python - 将 Facenet 模型 .pb 文件转换为 TFLITE 格式时出错

Question

我正在尝试基于我从 David Sandbergs Github 获得的 Inception ResNet 转换预训练的卡住 .pb使用以下命令在 Ubuntu 上使用 Tensorflow Lite 转换器:

/home/nils/.local/bin/tflite_convert
--output_file=/home/nils/Documents/frozen.tflite
--graph_def_file=/home/nils/Documents/20180402-114759/20180402-114759.pb 
--input_arrays=input 
--output_arrays=embeddings 
--input_shapes=1,160,160,3

但是，我收到以下错误:

2018-12-03 15:03:16.807431: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Traceback (most recent call last):
File "/home/nils/.local/bin/tflite_convert", line 11, in <module>
sys.exit(main())
File "/home/nils/.local/lib/python3.6/site-packages/tensorflow/contrib/lite/python/tflite_convert.py", line 412, in main
app.run(main=run_main, argv=sys.argv[:1])
File "/home/nils/.local/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "/home/nils/.local/lib/python3.6/site-packages/tensorflow/contrib/lite/python/tflite_convert.py", line 408, in run_main
_convert_model(tflite_flags)
File "/home/nils/.local/lib/python3.6/site-packages/tensorflow/contrib/lite/python/tflite_convert.py", line 162, in _convert_model
output_data = converter.convert()
File "/home/nils/.local/lib/python3.6/site-packages/tensorflow/contrib/lite/python/lite.py", line 453, in convert
**converter_kwargs)
File "/home/nils/.local/lib/python3.6/site-packages/tensorflow/contrib/lite/python/convert.py", line 342, in toco_convert_impl
input_data.SerializeToString())
File "/home/nils/.local/lib/python3.6/site-packages/tensorflow/contrib/lite/python/convert.py", line 135, in toco_convert_protos
(stdout, stderr))
RuntimeError: TOCO failed see console for info.
b'2018-12-03 15:03:26.006252: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1080] Converting unsupported operation: FIFOQueueV2\n2018-12-03 15:03:26.006322: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1127] Op node missing output type attribute: batch_join/fifo_queue\n2018-12-03 15:03:26.006339: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1080] Converting unsupported operation: QueueDequeueUpToV2\n2018-12-03 15:03:26.006352: I tensorflow/contrib/lite/toco/import_tensorflow.cc:1127] Op node missing output type attribute: batch_join\n2018-12-03 15:03:27.496676: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before Removing unused ops: 5601 operators, 9399 arrays (0 quantized)\n2018-12-03 15:03:28.603936: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] After Removing unused ops pass 1: 3578 operators, 6254 arrays (0 quantized)\n2018-12-03 15:03:29.418074: I tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.cc:39] Before general graph transformations: 3578 operators, 6254 arrays (0 quantized)\n2018-12-03 15:03:29.420354: F tensorflow/contrib/lite/toco/graph_transformations/resolve_batch_normalization.cc:42] 
Check failed: IsConstantParameterArray(*model, bn_op->inputs[1]) && IsConstantParameterArray(*model, bn_op->inputs[2]) && IsConstantParameterArray(*model, bn_op->inputs[3]) Batch normalization resolution requires that mean, multiplier and offset arrays be constant.\nAborted (core dumped)\n'
None

如果我猜对了，这可能是因为两个不受支持的操作，QueueDequeueUpToV2 和 FIFOQueueV2，但我不确定。您有什么想法可能是什么问题或者我如何解决这个错误？这个错误到底意味着什么？我希望这个模型在移动 Android 设备上运行，有其他选择吗？版本: tensorflow V1.12Python 3.6.7Ubuntu 18.04.1 LTS在 VirtualBox 上提前致谢!

