问题
i'm trying to convert a pre-trained frozen .pb based on Inception ResNet i got from David Sandbergs Github with the Tensorflow Lite Converter on Ubuntu using the following command:
/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
However, i get the following error:
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
If i get this right, this might be because of two unsupported Ops, QueueDequeueUpToV2 and FIFOQueueV2, but i don't know for sure. Do you have any ideas what might be the problem or how i can solve this error? What does that error even mean? I want this model to run on a mobile android device, are there any alternatives? Versions: Tensorflow V1.12 Python 3.6.7 Ubuntu 18.04.1 LTS on a VirtualBox Thanks in advance!
回答1:
I have solved this problem here, adding the snippet here too:
I could able to convert FaceNet .pb to .tflite model, and following are the instructions to do so:
We will quantise pre-trained Facenet model with 512 embedding size. This model is about 95MB in size before quantization.
$ ls -l model_pc
total 461248
-rw-rw-r--@ 1 milinddeore staff 95745767 Apr 9 2018 20180402-114759.pb
create a file inference_graph.py with following code:
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()
Run this file on pre-trained model, would generate model for inference. Download pre-trained model and unzip it to model_pre_trained/ directory. Make sure you have python ≥ 3.4 version.
python3 eval_graph.py model_pre_trained/ model_inference/
FaceNet provides freeze_graph.py file, which we will use to freeze the inference model.
python3 src/freeze_graph.py model_inference/ my_facenet.pb
Once the frozen model is generated, time to convert it to .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
Let us check the quantized model size:
$ ls -l model_mobile/
total 47232
-rw-r--r--@ 1 milinddeore staff 23667888 Feb 25 13:39 my_facenet.tflite
Interpeter code:
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)
Interpeter output:
$ 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'>}]
Hope this helps!
来源：https://stackoverflow.com/questions/53596521/error-converting-facenet-model-pb-file-to-tflite-format