Merged commit includes the following changes: (#8235)

298416930  by lzyuan:

    Explicitly mark base models' state inputs as 'raw_inputs/init_lstm_h_1' and 'raw_inputs_init_lstm_h_2' when pre_bottleneck=True.

--
298380851  by skligys:

    Fix LSTD LSTM cells to use fixed_quantize_op().

--
297662737  by Menglong Zhu:

    Explicitly replace "import tensorflow" with "tensorflow.compat.v1" for TF2.x migration

--
289667197  by lzyuan:

    Internal update.

--
288607438  by lzyuan:

    Enforce feature_extractor construction using arg keys.

--

PiperOrigin-RevId: 298416930
Co-authored-by: Menglong Zhu <menglong@google.com>

research/lstm_object_detection/metrics/coco_evaluation_all_frames_test.py

@@ -16,7 +16,7 @@
 """Tests for video_object_detection.metrics.coco_video_evaluation."""
 
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from lstm_object_detection.metrics import coco_evaluation_all_frames
 from object_detection.core import standard_fields
 

research/lstm_object_detection/model_builder_test.py

@@ -15,7 +15,7 @@
 
 """Tests for lstm_object_detection.tensorflow.model_builder."""
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from google.protobuf import text_format
 from lstm_object_detection import model_builder
 from lstm_object_detection.meta_architectures import lstm_ssd_meta_arch

research/lstm_object_detection/models/lstm_ssd_interleaved_mobilenet_v2_feature_extractor.py

@@ -15,7 +15,7 @@
 
 """LSTDInterleavedFeatureExtractor which interleaves multiple MobileNet V2."""
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from tensorflow.contrib import slim
 
 from tensorflow.python.framework import ops as tf_ops
@@ -64,8 +64,15 @@ class LSTMSSDInterleavedMobilenetV2FeatureExtractor(
         `conv_hyperparams_fn`.
     """
     super(LSTMSSDInterleavedMobilenetV2FeatureExtractor, self).__init__(
-        is_training, depth_multiplier, min_depth, pad_to_multiple,
-        conv_hyperparams_fn, reuse_weights, use_explicit_padding, use_depthwise,
+        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)
     # RANDOM_SKIP_SMALL means the training policy is random and the small model
     # does not update state during training.

research/lstm_object_detection/models/lstm_ssd_interleaved_mobilenet_v2_feature_extractor_test.py

@@ -17,7 +17,7 @@
 
 import itertools
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from tensorflow.contrib import slim
 from tensorflow.contrib import training as contrib_training
 
@@ -60,6 +60,47 @@ class LSTMSSDInterleavedMobilenetV2FeatureExtractorTest(
     feature_extractor.is_quantized = is_quantized
     return feature_extractor
 
+  def test_feature_extractor_construct_with_expected_params(self):
+    def conv_hyperparams_fn():
+      with (slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm) and
+            slim.arg_scope([slim.batch_norm], decay=0.97, epsilon=1e-3)) as sc:
+        return sc
+
+    params = {
+        'is_training': True,
+        'depth_multiplier': .55,
+        'min_depth': 9,
+        'pad_to_multiple': 3,
+        'conv_hyperparams_fn': conv_hyperparams_fn,
+        'reuse_weights': False,
+        'use_explicit_padding': True,
+        'use_depthwise': False,
+        'override_base_feature_extractor_hyperparams': True}
+
+    feature_extractor = (
+        lstm_ssd_interleaved_mobilenet_v2_feature_extractor
+        .LSTMSSDInterleavedMobilenetV2FeatureExtractor(**params))
+
+    self.assertEqual(params['is_training'],
+                     feature_extractor._is_training)
+    self.assertEqual(params['depth_multiplier'],
+                     feature_extractor._depth_multiplier)
+    self.assertEqual(params['min_depth'],
+                     feature_extractor._min_depth)
+    self.assertEqual(params['pad_to_multiple'],
+                     feature_extractor._pad_to_multiple)
+    self.assertEqual(params['conv_hyperparams_fn'],
+                     feature_extractor._conv_hyperparams_fn)
+    self.assertEqual(params['reuse_weights'],
+                     feature_extractor._reuse_weights)
+    self.assertEqual(params['use_explicit_padding'],
+                     feature_extractor._use_explicit_padding)
+    self.assertEqual(params['use_depthwise'],
+                     feature_extractor._use_depthwise)
+    self.assertEqual(params['override_base_feature_extractor_hyperparams'],
+                     (feature_extractor.
+                      _override_base_feature_extractor_hyperparams))
+
   def test_extract_features_returns_correct_shapes_128(self):
     image_height = 128
     image_width = 128

