Merged commit includes the following changes: (#7800)

280142968  by Zhichao Lu:

    Opensource MobilenetEdgeTPU + ssdlite into third-party object detection APIs on EdgeTPU.

--
280134001  by Zhichao Lu:

    Adds MobilenetEdgeTpu + ssdlite into internal object detection APIs on EdgeTPU.

--
278941778  by Zhichao Lu:

    Add support for fixed input shapes for 'encoded_image_string_tensor' and 'tf_example' inputs.

--
278933274  by Zhichao Lu:

      Adding fool proof check to avoid using 1x1 depthwise conv op.

--
278762192  by Zhichao Lu:

    Ensure correct number of iterations after training resumes.

--
278746440  by Zhichao Lu:

    Internal change.

--
278006953  by Zhichao Lu:

    Internal changes to tf.contrib symbols

--
278006330  by Zhichao Lu:

    Internal changes to tf.contrib symbols

--
277593959  by Zhichao Lu:

      Make the ssd_feature_extractor_test.py PY3 compatible. The "six.zip" will use "itertools.izip" in Python 2 and "zip" in Python 3....

research/object_detection/model_lib_v2.py

@@ -135,7 +135,6 @@ def eager_train_step(detection_model,
                      learning_rate,
                      add_regularization_loss=True,
                      clip_gradients_value=None,
-                     use_tpu=False,
                      global_step=None,
                      num_replicas=1.0):
   """Process a single training batch.
@@ -192,7 +191,6 @@ def eager_train_step(detection_model,
       regularization loss in the losses dictionary.
     clip_gradients_value: If this is present, clip the gradients global norm
       at this value using `tf.clip_by_global_norm`.
-    use_tpu: Whether computation should happen on a TPU.
     global_step: The current training step. Used for TensorBoard logging
       purposes. This step is not updated by this function and must be
       incremented separately.
@@ -223,10 +221,9 @@ def eager_train_step(detection_model,
                                 tf.constant(num_replicas, dtype=tf.float32))
     losses_dict['Loss/normalized_total_loss'] = total_loss
 
-  if not use_tpu:
-    for loss_type in losses_dict:
-      tf.compat.v2.summary.scalar(
-          loss_type, losses_dict[loss_type], step=global_step)
+  for loss_type in losses_dict:
+    tf.compat.v2.summary.scalar(
+        loss_type, losses_dict[loss_type], step=global_step)
 
   trainable_variables = detection_model.trainable_variables
 
@@ -235,10 +232,7 @@ def eager_train_step(detection_model,
   if clip_gradients_value:
     gradients, _ = tf.clip_by_global_norm(gradients, clip_gradients_value)
   optimizer.apply_gradients(zip(gradients, trainable_variables))
-
-  if not use_tpu:
-    tf.compat.v2.summary.scalar('learning_rate', learning_rate,
-                                step=global_step)
+  tf.compat.v2.summary.scalar('learning_rate', learning_rate, step=global_step)
 
   return total_loss
 
@@ -451,11 +445,10 @@ def train_loop(
                                   unpad_groundtruth_tensors)
 
       ckpt = tf.compat.v2.train.Checkpoint(
-          step=global_step, model=detection_model)
+          step=global_step, model=detection_model, optimizer=optimizer)
       manager = tf.compat.v2.train.CheckpointManager(
           ckpt, model_dir, max_to_keep=7)
-      ## Maybe re-enable checkpoint restoration depending on how it works:
-      # ckpt.restore(manager.latest_checkpoint)
+      ckpt.restore(manager.latest_checkpoint)
 
       def train_step_fn(features, labels):
         return eager_train_step(
@@ -467,7 +460,6 @@ def train_loop(
             learning_rate=learning_rate_fn(),
             add_regularization_loss=add_regularization_loss,
             clip_gradients_value=clip_gradients_value,
-            use_tpu=use_tpu,
             global_step=global_step,
             num_replicas=strategy.num_replicas_in_sync)
 
