def testIdTableWithHashBucketsWithMultipleInitializersDifferentDefault(self):
vocab_file = self._createVocabFile("feat_to_id_6.txt")
with self.test_session() as sess:
default_value1 = -1
vocab_size = 3
oov_buckets = 0
table1 = lookup_ops.IdTableWithHashBuckets(
lookup_ops.HashTable(
lookup_ops.TextFileIdTableInitializer(
vocab_file, vocab_size=vocab_size), default_value1),
oov_buckets)
default_value2 = -2
table2 = lookup_ops.IdTableWithHashBuckets(
lookup_ops.HashTable(
lookup_ops.TextFileIdTableInitializer(
vocab_file, vocab_size=vocab_size), default_value2),
oov_buckets)
lookup_ops.tables_initializer().run()
input_string_1 = constant_op.constant(
["brain", "salad", "surgery", "UNK"])
input_string_2 = constant_op.constant(["fruit", "salad", "UNK"])
out1 = table1.lookup(input_string_1)
out2 = table2.lookup(input_string_2)
out1, out2 = sess.run([out1, out2])
self.assertAllEqual([0, 1, 2, -1], out1)
self.assertAllEqual([-2, 1, -2], out2)
self.assertEquals(vocab_size + oov_buckets, table1.size().eval())
self.assertEquals(vocab_size + oov_buckets, table2.size().eval())
def test_asset_loading(self):
first_path = self._v1_asset_saved_model()
imported = load.load(first_path)
self.evaluate(lookup_ops.tables_initializer())
fn = imported.signatures["serving_default"]
self.assertAllClose({"output": [2, 0]},
fn(start=constant_op.constant(["gamma", "alpha"])))
second_path = os.path.join(self.get_temp_dir(), "saved_model",
str(ops.uid()))
save.save(imported, second_path, signatures=imported.signatures)
shutil.rmtree(first_path)
del ops.get_collection_ref(ops.GraphKeys.TABLE_INITIALIZERS)[:]
second_import = load.load(second_path)
self.evaluate(lookup_ops.tables_initializer())
fn = second_import.signatures["serving_default"]
self.assertAllClose({"output": [2, 0]},
fn(start=constant_op.constant(["gamma", "alpha"])))
third_path = os.path.join(self.get_temp_dir(), "saved_model",
str(ops.uid()))
save.save(second_import, third_path, signatures=second_import.signatures)
shutil.rmtree(second_path)
del ops.get_collection_ref(ops.GraphKeys.TABLE_INITIALIZERS)[:]
third_import = load.load(third_path)
self.evaluate(lookup_ops.tables_initializer())
fn = third_import.signatures["serving_default"]
self.assertAllClose({"output": [2, 0]},
fn(start=constant_op.constant(["gamma", "alpha"])))
def testMultipleHashTables(self):
with self.test_session() as sess:
default_val = -1
keys = constant_op.constant(["brain", "salad", "surgery"])
values = constant_op.constant([0, 1, 2], dtypes.int64)
table1 = lookup_ops.HashTable(
lookup_ops.KeyValueTensorInitializer(keys, values), default_val)
table2 = lookup_ops.HashTable(
lookup_ops.KeyValueTensorInitializer(keys, values), default_val)
table3 = lookup_ops.HashTable(
lookup_ops.KeyValueTensorInitializer(keys, values), default_val)
lookup_ops.tables_initializer().run()
self.assertAllEqual(3, table1.size().eval())
self.assertAllEqual(3, table2.size().eval())
self.assertAllEqual(3, table3.size().eval())
input_string = constant_op.constant(["brain", "salad", "tank"])
output1 = table1.lookup(input_string)
output2 = table2.lookup(input_string)
output3 = table3.lookup(input_string)
out1, out2, out3 = sess.run([output1, output2, output3])
self.assertAllEqual([0, 1, -1], out1)
self.assertAllEqual([0, 1, -1], out2)
self.assertAllEqual([0, 1, -1], out3)
def test_index_table_from_tensor_with_tensor_init(self):
with self.test_session():
table = lookup_ops.index_table_from_tensor(
vocabulary_list=("brain", "salad", "surgery"), num_oov_buckets=1)
ids = table.lookup(constant_op.constant(("salad", "surgery", "tarkus")))
self.assertRaises(errors_impl.OpError, ids.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual((1, 2, 3), ids.eval())
def test_duplicate_entries(self):
with self.test_session():
vocabulary_list = constant_op.constant(["hello", "hello"])
table = lookup_ops.index_to_string_table_from_tensor(