research/lstm_object_detection/models/lstm_ssd_mobilenet_v1_feature_extractor.py

@@ -15,7 +15,7 @@
 
 """LSTMSSDFeatureExtractor for MobilenetV1 features."""
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from tensorflow.contrib import slim as contrib_slim
 from tensorflow.python.framework import ops as tf_ops
 from lstm_object_detection.lstm import lstm_cells
@@ -66,8 +66,15 @@ class LSTMSSDMobileNetV1FeatureExtractor(
       lstm_state_depth: An integter of the depth of the lstm state.
     """
     super(LSTMSSDMobileNetV1FeatureExtractor, self).__init__(
-        is_training, depth_multiplier, min_depth, pad_to_multiple,
-        conv_hyperparams_fn, reuse_weights, use_explicit_padding, use_depthwise,
+        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)
     self._feature_map_layout = {
         'from_layer': ['Conv2d_13_pointwise_lstm', '', '', '', ''],

research/lstm_object_detection/models/lstm_ssd_mobilenet_v1_feature_extractor_test.py

@@ -16,11 +16,11 @@
 """Tests for models.lstm_ssd_mobilenet_v1_feature_extractor."""
 
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from tensorflow.contrib import slim as contrib_slim
 from tensorflow.contrib import training as contrib_training
 
-from lstm_object_detection.models import lstm_ssd_mobilenet_v1_feature_extractor as feature_extactor
+from lstm_object_detection.models import lstm_ssd_mobilenet_v1_feature_extractor as feature_extractor
 from object_detection.models import ssd_feature_extractor_test
 
 slim = contrib_slim
@@ -48,7 +48,7 @@ class LstmSsdMobilenetV1FeatureExtractorTest(
     """
     min_depth = 32
     extractor = (
-        feature_extactor.LSTMSSDMobileNetV1FeatureExtractor(
+        feature_extractor.LSTMSSDMobileNetV1FeatureExtractor(
             is_training,
             depth_multiplier,
             min_depth,
@@ -58,6 +58,46 @@ class LstmSsdMobilenetV1FeatureExtractorTest(
     extractor.lstm_state_depth = int(256 * depth_multiplier)
     return extractor
 
+  def test_feature_extractor_construct_with_expected_params(self):
+    def conv_hyperparams_fn():
+      with (slim.arg_scope([slim.conv2d], normalizer_fn=slim.batch_norm) and
+            slim.arg_scope([slim.batch_norm], decay=0.97, epsilon=1e-3)) as sc:
+        return sc
+
+    params = {
+        'is_training': True,
+        'depth_multiplier': .55,
+        'min_depth': 9,
+        'pad_to_multiple': 3,
+        'conv_hyperparams_fn': conv_hyperparams_fn,
+        'reuse_weights': False,
+        'use_explicit_padding': True,
+        'use_depthwise': False,
+        'override_base_feature_extractor_hyperparams': True}
+
+    extractor = (
+        feature_extractor.LSTMSSDMobileNetV1FeatureExtractor(**params))
+
+    self.assertEqual(params['is_training'],
+                     extractor._is_training)
+    self.assertEqual(params['depth_multiplier'],
+                     extractor._depth_multiplier)
+    self.assertEqual(params['min_depth'],
+                     extractor._min_depth)
+    self.assertEqual(params['pad_to_multiple'],
+                     extractor._pad_to_multiple)
+    self.assertEqual(params['conv_hyperparams_fn'],
+                     extractor._conv_hyperparams_fn)
+    self.assertEqual(params['reuse_weights'],
+                     extractor._reuse_weights)
+    self.assertEqual(params['use_explicit_padding'],
+                     extractor._use_explicit_padding)
+    self.assertEqual(params['use_depthwise'],
+                     extractor._use_depthwise)
+    self.assertEqual(params['override_base_feature_extractor_hyperparams'],
+                     (extractor.
+                      _override_base_feature_extractor_hyperparams))
+
   def test_extract_features_returns_correct_shapes_256(self):
     image_height = 256
     image_width = 256
@@ -87,8 +127,8 @@ class LstmSsdMobilenetV1FeatureExtractorTest(
 
   def test_preprocess_returns_correct_value_range(self):
     test_image = np.random.rand(5, 128, 128, 3)
-    feature_extractor = self._create_feature_extractor()
-    preprocessed_image = feature_extractor.preprocess(test_image)
+    extractor = self._create_feature_extractor()
+    preprocessed_image = extractor.preprocess(test_image)
     self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
 
