Speed up creation of visualizer html page for TensorFlow Lite.

Use the NumPy functionality of the object based flatbuffer API.
This speeds up a model that took 15 minutes to visualize.

PiperOrigin-RevId: 313255207
Change-Id: Ic9d43cbd97c6d5026d903ee947a0a56a0732f150

tensorflow/lite/tools/BUILD

@@ -17,7 +17,10 @@ py_binary(
     srcs = ["visualize.py"],
     python_version = "PY3",
     srcs_version = "PY2AND3",
-    deps = ["//tensorflow/lite/python:schema_py"],
+    deps = [
+        "//tensorflow/lite/python:schema_py",
+        "//third_party/py/numpy",
+    ],
 )
 
 py_test(

tensorflow/lite/tools/visualize.py

@@ -28,6 +28,7 @@ import json
 import os
 import re
 import sys
+import numpy as np
 
 from tensorflow.lite.python import schema_py_generated as schema_fb
 
@@ -377,23 +378,34 @@ def CamelCaseToSnakeCase(camel_case_input):
   return re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1).lower()
 
 
-def FlatbufferToDict(fb):
+def FlatbufferToDict(fb, preserve_as_numpy):
-  """Converts a hierarchy of FB objects into a nested dict."""
+  """Converts a hierarchy of FB objects into a nested dict.
-  if hasattr(fb, "__dict__"):
+
+  We avoid transforming big parts of the flat buffer into python arrays. This
+  speeds conversion from ten minutes to a few seconds on big graphs.
+
+  Args:
+    fb: a flat buffer structure. (i.e. ModelT)
+    preserve_as_numpy: true if all downstream np.arrays should be preserved.
+      false if all downstream np.array should become python arrays
+  Returns:
+    A dictionary representing the flatbuffer rather than a flatbuffer object.
+  """
+  if isinstance(fb, int) or isinstance(fb, float) or isinstance(fb, str):
+    return fb
+  elif hasattr(fb, "__dict__"):
     result = {}
     for attribute_name in dir(fb):
       attribute = fb.__getattribute__(attribute_name)
       if not callable(attribute) and attribute_name[0] != "_":
         snake_name = CamelCaseToSnakeCase(attribute_name)
-        result[snake_name] = FlatbufferToDict(attribute)
+        preserve = True if attribute_name == "buffers" else preserve_as_numpy
+        result[snake_name] = FlatbufferToDict(attribute, preserve)
     return result
-  elif isinstance(fb, str):
+  elif isinstance(fb, np.ndarray):
-    return fb
+    return fb if preserve_as_numpy else fb.tolist()
   elif hasattr(fb, "__len__"):
-    result = []
+    return [FlatbufferToDict(entry, preserve_as_numpy) for entry in fb]
-    for entry in fb:
-      result.append(FlatbufferToDict(entry))
-    return result
   else:
     return fb
 
@@ -401,7 +413,7 @@ def FlatbufferToDict(fb):
 def CreateDictFromFlatbuffer(buffer_data):
   model_obj = schema_fb.Model.GetRootAsModel(buffer_data, 0)
   model = schema_fb.ModelT.InitFromObj(model_obj)
-  return FlatbufferToDict(model)
+  return FlatbufferToDict(model, preserve_as_numpy=False)
 
 
 def CreateHtmlFile(tflite_input, html_output):