vocabulary_list=vocabulary_list)
indices = constant_op.constant([0, 1, 4], dtypes.int64)
features = table.lookup(indices)
lookup_ops.tables_initializer().run()
self.assertAllEqual((b"hello", b"hello", b"UNK"), features.eval())
def test_index_table_from_tensor_empty_vocabulary_list(self):
with self.test_session():
table = lookup_ops.index_table_from_tensor(
vocabulary_list=np.array([], dtype=np.str_), num_oov_buckets=1)
ids = table.lookup(constant_op.constant(["salad", "surgery", "brain"]))
self.assertRaises(errors_impl.OpError, ids.eval)
with self.assertRaisesRegexp(
errors_impl.OpError, "keys and values cannot be empty"):
lookup_ops.tables_initializer().run()
def test_string_index_table_from_file(self):
vocabulary_file = self._createVocabFile("f2i_vocab1.txt")
with self.test_session():
table = lookup_ops.index_table_from_file(
vocabulary_file=vocabulary_file, num_oov_buckets=1)
ids = table.lookup(constant_op.constant(["salad", "surgery", "tarkus"]))
self.assertRaises(errors_impl.OpError, ids.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual((1, 2, 3), ids.eval())
def test_index_to_string_table_with_vocab_size(self):
vocabulary_file = self._createVocabFile("f2i_vocab7.txt")
with self.test_session():
table = lookup_ops.index_to_string_table_from_file(
vocabulary_file=vocabulary_file, vocab_size=3)
features = table.lookup(constant_op.constant([1, 2, 4], dtypes.int64))
self.assertRaises(errors_impl.OpError, features.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual((b"salad", b"surgery", b"UNK"), features.eval())
def test_int64_index_table_from_tensor_with_tensor_init(self):
with self.test_session():
table = lookup_ops.index_table_from_tensor(
vocabulary_list=(42, 1, -1000), num_oov_buckets=1, dtype=dtypes.int64)
ids = table.lookup(
constant_op.constant((1, -1000, 11), dtype=dtypes.int64))
self.assertRaises(errors_impl.OpError, ids.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual((1, 2, 3), ids.eval())
def test_index_table_from_file_with_default_value(self):
default_value = -42
vocabulary_file = self._createVocabFile("f2i_vocab4.txt")
with self.test_session():
table = lookup_ops.index_table_from_file(
vocabulary_file=vocabulary_file, default_value=default_value)
ids = table.lookup(constant_op.constant(["salad", "surgery", "tarkus"]))
self.assertRaises(errors_impl.OpError, ids.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual((1, 2, default_value), ids.eval())
def test_index_table_from_tensor_with_default_value(self):
default_value = -42
with self.test_session():
table = lookup_ops.index_table_from_tensor(
vocabulary_list=["brain", "salad", "surgery"],
default_value=default_value)
ids = table.lookup(constant_op.constant(["salad", "surgery", "tarkus"]))
self.assertRaises(errors_impl.OpError, ids.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual((1, 2, default_value), ids.eval())
def test_index_table_from_file_with_vocab_size_too_small(self):
vocabulary_file = self._createVocabFile("f2i_vocab6.txt")
with self.test_session():
table = lookup_ops.index_table_from_file(
vocabulary_file=vocabulary_file, vocab_size=2)
ids = table.lookup(constant_op.constant(["salad", "surgery", "tarkus"]))
self.assertRaises(errors_impl.OpError, ids.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual((1, -1, -1), ids.eval())
self.assertEqual(2, table.size().eval())
def test_index_to_string_with_default_value(self):
default_value = b"NONE"
with self.test_session():
vocabulary_list = constant_op.constant(["brain", "salad", "surgery"])
table = lookup_ops.index_to_string_table_from_tensor(
vocabulary_list=vocabulary_list, default_value=default_value)
indices = constant_op.constant([1, 2, 4], dtypes.int64)
features = table.lookup(indices)
self.assertRaises(errors_impl.OpError, features.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual((b"salad", b"surgery", default_value),
