如何保存使用Tensorflow 1.xx中的.meta检查点模型作为部分的Tensorflow 2.0模型？
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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，我想将此模型保存为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！
答案
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'。
我使用的主要内容在此处描述：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 _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，使用所需的变量创建saver，然后使用self.saver.save(sess, path_to_model)再次加载模型。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数据的情况下执行此操作，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, queries):
pred, words = self.sess.run(
[self.probas, self.words],
feed_dict=
self.queries: queries
,
)
return pred, words
请注意__call__方法的实现。它使用我通过合并图创建的会话。
一旦您拥有带有已加载权重的完整图表，就可以轻松导出要投放的图表：
def export(self, export_dir):
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
predict_signature = tf.saved_model.signature_def_utils.build_signature_def(
inputs=
'queries': tf.saved_model.utils.build_tensor_info(self.queries),
,
outputs=
'probs': tf.saved_model.utils.build_tensor_info(self.probas),
'tokens': tf.saved_model.utils.build_tensor_info(self.words)
,
method_name='predict'
)
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()
有一些重要时刻：-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。-如果有需要初始化的表，则添加主main_op。
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。
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