Answer 1

我已经解决了这个问题here ，也在此处添加代码片段:

我可以将 FaceNet .pb 转换为 .tflite 模型，以下是执行此操作的说明:

我们将量化预训练的 Facenet model具有 512 嵌入尺寸。该模型在量化前的大小约为 95MB。

$ ls -l model_pc
total 461248
-rw-rw-r--@ 1 milinddeore  staff   95745767 Apr  9  2018 20180402-114759.pb

使用以下代码创建文件inference_graph.py:

import tensorflow as tf
from src.models import inception_resnet_v1
import sys
import click
from pathlib import Path

@click.command()
@click.argument('training_checkpoint_dir', type=click.Path(exists=True, file_okay=False, resolve_path=True))
@click.argument('eval_checkpoint_dir', type=click.Path(exists=True, file_okay=False, resolve_path=True))

def main(training_checkpoint_dir, eval_checkpoint_dir):
    traning_checkpoint = Path(training_checkpoint_dir) / "model-20180402-114759.ckpt-275"
    eval_checkpoint = Path(eval_checkpoint_dir) / "imagenet_facenet.ckpt"
    data_input = tf.placeholder(name='input', dtype=tf.float32, shape=[None, 160, 160, 3])
    output, _ = inception_resnet_v1.inference(data_input, keep_probability=0.8, phase_train=False, bottleneck_layer_size=512)
    label_batch= tf.identity(output, name='label_batch')
    embeddings = tf.identity(output, name='embeddings')
    init = tf.global_variables_initializer()
    with tf.Session() as sess:
        sess.run(init)
        saver = tf.train.Saver()
        saver.restore(sess, traning_checkpoint.as_posix())
        save_path = saver.save(sess, eval_checkpoint.as_posix())
        print("Model saved in file: %s" % save_path)

if __name__ == "__main__":
     main()

在预训练模型上运行此文件，将生成用于推理的模型。下载预训练模型并将其解压缩到 model_pre_trained/目录。确保您有 python ≥ 3.4 版本。

python3 eval_graph.py model_pre_trained/ model_inference/

FaceNet 提供了 freeze_graph.py 文件，我们将用它来卡住推理模型。

python3  src/freeze_graph.py model_inference/  my_facenet.pb

生成卡住模型后，需要将其转换为 .tflite

$ tflite_convert --output_file model_mobile/my_facenet.tflite --graph_def_file my_facenet.pb  --input_arrays "input" --input_shapes "1,160,160,3" --output_arrays embeddings --output_format TFLITE --mean_values 128 --std_dev_values 128 --default_ranges_min 0  --default_ranges_max 6 --inference_type QUANTIZED_UINT8 --inference_input_type QUANTIZED_UINT8

让我们检查一下量化模型的大小:

$ ls -l model_mobile/
total 47232
-rw-r--r--@ 1 milinddeore  staff  23667888 Feb 25 13:39 my_facenet.tflite

解释器代码:

 import numpy as np
 import tensorflow as tf


 # Load TFLite model and allocate tensors.
 interpreter = tf.lite.Interpreter(model_path="/Users/milinddeore/facenet/model_mobile/my_facenet.tflite")
 interpreter.allocate_tensors()

 # Get input and output tensors.
 input_details = interpreter.get_input_details()
 output_details = interpreter.get_output_details()

 # Test model on random input data.
 input_shape = input_details[0]['shape']
 input_data = np.array(np.random.random_sample(input_shape), dtype=np.uint8)
 interpreter.set_tensor(input_details[0]['index'], input_data)

 interpreter.invoke()
 output_data = interpreter.get_tensor(output_details[0]['index'])

 print('INPUTS: ')
 print(input_details)
 print('OUTPUTS: ')
 print(output_details)

解释器输出:

$ python inout.py
INPUTS:
[{'index': 451, 'shape': array([  1, 160, 160,   3], dtype=int32), 'quantization': (0.0078125, 128L), 'name': 'input', 'dtype': <type 'numpy.uint8'>}]
OUTPUTS:
[{'index': 450, 'shape': array([  1, 512], dtype=int32), 'quantization': (0.0235294122248888, 0L), 'name': 'embeddings', 'dtype': <type 'numpy.uint8'>}]

希望这有帮助!