features.eval())
def test_index_to_string_table_with_vocab_size_too_small(self):
default_value = b"NONE"
vocabulary_file = self._createVocabFile("f2i_vocab2.txt")
with self.test_session():
table = lookup_ops.index_to_string_table_from_file(
vocabulary_file=vocabulary_file,
vocab_size=2,
default_value=default_value)
features = table.lookup(constant_op.constant([1, 2, 4], dtypes.int64))
self.assertRaises(errors_impl.OpError, features.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual((b"salad", default_value, default_value),
features.eval())
def test_int64_index_table_from_file(self):
vocabulary_file = self._createVocabFile(
"f2i_vocab3.txt", values=("42", "1", "-1000"))
with self.test_session():
table = lookup_ops.index_table_from_file(
vocabulary_file=vocabulary_file,
num_oov_buckets=1,
key_dtype=dtypes.int64)
ids = table.lookup(
constant_op.constant((1, -1000, 11), dtype=dtypes.int64))
self.assertRaises(errors_impl.OpError, ids.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual((1, 2, 3), ids.eval())
def testDecodeExampleWithBranchedLookup(self):
example = example_pb2.Example(features=feature_pb2.Features(feature={
'image/object/class/text': self._BytesFeatureFromList(
np.array(['cat', 'dog', 'guinea pig'])),
}))
serialized_example = example.SerializeToString()
# 'dog' -> 0, 'guinea pig' -> 1, 'cat' -> 2
table = lookup_ops.index_table_from_tensor(
constant_op.constant(['dog', 'guinea pig', 'cat']))
with self.test_session() as sess:
sess.run(lookup_ops.tables_initializer())
serialized_example = array_ops.reshape(serialized_example, shape=[])
keys_to_features = {
'image/object/class/text': parsing_ops.VarLenFeature(dtypes.string),
}
items_to_handlers = {
'labels':
tf_example_decoder.LookupTensor('image/object/class/text', table),
}
decoder = slim_example_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
obtained_class_ids = decoder.decode(serialized_example)[0].eval()
self.assertAllClose([2, 0, 1], obtained_class_ids)
def _text_vocab_subsample_vocab_helper(self, vocab_freq_file, vocab_min_count,
vocab_freq_dtype, corpus_size=None):
# The outputs are non-deterministic, so set random seed to help ensure that
# the outputs remain constant for testing.
random_seed.set_random_seed(42)
input_tensor = constant_op.constant([
# keep_prob = (sqrt(30/(0.05*100)) + 1) * (0.05*100/30) = 0.57.
b"the",
b"answer", # Not in vocab. (Always discarded)
b"to", # keep_prob = 0.75.
b"life", # keep_prob > 1. (Always kept)
b"and", # keep_prob = 0.48.
b"universe" # Below vocab threshold of 3. (Always discarded)
])
# keep_prob calculated from vocab file with relative frequencies of:
# and: 40
# life: 8
# the: 30
# to: 20
# universe: 2
tokens, labels = text.skip_gram_sample_with_text_vocab(
input_tensor=input_tensor,
vocab_freq_file=vocab_freq_file,
vocab_token_index=0,
vocab_freq_index=1,
vocab_freq_dtype=vocab_freq_dtype,
vocab_min_count=vocab_min_count,
vocab_subsampling=0.05,
corpus_size=corpus_size,
min_skips=1,
max_skips=1,
seed=123)
expected_tokens, expected_labels = self._split_tokens_labels([
(b"the", b"to"),
(b"to", b"the"),
(b"to", b"life"),
(b"life", b"to"),
])
with self.test_session() as sess:
lookup_ops.tables_initializer().run()
tokens_eval, labels_eval = sess.run([tokens, labels])
self.assertAllEqual(expected_tokens, tokens_eval)
self.assertAllEqual(expected_labels, labels_eval)
def test_index_table_from_file_with_oov_buckets(self):
vocabulary_file = self._createVocabFile("f2i_vocab5.txt")
with self.test_session():
table = lookup_ops.index_table_from_file(
vocabulary_file=vocabulary_file, num_oov_buckets=1000)
ids = table.lookup(
constant_op.constant(["salad", "surgery", "tarkus", "toccata"]))
self.assertRaises(errors_impl.OpError, ids.eval)
lookup_ops.tables_initializer().run()
self.assertAllEqual(
(
1, # From vocabulary file.
2, # From vocabulary file.
867, # 3 + fingerprint("tarkus") mod 300.
