Merge pull request #1 from tensorflow/master

Updated

research/lstm_object_detection/eval.py

@@ -25,7 +25,7 @@ This executable is used to evaluate DetectionModels. Example usage:
 
 import functools
 import os
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from google.protobuf import text_format
 from lstm_object_detection import evaluator
 from lstm_object_detection import model_builder

research/lstm_object_detection/evaluator.py

@@ -20,7 +20,8 @@ DetectionModel.
 
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+from tensorflow.contrib import tfprof as contrib_tfprof
 from lstm_object_detection.metrics import coco_evaluation_all_frames
 from object_detection import eval_util
 from object_detection.core import prefetcher
@@ -105,13 +106,13 @@ def _extract_prediction_tensors(model,
   detections = _create_detection_op(model, input_dict, batch)
 
   # Print out anaylsis of the model.
-  tf.contrib.tfprof.model_analyzer.print_model_analysis(
+  contrib_tfprof.model_analyzer.print_model_analysis(
       tf.get_default_graph(),
-      tfprof_options=tf.contrib.tfprof.model_analyzer.
-      TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
-  tf.contrib.tfprof.model_analyzer.print_model_analysis(
+      tfprof_options=contrib_tfprof.model_analyzer
+      .TRAINABLE_VARS_PARAMS_STAT_OPTIONS)
+  contrib_tfprof.model_analyzer.print_model_analysis(
       tf.get_default_graph(),
-      tfprof_options=tf.contrib.tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
+      tfprof_options=contrib_tfprof.model_analyzer.FLOAT_OPS_OPTIONS)
 
   num_frames = len(input_dict[fields.InputDataFields.image])
   ret = []

research/lstm_object_detection/export_tflite_lstd_graph.py

@@ -84,7 +84,7 @@ python lstm_object_detection/export_tflite_lstd_graph.py \
        "
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from lstm_object_detection import export_tflite_lstd_graph_lib
 from lstm_object_detection.utils import config_util

research/lstm_object_detection/export_tflite_lstd_graph_lib.py

@@ -20,7 +20,7 @@ import os
 import tempfile
 
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from tensorflow.core.framework import attr_value_pb2
 from tensorflow.core.framework import types_pb2

research/lstm_object_detection/export_tflite_lstd_model.py

@@ -17,7 +17,7 @@
 
 import os
 from absl import flags
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from lstm_object_detection.utils import config_util
 

research/lstm_object_detection/inputs/seq_dataset_builder.py

@@ -22,7 +22,8 @@ Note: If users wishes to also use their own InputReaders with the Object
 Detection configuration framework, they should define their own builder function
 that wraps the build function.
 """
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+from tensorflow.contrib import slim as contrib_slim
 from tensorflow.contrib.training.python.training import sequence_queueing_state_saver as sqss
 from lstm_object_detection.inputs import tf_sequence_example_decoder
 from lstm_object_detection.protos import input_reader_google_pb2
@@ -32,7 +33,7 @@ from object_detection.core import standard_fields as fields
 from object_detection.protos import input_reader_pb2
 from object_detection.utils import ops as util_ops
 
-parallel_reader = tf.contrib.slim.parallel_reader
+parallel_reader = contrib_slim.parallel_reader
 # TODO(yinxiao): Make the following variable into configurable proto.
 # Padding size for the labeled objects in each frame. Here we assume each
 # frame has a total number of objects less than _PADDING_SIZE.

research/lstm_object_detection/inputs/seq_dataset_builder_test.py

@@ -17,7 +17,7 @@
 
 import os
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from google.protobuf import text_format
 from tensorflow.core.example import example_pb2
@@ -33,6 +33,68 @@ from object_detection.protos import preprocessor_pb2
 
 class DatasetBuilderTest(tf.test.TestCase):
 
