Check in seq_flow_lite (#10502)

research/seq_flow_lite/.bazelrc

@@ -19,6 +19,7 @@ build:manylinux2010 --crosstool_top=//third_party/toolchains/preconfig/ubuntu16.
 
 build -c opt
 build --cxxopt="-std=c++14"
+build --host_cxxopt="-std=c++14"
 build --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0"
 build --auto_output_filter=subpackages
 build --copt="-Wall" --copt="-Wno-sign-compare"
@@ -60,11 +61,6 @@ build --define=grpc_no_ares=true
 # archives in -whole_archive -no_whole_archive.
 build --noincompatible_remove_legacy_whole_archive
 
-# These are bazel 2.0's incompatible flags. Tensorflow needs to use bazel 2.0.0
-# to use cc_shared_library, as part of the Tensorflow Build Improvements RFC:
-# https://github.com/tensorflow/community/pull/179
-build --noincompatible_prohibit_aapt1
-
 # Build TF with C++ 17 features.
 build:c++17 --cxxopt=-std=c++1z
 build:c++17 --cxxopt=-stdlib=libc++

research/seq_flow_lite/utils/tflite_utils.py

@@ -28,6 +28,40 @@ def _dump_graph_in_text_format(filename, graph_def):
   f.close()
 
 
+def get_mean_stddev_values(min_value_of_features, max_value_of_features):
+  """Gets Mean and Stddev values for given min/max float values."""
+  quant_min = 0
+  quant_max = 255
+
+  min_global = min_value_of_features
+  max_global = max_value_of_features
+
+  quant_min_float = float(quant_min)
+  quant_max_float = float(quant_max)
+
+  nudged_scale = (max_global - min_global) / (quant_max_float - quant_min_float)
+
+  zero_point_from_min = quant_min_float - min_global / nudged_scale
+
+  if zero_point_from_min < quant_min_float:
+    nudged_zero_point = int(quant_min)
+  elif zero_point_from_min > quant_max_float:
+    nudged_zero_point = int(quant_max)
+  else:
+    nudged_zero_point = int(round(zero_point_from_min))
+
+  nudged_min = (quant_min_float - nudged_zero_point) * (nudged_scale)
+  nudged_max = (quant_max_float - nudged_zero_point) * (nudged_scale)
+
+  zero_point = (quant_min - min_global) / (max_global - min_global) * quant_max
+  scale = (nudged_max - nudged_min) / 255.0
+
+  mean_value = zero_point
+  stddev_value = 1 / scale
+
+  return mean_value, stddev_value
+
+
 class InterpreterWithCustomOps(tf.lite.Interpreter):
   """Extended tf.lite.Interpreter."""