860), # 3 + fingerprint("toccata") mod 300.
ids.eval())
def testMissingValueInOneHotColumnForSparseColumnWithKeys(self):
ids = fc.sparse_column_with_keys("ids", ["marlo", "omar", "stringer"])
one_hot = fc.one_hot_column(ids)
features = {"ids": constant_op.constant([["marlo", "unknown", "omar"]])}
one_hot_tensor = feature_column_ops.input_from_feature_columns(
features, [one_hot])
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
sess.run(lookup_ops.tables_initializer())
self.assertAllEqual([[1., 1., 0.]], one_hot_tensor.eval())
def test_index_to_string_table_with_vocab_size_too_large(self):
vocabulary_file = self._createVocabFile("f2i_vocab6.txt")
with self.test_session():
table = lookup_ops.index_to_string_table_from_file(
vocabulary_file=vocabulary_file, vocab_size=4)
features = table.lookup(constant_op.constant([1, 2, 4], dtypes.int64))
self.assertRaises(errors_impl.OpError, features.eval)
init = lookup_ops.tables_initializer()
self.assertRaisesRegexp(errors_impl.InvalidArgumentError,
"Invalid vocab_size", init.run)
def _export_graph(graph, saver, checkpoint_path, export_dir,
default_graph_signature, named_graph_signatures,
exports_to_keep):
"""Exports graph via session_bundle, by creating a Session."""
with graph.as_default():
with tf_session.Session('') as session:
variables.local_variables_initializer()
lookup_ops.tables_initializer()
saver.restore(session, checkpoint_path)
export = exporter.Exporter(saver)
export.init(
init_op=control_flow_ops.group(
variables.local_variables_initializer(),
lookup_ops.tables_initializer()),
default_graph_signature=default_graph_signature,
named_graph_signatures=named_graph_signatures,
assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS))
return export.export(export_dir, contrib_variables.get_global_step(),
session, exports_to_keep=exports_to_keep)
def testInitializeSameTableWithMultipleNodes(self):
vocabulary_file = self._createVocabFile("one_column_5.txt")
with self.test_session() as sess:
shared_name = "shared-one-columm"
default_value = -1
table1 = lookup_ops.HashTable(
lookup_ops.TextFileInitializer(vocabulary_file, dtypes.string,
lookup_ops.TextFileIndex.WHOLE_LINE,
dtypes.int64,
lookup_ops.TextFileIndex.LINE_NUMBER),
default_value,
shared_name=shared_name)
table2 = lookup_ops.HashTable(
lookup_ops.TextFileInitializer(vocabulary_file, dtypes.string,
lookup_ops.TextFileIndex.WHOLE_LINE,
dtypes.int64,
lookup_ops.TextFileIndex.LINE_NUMBER),
default_value,
shared_name=shared_name)
table3 = lookup_ops.HashTable(
lookup_ops.TextFileInitializer(vocabulary_file, dtypes.string,
lookup_ops.TextFileIndex.WHOLE_LINE,
dtypes.int64,
lookup_ops.TextFileIndex.LINE_NUMBER),
default_value,
shared_name=shared_name)
lookup_ops.tables_initializer().run()
input_string = constant_op.constant(["brain", "salad", "tank"])
output1 = table1.lookup(input_string)
output2 = table2.lookup(input_string)
output3 = table3.lookup(input_string)
out1, out2, out3 = sess.run([output1, output2, output3])
self.assertAllEqual([0, 1, -1], out1)
self.assertAllEqual([0, 1, -1], out2)
self.assertAllEqual([0, 1, -1], out3)
def testIdTableWithHashBucketsWithMultipleInitializers(self):
vocab_file = self._createVocabFile("feat_to_id_4.txt")
with self.test_session() as sess:
default_value = -1
vocab_size = 3
oov_buckets = 3
vocab_table = lookup_ops.HashTable(
lookup_ops.TextFileIdTableInitializer(
vocab_file, vocab_size=vocab_size), default_value)
table1 = lookup_ops.IdTableWithHashBuckets(
vocab_table,
oov_buckets,
hasher_spec=lookup_ops.FastHashSpec,
name="table1")
table2 = lookup_ops.IdTableWithHashBuckets(
vocab_table,
oov_buckets,
hasher_spec=lookup_ops.StrongHashSpec((1, 2)),
name="table2")
lookup_ops.tables_initializer().run()
input_string = constant_op.constant(
["fruit", "brain", "salad", "surgery", "UNK"])
out1 = table1.lookup(input_string)
out2 = table2.lookup(input_string)
out1, out2 = sess.run([out1, out2])
self.assertAllEqual([5, 0, 1, 2, 5], out1)
self.assertAllEqual([5, 0, 1, 2, 3], out2)
self.assertEquals(vocab_size + oov_buckets, table1.size().eval())
self.assertEquals(vocab_size + oov_buckets, table2.size().eval())
test_util.assert_ops_in_graph({
"table1_Lookup/hash_bucket": "StringToHashBucketFast",
"table2_Lookup/hash_bucket": "StringToHashBucketStrong",
}, sess.graph)
def main_op():
"""Returns a main op to init variables and tables.
