我使用 tensorflow 1.15 训练了模型并将其保存为检查点(使用 .meta 、 .index 和 .data 文件)。
我需要的是在该图的开头和结尾添加一些额外的操作。其中一些操作仅存在于 tensorflow 2.0 和 tensorflow_text 2.0 中。之后我想将此模型保存为 tensorflow-serving 。
我尝试做的事情:使用 tensorflow 2.0 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 tf.keras.Model 来加载 .pb 模型并执行我需要的所有工作人员:
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)
当我调用这段代码时，它工作得很好:
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]]]>
但我不知道如何保存它。如果我理解正确的 tf.keras.Model 不要将 self.model 和 self.bert_tokenizer 视为模型的一部分。如果我调用 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 一起运行它，但在这种情况下，我无法正确加载模型。与 import tensorflow.compat.v1 as tf 相同的代码和 tensorflow 1.xx 的样板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]]
如果您有一些想法如何修复我保存图表的方法，或者您知道如何做得更好 - 请告诉我。谢谢!
最佳答案
我解决了。首先，我无法使用 tf.keras 来实现它。我用过
import tensorflow.compat.v1 as tf
此外，我使用了 .meta、.index 和 bla bla 检查点，而没有使用“.pb”。
我使用的主要内容描述如下:Tensorflow: How to replace a node in a calculation graph?
我制作了两个不同的图表，然后将它们合并起来，就像这部分代码一样:
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) 来加载模型，使用所需变量创建 saver ，然后使用 再次加载模型self.saver.save(sess，path_to_model)。需要第一次加载来读取预先保存的图形并访问它的张量。其次需要在另一个 session 中使用 g_combined 合并图加载权重。我认为有一种方法可以做到这一点，而无需从磁盘加载数据两次，但它有效，我不想破坏它:-)。
还有一件更重要的事情是vars_dict_to_save。需要这个字典来在图中加载的权重和张量之间进行映射。
之后，您就拥有了包含所有操作的完整图表，因此您可以这样调用它:
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 session 。
一旦您拥有带有加载权重的完整图表，就可以轻松导出图表以供服务:
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()
有几个重要时刻:
- 使用合并图.as_default()
- 使用与合并图相同的 session 。
- 使用与在合并图中加载权重相同的保护程序。
- 如果您有需要初始化的表，请添加 main main_op。
如果它能帮助别人，我会很高兴:-)。这对我来说并不是一件小事，我花了很多时间才让它发挥作用。
附注此代码中的 BertTokenizer 与 tensorflow_text 中的此类略有不同，但与问题无关。
关于tensorflow - 如何保存使用来自 Tensorflow 1.xx 的 .meta 检查点的模型作为一部分的 Tensorflow 2.0 模型？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/58736787/