+  def _create_tf_record(self):
+    path = os.path.join(self.get_temp_dir(), 'tfrecord')
+    writer = tf.python_io.TFRecordWriter(path)
+
+    image_tensor = np.random.randint(255, size=(16, 16, 3)).astype(np.uint8)
+    with self.test_session():
+      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
+
+    sequence_example = example_pb2.SequenceExample(
+        context=feature_pb2.Features(
+            feature={
+                'image/format':
+                    feature_pb2.Feature(
+                        bytes_list=feature_pb2.BytesList(
+                            value=['jpeg'.encode('utf-8')])),
+                'image/height':
+                    feature_pb2.Feature(
+                        int64_list=feature_pb2.Int64List(value=[16])),
+                'image/width':
+                    feature_pb2.Feature(
+                        int64_list=feature_pb2.Int64List(value=[16])),
+            }),
+        feature_lists=feature_pb2.FeatureLists(
+            feature_list={
+                'image/encoded':
+                    feature_pb2.FeatureList(feature=[
+                        feature_pb2.Feature(
+                            bytes_list=feature_pb2.BytesList(
+                                value=[encoded_jpeg])),
+                    ]),
+                'image/object/bbox/xmin':
+                    feature_pb2.FeatureList(feature=[
+                        feature_pb2.Feature(
+                            float_list=feature_pb2.FloatList(value=[0.0])),
+                    ]),
+                'image/object/bbox/xmax':
+                    feature_pb2.FeatureList(feature=[
+                        feature_pb2.Feature(
+                            float_list=feature_pb2.FloatList(value=[1.0]))
+                    ]),
+                'image/object/bbox/ymin':
+                    feature_pb2.FeatureList(feature=[
+                        feature_pb2.Feature(
+                            float_list=feature_pb2.FloatList(value=[0.0])),
+                    ]),
+                'image/object/bbox/ymax':
+                    feature_pb2.FeatureList(feature=[
+                        feature_pb2.Feature(
+                            float_list=feature_pb2.FloatList(value=[1.0]))
+                    ]),
+                'image/object/class/label':
+                    feature_pb2.FeatureList(feature=[
+                        feature_pb2.Feature(
+                            int64_list=feature_pb2.Int64List(value=[2]))
+                    ]),
+            }))
+
+    writer.write(sequence_example.SerializeToString())
+    writer.close()
+
+    return path
+
   def _get_model_configs_from_proto(self):
     """Creates a model text proto for testing.
 

research/lstm_object_detection/inputs/tf_sequence_example_decoder.py

@@ -18,11 +18,12 @@
 A decoder to decode string tensors containing serialized
 tensorflow.SequenceExample protos.
 """
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+from tensorflow.contrib import slim as contrib_slim
 from object_detection.core import data_decoder
 from object_detection.core import standard_fields as fields
 
-tfexample_decoder = tf.contrib.slim.tfexample_decoder
+tfexample_decoder = contrib_slim.tfexample_decoder
 
 
 class BoundingBoxSequence(tfexample_decoder.ItemHandler):

research/lstm_object_detection/inputs/tf_sequence_example_decoder_test.py

@@ -16,7 +16,7 @@
 """Tests for lstm_object_detection.tf_sequence_example_decoder."""
 
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from tensorflow.core.example import example_pb2
 from tensorflow.core.example import feature_pb2
 from tensorflow.python.framework import dtypes

research/lstm_object_detection/lstm/lstm_cells.py

@@ -14,7 +14,7 @@
 # ==============================================================================
 """BottleneckConvLSTMCell implementation."""
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from tensorflow.contrib import layers as contrib_layers
 from tensorflow.contrib import rnn as contrib_rnn
@@ -494,11 +494,10 @@ class GroupedConvLSTMCell(contrib_rnn.RNNCell):
         f_act = tf.sigmoid(f_add)
         # The quantization range is fixed for the sigmoid to ensure that zero
         # is exactly representable.
-        f_act = lstm_utils.quantize_op(
+        f_act = lstm_utils.fixed_quantize_op(
             f_act,
-            is_training=False,
-            default_min=0,
-            default_max=1,
+            fixed_min=0.0,
+            fixed_max=1.0,
             is_quantized=self._is_quantized,
             scope='forget_gate_%d/act_quant' % k)
 
@@ -512,22 +511,20 @@ class GroupedConvLSTMCell(contrib_rnn.RNNCell):
         i_act = tf.sigmoid(i)
         # The quantization range is fixed for the sigmoid to ensure that zero
         # is exactly representable.
-        i_act = lstm_utils.quantize_op(
+        i_act = lstm_utils.fixed_quantize_op(
             i_act,
-            is_training=False,
-            default_min=0,
-            default_max=1,
+            fixed_min=0.0,
+            fixed_max=1.0,
             is_quantized=self._is_quantized,
             scope='input_gate_%d/act_quant' % k)
 
         j_act = self._activation(j)
         # The quantization range is fixed for the relu6 to ensure that zero
         # is exactly representable.
-        j_act = lstm_utils.quantize_op(
+        j_act = lstm_utils.fixed_quantize_op(
             j_act,
-            is_training=False,
-            default_min=0,
-            default_max=6,
+            fixed_min=0.0,
+            fixed_max=6.0,
             is_quantized=self._is_quantized,
             scope='new_input_%d/act_quant' % k)
 
