Tensorflow-bin

Prebuilt binary with Tensorflow Lite enabled for RaspberryPi

Forked from https://github.com/PINTO0309/Tensorflow-bin for use with AutoML cloud TPU models until they support v1.14

Python 3.x + Tensorflow v1.13.1

Best	.whl	jemalloc	MPI	4Threads	Note
⭐	tensorflow-1.13.1-cp35-cp35m-linux_armv7l.whl	○		○

Example of Python 3.x series

$ sudo apt-get install -y libhdf5-dev libc-ares-dev libeigen3-dev
$ sudo pip3 install keras_applications==1.0.7 --no-deps
$ sudo pip3 install keras_preprocessing==1.0.9 --no-deps
$ sudo pip3 install h5py==2.9.0
$ sudo apt-get install -y openmpi-bin libopenmpi-dev
$ sudo apt-get install -y libatlas-base-dev
$ pip3 install -U --user six wheel mock
$ sudo pip3 uninstall tensorflow
$ wget https://github.com/mefitzgerald/Tensorflow-bin/raw/master/tensorflow-1.13.1-cp35-cp35m-linux_armv7l.whl
$ sudo pip3 install tensorflow-1.13.1-cp35-cp35m-linux_armv7l.whl

【Required】 Restart the terminal.

Example of Python 3.x series

$ python3
>>> import tensorflow
>>> tensorflow.__version__
1.13.0
>>> exit()

Sample of MultiThread x4

• Preparation of test environment

$ cd ~;mkdir test
$ curl https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/lite/examples/label_image/testdata/grace_hopper.bmp > ~/test/grace_hopper.bmp
$ curl https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_1.0_224_frozen.tgz | tar xzv -C ~/test mobilenet_v1_1.0_224/labels.txt
$ mv ~/test/mobilenet_v1_1.0_224/labels.txt ~/test/
$ curl http://download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224_quant.tgz | tar xzv -C ~/test
$ cp tensorflow/tensorflow/contrib/lite/examples/python/label_image.py ~/test

[Sample Code] label_image.py

import argparse
import numpy as np
import time

from PIL import Image

# Tensorflow v1.13.0+, v2.x.x
from tensorflow.lite.python import interpreter as interpreter_wrapper

def load_labels(filename):
  my_labels = []
  input_file = open(filename, 'r')
  for l in input_file:
    my_labels.append(l.strip())
  return my_labels
if __name__ == "__main__":
  floating_model = False
  parser = argparse.ArgumentParser()
  parser.add_argument("-i", "--image", default="/tmp/grace_hopper.bmp", \
    help="image to be classified")
  parser.add_argument("-m", "--model_file", \
    default="/tmp/mobilenet_v1_1.0_224_quant.tflite", \
    help=".tflite model to be executed")
  parser.add_argument("-l", "--label_file", default="/tmp/labels.txt", \
    help="name of file containing labels")
  parser.add_argument("--input_mean", default=127.5, help="input_mean")
  parser.add_argument("--input_std", default=127.5, \
    help="input standard deviation")
  parser.add_argument("--num_threads", default=1, help="number of threads")
  args = parser.parse_args()

  interpreter = interpreter_wrapper.Interpreter(model_path=args.model_file)
  interpreter.allocate_tensors()
  input_details = interpreter.get_input_details()
  output_details = interpreter.get_output_details()
  # check the type of the input tensor
  if input_details[0]['dtype'] == np.float32:
    floating_model = True
  # NxHxWxC, H:1, W:2
  height = input_details[0]['shape'][1]
  width = input_details[0]['shape'][2]
  img = Image.open(args.image)
  img = img.resize((width, height))
  # add N dim
  input_data = np.expand_dims(img, axis=0)
  if floating_model:
    input_data = (np.float32(input_data) - args.input_mean) / args.input_std

  interpreter.set_num_threads(int(args.num_threads))
  interpreter.set_tensor(input_details[0]['index'], input_data)

  start_time = time.time()
  interpreter.invoke()
  stop_time = time.time()

  output_data = interpreter.get_tensor(output_details[0]['index'])
  results = np.squeeze(output_data)
  top_k = results.argsort()[-5:][::-1]
  labels = load_labels(args.label_file)
  for i in top_k:
    if floating_model:
      print('{0:08.6f}'.format(float(results[i]))+":", labels[i])
    else:
      print('{0:08.6f}'.format(float(results[i]/255.0))+":", labels[i])

  print("time: ", stop_time - start_time)

