我已使用tensorflow 1.15训练模型并保存为检查点（带有.meta，.index和.data文件）。
我需要在此图的开头和结尾添加一些其他操作。其中一些操作仅在tensorflow 2.0和tensorflow_text 2.0中存在。之后，我想将此模型另存为.pb的tensorflow-serving。
我试图做的事情：使用tensorflow 2.0，我将其保存为.pb文件，如下所示。
trained_checkpoint_prefix = 'path/to/model'
export_dir = os.path.join('path/to/export', '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)
classification_signature = tf.compat.v1.saved_model.signature_def_utils.build_signature_def(
inputs={
'token_indices': get_tensor_info('token_indices_ph:0'),
'token_mask': get_tensor_info('token_mask_ph:0'),
'y_mask': get_tensor_info('y_mask_ph:0'),
},
outputs={'probas': get_tensor_info('ner/Softmax:0'), 'seq_lengths': get_tensor_info('ner/Sum:0')},
method_name='predict',
)
builder.add_meta_graph_and_variables(sess,
[tf.saved_model.TRAINING, tf.saved_model.SERVING],
strip_default_attrs=True, saver=loader,
signature_def_map={'predict': classification_signature}) # , clear_devices=True)
builder.save()
[之后，我创建了一个加载tf.keras.Model模型并执行我需要的所有人员的.pb：
import os
from pathlib import Path
import tensorflow as tf
import tensorflow_text as tf_text
class BertPipeline(tf.keras.Model):
def __init__(self):
super().__init__()
vocab_file = Path('path/to/vocab.txt')
vocab = vocab_file.read_text().split('\n')[:-1]
self.vocab_table = self.create_table(vocab)
export_dir = 'path/to/pb/model'
self.model = tf.saved_model.load(export_dir)
self.bert_tokenizer = BertTokenizer(
self.vocab_table,
max_chars_per_token=15,
token_out_type=tf.int64
,
lower_case=True,
)
self.to_dense = tf_text.keras.layers.ToDense()
def call(self, texts):
tokens = self.bert_tokenizer.tokenize(texts)
tokens = tf.cast(tokens, dtype=tf.int32)
mask = self.make_mask(tokens)
token_ids = self.make_token_ids(tokens)
token_indices = self.to_dense(token_ids)
token_mask = self.to_dense(tf.ones_like(mask))
y_mask = self.to_dense(mask)
res = self.model.signatures['predict'](
token_indices=token_indices,
token_mask=token_mask,
y_mask=y_mask,
)
starts_range = tf.range(0, tf.shape(res['seq_lengths'])[0]) * tf.shape(res['probas'])[1]
row_splits = tf.reshape(
tf.stack(
[
starts_range,
starts_range + res['seq_lengths'],
],
axis=1,
),
[-1],
)
row_splits = tf.concat(
[
row_splits,
tf.expand_dims(tf.shape(res['probas'])[0] * tf.shape(res['probas'])[1], 0),
],
axis=0,
)
probas = tf.RaggedTensor.from_row_splits(
tf.reshape(res['probas'], [-1, 2]),
row_splits,
)[::2]
probas
return probas
def make_mask(self, tokens):
masked_suff = tf.concat(
[
tf.ones_like(tokens[:, :, :1], dtype=tf.int32),
tf.zeros_like(tokens[:, :, 1:], dtype=tf.int32),
],
axis=-1,
)
joined_mask = self.join_wordpieces(masked_suff)
return tf.concat(
[
tf.zeros_like(joined_mask[:, :1], dtype=tf.int32),
joined_mask,
tf.zeros_like(joined_mask[:, :1], dtype=tf.int32),
],
axis=-1,
)
def make_token_ids(self, tokens):
joined_tokens = self.join_wordpieces(tokens)
return tf.concat(
[
tf.fill(
[joined_tokens.nrows(), 1],
tf.dtypes.cast(
self.vocab_table.lookup(tf.constant('[CLS]')),
dtype=tf.int32,
)
),
self.join_wordpieces(tokens),
tf.fill(
[joined_tokens.nrows(), 1],
tf.dtypes.cast(
self.vocab_table.lookup(tf.constant('[SEP]')),
dtype=tf.int32,
)
),
],
axis=-1,
)
def join_wordpieces(self, wordpieces):
return tf.RaggedTensor.from_row_splits(
wordpieces.flat_values, tf.gather(wordpieces.values.row_splits,
wordpieces.row_splits))
def create_table(self, vocab, num_oov=1):
init = tf.lookup.KeyValueTensorInitializer(
vocab,
tf.range(tf.size(vocab, out_type=tf.int64), dtype=tf.int64),
key_dtype=tf.string,
value_dtype=tf.int64)
return tf.lookup.StaticVocabularyTable(init, num_oov, lookup_key_dtype=tf.string)
当我调用此代码时，它运行良好：
bert_pipeline = BertPipeline()
print(bbert_pipeline(["Some test string", "another string"]))
---
