Migrasi kode TFLite Anda ke TF2

Lihat di TensorFlow.org Jalankan di Google Colab Lihat sumber di GitHub Unduh buku catatan

TensorFlow Lite (TFLite) adalah seperangkat alat yang membantu pengembang menjalankan inferensi ML di perangkat (perangkat seluler, tersemat, dan IoT). Konverter TFLite adalah salah satu alat yang mengubah model TF yang ada menjadi format model TFLite yang dioptimalkan yang dapat dijalankan secara efisien di perangkat.

Dalam dokumen ini, Anda akan mempelajari perubahan apa yang perlu Anda buat pada kode konversi TF ke TFLite, diikuti dengan beberapa contoh yang melakukan hal yang sama.

Perubahan pada kode konversi TF ke TFLite Anda

  • Jika Anda menggunakan format model TF1 lama (file Keras, GraphDef beku, pos pemeriksaan, tf.Session, dll), perbarui ke TF1/TF2 SavedModel dan gunakan TF2 converter API tf.lite.TFLiteConverter.from_saved_model(...) untuk mengubahnya menjadi model TFLite (lihat Tabel 1).

  • Perbarui flag converter API (lihat Tabel 2).

  • Hapus API lama seperti tf.lite.constants . (misalnya: Ganti tf.lite.constants.INT8 dengan tf.int8 )

// Tabel 1 // Pembaruan API Konverter Python TFLite

API TF1 TF2 API
tf.lite.TFLiteConverter.from_saved_model('saved_model/',..) didukung
tf.lite.TFLiteConverter.from_keras_model_file('model.h5',..) dihapus (perbarui ke format SavedModel)
tf.lite.TFLiteConverter.from_frozen_graph('model.pb',..) dihapus (perbarui ke format SavedModel)
tf.lite.TFLiteConverter.from_session(sess,...) dihapus (perbarui ke format SavedModel)

// Tabel 2 // Pembaruan Bendera TFLite Python Converter API

API TF1 TF2 API
allow_custom_ops
optimizations
representative_dataset
target_spec
inference_input_type
inference_output_type
experimental_new_converter
experimental_new_quantizer		 didukung







input_tensors
output_tensors
input_arrays_with_shape
output_arrays
experimental_debug_info_func		 dihapus (argumen API konverter tidak didukung)




change_concat_input_ranges
default_ranges_stats
get_input_arrays()
inference_type
quantized_input_stats
reorder_across_fake_quant		 dihapus (alur kerja kuantisasi tidak didukung)





conversion_summary_dir
dump_graphviz_dir
dump_graphviz_video		 dihapus (sebagai gantinya, visualisasikan model menggunakan Netron atau visualize.py )


output_format
drop_control_dependency		 dihapus (fitur tidak didukung di TF2)

Contoh

Sekarang Anda akan menelusuri beberapa contoh untuk mengonversi model TF1 lawas ke TF1/TF2 SavedModels dan kemudian mengonversinya ke model TF2 TFLite.

Mempersiapkan

Mulailah dengan impor TensorFlow yang diperlukan.

import tensorflow as tf
import tensorflow.compat.v1 as tf1
import numpy as np

import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)

import shutil
def remove_dir(path):
  try:
    shutil.rmtree(path)
  except:
    pass

Buat semua format model TF1 yang diperlukan.

# Create a TF1 SavedModel
SAVED_MODEL_DIR = "tf_saved_model/"
remove_dir(SAVED_MODEL_DIR)
with tf1.Graph().as_default() as g:
  with tf1.Session() as sess:
    input = tf1.placeholder(tf.float32, shape=(3,), name='input')
    output = input + 2
    # print("result: ", sess.run(output, {input: [0., 2., 4.]}))
    tf1.saved_model.simple_save(
        sess, SAVED_MODEL_DIR,
        inputs={'input': input}, 
        outputs={'output': output})
print("TF1 SavedModel path: ", SAVED_MODEL_DIR)

# Create a TF1 Keras model
KERAS_MODEL_PATH = 'tf_keras_model.h5'
model = tf1.keras.models.Sequential([
    tf1.keras.layers.InputLayer(input_shape=(128, 128, 3,), name='input'),
    tf1.keras.layers.Dense(units=16, input_shape=(128, 128, 3,), activation='relu'),
    tf1.keras.layers.Dense(units=1, name='output')
])
model.save(KERAS_MODEL_PATH, save_format='h5')
print("TF1 Keras Model path: ", KERAS_MODEL_PATH)

# Create a TF1 frozen GraphDef model
GRAPH_DEF_MODEL_PATH = tf.keras.utils.get_file(
    'mobilenet_v1_0.25_128',
    origin='https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.25_128_frozen.tgz',
    untar=True,
) + '/frozen_graph.pb'

print("TF1 frozen GraphDef path: ", GRAPH_DEF_MODEL_PATH)

TF1 SavedModel path:  tf_saved_model/
TF1 Keras Model path:  tf_keras_model.h5
Downloading data from https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_0.25_128_frozen.tgz
2621440/2617289 [==============================] - 0s 0us/step
2629632/2617289 [==============================] - 0s 0us/step
TF1 frozen GraphDef path:  /home/kbuilder/.keras/datasets/mobilenet_v1_0.25_128/frozen_graph.pb

1. Ubah TF1 SavedModel menjadi model TFLite

Sebelum: Mengonversi dengan TF1

Ini adalah kode khas untuk konversi TFlite gaya TF1.

converter = tf1.lite.TFLiteConverter.from_saved_model(
    saved_model_dir=SAVED_MODEL_DIR,
    input_arrays=['input'],
    input_shapes={'input' : [3]}
)
converter.optimizations = {tf.lite.Optimize.DEFAULT}
converter.change_concat_input_ranges = True
tflite_model = converter.convert()
# Ignore warning: "Use '@tf.function' or '@defun' to decorate the function."