• Run test

$ cd ~/test
$ python3 label_image.py \
--num_threads 1 \
--image grace_hopper.bmp \
--model_file mobilenet_v1_1.0_224_quant.tflite \
--label_file labels.txt

0.415686: 653:military uniform
0.352941: 907:Windsor tie
0.058824: 668:mortarboard
0.035294: 458:bow tie, bow-tie, bowtie
0.035294: 835:suit, suit of clothes
time:  0.4152982234954834

$ cd ~/test
$ python3 label_image.py \
--num_threads 4 \
--image grace_hopper.bmp \
--model_file mobilenet_v1_1.0_224_quant.tflite \
--label_file labels.txt

0.415686: 653:military uniform
0.352941: 907:Windsor tie
0.058824: 668:mortarboard
0.035294: 458:bow tie, bow-tie, bowtie
0.035294: 835:suit, suit of clothes
time:  0.1647195816040039

Tensorflow v1.13.1

============================================================

Tensorflow v1.13.1 - Bazel 0.19.2

============================================================

Python3.x

$ sudo nano /etc/dphys-swapfile
CONF_SWAPFILE=2048
CONF_MAXSWAP=2048

$ sudo systemctl stop dphys-swapfile
$ sudo systemctl start dphys-swapfile

$ wget https://github.com/PINTO0309/Tensorflow-bin/raw/master/zram.sh
$ chmod 755 zram.sh
$ sudo mv zram.sh /etc/init.d/
$ sudo update-rc.d zram.sh defaults
$ sudo reboot

$ sudo apt-get install -y libhdf5-dev libc-ares-dev libeigen3-dev
$ sudo pip3 install keras_applications==1.0.7 --no-deps
$ sudo pip3 install keras_preprocessing==1.0.9 --no-deps
$ sudo pip3 install h5py==2.9.0
$ sudo apt-get install -y openmpi-bin libopenmpi-dev
$ sudo -H pip3 install -U --user six numpy wheel mock
$ sudo apt update;sudo apt upgrade

$ cd ~
$ git clone https://github.com/PINTO0309/Bazel_bin.git
$ cd Bazel_bin
$ ./0.19.2/Raspbian_armhf/install.sh

$ cd ~
$ git clone https://github.com/tensorflow/tensorflow.git
$ cd tensorflow

• tensorflow/lite/python/interpreter.py

import sys
import numpy as np

# pylint: disable=g-import-not-at-top
try:
  from tensorflow.python.util.lazy_loader import LazyLoader
  from tensorflow.python.util.tf_export import tf_export as _tf_export

  # Lazy load since some of the performance benchmark skylark rules
  # break dependencies. Must use double quotes to match code internal rewrite
  # rule.
  # pylint: disable=g-inconsistent-quotes
  _interpreter_wrapper = LazyLoader(
      "_interpreter_wrapper", globals(),
      "tensorflow.lite.python.interpreter_wrapper."
      "tensorflow_wrap_interpreter_wrapper")
  # pylint: enable=g-inconsistent-quotes

  del LazyLoader
except ImportError:
  # When full Tensorflow Python PIP is not available do not use lazy load
  # and instead uf the tflite_runtime path.
  from tflite_runtime.lite.python import interpreter_wrapper as _interpreter_wrapper

  def tf_export_dummy(*x, **kwargs):
    del x, kwargs
    return lambda x: x
  _tf_export = tf_export_dummy


@_tf_export('lite.Interpreter')
class Interpreter(object):
  """Interpreter inferace for TF-Lite Models."""