<tf.RaggedTensor [[[0.17896245419979095, 0.8210375308990479], [0.8825045228004456, 0.11749550700187683], [0.9141901731491089, 0.0858098641037941]], [[0.2768123149871826, 0.7231876850128174], [0.9391192197799683, 0.060880810022354126]]]>
但是我不知道如何保存。如果我理解正确，tf.keras.Model请勿将self.model和self.bert_tokenizer视为模型的一部分。如果我呼叫bert_pipeline.summary()，则没有操作：
bert_pipeline.build([])
bert_pipeline.summary()
---
Model: "bert_pipeline_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
to_dense (ToDense) multiple 0
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
_________________________________________________________________
此外，我尝试使用显式tensorflow.compat.v1和Session与Graph一起运行，但是在这种情况下，我只是无法正确加载模型。与import tensorflow.compat.v1 as tf和tensorflow 1.xx样板相同的代码无法初始化某些变量：
# tf.saved_model.load(export_dir) changed to tf.saved_model.load_v2(export_dir) above
import tensorflow.compat.v1 as tf
graph = tf.Graph()
with tf.Session(graph=graph) as sess:
bert_pipeline = BertPipeline()
texts = tf.placeholder(tf.string, shape=[None], name='texts')
res_tensor = bert_pipeline(texts)
sess.run(tf.tables_initializer())
sess.run(tf.global_variables_initializer())
sess.run(res_tensor, feed_dict={texts: ["Some test string", "another string"]})
---
FailedPreconditionError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _do_call(self, fn, *args)
1364 try:
-> 1365 return fn(*args)
1366 except errors.OpError as e:
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1349 return self._call_tf_sessionrun(options, feed_dict, fetch_list,
-> 1350 target_list, run_metadata)
1351
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1442 fetch_list, target_list,
-> 1443 run_metadata)
1444
FailedPreconditionError: [_Derived_]{{function_node __inference_pruned_77348}} {{function_node __inference_pruned_77348}} Attempting to use uninitialized value bert/encoder/layer_3/attention/self/query/kernel
[[{{node bert/encoder/layer_3/attention/self/query/kernel/read}}]]
[[bert_pipeline/StatefulPartitionedCall]]
During handling of the above exception, another exception occurred:
FailedPreconditionError Traceback (most recent call last)
<ipython-input-15-5a0a45327337> in <module>
21 sess.run(tf.global_variables_initializer())
22
---> 23 sess.run(res_tensor, feed_dict={texts: ["Some test string", "another string"]})
24
25 # print(res)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
954 try:
955 result = self._run(None, fetches, feed_dict, options_ptr,
--> 956 run_metadata_ptr)
957 if run_metadata:
958 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1178 if final_fetches or final_targets or (handle and feed_dict_tensor):
1179 results = self._do_run(handle, final_targets, final_fetches,
-> 1180 feed_dict_tensor, options, run_metadata)
1181 else:
1182 results = []
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1357 if handle is None:
1358 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1359 run_metadata)
1360 else:
1361 return self._do_call(_prun_fn, handle, feeds, fetches)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _do_call(self, fn, *args)
1382 '\nsession_config.graph_options.rewrite_options.'
1383 'disable_meta_optimizer = True')
-> 1384 raise type(e)(node_def, op, message)
1385
1386 def _extend_graph(self):
FailedPreconditionError: [_Derived_] Attempting to use uninitialized value bert/encoder/layer_3/attention/self/query/kernel
[[{{node bert/encoder/layer_3/attention/self/query/kernel/read}}]]
[[bert_pipeline/StatefulPartitionedCall]]
[请，如果您有一些想法如何解决我保存图形的方法，或者您知道如何做得更好-请告诉我。谢谢！
我解决了。首先，我无法使用tf.keras做到这一点。我使用了与tf v1代码兼容的代码。
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
除了我使用.meta，.index和bla bla checkpoint时，不使用'.pb'。
我在这里使用的主要内容：Tensorflow: How to replace a node in a calculation graph?