2021-09-22 20:02:56.143221: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:351] Ignored output_format.
2021-09-22 20:02:56.143267: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored drop_control_dependency.
2021-09-22 20:02:56.143274: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:360] Ignored change_concat_input_ranges.

Setelah: Mengonversi dengan TF2

Konversikan TF1 SavedModel secara langsung ke model TFLite, dengan set flag konverter v2 yang lebih kecil.

# Convert TF1 SavedModel to a TFLite model.
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir=SAVED_MODEL_DIR)
converter.optimizations = {tf.lite.Optimize.DEFAULT}
tflite_model = converter.convert()

2021-09-22 20:02:56.207882: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:351] Ignored output_format.
2021-09-22 20:02:56.207923: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored drop_control_dependency.
2021-09-22 20:02:56.207930: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:360] Ignored change_concat_input_ranges.

2. Konversi file model TF1 Keras ke model TFLite

Sebelum: Mengonversi dengan TF1

Ini adalah kode khas untuk konversi TFlite gaya TF1.

converter = tf1.lite.TFLiteConverter.from_keras_model_file(model_file=KERAS_MODEL_PATH)
converter.optimizations = {tf.lite.Optimize.DEFAULT}
converter.change_concat_input_ranges = True
tflite_model = converter.convert()

2021-09-22 20:02:56.608343: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
2021-09-22 20:02:57.119836: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:351] Ignored output_format.
2021-09-22 20:02:57.119881: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored drop_control_dependency.
2021-09-22 20:02:57.119888: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:360] Ignored change_concat_input_ranges.

Setelah: Mengonversi dengan TF2

Pertama, konversi file model TF1 Keras ke TF2 SavedModel dan kemudian ubah ke model TFLite, dengan set flag konverter v2 yang lebih kecil.

# Convert TF1 Keras model file to TF2 SavedModel.
model = tf.keras.models.load_model(KERAS_MODEL_PATH)
model.save(filepath='saved_model_2/')

# Convert TF2 SavedModel to a TFLite model.
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir='saved_model_2/')
tflite_model = converter.convert()

2021-09-22 20:02:57.943564: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:351] Ignored output_format.
2021-09-22 20:02:57.943608: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored drop_control_dependency.
2021-09-22 20:02:57.943614: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:360] Ignored change_concat_input_ranges.

3. Konversikan GraphDef beku TF1 ke model TFLite

Sebelum: Mengonversi dengan TF1

Ini adalah kode khas untuk konversi TFlite gaya TF1.

converter = tf1.lite.TFLiteConverter.from_frozen_graph(
    graph_def_file=GRAPH_DEF_MODEL_PATH,
    input_arrays=['input'],
    input_shapes={'input' : [1, 128, 128, 3]},
    output_arrays=['MobilenetV1/Predictions/Softmax'],
)
converter.optimizations = {tf.lite.Optimize.DEFAULT}
converter.change_concat_input_ranges = True
tflite_model = converter.convert()

2021-09-22 20:02:58.139650: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:351] Ignored output_format.
2021-09-22 20:02:58.139707: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored drop_control_dependency.
2021-09-22 20:02:58.139721: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:360] Ignored change_concat_input_ranges.

Setelah: Mengonversi dengan TF2

Pertama, konversikan GraphDef beku TF1 ke TF1 SavedModel dan kemudian ubah ke model TFLite, dengan set flag konverter v2 yang lebih kecil.

## Convert TF1 frozen Graph to TF1 SavedModel.

# Load the graph as a v1.GraphDef
import pathlib
gdef = tf.compat.v1.GraphDef()
gdef.ParseFromString(pathlib.Path(GRAPH_DEF_MODEL_PATH).read_bytes())

# Convert the GraphDef to a tf.Graph
with tf.Graph().as_default() as g:
  tf.graph_util.import_graph_def(gdef, name="")

# Lookup the input and output tensors.
input_tensor = g.get_tensor_by_name('input:0') 
output_tensor = g.get_tensor_by_name('MobilenetV1/Predictions/Softmax:0')

# Save the graph as a TF1 Savedmodel
remove_dir('saved_model_3/')
with tf.compat.v1.Session(graph=g) as s:
  tf.compat.v1.saved_model.simple_save(
      session=s,
      export_dir='saved_model_3/',
      inputs={'input':input_tensor},
      outputs={'output':output_tensor})

# Convert TF1 SavedModel to a TFLite model.
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir='saved_model_3/')
converter.optimizations = {tf.lite.Optimize.DEFAULT}
tflite_model = converter.convert()

2021-09-22 20:02:58.874490: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:351] Ignored output_format.
2021-09-22 20:02:58.874538: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:354] Ignored drop_control_dependency.
2021-09-22 20:02:58.874545: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:360] Ignored change_concat_input_ranges.

Bacaan lebih lanjut

  • Lihat Panduan TFLite untuk mempelajari lebih lanjut tentang alur kerja dan fitur terbaru.
  • Jika Anda menggunakan kode TF1 atau format model TF1 lama (file Keras .h5 , GraphDef beku .pb , dll), perbarui kode Anda dan migrasikan model Anda ke format TF2 SavedModel .