  def __init__(self, model_path=None, model_content=None):
    """Constructor.
    Args:
      model_path: Path to TF-Lite Flatbuffer file.
      model_content: Content of model.
    Raises:
      ValueError: If the interpreter was unable to create.
    """
    if model_path and not model_content:
      self._interpreter = (
          _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromFile(
              model_path))
      if not self._interpreter:
        raise ValueError('Failed to open {}'.format(model_path))
    elif model_content and not model_path:
      # Take a reference, so the pointer remains valid.
      # Since python strings are immutable then PyString_XX functions
      # will always return the same pointer.
      self._model_content = model_content
      self._interpreter = (
          _interpreter_wrapper.InterpreterWrapper_CreateWrapperCPPFromBuffer(
              model_content))
    elif not model_path and not model_path:
      raise ValueError('`model_path` or `model_content` must be specified.')
    else:
      raise ValueError('Can\'t both provide `model_path` and `model_content`')

  def allocate_tensors(self):
    self._ensure_safe()
    return self._interpreter.AllocateTensors()

  def _safe_to_run(self):
    """Returns true if there exist no numpy array buffers.
    This means it is safe to run tflite calls that may destroy internally
    allocated memory. This works, because in the wrapper.cc we have made
    the numpy base be the self._interpreter.
    """
    # NOTE, our tensor() call in cpp will use _interpreter as a base pointer.
    # If this environment is the only _interpreter, then the ref count should be
    # 2 (1 in self and 1 in temporary of sys.getrefcount).
    return sys.getrefcount(self._interpreter) == 2

  def _ensure_safe(self):
    """Makes sure no numpy arrays pointing to internal buffers are active.
    This should be called from any function that will call a function on
    _interpreter that may reallocate memory e.g. invoke(), ...
    Raises:
      RuntimeError: If there exist numpy objects pointing to internal memory
        then we throw.
    """
    if not self._safe_to_run():
      raise RuntimeError("""There is at least 1 reference to internal data
      in the interpreter in the form of a numpy array or slice. Be sure to
      only hold the function returned from tensor() if you are using raw
      data access.""")

  def _get_tensor_details(self, tensor_index):
    """Gets tensor details.
    Args:
      tensor_index: Tensor index of tensor to query.
    Returns:
      a dictionary containing the name, index, shape and type of the tensor.
    Raises:
      ValueError: If tensor_index is invalid.
    """
    tensor_index = int(tensor_index)
    tensor_name = self._interpreter.TensorName(tensor_index)
    tensor_size = self._interpreter.TensorSize(tensor_index)
    tensor_type = self._interpreter.TensorType(tensor_index)
    tensor_quantization = self._interpreter.TensorQuantization(tensor_index)

    if not tensor_name or not tensor_type:
      raise ValueError('Could not get tensor details')

    details = {
        'name': tensor_name,
        'index': tensor_index,
        'shape': tensor_size,
        'dtype': tensor_type,
        'quantization': tensor_quantization,
    }

    return details

  def get_tensor_details(self):
    """Gets tensor details for every tensor with valid tensor details.
    Tensors where required information about the tensor is not found are not
    added to the list. This includes temporary tensors without a name.
    Returns:
      A list of dictionaries containing tensor information.
    """
    tensor_details = []
    for idx in range(self._interpreter.NumTensors()):
      try:
        tensor_details.append(self._get_tensor_details(idx))
      except ValueError:
        pass
    return tensor_details

  def get_input_details(self):
    """Gets model input details.
    Returns:
      A list of input details.
    """
    return [
        self._get_tensor_details(i) for i in self._interpreter.InputIndices()
    ]

  def set_tensor(self, tensor_index, value):
    """Sets the value of the input tensor. Note this copies data in `value`.
    If you want to avoid copying, you can use the `tensor()` function to get a
    numpy buffer pointing to the input buffer in the tflite interpreter.
    Args:
      tensor_index: Tensor index of tensor to set. This value can be gotten from
                    the 'index' field in get_input_details.
      value: Value of tensor to set.
    Raises:
      ValueError: If the interpreter could not set the tensor.
    """
    self._interpreter.SetTensor(tensor_index, value)

  def resize_tensor_input(self, input_index, tensor_size):
    """Resizes an input tensor.
    Args:
      input_index: Tensor index of input to set. This value can be gotten from
                   the 'index' field in get_input_details.
      tensor_size: The tensor_shape to resize the input to.
    Raises:
      ValueError: If the interpreter could not resize the input tensor.
    """
    self._ensure_safe()
    # `ResizeInputTensor` now only accepts int32 numpy array as `tensor_size
    # parameter.
    tensor_size = np.array(tensor_size, dtype=np.int32)
    self._interpreter.ResizeInputTensor(input_index, tensor_size)