我制作了3个不同的图，然后像这部分代码一样将它们合并：
def __call__(self, queries):
pred, words = self.sess.run(
[self.probas, self.words],
feed_dict={
self.queries: queries
},
)
return pred, words
...
def _build_model(self):
with tf.Graph().as_default() as g_1:
self.lookup_table = self._make_lookup_table()
init_table = tf.initialize_all_tables()
self.bert_tokenizer = BertTokenizer(
self.lookup_table,
max_chars_per_token=15, token_out_type=tf.int64,
lower_case=True,
)
self.texts_ph = tf.placeholder(tf.string, shape=(None,), name="texts_ph") # input
words_without_name, tokens_int_64 = self.bert_tokenizer.tokenize(self.texts_ph)
words = tf.identity(words_without_name, name='tokens')
tokens = tf.cast(tokens_int_64, dtype=tf.int32)
mask = self._make_mask(tokens)
token_ids = self._make_token_ids(tokens)
self.token_indices = token_ids.to_tensor(default_value=0, name='token_indices') # output 1
self.token_mask = tf.ones_like(mask).to_tensor(default_value=0, name='token_mask') # output 2
self.y_mask = mask.to_tensor(default_value=0, name='y_mask') # output 3
with tf.Graph().as_default() as g_2:
sess = tf.Session()
path_to_model = 'path/to/model'
self._load_model(sess, path_to_model)
token_indices_2 = g_2.get_tensor_by_name('token_indices_ph:0'),
token_mask_2 = g_2.get_tensor_by_name('token_mask_ph:0'),
y_mask_2 = g_2.get_tensor_by_name('y_mask_ph:0'),
probas = g_2.get_tensor_by_name('ner/Softmax:0')
seq_lengths = g_2.get_tensor_by_name('ner/Sum:0')
exclude_scopes = ('Optimizer', 'learning_rate', 'momentum', 'EMA/BackupVariables')
all_vars = variables._all_saveable_objects()
self.vars_to_save = [var for var in all_vars if all(sc not in var.name for sc in exclude_scopes)]
self.saver = tf.train.Saver(self.vars_to_save)
with tf.Graph().as_default() as g_3:
softmax_out = tf.placeholder(dtype=tf.float32, name="softmax_out")
sum_out = tf.placeholder(dtype=tf.int32, name="sum_out")
final_probas = tf.identity(self._get_probas(softmax_out, sum_out), name='probas')
g_1_def = g_1.as_graph_def()
g_2_def = g_2.as_graph_def()
g_3_def = g_3.as_graph_def()
with tf.Graph().as_default() as g_combined:
self.texts = tf.placeholder(tf.string, shape=(None,), name="texts")
y1, y2, y3, self.init_table, self.words = tf.import_graph_def(
g_1_def, input_map={"texts_ph:0": self.texts},
return_elements=["token_indices/GatherV2:0", "token_mask/GatherV2:0", "y_mask/GatherV2:0", 'init_all_tables', 'tokens_1:0'],
name='',
)
z1, z2 = tf.import_graph_def(
g_2_def, input_map={"token_indices_ph:0": y1, "token_mask_ph:0": y2, "y_mask_ph:0": y3},
return_elements=["ner/Softmax:0", "ner/Sum:0"],
name='',
)
self.probas, = tf.import_graph_def(
g_3_def, input_map={"softmax_out:0": z1, "sum_out:0": z2},
return_elements=["probas_1:0"],
name='',
)
self.sess = tf.Session(graph=g_combined)
self.graph = g_combined
self.sess.run(self.init_table)
vars_dict_to_save = {v.name[:-2]: g_2.get_tensor_by_name(v.name) for v in self.vars_to_save}
self.saver.restore(self.sess, path_to_model)
[您可能会注意到我调用self._load_model(sess, path_to_model)来加载模型，用所需的变量创建saver，然后第二次加载模型self.saver.save(sess, path_to_model)。需要首先加载才能读取保存的图并可以访问其张量。其次需要使用g_combined合并图在另一个会话中加载权重。我认为有一种方法可以在不两次从磁盘加载的情况下做到这一点，但是它可以正常工作，我不想破坏它：-）。
更重要的是vars_dict_to_save。需要此dict才能在图中的加载权重和张量之间进行映射。
此外，请注意__call__方法。它使用我通过合并图创建的会话。
如果能帮助别人，我会很高兴的：-)。对我来说这不是小事，我花了很多时间使它起作用。
P.S。此代码中的BertTokenizer与此类与tensorflow_text稍有不同，但与问题无关。