@@ -487,19 +479,22 @@ def train_loop(
         return mean_loss
 
       train_input_iter = iter(train_input)
-      for _ in range(train_steps):
+      for _ in range(train_steps - global_step.value()):
         start_time = time.time()
 
         loss = _dist_train_step(train_input_iter)
         global_step.assign_add(1)
         end_time = time.time()
-        if not use_tpu:
-          tf.compat.v2.summary.scalar(
-              'steps_per_sec', 1.0 / (end_time - start_time), step=global_step)
-        # TODO(kaftan): Remove this print after it is no longer helpful for
-        ## debugging.
-        print('Finished step', global_step, end_time, loss)
-        if int(global_step.value().numpy()) % checkpoint_every_n == 0:
+
+        tf.compat.v2.summary.scalar(
+            'steps_per_sec', 1.0 / (end_time - start_time),
+            step=global_step)
+        if (int(global_step.value()) % 100) == 0:
+          tf.logging.info(
+              'Step {} time taken {:.3f}s loss={:.3f}'.format(
+                  global_step.value(), end_time - start_time, loss))
+
+        if int(global_step.value()) % checkpoint_every_n == 0:
           manager.save()
 
 
@@ -620,14 +615,11 @@ def eager_eval_loop(
 
     return eval_dict, losses_dict, class_agnostic
 
-  i = 0
-  for features, labels in eval_dataset:
+  for i, (features, labels) in enumerate(eval_dataset):
     eval_dict, losses_dict, class_agnostic = compute_eval_dict(features, labels)
-    end_time = time.time()
-    # TODO(kaftan): Remove this print after it is no longer helpful for
-    ## debugging.
-    tf.print('Finished eval dict computation', i, end_time)
-    i += 1
+
+    if i % 100 == 0:
+      tf.logging.info('Finished eval step %d', i)
 
     if evaluators is None:
       if class_agnostic:

research/object_detection/models/embedded_ssd_mobilenet_v1_feature_extractor.py

@@ -16,6 +16,7 @@
 """Embedded-friendly SSDFeatureExtractor for MobilenetV1 features."""
 
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import ssd_meta_arch
 from object_detection.models import feature_map_generators
@@ -23,7 +24,7 @@ from object_detection.utils import context_manager
 from object_detection.utils import ops
 from nets import mobilenet_v1
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 class EmbeddedSSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):

research/object_detection/models/faster_rcnn_inception_resnet_v2_feature_extractor.py

@@ -23,12 +23,13 @@ Huang et al. (https://arxiv.org/abs/1611.10012)
 """
 
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import faster_rcnn_meta_arch
 from object_detection.utils import variables_helper
 from nets import inception_resnet_v2
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 class FasterRCNNInceptionResnetV2FeatureExtractor(
@@ -211,4 +212,3 @@ class FasterRCNNInceptionResnetV2FeatureExtractor(
             second_stage_feature_extractor_scope + '/', '')
         variables_to_restore[var_name] = variable
     return variables_to_restore
-

research/object_detection/models/faster_rcnn_inception_v2_feature_extractor.py

@@ -19,11 +19,12 @@ See "Rethinking the Inception Architecture for Computer Vision"
 https://arxiv.org/abs/1512.00567
 """
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import faster_rcnn_meta_arch
 from nets import inception_v2
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 def _batch_norm_arg_scope(list_ops,

research/object_detection/models/faster_rcnn_mobilenet_v1_feature_extractor.py

@@ -17,12 +17,13 @@
 import numpy as np
 
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import faster_rcnn_meta_arch
 from object_detection.utils import shape_utils
 from nets import mobilenet_v1
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 def _get_mobilenet_conv_no_last_stride_defs(conv_depth_ratio_in_percentage):

research/object_detection/models/faster_rcnn_nas_feature_extractor.py

@@ -21,14 +21,16 @@ https://arxiv.org/abs/1707.07012
 """
 
 import tensorflow as tf
+from tensorflow.contrib import framework as contrib_framework
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import faster_rcnn_meta_arch
 from object_detection.utils import variables_helper
 from nets.nasnet import nasnet
 from nets.nasnet import nasnet_utils
 
-arg_scope = tf.contrib.framework.arg_scope
-slim = tf.contrib.slim
+arg_scope = contrib_framework.arg_scope
+slim = contrib_slim
 
 
 def nasnet_large_arg_scope_for_detection(is_batch_norm_training=False):
@@ -322,4 +324,3 @@ class FasterRCNNNASFeatureExtractor(
         var_name += '/ExponentialMovingAverage'
         variables_to_restore[var_name] = variable
     return variables_to_restore
-

research/object_detection/models/faster_rcnn_pnas_feature_extractor.py

@@ -19,14 +19,16 @@ Based on PNASNet model: https://arxiv.org/abs/1712.00559
 """
 
 import tensorflow as tf
+from tensorflow.contrib import framework as contrib_framework
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import faster_rcnn_meta_arch
 from object_detection.utils import variables_helper
 from nets.nasnet import nasnet_utils
 from nets.nasnet import pnasnet
 