Returns the main op including the group of ops that initializes all
variables, initializes local variables and initialize all tables.
Returns:
The set of ops to be run as part of the main op upon the load operation.
"""
init = variables.global_variables_initializer()
init_local = variables.local_variables_initializer()
init_tables = lookup_ops.tables_initializer()
return control_flow_ops.group(init, init_local, init_tables)
def _get_local_init_op():
"""Returns the local init ops to initialize tables and local variables."""
local_init_op = _get_first_op_from_collection(
ops.GraphKeys.LOCAL_INIT_OP)
if local_init_op is None:
op_list = [
variables.local_variables_initializer(),
lookup_ops.tables_initializer()
]
if op_list:
local_init_op = control_flow_ops.group(*op_list)
ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
return local_init_op
def _test_prepare_inputs_for_rnn(self, sequence_features, context_features,
sequence_feature_columns, num_unroll,
expected):
features_by_time = ssre._prepare_inputs_for_rnn(sequence_features,
context_features,
sequence_feature_columns,
num_unroll)
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
sess.run(lookup_ops.tables_initializer())
features_val = sess.run(features_by_time)
self.assertAllEqual(expected, features_val)
def testDecodeExampleWithBranchedBackupHandler(self):
example1 = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/object/class/text':
self._BytesFeatureFromList(
np.array(['cat', 'dog', 'guinea pig'])),
'image/object/class/label':
self._Int64FeatureFromList(np.array([42, 10, 900]))
}))
example2 = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/object/class/text':
self._BytesFeatureFromList(
np.array(['cat', 'dog', 'guinea pig'])),
}))
example3 = example_pb2.Example(
features=feature_pb2.Features(
feature={
'image/object/class/label':
self._Int64FeatureFromList(np.array([42, 10, 901]))
}))
# 'dog' -> 0, 'guinea pig' -> 1, 'cat' -> 2
table = lookup_ops.index_table_from_tensor(
constant_op.constant(['dog', 'guinea pig', 'cat']))
keys_to_features = {
'image/object/class/text': parsing_ops.VarLenFeature(dtypes.string),
'image/object/class/label': parsing_ops.VarLenFeature(dtypes.int64),
}
backup_handler = tf_example_decoder.BackupHandler(
handler=slim_example_decoder.Tensor('image/object/class/label'),
backup=tf_example_decoder.LookupTensor('image/object/class/text',
table))
items_to_handlers = {
'labels': backup_handler,
}
decoder = slim_example_decoder.TFExampleDecoder(keys_to_features,
items_to_handlers)
obtained_class_ids_each_example = []
with self.test_session() as sess:
sess.run(lookup_ops.tables_initializer())
for example in [example1, example2, example3]:
serialized_example = array_ops.reshape(
example.SerializeToString(), shape=[])
obtained_class_ids_each_example.append(
decoder.decode(serialized_example)[0].eval())
self.assertAllClose([42, 10, 900], obtained_class_ids_each_example[0])
self.assertAllClose([2, 0, 1], obtained_class_ids_each_example[1])
self.assertAllClose([42, 10, 901], obtained_class_ids_each_example[2])
def testBuildSequenceInputInput(self):
sequence_input = dynamic_rnn_estimator.build_sequence_input(
self.GetColumnsToTensors(), self.sequence_feature_columns,
self.context_feature_columns)
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
sess.run(lookup_ops.tables_initializer())
sequence_input_val = sess.run(sequence_input)
expected_shape = np.array([
3, # expected batch size
2, # padded sequence length
3 + 8 + 2 # location keys + embedding dim + measurement dimension
])
self.assertAllEqual(expected_shape, sequence_input_val.shape)
def run_feeds_iter(output_dict, feed_dicts, restore_checkpoint_path=None):
"""Run `output_dict` tensors with each input in `feed_dicts`.
If `restore_checkpoint_path` is supplied, restore from checkpoint. Otherwise,
init all variables.
