Change the data type of mean_values and std_dev_values to float.

PiperOrigin-RevId: 211010293

4 files changed, 23 insertions, 9 deletions

diff --git a/tensorflow/contrib/lite/python/convert.py b/tensorflow/contrib/lite/python/convert.py
index 0b2192e031..69a3d562b3 100644
--- a/tensorflow/contrib/lite/python/convert.py
+++ b/tensorflow/contrib/lite/python/convert.py
@@ -149,9 +149,11 @@ def build_toco_convert_protos(input_tensors,
       as `input_tensors`, or None. (default None)
     output_format: Output file format. Currently must be `{TFLITE,
       GRAPHVIZ_DOT}`. (default TFLITE)
-    quantized_input_stats: List of tuples of integers representing the mean and
+    quantized_input_stats: List of tuples of floats representing the mean and
       standard deviation. Each tuple maps to the corresponding input tensor.
-      Only need if `inference_type` is `QUANTIZED_UINT8`. (default None)
+      Only need if `inference_input_type` is `QUANTIZED_UINT8`.
+      real_input_value = (quantized_input_value - mean_value) / std_dev_value.
+      (default None)
     default_ranges_stats: Tuple of integers representing (min, max) range values
       for all arrays without a specified range. Intended for experimenting with
       quantization via "dummy quantization". (default None)
@@ -197,6 +199,8 @@ def build_toco_convert_protos(input_tensors,
   toco.inference_type = inference_type
   if inference_input_type:
     toco.inference_input_type = inference_input_type
+  else:
+    toco.inference_input_type = toco.inference_type
   toco.drop_control_dependency = drop_control_dependency
   toco.reorder_across_fake_quant = reorder_across_fake_quant
   toco.allow_custom_ops = allow_custom_ops
@@ -212,7 +216,7 @@ def build_toco_convert_protos(input_tensors,
   model.change_concat_input_ranges = change_concat_input_ranges
   for idx, input_tensor in enumerate(input_tensors):
     input_array = model.input_arrays.add()
-    if inference_type == lite_constants.QUANTIZED_UINT8:
+    if toco.inference_input_type == lite_constants.QUANTIZED_UINT8:
       input_array.mean_value, input_array.std_value = quantized_input_stats[idx]
     input_array.name = tensor_name(input_tensor)
     if input_shapes is None:
diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py
index a4c9a2381c..80cbb12825 100644
--- a/tensorflow/contrib/lite/python/lite.py
+++ b/tensorflow/contrib/lite/python/lite.py
@@ -78,9 +78,11 @@ class TocoConverter(object):
     output_format: Output file format. Currently must be `{TFLITE,
       GRAPHVIZ_DOT}`. (default TFLITE)
     quantized_input_stats: Dict of strings representing input tensor names
-      mapped to tuple of integers representing the mean and standard deviation
+      mapped to tuple of floats representing the mean and standard deviation
       of the training data (e.g., {"foo" : (0., 1.)}). Only need if
-      `inference_type` is `QUANTIZED_UINT8`. (default {})
+      `inference_input_type` is `QUANTIZED_UINT8`.
+      real_input_value = (quantized_input_value - mean_value) / std_dev_value.
+      (default {})
     default_ranges_stats: Tuple of integers representing (min, max) range values
       for all arrays without a specified range. Intended for experimenting with
       quantization via "dummy quantization". (default None)
diff --git a/tensorflow/contrib/lite/python/lite_test.py b/tensorflow/contrib/lite/python/lite_test.py
index 8c9cfa943f..e6aa8b0d99 100644
--- a/tensorflow/contrib/lite/python/lite_test.py
+++ b/tensorflow/contrib/lite/python/lite_test.py
@@ -319,6 +319,7 @@ class FromSessionTest(test_util.TensorFlowTestCase):
     # Convert model and ensure model is not None.
     converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
     converter.inference_input_type = lite_constants.QUANTIZED_UINT8
+    converter.quantized_input_stats = {'Placeholder': (0., 1.)}  # mean, std_dev
     tflite_model = converter.convert()
     self.assertTrue(tflite_model)
 
diff --git a/tensorflow/contrib/lite/python/tflite_convert.py b/tensorflow/contrib/lite/python/tflite_convert.py
index ce12a9abde..dc078ffd21 100644
--- a/tensorflow/contrib/lite/python/tflite_convert.py
+++ b/tensorflow/contrib/lite/python/tflite_convert.py
@@ -109,8 +109,14 @@ def _convert_model(flags):
 
   if flags.mean_values and flags.std_dev_values:
     input_arrays = converter.get_input_arrays()
-    std_dev_values = _parse_array(flags.std_dev_values, type_fn=int)
-    mean_values = _parse_array(flags.mean_values, type_fn=int)
+    std_dev_values = _parse_array(flags.std_dev_values, type_fn=float)
+
+    # In quantized inference, mean_value has to be integer so that the real
+    # value 0.0 is exactly representable.
+    if flags.inference_type == lite_constants.QUANTIZED_UINT8:
+      mean_values = _parse_array(flags.mean_values, type_fn=int)
+    else:
+      mean_values = _parse_array(flags.mean_values, type_fn=float)
     quant_stats = list(zip(mean_values, std_dev_values))
     if ((not flags.input_arrays and len(input_arrays) > 1) or
         (len(input_arrays) != len(quant_stats))):
@@ -293,12 +299,13 @@ def run_main(_):
       "--std_dev_values",
       type=str,
       help=("Standard deviation of training data for each input tensor, "
-            "comma-separated integers. Used for quantization. (default None)"))
+            "comma-separated floats. Used for quantized input tensors. "
+            "(default None)"))
   parser.add_argument(
       "--mean_values",
       type=str,
       help=("Mean of training data for each input tensor, comma-separated "
-            "integers. Used for quantization. (default None)"))
+            "floats. Used for quantized input tensors. (default None)"))
   parser.add_argument(
       "--default_ranges_min",
       type=int,