-arg_scope = tf.contrib.framework.arg_scope
-slim = tf.contrib.slim
+arg_scope = contrib_framework.arg_scope
+slim = contrib_slim
 
 
 def pnasnet_large_arg_scope_for_detection(is_batch_norm_training=False):

research/object_detection/models/faster_rcnn_resnet_v1_feature_extractor.py

@@ -25,12 +25,13 @@ the MSRA provided checkpoints
 same preprocessing, batch norm scaling, etc.
 """
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import faster_rcnn_meta_arch
 from nets import resnet_utils
 from nets import resnet_v1
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 class FasterRCNNResnetV1FeatureExtractor(

research/object_detection/models/feature_map_generators.py

@@ -26,9 +26,10 @@ of final feature maps.
 import collections
 import functools
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 from object_detection.utils import ops
 from object_detection.utils import shape_utils
-slim = tf.contrib.slim
+slim = contrib_slim
 
 # Activation bound used for TPU v1. Activations will be clipped to
 # [-ACTIVATION_BOUND, ACTIVATION_BOUND] when training with

research/object_detection/models/ssd_feature_extractor_test.py

@@ -17,11 +17,12 @@
 
 from abc import abstractmethod
 
-import itertools
 import numpy as np
+from six.moves import zip
 import tensorflow as tf
 
 from google.protobuf import text_format
+from tensorflow.contrib import slim as contrib_slim
 from object_detection.builders import hyperparams_builder
 from object_detection.protos import hyperparams_pb2
 from object_detection.utils import test_case
@@ -53,7 +54,7 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
     return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
 
   def conv_hyperparams_fn(self):
-    with tf.contrib.slim.arg_scope([]) as sc:
+    with contrib_slim.arg_scope([]) as sc:
       return sc
 
   @abstractmethod
@@ -135,7 +136,7 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
     image_tensor = np.random.rand(batch_size, image_height, image_width,
                                   3).astype(np.float32)
     feature_maps = self.execute(graph_fn, [image_tensor])
-    for feature_map, expected_shape in itertools.izip(
+    for feature_map, expected_shape in zip(
         feature_maps, expected_feature_map_shapes):
       self.assertAllEqual(feature_map.shape, expected_shape)
 
@@ -168,7 +169,7 @@ class SsdFeatureExtractorTestBase(test_case.TestCase):
         np.array(image_height, dtype=np.int32),
         np.array(image_width, dtype=np.int32)
     ])
-    for feature_map, expected_shape in itertools.izip(
+    for feature_map, expected_shape in zip(
         feature_maps, expected_feature_map_shapes):
       self.assertAllEqual(feature_map.shape, expected_shape)
 

research/object_detection/models/ssd_inception_v2_feature_extractor.py

@@ -15,6 +15,7 @@
 
 """SSDFeatureExtractor for InceptionV2 features."""
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import ssd_meta_arch
 from object_detection.models import feature_map_generators
@@ -22,7 +23,7 @@ from object_detection.utils import ops
 from object_detection.utils import shape_utils
 from nets import inception_v2
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 class SSDInceptionV2FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):

research/object_detection/models/ssd_inception_v3_feature_extractor.py

@@ -15,6 +15,7 @@
 
 """SSDFeatureExtractor for InceptionV3 features."""
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import ssd_meta_arch
 from object_detection.models import feature_map_generators
@@ -22,7 +23,7 @@ from object_detection.utils import ops
 from object_detection.utils import shape_utils
 from nets import inception_v3
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 class SSDInceptionV3FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):

