python - 将 FaceNet 模型转换为 ONNX 格式时出错

Question

系统信息

• 操作系统平台和发行版:Linux Ubuntu 19.10
• Tensorflow 版本:1.15
• Python 版本:3.7

问题

我从这个 page 下载了一个 FaceNet 的 tensorflow 模型，并且我正在尝试将其从 .pb 转换为 .onnx 文件，但是它引发了以下错误:

复制

root@xesk-VirtualBox:/home/xesk/Desktop# python -m tf2onnx.convert --saved-model home/xesk/Desktop/2s/20180402-114759/20180402-114759.pb --output model.onnx

    2020-08-03 20:18:05.081538: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory
    2020-08-03 20:18:05.081680: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
    2020-08-03 20:18:07,431 - WARNING - '--tag' not specified for saved_model. Using --tag serve
    Traceback (most recent call last):
    File "/usr/lib/python3.8/runpy.py", line 193, in _run_module_as_main
    return _run_code(code, main_globals, None,
    File "/usr/lib/python3.8/runpy.py", line 86, in _run_code
    exec(code, run_globals)
    File "/usr/local/lib/python3.8/dist-packages/tf2onnx/convert.py", line 171, in
    main()
    File "/usr/local/lib/python3.8/dist-packages/tf2onnx/convert.py", line 131, in main
    graph_def, inputs, outputs = tf_loader.from_saved_model(
    File "/usr/local/lib/python3.8/dist-packages/tf2onnx/tf_loader.py", line 288, in from_saved_model
    _from_saved_model_v2(model_path, input_names, output_names, tag, signatures, concrete_function)
    File "/usr/local/lib/python3.8/dist-packages/tf2onnx/tf_loader.py", line 247, in _from_saved_model_v2
    imported = tf.saved_model.load(model_path, tags=tag) # pylint: disable=no-value-for-parameter
    File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 603, in load
    return load_internal(export_dir, tags, options)
    File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/load.py", line 614, in load_internal
    loader_impl.parse_saved_model_with_debug_info(export_dir))
    File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/loader_impl.py", line 56, in parse_saved_model_with_debug_info
    saved_model = _parse_saved_model(export_dir)
    File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/saved_model/loader_impl.py", line 110, in parse_saved_model
    raise IOError("SavedModel file does not exist at: %s/{%s|%s}" %
    OSError: SavedModel file does not exist at: home/xesk/Desktop/2s/20180402-114759/20180402-114759.pb/{saved_model.pbtxt|saved_model.pb}

其他上下文

我没有运行任何 CUDA 或类似的东西，只有 CPU。下载的模型是20180402-114759 .这是我第一次使用这些工具，而且我在这个 AI 世界中还是个初学者，所以我可能遗漏了一些明显的东西。当然，我多次检查了路径和命令语法。可能与我下载的文件格式有关？

编辑

根据 Venkatesh Wadawadagi 的回答，我选择选项 1。更改 .meta 文件的名称解决了脚本无法识别的问题。

脚本或多或少正确运行，并完成创建 export_dir 目录，其中包含 export_dir > 0 > variables 子文件夹。然而，它们是空的。

控制台输出是这样的:

xesk@xesk:~/Desktop/UP2S/ACROMEGALLY/20180402-114759$ python3 ./pb2sm
2020-08-10 16:02:26.128846: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2020-08-10 16:02:26.129114: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)
2020-08-10 16:02:26.129137: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (xesk): /proc/driver/nvidia/version does not exist
2020-08-10 16:02:26.129501: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2020-08-10 16:02:26.139076: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2592000000 Hz
2020-08-10 16:02:26.139506: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x44018d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2020-08-10 16:02:26.139520: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/queue_runner_impl.py:391: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the `tf.data` module.
2020-08-10 16:02:32.681265: W tensorflow/core/framework/cpu_allocator_impl.cc:81] Allocation of 17676288 exceeds 10% of system memory.
Traceback (most recent call last):
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/client/session.py", line 1365, in _do_call
    return fn(*args)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/client/session.py", line 1350, in _run_fn
    target_list, run_metadata)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/client/session.py", line 1443, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value InceptionResnetV1/Block8/Branch_0/Conv2d_1x1/BatchNorm/beta/Adam
     [[{{node save/SaveV2_1}}]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "./pb2sm", line 17, in <module>
    strip_default_attrs=True)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/saved_model/builder_impl.py", line 595, in add_meta_graph_and_variables
    saver.save(sess, variables_path, write_meta_graph=False, write_state=False)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/saver.py", line 1193, in save
    raise exc
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/saver.py", line 1176, in save
    {self.saver_def.filename_tensor_name: checkpoint_file})
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/client/session.py", line 956, in run
    run_metadata_ptr)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/client/session.py", line 1180, in _run
    feed_dict_tensor, options, run_metadata)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/client/session.py", line 1359, in _do_run
    run_metadata)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/client/session.py", line 1384, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value InceptionResnetV1/Block8/Branch_0/Conv2d_1x1/BatchNorm/beta/Adam
     [[node save/SaveV2_1 (defined at /usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/ops.py:1748) ]]

