6 +33,7 @@
3333
from six.moves import map
3434
from six.moves import range
3535
import tensorflow.compat.v1 as tf
36+
from tensorflow.compat.v1 import estimator as tf_estimator
3637
from tensorflow.contrib import data as contrib_data
3738
from tensorflow.contrib import layers as contrib_layers
3839
from tensorflow.contrib import tpu as contrib_tpu
@@ -767,7 +768,7 @@ def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
767768
input_mask = features["input_mask"]
768769
segment_ids = features["segment_ids"]
769770

770-
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
771+
is_training = (mode == tf_estimator.ModeKeys.TRAIN)
771772

772773
(start_logits, end_logits) = create_v1_model(
773774
albert_config=albert_config,
@@ -809,7 +810,7 @@ def tpu_scaffold():
809810
init_string)
810811

811812
output_spec = None
812-
if mode == tf.estimator.ModeKeys.TRAIN:
813+
if mode == tf_estimator.ModeKeys.TRAIN:
813814
seq_length = modeling.get_shape_list(input_ids)[1]
814815

815816
def compute_loss(logits, positions):
@@ -836,7 +837,7 @@ def compute_loss(logits, positions):
836837
loss=total_loss,
837838
train_op=train_op,
838839
scaffold_fn=scaffold_fn)
839-
elif mode == tf.estimator.ModeKeys.PREDICT:
840+
elif mode == tf_estimator.ModeKeys.PREDICT:
840841
predictions = {
841842
"start_log_prob": start_logits,
842843
"end_log_prob": end_logits,
@@ -1594,7 +1595,7 @@ def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
15941595
input_mask = features["input_mask"]
15951596
segment_ids = features["segment_ids"]
15961597

1597-
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
1598+
is_training = (mode == tf_estimator.ModeKeys.TRAIN)
15981599

15991600
outputs = create_v2_model(
16001601
albert_config=albert_config,
@@ -1636,7 +1637,7 @@ def tpu_scaffold():
16361637
init_string)
16371638

16381639
output_spec = None
1639-
if mode == tf.estimator.ModeKeys.TRAIN:
1640+
if mode == tf_estimator.ModeKeys.TRAIN:
16401641
seq_length = modeling.get_shape_list(input_ids)[1]
16411642

16421643
def compute_loss(log_probs, positions):
@@ -1671,7 +1672,7 @@ def compute_loss(log_probs, positions):
16711672
loss=total_loss,
16721673
train_op=train_op,
16731674
scaffold_fn=scaffold_fn)
1674-
elif mode == tf.estimator.ModeKeys.PREDICT:
1675+
elif mode == tf_estimator.ModeKeys.PREDICT:
16751676
predictions = {
16761677
"unique_ids": features["unique_ids"],
16771678
"start_top_index": outputs["start_top_index"],

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