Args:
output_dict: A `dict` mapping string names to `Tensor` objects to run.
Tensors must all be from the same graph.
feed_dicts: Iterable of `dict` objects of input values to feed.
restore_checkpoint_path: A string containing the path to a checkpoint to
restore.
Yields:
A sequence of dicts of values read from `output_dict` tensors, one item
yielded for each item in `feed_dicts`. Keys are the same as `output_dict`,
values are the results read from the corresponding `Tensor` in
`output_dict`.
Raises:
ValueError: if `output_dict` or `feed_dicts` is None or empty.
"""
if not output_dict:
raise ValueError('output_dict is invalid: %s.' % output_dict)
if not feed_dicts:
raise ValueError('feed_dicts is invalid: %s.' % feed_dicts)
graph = contrib_ops.get_graph_from_inputs(output_dict.values())
with graph.as_default() as g:
with tf_session.Session('') as session:
session.run(
resources.initialize_resources(resources.shared_resources() +
resources.local_resources()))
if restore_checkpoint_path:
_restore_from_checkpoint(session, g, restore_checkpoint_path)
else:
session.run(variables.global_variables_initializer())
session.run(variables.local_variables_initializer())
session.run(lookup_ops.tables_initializer())
coord = coordinator.Coordinator()
threads = None
try:
threads = queue_runner.start_queue_runners(session, coord=coord)
for f in feed_dicts:
yield session.run(output_dict, f)
finally:
coord.request_stop()
if threads:
coord.join(threads, stop_grace_period_secs=120)
def default_local_init_op():
"""Returns an op that groups the default local init ops.
This op is used during session initialization when a Scaffold is
initialized without specifying the local_init_op arg. It includes
`tf.local_variables_initializer`, `tf.tables_initializer`, and also
initializes local session resources.
Returns:
The default Scaffold local init op.
"""
return control_flow_ops.group(
variables.local_variables_initializer(),
lookup_ops.tables_initializer(),
resources.initialize_resources(resources.local_resources()))
def my_main_op():
init_local = variables.local_variables_initializer()
init_tables = lookup_ops.tables_initializer()
return tf.group(init_local, init_tables)
def _default_local_init_op():
return control_flow_ops.group(variables.local_variables_initializer(),
lookup_ops.tables_initializer())
def _initialize(self, init_op, sess):
sess.run(variables.global_variables_initializer())
sess.run(lookup_ops.tables_initializer())
sess.run(init_op)
def _restore(self, saver, sess):
sess.run(lookup_ops.tables_initializer())
saver.restore(sess, self._latest_ckpt())
def test_dense_features(self, use_safe_embedding_lookup,
partition_variables):
# Inputs.
vocabulary_size = 4
sparse_input = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
# example 2, ids []
# example 3, ids [1]
indices=((0, 0), (1, 0), (1, 4), (3, 0)),
values=(2, 0, 1, 1),
dense_shape=(4, 5))
# Embedding variable.
embedding_dimension = 2
embedding_values = (
(1., 2.), # id 0
(3., 5.), # id 1
(7., 11.), # id 2
(9., 13.) # id 3
)
def _initializer(shape, dtype, partition_info=None):
if partition_variables:
self.assertEqual([vocabulary_size, embedding_dimension],
partition_info.full_shape)
self.assertAllEqual((2, embedding_dimension), shape)
else:
self.assertAllEqual((vocabulary_size, embedding_dimension),
shape)
self.assertIsNone(partition_info)
self.assertEqual(dtypes.float32, dtype)
return embedding_values
# Expected lookup result, using combiner='mean'.
expected_lookups = (
# example 0, ids [2], embedding = [7, 11]
(7., 11.),
# example 1, ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
(2., 3.5),
# example 2, ids [], embedding = [0, 0]
(0., 0.),
# example 3, ids [1], embedding = [3, 5]
(3., 5.),
)
# Build columns.
categorical_column = fc.categorical_column_with_identity(
key='aaa', num_buckets=vocabulary_size)
partitioner = None
if partition_variables:
partitioner = partitioned_variables.fixed_size_partitioner(2,
axis=0)
with variable_scope.variable_scope('vars', partitioner=partitioner):
embedding_column = fc.embedding_column(
categorical_column,
dimension=embedding_dimension,
initializer=_initializer,
use_safe_embedding_lookup=use_safe_embedding_lookup)
