[TensorFlow Lite] The performance experiment of TensorFlow Lite

Based on my previous post: [TensorFlow Lite] My first try with TensorFlow Lite ,
I am just curious about the performance of TensorFlow Lite on the different platforms so that I do a performance experiment for it.
There are a X86 server (no GPU enabled) and a Nvidia TX2 board ( which is ARM based ) to do the experiment. They both run TFLite’s official example using Inception_v3 model and get average inference time as the following table.

Hardware Information:
Nvidia TX2: 4 CPU cores and 1 GPU ( but does not use )
X86 server: 24 CPU cores and no GPU enabled

[XLA] Build Tensorflow XLA AOT Shared Library

Build Tensorflow XLA AOT Shared Library:

Add the following code into tensorflow/compiler/aot/BUILD in TensorFlow source:

cc_binary(
    name = "libxlaaot.so",
    deps = [":embedded_protocol_buffers",
        "//tensorflow/compiler/tf2xla:xla_jit_compiled_cpu_function",
        "//tensorflow/compiler/tf2xla",
        "//tensorflow/compiler/tf2xla:cpu_function_runtime",
        "//tensorflow/compiler/tf2xla:tf2xla_proto",
        "//tensorflow/compiler/tf2xla:tf2xla_util",
        "//tensorflow/compiler/tf2xla:xla_compiler",
        "//tensorflow/compiler/tf2xla/kernels:xla_cpu_only_ops",
        "//tensorflow/compiler/tf2xla/kernels:xla_dummy_ops",
        "//tensorflow/compiler/tf2xla/kernels:xla_ops",
        "//tensorflow/compiler/xla:shape_util",
        "//tensorflow/compiler/xla:statusor",
        "//tensorflow/compiler/xla:util",
        "//tensorflow/compiler/xla:xla_data_proto",
        "//tensorflow/compiler/xla/client:client_library",
        "//tensorflow/compiler/xla/client:compile_only_client",
        "//tensorflow/compiler/xla/client:xla_computation",
        "//tensorflow/compiler/xla/service:compiler",
        "//tensorflow/compiler/xla/service/cpu:buffer_info_util",
        "//tensorflow/compiler/xla/service/cpu:cpu_compiler",
        "//tensorflow/core:core_cpu_internal",
        "//tensorflow/core:framework_internal",
        "//tensorflow/core:lib",
        "//tensorflow/core:lib_internal",
        "//tensorflow/core:protos_all_cc",
        ":tfcompile_lib",
        "//tensorflow/compiler/xla/legacy_flags:debug_options_flags",
        "//tensorflow/core:core_cpu",
        "//tensorflow/core:framework",
        "//tensorflow/core:graph",
        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/types:span",
    ],
    linkopts=["-shared -Wl,--whole-archive" ],
    linkshared=1

)

[TensorFlow Lite] Build Tensorflow Lite C++ shared library

Build Tensorflow Lite shared library:

Modify the code: tensorflow/contrib/lite/BUILD in TensorFlow source:

cc_binary(
    name = "libtflite.so",
    deps = [":framework",
        "//tensorflow/contrib/lite/kernels:builtin_ops",
        "//tensorflow/contrib/lite/kernels:eigen_support",
        "//tensorflow/contrib/lite/kernels:gemm_support",
    ],
    linkopts=["-shared -Wl,--whole-archive" ],
    linkshared=1

)

[TFCompile] Use XLA AOT Compiler to compile Resnet50 model and do inference

I inspired by this following article and try to do something different because it's approach using Keras has an issue for XLA AOT compiler.
Kerasモデルをtfcompileでコンパイルする
Instead, I download the pre-trained Resnet50 model and optimize simply by the tool:transform_graph

Download:

wget http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v2_fp32_savedmodel_NHWC.tar.gz
or
wget http://download.tensorflow.org/models/official/20181001_resnet/savedmodels/resnet_v2_fp32_savedmodel_NHWC_jpg.tar.gz

Transform:

bazel-bin/tensorflow/tools/graph_transforms/transform_graph \
--in_graph='/home/liudanny/git/tensorflow_danny/tensorflow/compiler/aot/myaot_resnet50/resnetv2_imagenet_frozen_graph.pb' \
--out_graph='/home/liudanny/workspace/pyutillib/my_resnet50/optimized_frozen_graph.pb' \
--inputs='input_tensor:0' \
--outputs='softmax_tensor:0' \
--transforms='
  strip_unused_nodes
  fold_constants'

#I don't enable the follwoing options due to worse performance.
#fold_batch_norms      <== For XLA AOT, it is not good in the performance
#fold_old_batch_norms  <== For XLA AOT, it is not good in the performance
#quantize_weights' <== XLA AOT doesn't support

[AutoKeras] My first try with a simple example of AutoKeras

AutoKeras only supports Python 3.6 so that the running environment has to install Python 3.6. My operation system is Ubuntu 16.04 and it needs to add apt repository first.

Install Python 3.6 and AutoKeras ( Don't remove Python 3.5)

# Install pip3
apt-get install python3-pip
# Install Python 3.6
apt-get install software-properties-common
add-apt-repository ppa:jonathonf/python-3.6
apt-get update
apt-get install python3.6

update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.5 1
update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.6 2
update-alternatives --config python3

ln -s /usr/include/python3.5 /usr/include/python3.6m
pip3 install lws
pip3 install autokeras

[TensorFlow] Build TensorFlow v1.12 from the source on Ubuntu 16.04

My previous post: [TensorFlow] How to build your C++ program or application with TensorFlow library using CMake

It is for building TensorFlow from the source based on v1.10. Currently, I want to upgrade it to v1.12 and encounter some problems. 

First, my version of ProtoBuf library on my system is v3.6.1 so that we should align its version in the TensorFlow.
Second, it seems that there are a few issues when building TensorFlow v1.12 that we need to deal with it case by case.

[TensorFlow Lite] My first try with TensorFlow Lite

I just take my first try with the example: label_image (tensorflow/contrib/lite/examples/label_image) in TensorFlow Lite and write down the commands that I used.
There are a bunch of information from the offical TensorFlow Lite guide:
https://www.tensorflow.org/lite/guide

1. convert the example of model to tflite format

[Tool] Convert TensorFlow graph to UFF format

The previous post: How to use TensorRT to do inference with TensorFlow model ? has introduced away to do the converting job for UFF format model. But, basically there are 2 ways to do that:

1. Convert TensorFlow's Session GraphDef directly on the fly to UFF format model
    ==> convert_uff_from_tensorflow()
2. Convert the frozen model file to UFF format model
    ==> convert_uff_from_frozen_model()

The following code is about the functions to convert TensorFlow graph to UFF format for running with TensorRT.