@@ -546,11 +543,10 @@ class GroupedConvLSTMCell(contrib_rnn.RNNCell):
         # to the concat have the same range, removing the need for rescaling.
         # The quantization ranges input to the relu6 are propagated to its
         # output. Any mismatch between these two ranges will cause an error.
-        new_c = lstm_utils.quantize_op(
+        new_c = lstm_utils.fixed_quantize_op(
             new_c,
-            is_training=False,
-            default_min=0,
-            default_max=6,
+            fixed_min=0.0,
+            fixed_max=6.0,
             is_quantized=self._is_quantized,
             scope='new_c_%d/add_quant' % k)
 
@@ -565,22 +561,20 @@ class GroupedConvLSTMCell(contrib_rnn.RNNCell):
         new_c_act = self._activation(new_c)
         # The quantization range is fixed for the relu6 to ensure that zero
         # is exactly representable.
-        new_c_act = lstm_utils.quantize_op(
+        new_c_act = lstm_utils.fixed_quantize_op(
             new_c_act,
-            is_training=False,
-            default_min=0,
-            default_max=6,
+            fixed_min=0.0,
+            fixed_max=6.0,
             is_quantized=self._is_quantized,
             scope='new_c_%d/act_quant' % k)
 
         o_act = tf.sigmoid(o)
         # The quantization range is fixed for the sigmoid to ensure that zero
         # is exactly representable.
-        o_act = lstm_utils.quantize_op(
+        o_act = lstm_utils.fixed_quantize_op(
             o_act,
-            is_training=False,
-            default_min=0,
-            default_max=1,
+            fixed_min=0.0,
+            fixed_max=1.0,
             is_quantized=self._is_quantized,
             scope='output_%d/act_quant' % k)
 
@@ -588,11 +582,10 @@ class GroupedConvLSTMCell(contrib_rnn.RNNCell):
         # The quantization range is fixed since it is input to a concat.
         # A range of [0, 6] is used since |new_h| is a product of ranges [0, 6]
         # and [0, 1].
-        new_h_act = lstm_utils.quantize_op(
+        new_h_act = lstm_utils.fixed_quantize_op(
             new_h,
-            is_training=False,
-            default_min=0,
-            default_max=6,
+            fixed_min=0.0,
+            fixed_max=6.0,
             is_quantized=self._is_quantized,
             scope='new_h_%d/act_quant' % k)
 
@@ -710,7 +703,8 @@ class GroupedConvLSTMCell(contrib_rnn.RNNCell):
       raise ValueError('Expect rank 2 state tensor when flatten_state is set.')
 
     with tf.name_scope(None):
-      state = tf.identity(state, name='raw_inputs/init_lstm_h')
+      state = tf.identity(
+          state, name='raw_inputs/init_lstm_h_%d' % (input_index + 1))
     if self._flatten_state:
       batch_size = inputs.shape[0]
       height = inputs.shape[1]

research/lstm_object_detection/lstm/lstm_cells_test.py

@@ -19,7 +19,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from lstm_object_detection.lstm import lstm_cells
 

research/lstm_object_detection/lstm/rnn_decoder.py

@@ -15,7 +15,7 @@
 
 """Custom RNN decoder."""
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 import lstm_object_detection.lstm.utils as lstm_utils
 
 

research/lstm_object_detection/lstm/rnn_decoder_test.py

@@ -20,7 +20,7 @@ from __future__ import division
 from __future__ import print_function
 
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from tensorflow.contrib import layers as contrib_layers
 from tensorflow.contrib import rnn as contrib_rnn