research/object_detection/models/ssd_mobilenet_edgetpu_feature_extractor.py

+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""SSDFeatureExtractor for MobileNetEdgeTPU features."""
+
+import tensorflow as tf
+
+from object_detection.models import ssd_mobilenet_v3_feature_extractor
+from nets.mobilenet import mobilenet_v3
+
+slim = tf.contrib.slim
+
+
+class SSDMobileNetEdgeTPUFeatureExtractor(
+    ssd_mobilenet_v3_feature_extractor.SSDMobileNetV3FeatureExtractorBase):
+  """MobileNetEdgeTPU feature extractor."""
+
+  def __init__(self,
+               is_training,
+               depth_multiplier,
+               min_depth,
+               pad_to_multiple,
+               conv_hyperparams_fn,
+               reuse_weights=None,
+               use_explicit_padding=False,
+               use_depthwise=False,
+               override_base_feature_extractor_hyperparams=False,
+               scope_name='MobilenetEdgeTPU'):
+    super(SSDMobileNetEdgeTPUFeatureExtractor, self).__init__(
+        conv_defs=mobilenet_v3.V3_EDGETPU,
+        from_layer=['layer_18/expansion_output', 'layer_23'],
+        is_training=is_training,
+        depth_multiplier=depth_multiplier,
+        min_depth=min_depth,
+        pad_to_multiple=pad_to_multiple,
+        conv_hyperparams_fn=conv_hyperparams_fn,
+        reuse_weights=reuse_weights,
+        use_explicit_padding=use_explicit_padding,
+        use_depthwise=use_depthwise,
+        override_base_feature_extractor_hyperparams=override_base_feature_extractor_hyperparams,
+        scope_name=scope_name
+    )

research/object_detection/models/ssd_mobilenet_edgetpu_feature_extractor_test.py

+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for ssd_mobilenet_edgetpu_feature_extractor."""
+
+import tensorflow as tf
+
+from tensorflow.contrib import slim as contrib_slim
+from object_detection.models import ssd_mobilenet_edgetpu_feature_extractor
+from object_detection.models import ssd_mobilenet_edgetpu_feature_extractor_testbase
+
+slim = contrib_slim
+
+
+class SsdMobilenetEdgeTPUFeatureExtractorTest(
+    ssd_mobilenet_edgetpu_feature_extractor_testbase
+    ._SsdMobilenetEdgeTPUFeatureExtractorTestBase):
+
+  def _get_input_sizes(self):
+    """Return first two input feature map sizes."""
+    return [384, 192]
+
+  def _create_feature_extractor(self,
+                                depth_multiplier,
+                                pad_to_multiple,
+                                use_explicit_padding=False,
+                                use_keras=False):
+    """Constructs a new MobileNetEdgeTPU feature extractor.
+
+    Args:
+      depth_multiplier: float depth multiplier for feature extractor
+      pad_to_multiple: the nearest multiple to zero pad the input height and
+        width dimensions to.
+      use_explicit_padding: use 'VALID' padding for convolutions, but prepad
+        inputs so that the output dimensions are the same as if 'SAME' padding
+        were used.
+      use_keras: if True builds a keras-based feature extractor, if False builds
+        a slim-based one.
+
+    Returns:
+      an ssd_meta_arch.SSDFeatureExtractor object.
+    """
+    min_depth = 32
+    return (ssd_mobilenet_edgetpu_feature_extractor
+            .SSDMobileNetEdgeTPUFeatureExtractor(
+                False,
+                depth_multiplier,
+                min_depth,
+                pad_to_multiple,
+                self.conv_hyperparams_fn,
+                use_explicit_padding=use_explicit_padding))
+
+
+if __name__ == '__main__':
+  tf.test.main()

research/object_detection/models/ssd_mobilenet_edgetpu_feature_extractor_testbase.py