Original stack trace for 'save/SaveV2_1':
  File "./pb2sm", line 17, in <module>
    strip_default_attrs=True)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/saved_model/builder_impl.py", line 589, in add_meta_graph_and_variables
    saver = self._maybe_create_saver(saver)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/saved_model/builder_impl.py", line 227, in _maybe_create_saver
    allow_empty=True)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/saver.py", line 828, in __init__
    self.build()
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/saver.py", line 840, in build
    self._build(self._filename, build_save=True, build_restore=True)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/saver.py", line 878, in _build
    build_restore=build_restore)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/saver.py", line 499, in _build_internal
    save_tensor = self._AddShardedSaveOps(filename_tensor, per_device)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/saver.py", line 291, in _AddShardedSaveOps
    return self._AddShardedSaveOpsForV2(filename_tensor, per_device)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/saver.py", line 265, in _AddShardedSaveOpsForV2
    sharded_saves.append(self._AddSaveOps(sharded_filename, saveables))
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/saver.py", line 206, in _AddSaveOps
    save = self.save_op(filename_tensor, saveables)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/training/saver.py", line 122, in save_op
    tensors)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/ops/gen_io_ops.py", line 1946, in save_v2
    name=name)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/op_def_library.py", line 794, in _apply_op_helper
    op_def=op_def)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/ops.py", line 3357, in create_op
    attrs, op_def, compute_device)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/ops.py", line 3426, in _create_op_internal
    op_def=op_def)
  File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/framework/ops.py", line 1748, in __init__
    self._traceback = tf_stack.extract_stack()

我是否可能缺少一些要安装的库？似乎与某些 CUDA 实现有关，但我没有。可能吗？

Answer 1

您正在使用的命令:

python -m tf2onnx.convert --saved-model home/xesk/Desktop/2s/20180402-114759/20180402-114759.pb --output model.onnx

请注意 Facenet您正在使用的经过训练的模型只有卡住图( .pb 文件)和检查点( .ckpt )并且没有 saved-model 您的命令正在查找。

所以基本上您是将路径传递给 .pb卡住图的文件，不同于.pb SavedModel 文件(您没有)。保存的模型将具有 variables 文件夹以及 saved_model.pb文件。

这就是错误的原因:

OSError: SavedModel file does not exist

阅读有关 SavedModel 的更多信息 here .

要继续进行 ONNX 转换，您有两个选择:

1. 将检查点转换为 SavedModel:

使用以下 code为此:

import os
import tensorflow as tf

trained_checkpoint_prefix = 'model-20180402-114759.ckpt-275'
export_dir = os.path.join('export_dir', '0')

graph = tf.Graph()
with tf.compat.v1.Session(graph=graph) as sess:
    # Restore from checkpoint
    loader = tf.compat.v1.train.import_meta_graph(trained_checkpoint_prefix + '.meta')
    loader.restore(sess, trained_checkpoint_prefix)

    # Export checkpoint to SavedModel
    builder = tf.compat.v1.saved_model.builder.SavedModelBuilder(export_dir)
    builder.add_meta_graph_and_variables(sess,
                                         [tf.saved_model.TRAINING, tf.saved_model.SERVING],
                                         strip_default_attrs=True)
    builder.save() 

注意: .data , .index和 .meta应该有相同的前缀然后这个代码将工作。所以重命名 .meta文件。

mv model-20180402-114759.meta model-20180402-114759.ckpt-275.meta

例如:

1. 利用ckpt文件或 frozen-graph.pb用于onnx转换

来自检查点格式:

python -m tf2onnx.convert --checkpoint tensorflow-model-meta-file-path --output model.onnx --inputs input0:0,input1:0 --outputs output0:0

来自 graphdef/frozen-graph 格式:

python -m tf2onnx.convert --graphdef tensorflow-model-graphdef-file --output model.onnx --inputs input0:0,input1:0 --outputs output0:0

如果您的 TensorFlow 模型的格式不是 saved model ，那么你需要提供 inputs 和 outputs 模型图。

来自 this :

If your model is in checkpoint or graphdef format and you do not knowthe input and output nodes of the model, you can use thesummarize_graph TensorFlow utility. The summarize_graph tool doesneed to be downloaded and built from source. If you have the option ofgoing to your model provider and obtaining the model in saved modelformat, then we recommend doing so.