how to use my own trained model with facenet implemented in tensorflow?

1
votes

I train the model with the shell command:

python src/facenet_train.py \
           --batch_size 15 \
           --gpu_memory_fraction 0.25 \
           --models_base_dir trained_model_2017_05_15_10_24 \
           --pretrained_model trained_model_2017_05_15_10_24/20170515-121856/model-20170515-121856.ckpt-182784 \
           --model_def models.nn2 \
           --logs_base_dir logs \
           --data_dir /data/user_set/training/2017_05_15_10_24 \
           --lfw_pairs /data/user_set/lfw_pairs.txt \
           --image_size 224 \
           --lfw_dir /data/user_set/lfw \
           --optimizer ADAM \
           --max_nrof_epochs 1000 \
           --learning_rate 0.00001

but i get error infomation like this when use my own trained model:

2017-05-17 14:23:05.448285: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2017-05-17 14:23:05.448318: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. 2017-05-17 14:23:05.448324: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. 2017-05-17 14:23:05.448329: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. 2017-05-17 14:23:05.448334: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. 2017-05-17 14:23:05.674872: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties: name: Quadro M4000 major: 5 minor: 2 memoryClockRate (GHz) 0.7725 pciBusID 0000:03:00.0 Total memory: 7.93GiB Free memory: 2.89GiB 2017-05-17 14:23:05.674917: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 2017-05-17 14:23:05.674935: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y 2017-05-17 14:23:05.674957: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro M4000, pci bus id: 0000:03:00.0) Traceback (most recent call last): File "forward.py", line 21, in images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0") File "/home/chen/.pyenv/versions/anaconda3-4.2.0/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2563, in get_tensor_by_name return self.as_graph_element(name, allow_tensor=True, allow_operation=False) File "/home/chen/.pyenv/versions/anaconda3-4.2.0/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2414, in as_graph_element return self._as_graph_element_locked(obj, allow_tensor, allow_operation) File "/home/chen/.pyenv/versions/anaconda3-4.2.0/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2456, in _as_graph_element_locked "graph." % (repr(name), repr(op_name))) KeyError: "The name 'input:0' refers to a Tensor which does not exist. The operation, 'input', does not exist in the graph."

get feature code:

import tensorflow as tf
import facenet
w_MODEL_PATH_='/home/chen/demo_dir/facenet_tensorflow_train/trained_model_2017_05_15_10_24/20170515-121856'

with tf.Graph().as_default():
    with tf.Session() as sess:
        # load the model
        meta_file, ckpt_file = facenet.get_model_filenames(w_MODEL_PATH_)
        facenet.load_model(w_MODEL_PATH_, meta_file, ckpt_file)
        # print("model_path:", w_MODEL_PATH_,"meta_file:", meta_file,"ckpt_file:", ckpt_file)

        # Get input and output tensors
        # ops = tf.get_default_graph().get_operations()
        #
        # print(ops)

        images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
        embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
        phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")

        image_size = images_placeholder.get_shape()[1]
        embedding_size = embeddings.get_shape()[1]

        # print(image_size)

        paths = ['one.png', 'two.png']

        # Run forward pass to calculate embeddings
        images = facenet.load_data(paths, do_random_crop=False, do_random_flip=False, image_size=image_size,
                                   do_prewhiten=True)
        # print("images:", idx, images)
        feed_dict = {images_placeholder: images, phase_train_placeholder: False}
        # print(idx,"embeddings:", embeddings)
        emb_array = sess.run(embeddings, feed_dict=feed_dict)
        # print(idx, "emb_array:", emb_array)
        print(emb_array)

I don't know how to use my own trained model, please help.

1
tensorflow

1 Answers

0
votes

If you are talking about the last part then use this code to see what operations your model has.

for i in tf.get_default_graph().get_operations():
    print(i.name)

If you are talking about the optimizations.

You are getting this error because you need to compile tensorflow on your own machine. It is very easy to do.

You can read the documentation for the full list of options, but essentially you need to do a few steps.

https://www.tensorflow.org/install/install_sources

  1. git clone tensorflow
  2. repo install
  3. bazel a tensorflow build system
  4. configure tensorflow
  5. build tensorflow
  6. install tensorflow in your environment if that is anaconda or virtualenv if you are using python

that is it, of course other required libraries will need to be installed. It is pretty easy to do on Ubuntu.

Alternatively you could try the conda forge version of tensorflow-gpu if you are using anaconda, but I cannot verify it is also compiled with optimizations for your cpu.

https://conda-forge.org/

  1. install anaconda
  2. add the conda forge repo url
  3. update conda
  4. install tensorflow-gpu