  def get_output_details(self):
    """Gets model output details.
    Returns:
      A list of output details.
    """
    return [
        self._get_tensor_details(i) for i in self._interpreter.OutputIndices()
    ]

  def get_tensor(self, tensor_index):
    """Gets the value of the input tensor (get a copy).
    If you wish to avoid the copy, use `tensor()`. This function cannot be used
    to read intermediate results.
    Args:
      tensor_index: Tensor index of tensor to get. This value can be gotten from
                    the 'index' field in get_output_details.
    Returns:
      a numpy array.
    """
    return self._interpreter.GetTensor(tensor_index)

  def tensor(self, tensor_index):
    """Returns function that gives a numpy view of the current tensor buffer.
    This allows reading and writing to this tensors w/o copies. This more
    closely mirrors the C++ Interpreter class interface's tensor() member, hence
    the name. Be careful to not hold these output references through calls
    to `allocate_tensors()` and `invoke()`. This function cannot be used to read
    intermediate results.
    Usage:
    ```
    interpreter.allocate_tensors()
    input = interpreter.tensor(interpreter.get_input_details()[0]["index"])
    output = interpreter.tensor(interpreter.get_output_details()[0]["index"])
    for i in range(10):
      input().fill(3.)
      interpreter.invoke()
      print("inference %s" % output())
    ```
    Notice how this function avoids making a numpy array directly. This is
    because it is important to not hold actual numpy views to the data longer
    than necessary. If you do, then the interpreter can no longer be invoked,
    because it is possible the interpreter would resize and invalidate the
    referenced tensors. The NumPy API doesn't allow any mutability of the
    the underlying buffers.
    WRONG:
    ```
    input = interpreter.tensor(interpreter.get_input_details()[0]["index"])()
    output = interpreter.tensor(interpreter.get_output_details()[0]["index"])()
    interpreter.allocate_tensors()  # This will throw RuntimeError
    for i in range(10):
      input.fill(3.)
      interpreter.invoke()  # this will throw RuntimeError since input,output
    ```
    Args:
      tensor_index: Tensor index of tensor to get. This value can be gotten from
                    the 'index' field in get_output_details.
    Returns:
      A function that can return a new numpy array pointing to the internal
      TFLite tensor state at any point. It is safe to hold the function forever,
      but it is not safe to hold the numpy array forever.
    """
    return lambda: self._interpreter.tensor(self._interpreter, tensor_index)

  def invoke(self):
    """Invoke the interpreter.
    Be sure to set the input sizes, allocate tensors and fill values before
    calling this.
    Raises:
      ValueError: When the underlying interpreter fails raise ValueError.
    """
    self._ensure_safe()
    self._interpreter.Invoke()

  def reset_all_variables(self):
    return self._interpreter.ResetVariableTensors()

  def set_num_threads(self, i):
    """Set number of threads used by TFLite kernels.
    If not set, kernels are running single-threaded. Note that currently,
    only some kernels, such as conv, are multithreaded.
    Args:
      i: number of threads.
    """
    return self._interpreter.SetNumThreads(i)

• tensorflow/lite/python/interpreter_wrapper/interpreter_wrapper.cc

// Corrected the vicinity of the last line as follows
PyObject* InterpreterWrapper::ResetVariableTensors() {
  TFLITE_PY_ENSURE_VALID_INTERPRETER();
  TFLITE_PY_CHECK(interpreter_->ResetVariableTensors());
  Py_RETURN_NONE;
}

PyObject* InterpreterWrapper::SetNumThreads(int i) {
  interpreter_->SetNumThreads(i);
  Py_RETURN_NONE;
}

}  // namespace interpreter_wrapper
}  // namespace tflite

• tensorflow/lite/python/interpreter_wrapper/interpreter_wrapper.h

  // should be the interpreter object providing the memory.
  PyObject* tensor(PyObject* base_object, int i);

  PyObject* SetNumThreads(int i);

 private:
  // Helper function to construct an `InterpreterWrapper` object.
  // It only returns InterpreterWrapper if it can construct an `Interpreter`.