# Provide sparse input and get dense result.
l = df.DenseFeatures((embedding_column, ))
dense_features = l({'aaa': sparse_input})
# Assert expected embedding variable and lookups.
global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
if partition_variables:
self.assertCountEqual((
'vars/dense_features/aaa_embedding/embedding_weights/part_0:0',
'vars/dense_features/aaa_embedding/embedding_weights/part_1:0'
), tuple([v.name for v in global_vars]))
else:
self.assertCountEqual(
('vars/dense_features/aaa_embedding/embedding_weights:0', ),
tuple([v.name for v in global_vars]))
for v in global_vars:
self.assertIsInstance(v, variables_lib.Variable)
trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
if partition_variables:
self.assertCountEqual((
'vars/dense_features/aaa_embedding/embedding_weights/part_0:0',
'vars/dense_features/aaa_embedding/embedding_weights/part_1:0'
), tuple([v.name for v in trainable_vars]))
else:
self.assertCountEqual(
('vars/dense_features/aaa_embedding/embedding_weights:0', ),
tuple([v.name for v in trainable_vars]))
self.evaluate(variables_lib.global_variables_initializer())
self.evaluate(lookup_ops.tables_initializer())
self.assertAllEqual(embedding_values, self.evaluate(trainable_vars[0]))
self.assertAllEqual(expected_lookups, self.evaluate(dense_features))
if use_safe_embedding_lookup:
self.assertIn(
'SparseFillEmptyRows',
[x.type for x in ops.get_default_graph().get_operations()])
else:
self.assertNotIn(
'SparseFillEmptyRows',
[x.type for x in ops.get_default_graph().get_operations()])
def test_encode_features(self):
# Inputs.
vocabulary_size = 4
# -1 values are ignored.
input_a = np.array([
[3, -1, -1], # example 0, ids [3]
[0, 1, -1], # example 1, ids [0, 1]
])
input_b = np.array([
[0, -1, -1], # example 0, ids [0]
[-1, -1, -1], # example 1, ids []
])
input_features = {"aaa": input_a, "bbb": input_b}
# Embedding variable.
embedding_dimension = 2
embedding_values = (
(1., 2.), # id 0
(3., 5.), # id 1
(7., 11.), # id 2
(9., 13.) # id 3
)
# Expected lookup result, using combiner='mean'.
expected_lookups_a = (
# example 0:
(9., 13.), # ids [3], embedding = [9, 13]
# example 1:
(2., 3.5
), # ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
)
expected_lookups_b = (
# example 0:
(1., 2.), # ids [0], embedding = [1, 2]
# example 1:
(0., 0.), # ids [], embedding = [0, 0]
)
# Build columns.
categorical_column_a = feature_column.categorical_column_with_identity(
key="aaa", num_buckets=vocabulary_size)
categorical_column_b = feature_column.categorical_column_with_identity(
key="bbb", num_buckets=vocabulary_size)
embed_column_a, embed_column_b = feature_column.shared_embedding_columns(
[categorical_column_a, categorical_column_b],
dimension=embedding_dimension,
initializer=lambda shape, dtype, partition_info: embedding_values,
shared_embedding_collection_name="custom_collection_name")
feature_columns = {"aaa": embed_column_a, "bbb": embed_column_b}
cols_to_tensors = feature_lib.encode_features(
input_features,
feature_columns.values(),
mode=model_fn.ModeKeys.EVAL)
embedding_lookup_a = cols_to_tensors[feature_columns["aaa"]]
embedding_lookup_b = cols_to_tensors[feature_columns["bbb"]]
# Assert expected embedding variable and lookups.
global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
embedding_var = global_vars[0]
with session.Session() as sess:
sess.run(variables.global_variables_initializer())
sess.run(lookup_ops.tables_initializer())
self.assertAllEqual(embedding_values, embedding_var.eval())
self.assertAllEqual(expected_lookups_a, embedding_lookup_a.eval())
self.assertAllEqual(expected_lookups_b, embedding_lookup_b.eval())
def test_encode_listwise_features(self):