+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Base test class for ssd_mobilenet_edgetpu_feature_extractor."""
+
+import abc
+
+import numpy as np
+import tensorflow as tf
+
+from object_detection.models import ssd_feature_extractor_test
+
+
+slim = tf.contrib.slim
+
+
+class _SsdMobilenetEdgeTPUFeatureExtractorTestBase(
+    ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
+  """Base class for MobilenetEdgeTPU tests."""
+
+  @abc.abstractmethod
+  def _get_input_sizes(self):
+    """Return feature map sizes for the two inputs to SSD head."""
+    pass
+
+  def test_extract_features_returns_correct_shapes_128(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    input_feature_sizes = self._get_input_sizes()
+    expected_feature_map_shape = [(2, 8, 8, input_feature_sizes[0]),
+                                  (2, 4, 4, input_feature_sizes[1]),
+                                  (2, 2, 2, 512), (2, 1, 1, 256), (2, 1, 1,
+                                                                   256),
+                                  (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2,
+        image_height,
+        image_width,
+        depth_multiplier,
+        pad_to_multiple,
+        expected_feature_map_shape,
+        use_keras=False)
+
+  def test_extract_features_returns_correct_shapes_299(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 1
+    input_feature_sizes = self._get_input_sizes()
+    expected_feature_map_shape = [(2, 19, 19, input_feature_sizes[0]),
+                                  (2, 10, 10, input_feature_sizes[1]),
+                                  (2, 5, 5, 512), (2, 3, 3, 256), (2, 2, 2,
+                                                                   256),
+                                  (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2,
+        image_height,
+        image_width,
+        depth_multiplier,
+        pad_to_multiple,
+        expected_feature_map_shape,
+        use_keras=False)
+
+  def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
+    image_height = 299
+    image_width = 299
+    depth_multiplier = 1.0
+    pad_to_multiple = 32
+    input_feature_sizes = self._get_input_sizes()
+    expected_feature_map_shape = [(2, 20, 20, input_feature_sizes[0]),
+                                  (2, 10, 10, input_feature_sizes[1]),
+                                  (2, 5, 5, 512), (2, 3, 3, 256), (2, 2, 2,
+                                                                   256),
+                                  (2, 1, 1, 128)]
+    self.check_extract_features_returns_correct_shape(
+        2, image_height, image_width, depth_multiplier, pad_to_multiple,
+        expected_feature_map_shape)
+
+  def test_preprocess_returns_correct_value_range(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    test_image = np.random.rand(4, image_height, image_width, 3)
+    feature_extractor = self._create_feature_extractor(
+        depth_multiplier, pad_to_multiple, use_keras=False)
+    preprocessed_image = feature_extractor.preprocess(test_image)
+    self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
+
+  def test_has_fused_batchnorm(self):
+    image_height = 128
+    image_width = 128
+    depth_multiplier = 1
+    pad_to_multiple = 1
+    image_placeholder = tf.placeholder(tf.float32,
+                                       [1, image_height, image_width, 3])
+    feature_extractor = self._create_feature_extractor(
+        depth_multiplier, pad_to_multiple, use_keras=False)
+    preprocessed_image = feature_extractor.preprocess(image_placeholder)
+    _ = feature_extractor.extract_features(preprocessed_image)
+    self.assertTrue(any('FusedBatchNorm' in op.type
+                        for op in tf.get_default_graph().get_operations()))

research/object_detection/models/ssd_mobilenet_v1_feature_extractor.py

@@ -16,6 +16,7 @@
 """SSDFeatureExtractor for MobilenetV1 features."""
 
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import ssd_meta_arch
 from object_detection.models import feature_map_generators
@@ -24,7 +25,7 @@ from object_detection.utils import ops
 from object_detection.utils import shape_utils
 from nets import mobilenet_v1
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 class SSDMobileNetV1FeatureExtractor(ssd_meta_arch.SSDFeatureExtractor):

research/object_detection/models/ssd_mobilenet_v1_feature_extractor_test.py

@@ -21,12 +21,13 @@ from absl.testing import parameterized
 
 import numpy as np
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.models import ssd_feature_extractor_test
 from object_detection.models import ssd_mobilenet_v1_feature_extractor
 from object_detection.models import ssd_mobilenet_v1_keras_feature_extractor
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 @parameterized.parameters(

research/object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor.py

@@ -18,6 +18,7 @@
 import copy
 import functools
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import ssd_meta_arch
 from object_detection.models import feature_map_generators
@@ -26,7 +27,7 @@ from object_detection.utils import ops
 from object_detection.utils import shape_utils
 from nets import mobilenet_v1
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 # A modified config of mobilenet v1 that makes it more detection friendly,

research/object_detection/models/ssd_mobilenet_v1_fpn_feature_extractor_test.py

@@ -21,12 +21,13 @@ Keras-based Mobilenet V1 FPN feature extractors in SSD.
 from absl.testing import parameterized
 import numpy as np
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.models import ssd_feature_extractor_test
 from object_detection.models import ssd_mobilenet_v1_fpn_feature_extractor
 from object_detection.models import ssd_mobilenet_v1_fpn_keras_feature_extractor
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 @parameterized.parameters(

research/object_detection/models/ssd_mobilenet_v1_keras_feature_extractor.py

@@ -16,6 +16,7 @@
 """SSDFeatureExtractor for Keras MobilenetV1 features."""
 
 import tensorflow as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.meta_architectures import ssd_meta_arch
 from object_detection.models import feature_map_generators
@@ -23,7 +24,7 @@ from object_detection.models.keras_models import mobilenet_v1
 from object_detection.utils import ops
 from object_detection.utils import shape_utils
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 class SSDMobileNetV1KerasFeatureExtractor(