research/lstm_object_detection/lstm/utils.py

@@ -18,7 +18,7 @@
 from __future__ import absolute_import
 from __future__ import division
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 from tensorflow.contrib import framework as contrib_framework
 from tensorflow.contrib import layers as contrib_layers
 from tensorflow.python.training import moving_averages
@@ -204,19 +204,44 @@ def quantize_op(inputs,
   Returns:
     Tensor resulting from quantizing the input tensors.
   """
-  if is_quantized:
-    with tf.variable_scope(scope):
-      min_var = _quant_var('min', default_min)
-      max_var = _quant_var('max', default_max)
-      if is_training:
-        # TFLite requires that 0.0 is always in the [min; max] range.
-        range_min = tf.minimum(tf.reduce_min(inputs), 0.0, 'SafeQuantRangeMin')
-        range_max = tf.maximum(tf.reduce_max(inputs), 0.0, 'SafeQuantRangeMax')
-        min_val = moving_averages.assign_moving_average(
-            min_var, range_min, ema_decay, name='AssignMinEma')
-        max_val = moving_averages.assign_moving_average(
-            max_var, range_max, ema_decay, name='AssignMaxEma')
-        inputs = tf.fake_quant_with_min_max_vars(inputs, min_val, max_val)
-      else:
-        inputs = tf.fake_quant_with_min_max_vars(inputs, min_var, max_var)
-  return inputs
+  if not is_quantized:
+    return inputs
+
+  with tf.variable_scope(scope):
+    min_var = _quant_var('min', default_min)
+    max_var = _quant_var('max', default_max)
+    if not is_training:
+      # Just use variables in the checkpoint.
+      return tf.fake_quant_with_min_max_vars(inputs, min_var, max_var)
+
+    # While training, collect EMAs of ranges seen, store in min_var, max_var.
+    # TFLite requires that 0.0 is always in the [min; max] range.
+    range_min = tf.minimum(tf.reduce_min(inputs), 0.0, 'SafeQuantRangeMin')
+    range_max = tf.maximum(tf.reduce_max(inputs), 0.0, 'SafeQuantRangeMax')
+    min_val = moving_averages.assign_moving_average(
+        min_var, range_min, ema_decay, name='AssignMinEma')
+    max_val = moving_averages.assign_moving_average(
+        max_var, range_max, ema_decay, name='AssignMaxEma')
+    return tf.fake_quant_with_min_max_vars(inputs, min_val, max_val)
+
+
+def fixed_quantize_op(inputs, is_quantized=True,
+                      fixed_min=0.0, fixed_max=6.0, scope='quant'):
+  """Inserts a fake quantization op with fixed range after inputs.
+
+  Args:
+    inputs: A tensor of size [batch_size, height, width, channels].
+    is_quantized: flag to enable/disable quantization.
+    fixed_min: fixed min value for fake quant op.
+    fixed_max: fixed max value for fake quant op.
+    scope: Optional scope for variable_scope.
+
+  Returns:
+    Tensor resulting from quantizing the input tensors.
+  """
+  if not is_quantized:
+    return inputs
+
+  with tf.variable_scope(scope):
+    # Just use fixed quantization range.
+    return tf.fake_quant_with_min_max_args(inputs, fixed_min, fixed_max)

research/lstm_object_detection/lstm/utils_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.lstm import utils
 
 
@@ -73,13 +73,20 @@ class QuantizableUtilsTest(tf.test.TestCase):
     self._check_min_max_ema(tf.get_default_graph())
     self._check_min_max_vars(tf.get_default_graph())
 
-  def test_quantize_op_inferene(self):
+  def test_quantize_op_inference(self):
     inputs = tf.zeros([4, 10, 10, 128], dtype=tf.float32)
     outputs = utils.quantize_op(inputs, is_training=False)
     self.assertAllEqual(inputs.shape.as_list(), outputs.shape.as_list())
     self._check_no_min_max_ema(tf.get_default_graph())
     self._check_min_max_vars(tf.get_default_graph())
 
+  def test_fixed_quantize_op(self):
+    inputs = tf.zeros([4, 10, 10, 128], dtype=tf.float32)
+    outputs = utils.fixed_quantize_op(inputs)
+    self.assertAllEqual(inputs.shape.as_list(), outputs.shape.as_list())
+    self._check_no_min_max_ema(tf.get_default_graph())
+    self._check_no_min_max_vars(tf.get_default_graph())
+
   def _check_min_max_vars(self, graph):
     op_types = [op.type for op in graph.get_operations()]
     self.assertTrue(

research/lstm_object_detection/meta_architectures/lstm_ssd_meta_arch.py

@@ -24,7 +24,8 @@ for details.
 """
 import abc
 import re
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from object_detection.core import box_list_ops
 from object_detection.core import matcher
@@ -33,7 +34,7 @@ from object_detection.meta_architectures import ssd_meta_arch
 from object_detection.utils import ops
 from object_detection.utils import shape_utils
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 
 class LSTMSSDMetaArch(ssd_meta_arch.SSDMetaArch):

research/lstm_object_detection/meta_architectures/lstm_ssd_meta_arch_test.py

@@ -22,7 +22,8 @@ from __future__ import print_function
 import functools
 
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+from tensorflow.contrib import slim as contrib_slim
 
 from lstm_object_detection.lstm import lstm_cells
 from lstm_object_detection.meta_architectures import lstm_ssd_meta_arch
@@ -38,7 +39,7 @@ from object_detection.utils import test_case
 from object_detection.utils import test_utils
 
 
-slim = tf.contrib.slim
+slim = contrib_slim
 
 MAX_TOTAL_NUM_BOXES = 5
 NUM_CLASSES = 1

research/lstm_object_detection/metrics/coco_evaluation_all_frames.py

@@ -15,7 +15,7 @@
 
 """Class for evaluating video object detections with COCO metrics."""
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 
 from object_detection.core import standard_fields
 from object_detection.metrics import coco_evaluation

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