python - 未知 : KeyError: 'pyfunc_0'

Question

我正在导出一个savedModel，它采用字符串占位符作为输入张量。我注入(inject)了一个图来预处理这个字符串张量，以便它可以传递到模型中。但是，我正在使用 py_func 对张量执行 python 字符串操作。

这里input_text是savedModel签名中的输入张量。我使用默认的 input_ints 创建了另一个占位符，该占位符是使用对 input_text 执行 py_func 的结果进行初始化的。我最初将 input_text 作为一项操作 (input_ints =tf.py_func(preprocess, [input_text], tf.int64))，但随后 tf.nn.dynamic_rnn 不接受形状未指定的张量。

    # Create the graph object
with tf.name_scope('inputs'):
    input_text = tf.placeholder(tf.string, name="input_text")
    input_ints = tf.placeholder_with_default(
        tf.py_func(preprocess, [input_text], tf.int64), shape=[None, None])

def lstm_cell():
    # Your basic LSTM cell
    lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size, reuse=tf.get_variable_scope().reuse)
    # Add dropout to the cell
    return tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)


# def create_rnn():
with tf.name_scope("Embeddings"):
    embedding = tf.Variable(tf.random_uniform((vocab_size, embed_size), -1, 1))
    embed = tf.nn.embedding_lookup(embedding, input_ints)
with tf.name_scope("RNN_layers"):
    cell = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(lstm_layers)])
initial_state = cell.zero_state(batch_size, tf.float32)
with tf.name_scope("RNN_forward"):
    outputs, final_state = tf.nn.dynamic_rnn(cell, embed, initial_state=initial_state)
with tf.name_scope('predictions'):
    predictions = tf.contrib.layers.fully_connected(outputs[:, -1], 1, activation_fn=tf.sigmoid)

现在使用上述实现，我可以正确导出模型，但是在恢复模型时，出现以下错误:

2017-11-23 17:29:14.600184: W tensorflow/core/framework/op_kernel.cc:1192] Unknown: KeyError: 'pyfunc_0'
Traceback (most recent call last):
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1327, in _do_call
    return fn(*args)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1306, in _run_fn
    status, run_metadata)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/contextlib.py", line 89, in __exit__
    next(self.gen)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.UnknownError: KeyError: 'pyfunc_0'
     [[Node: inputs/PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_INT64], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_inputs/input_text_0_0)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "neural_load_model.py", line 85, in <module>
    result = sess.run(output_tensor, {input_tensor: "Charter Communications, Inc. (CHTR) Stock Rating Reaffirmed by Goldman Sachs Group, Inc. (The)"})
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 895, in run
    run_metadata_ptr)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1124, in _run
    feed_dict_tensor, options, run_metadata)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1321, in _do_run
    options, run_metadata)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1340, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.UnknownError: KeyError: 'pyfunc_0'
     [[Node: inputs/PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_INT64], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_inputs/input_text_0_0)]]

Caused by op 'inputs/PyFunc', defined at:
  File "neural_load_model.py", line 74, in <module>
    model = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], import_path)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/saved_model/loader_impl.py", line 216, in load
    saver = tf_saver.import_meta_graph(meta_graph_def_to_load, **saver_kwargs)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/training/saver.py", line 1698, in import_meta_graph
    **kwargs)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/meta_graph.py", line 656, in import_scoped_meta_graph
    producer_op_list=producer_op_list)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/importer.py", line 313, in import_graph_def
    op_def=op_def)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2630, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/Users/sakibarrahman/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1204, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

UnknownError (see above for traceback): KeyError: 'pyfunc_0'
     [[Node: inputs/PyFunc = PyFunc[Tin=[DT_STRING], Tout=[DT_INT64], token="pyfunc_0", _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_inputs/input_text_0_0)]]

我看过这个issue发布在 Github 上，但我不确定如何实现。另外，我只是加载模型并传入一个字符串进行输入，而不是使用“freeze_graph”。

我保存模型的代码:

saver = tf.train.Saver()

#Define new functions
def preprocess(text):
.
.
.
tf.reset_default_graph()
.
.
.
#Define new placeholder that was not in the original model graph
#Define new placeholder with default value initialized with py_func that was not in the original model graph
with tf.name_scope('inputs'):
    input_text = tf.placeholder(tf.string, name="input_text")
    input_ints = tf.placeholder_with_default(
        tf.py_func(preprocess, [input_text], tf.int64), shape=[None, None])
.
.
.
#Define placeholders and ops that I need and were in the original graph



saver = tf.train.Saver()
#Serving the model
with tf.Session() as sess:


#Restore from old checkpoint
saver.restore(sess, import_path)

print ('Exporting trained model to %s'%(export_path))

builder = saved_model_builder.SavedModelBuilder(export_path)

original_assets_directory = export_path + '/assets'
original_assets_filename = "vocabulary.pickle"
original_assets_filepath = write_vocab(original_assets_directory,
                                         original_assets_filename)

# Set up the assets collection.
assets_filepath = tf.constant(original_assets_filepath)
tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, assets_filepath)
filename_tensor = tf.Variable(
    original_assets_filename,
    name="vocab_tensor",
    trainable=False,
    collections=[])
assign_filename_op = filename_tensor.assign(original_assets_filename)


# Build the signature_def_map.
classification_inputs = utils.build_tensor_info(input_text)
classification_outputs_classes = utils.build_tensor_info(predictions)
classification_signature = signature_def_utils.build_signature_def(
    inputs={signature_constants.CLASSIFY_INPUTS: classification_inputs},
    outputs={
      signature_constants.CLASSIFY_OUTPUT_CLASSES:
          classification_outputs_classes,
    },
  method_name=signature_constants.CLASSIFY_METHOD_NAME)

legacy_init_op = tf.group(
    tf.tables_initializer(), name='legacy_init_op')
#add the sigs to the servable
builder.add_meta_graph_and_variables(
    sess, [tag_constants.SERVING],
    signature_def_map={
        signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
            classification_signature
    },
    assets_collection=tf.get_collection(tf.GraphKeys.ASSET_FILEPATHS),
    legacy_init_op=tf.group(assign_filename_op))
print ("added meta graph and variables")

builder.save()
print("model saved")

我的加载模型的代码。未定义函数或占位符会导致“pyfunc_0”错误:

#Define preprocess function
def preprocess(text_bin):

#Define new placeholders
with tf.name_scope('inputs'):
    input_text = tf.placeholder(tf.string, name="input_text")
    input_ints = tf.placeholder_with_default(
        tf.py_func(preprocess, [input_text], tf.int64), shape=[None, None])

with tf.Session(graph=tf.Graph()) as sess:
    # restore save model
    model = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], import_path)
    print("model restored")
    loaded_graph = tf.get_default_graph()

    # get necessary tensors by name
    input_tensor_name = model.signature_def[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].inputs[signature_constants.CLASSIFY_INPUTS].name
    input_tensor = loaded_graph.get_tensor_by_name(input_tensor_name)
    output_tensor_name = model.signature_def[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY].outputs[signature_constants.CLASSIFY_OUTPUT_CLASSES].name
    output_tensor = loaded_graph.get_tensor_by_name(output_tensor_name)

    result = sess.run(output_tensor, {input_tensor: "Some String"})
    print (result)

更新:

在加载savedModel时定义函数和占位符似乎有效。但是，我不知道为什么在使用构建器保存模型之前没有将它们添加到图表中

Answer 1

看起来您的模型有一个自定义层。您可以按照模型代码找到它。因此，您可以在图形加载之前定义该函数。此外，函数定义顺序也很重要。