• tensorflow/tensorflow/core/kernels/BUILD

cc_library(
    name = "linalg",
    deps = [
        ":cholesky_grad",
        ":cholesky_op",
        ":determinant_op",
        ":lu_op",
        ":matrix_exponential_op",
        ":matrix_inverse_op",
        ":matrix_logarithm_op",
        ":matrix_solve_ls_op",
        ":matrix_solve_op",
        ":matrix_triangular_solve_op",
        ":qr_op",
        ":self_adjoint_eig_op",
        ":self_adjoint_eig_v2_op",
        ":svd_op",
    ],
)

• tensorflow/tensorflow/core/kernels/BUILD - Delete the following

tf_kernel_library(
    name = "matrix_square_root_op",
    prefix = "matrix_square_root_op",
    deps = LINALG_DEPS,
)

• tensorflow/lite/tools/make/Makefile

BUILD_WITH_NNAPI=false
ifeq ($(BUILD_WITH_NNAPI),true)
	CORE_CC_EXCLUDE_SRCS += tensorflow/lite/nnapi_delegate_disabled.cc
else
	CORE_CC_EXCLUDE_SRCS += tensorflow/lite/nnapi_delegate.cc
endif

ifeq ($(TARGET),ios)
	CORE_CC_EXCLUDE_SRCS += tensorflow/lite/minimal_logging_android.cc
	CORE_CC_EXCLUDE_SRCS += tensorflow/lite/minimal_logging_default.cc
else
	CORE_CC_EXCLUDE_SRCS += tensorflow/lite/minimal_logging_android.cc
	CORE_CC_EXCLUDE_SRCS += tensorflow/lite/minimal_logging_ios.cc
endif

• configure

$ ./configure 
Extracting Bazel installation...
WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown".
You have bazel 0.19.2- (@non-git) installed.
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3


Found possible Python library paths:
  /usr/local/lib
  /home/b920405/git/caffe-jacinto/python
  /opt/intel//computer_vision_sdk_2018.5.455/python/python3.5/ubuntu16
  /opt/intel//computer_vision_sdk_2018.5.455/python/python3.5
  .
  /opt/intel//computer_vision_sdk_2018.5.455/deployment_tools/model_optimizer
  /opt/movidius/caffe/python
  /usr/lib/python3/dist-packages
  /usr/local/lib/python3.5/dist-packages
Please input the desired Python library path to use.  Default is [/usr/local/lib]
/usr/local/lib/python3.5/dist-packages
Do you wish to build TensorFlow with XLA JIT support? [Y/n]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with ROCm support? [y/N]: n
No ROCm support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: n
No CUDA support will be enabled for TensorFlow.

Do you wish to download a fresh release of clang? (Experimental) [y/N]: n
Clang will not be downloaded.

Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]: 


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
    --config=mkl            # Build with MKL support.
    --config=monolithic     # Config for mostly static monolithic build.
    --config=gdr            # Build with GDR support.
    --config=verbs          # Build with libverbs support.
    --config=ngraph         # Build with Intel nGraph support.
    --config=dynamic_kernels    # (Experimental) Build kernels into separate shared objects.
Preconfigured Bazel build configs to DISABLE default on features:
    --config=noaws          # Disable AWS S3 filesystem support.
    --config=nogcp          # Disable GCP support.
    --config=nohdfs         # Disable HDFS support.
    --config=noignite       # Disable Apache Ignite support.
    --config=nokafka        # Disable Apache Kafka support.
    --config=nonccl         # Disable NVIDIA NCCL support.
Configuration finished

• build

$ sudo bazel --host_jvm_args=-Xmx512m build \
--config=opt \
--config=noaws \
--config=nogcp \
--config=nohdfs \
--config=noignite \
--config=nokafka \
--config=nonccl \
--local_resources=1024.0,0.5,0.5 \
--copt=-mfpu=neon-vfpv4 \
--copt=-ftree-vectorize \
--copt=-funsafe-math-optimizations \
--copt=-ftree-loop-vectorize \
--copt=-fomit-frame-pointer \
--copt=-DRASPBERRY_PI \
--host_copt=-DRASPBERRY_PI \
//tensorflow/tools/pip_package:build_pip_package

$ su --preserve-environment
# ./bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
# exit
$ sudo cp /tmp/tensorflow_pkg/tensorflow-1.13.1-cp35-cp35m-linux_arm7l.whl ~

$ cd ~
$ sudo pip3 uninstall tensorflow
$ sudo -H pip3 install tensorflow-1.13.1-cp35-cp35m-linux_armv7l.whl