我有模型一起训练tensorflow 1.15，并保存为检查点（与.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。其中一些操作仅存在于tensorflow 2.0和中tensorflow_text 2.0。之后，我想将此模型保存为tensorflow-serving。
我想做的是：使用tensorflow 2.0这样的.pb文件将其保存为文件。
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tf.keras.Model负载.pb模型并执行了我需要的所有人员：
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，它可以完美运行：
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它。如果我理解正确，tf.keras.Model请不要将self.model其self.bert_tokenizer视为模型的一部分。如果我致电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
_________________________________________________________________
另外，我尝试tensorflow.compat.v1使用explicitSession和来运行它Graph，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和样板相同的代码tensorflow 1.xx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 首先我无法做到tf.keras。我用了
import tensorflow.compat.v1 as tf
除此之外，我用.meta，.index和血乳酸血乳酸检查点，而saing为“.pb”。
我在这里使用的主要东西描述如下：Tensorflow：如何替换计算图中的节点？
我制作了2个不同的图，然后像这部分代码一样将它们合并：
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 = words_without_name.to_tensor(default_value='', 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
g_1_def = g_1.as_graph_def()
g_2_def = g_2.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/GatherV2:0'],
name='',
)
self.dense_probas, self.lengths = 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.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)加载模型，saver使用所需的变量创建，然后在加载模型之后再次使用self.saver.save(sess, path_to_model)。需要首先加载才能读取保存的图形并可以访问其张量。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数据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。
之后，您便拥有了包含所有操作的完整图形，因此可以这样称呼它：
def __call__(self, texts):
lengths, words, probs = self.sess.run(
[self.lengths, self.words, self.dense_probas],
feed_dict={
self.texts: texts
},
)
return lengths, words, probs
注意__call__方法的实现。它使用我通过合并图创建的会话。
一旦您拥有带有权重的完整图表，就可以轻松导出用于服务的图表：
def export(self, export_dir):
with self.graph.as_default():
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
predict_signature = tf.saved_model.signature_def_utils.predict_signature_def(
inputs={
'texts': self.texts,
},
outputs={
'lengths': self.lengths,
'tokens': self.words,
'probs': self.dense_probas,
},
)
builder.add_meta_graph_and_variables(
self.sess,
[tf.saved_model.SERVING],
strip_default_attrs=True,
signature_def_map={'predict': predict_signature},
saver=self.saver,
main_op=self.init_table,
)
builder.save()
有一些重要的时刻：-使用合并图.as_default()-使用与合并图相同的会话。-使用与合并图中的权重相同的保护程序。-main_op如果您有需要初始化的表，请添加main 。
如果能帮助到别人，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。
BertTokenizer这段代码中的PS与此类的类略有不同tensorflow_text，但与问题无关。
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我来说两句