   def test_variables_only_created_in_scope(self):
@@ -96,8 +136,8 @@ class LstmSsdMobilenetV1FeatureExtractorTest(
     g = tf.Graph()
     with g.as_default():
       preprocessed_inputs = tf.placeholder(tf.float32, (5, 256, 256, 3))
-      feature_extractor = self._create_feature_extractor()
-      feature_extractor.extract_features(preprocessed_inputs)
+      extractor = self._create_feature_extractor()
+      extractor.extract_features(preprocessed_inputs)
       variables = g.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
       find_scope = False
       for variable in variables:
@@ -122,10 +162,10 @@ class LstmSsdMobilenetV1FeatureExtractorTest(
         input_context={},
         initial_states=init_state,
         capacity=1)
-    feature_extractor = self._create_feature_extractor()
+    extractor = self._create_feature_extractor()
     image = tf.random_uniform([5, 256, 256, 3])
     with tf.variable_scope('zero_state'):
-      feature_map = feature_extractor.extract_features(
+      feature_map = extractor.extract_features(
           image, stateful_reader.next_batch)
     with tf.Session() as sess:
       sess.run(tf.global_variables_initializer())

research/lstm_object_detection/models/mobilenet_defs.py

@@ -14,7 +14,7 @@
 # ==============================================================================
 """Definitions for modified MobileNet models used in LSTD."""
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from tensorflow.contrib import slim as contrib_slim
 
 from nets import mobilenet_v1

research/lstm_object_detection/models/mobilenet_defs_test.py

@@ -18,7 +18,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from lstm_object_detection.models import mobilenet_defs
 from nets import mobilenet_v1
 from nets.mobilenet import mobilenet_v2

research/lstm_object_detection/test_tflite_model.py

@@ -18,7 +18,7 @@
 from __future__ import print_function
 from absl import flags
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 flags.DEFINE_string('model_path', None, 'Path to model.')
 FLAGS = flags.FLAGS

research/lstm_object_detection/tflite/BUILD

@@ -50,7 +50,9 @@ cc_library(
         "//utils:ssd_utils",
     ] + select({
         "//conditions:default": [],
-        "enable_edgetpu": ["@libedgetpu//libedgetpu:header"],
+        "enable_edgetpu": [
+            "@libedgetpu//libedgetpu:header",
+        ],
     }),
     alwayslink = 1,
 )
@@ -71,7 +73,9 @@ cc_library(
         "@org_tensorflow//tensorflow/lite/kernels:builtin_ops",
     ] + select({
         "//conditions:default": [],
-        "enable_edgetpu": ["@libedgetpu//libedgetpu:header"],
+        "enable_edgetpu": [
+            "@libedgetpu//libedgetpu:header",
+        ],
     }),
     alwayslink = 1,
 )

research/lstm_object_detection/tflite/WORKSPACE

@@ -22,6 +22,12 @@ http_archive(
     strip_prefix = "abseil-cpp-a02f62f456f2c4a7ecf2be3104fe0c6e16fbad9a",
 )
 
+http_archive(
+    name = "rules_cc",
+    strip_prefix = "rules_cc-master",
+    urls = ["https://github.com/bazelbuild/rules_cc/archive/master.zip"],
+)
+
 # GoogleTest/GoogleMock framework. Used by most unit-tests.
 http_archive(
      name = "com_google_googletest",
@@ -90,6 +96,12 @@ http_archive(
     sha256 = "79d102c61e2a479a0b7e5fc167bcfaa4832a0c6aad4a75fa7da0480564931bcc",
 )
 
+#
+# http_archive(
+#     name = "com_google_protobuf",
+#     strip_prefix = "protobuf-master",
+#     urls = ["https://github.com/protocolbuffers/protobuf/archive/master.zip"],
+# )
 
 # Needed by TensorFlow
 http_archive(

research/lstm_object_detection/train.py

@@ -46,7 +46,7 @@ import functools
 import json
 import os
 from absl import flags
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from lstm_object_detection import model_builder
 from lstm_object_detection import trainer
 from lstm_object_detection.inputs import seq_dataset_builder

research/lstm_object_detection/trainer.py

@@ -20,7 +20,8 @@ DetectionModel.
 """
 
 import functools
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.builders import optimizer_builder
 from object_detection.core import standard_fields as fields
@@ -28,7 +29,7 @@ from object_detection.utils import ops as util_ops
 from object_detection.utils import variables_helper
 from deployment import model_deploy
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 def create_input_queue(create_tensor_dict_fn):

research/lstm_object_detection/utils/config_util.py

@@ -19,7 +19,7 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from google.protobuf import text_format
 from lstm_object_detection.protos import input_reader_google_pb2  # pylint: disable=unused-import

research/lstm_object_detection/utils/config_util_test.py

@@ -20,7 +20,7 @@ from __future__ import division
 from __future__ import print_function
 
 import os
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from google.protobuf import text_format
 from lstm_object_detection.protos import pipeline_pb2 as internal_pipeline_pb2