# Batch size = 2, list_size = 2.
features = {
"query_length":
ops.convert_to_tensor([[1], [2]]),
"utility":
ops.convert_to_tensor([[[1.0], [0.0]], [[0.0], [1.0]]]),
"unigrams":
sparse_tensor_lib.SparseTensor(
indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0]],
values=["ranking", "regression", "classification", "ordinal"],
dense_shape=[2, 2, 1])
}
context_feature_columns = {
"query_length":
feature_column.numeric_column("query_length",
shape=(1, ),
default_value=0,
dtype=dtypes.int64)
}
example_feature_columns = {
"utility":
feature_column.numeric_column("utility",
shape=(1, ),
default_value=0.0,
dtype=dtypes.float32),
"unigrams":
feature_column.embedding_column(
feature_column.categorical_column_with_vocabulary_list(
"unigrams",
vocabulary_list=[
"ranking", "regression", "classification", "ordinal"
]),
dimension=10)
}
with self.assertRaisesRegexp(
ValueError,
r"2nd dimesion of tensor must be equal to input size: 3, but found .*"
):
feature_lib.encode_listwise_features(
features,
input_size=3,
context_feature_columns=context_feature_columns,
example_feature_columns=example_feature_columns)
context_features, example_features = feature_lib.encode_listwise_features(
features,
input_size=2,
context_feature_columns=context_feature_columns,
example_feature_columns=example_feature_columns)
self.assertAllEqual(["query_length"], sorted(context_features))
self.assertAllEqual(["unigrams", "utility"], sorted(example_features))
self.assertAllEqual([2, 2, 10],
example_features["unigrams"].get_shape().as_list())
with session.Session() as sess:
sess.run(variables.global_variables_initializer())
sess.run(lookup_ops.tables_initializer())
context_features, example_features = sess.run(
[context_features, example_features])
self.assertAllEqual([[1], [2]], context_features["query_length"])
self.assertAllEqual([[[1.0], [0.0]], [[0.0], [1.0]]],
example_features["utility"])
def testPrepareFeaturesForSQSS(self):
mode = model_fn_lib.ModeKeys.TRAIN
seq_feature_name = 'seq_feature'
sparse_seq_feature_name = 'wire_cast'
ctx_feature_name = 'ctx_feature'
sequence_length = 4
embedding_dimension = 8
features = {
sparse_seq_feature_name:
sparse_tensor.SparseTensor(indices=[[0, 0, 0], [0, 1,
0], [1, 0, 0],
[1, 1, 0], [1, 1, 1],
[2, 0, 0], [2, 1, 1]],
values=[
b'marlo', b'stringer', b'omar',
b'stringer', b'marlo', b'marlo',
b'omar'
],
dense_shape=[3, 2, 2]),
seq_feature_name:
constant_op.constant(1.0, shape=[sequence_length]),
ctx_feature_name:
constant_op.constant(2.0)
}
labels = constant_op.constant(5.0, shape=[sequence_length])
wire_cast = feature_column.sparse_column_with_keys(
'wire_cast', ['marlo', 'omar', 'stringer'])
sequence_feature_columns = [
feature_column.real_valued_column(seq_feature_name, dimension=1),
feature_column.embedding_column(
wire_cast,
dimension=embedding_dimension,
initializer=init_ops.ones_initializer())
]
context_feature_columns = [
feature_column.real_valued_column(ctx_feature_name, dimension=1)
]
expected_sequence = {
rnn_common.RNNKeys.LABELS_KEY:
np.array([5., 5., 5., 5.]),
seq_feature_name:
np.array([1., 1., 1., 1.]),
sparse_seq_feature_name:
sparse_tensor.SparseTensor(indices=[[0, 0, 0], [0, 1,
0], [1, 0, 0],
[1, 1, 0], [1, 1, 1],
[2, 0, 0], [2, 1, 1]],
values=[
b'marlo', b'stringer', b'omar',
b'stringer', b'marlo', b'marlo',
b'omar'
],
dense_shape=[3, 2, 2]),
}
expected_context = {ctx_feature_name: 2.}
sequence, context = ssre._prepare_features_for_sqss(
features, labels, mode, sequence_feature_columns,
context_feature_columns)
def assert_equal(expected, got):
self.assertEqual(sorted(expected), sorted(got))
for k, v in expected.items():
if isinstance(v, sparse_tensor.SparseTensor):
self.assertAllEqual(v.values.eval(), got[k].values)
self.assertAllEqual(v.indices.eval(), got[k].indices)
self.assertAllEqual(v.dense_shape.eval(),
got[k].dense_shape)
else:
self.assertAllEqual(v, got[k])
with self.cached_session() as sess:
sess.run(variables.global_variables_initializer())
sess.run(lookup_ops.tables_initializer())
actual_sequence, actual_context = sess.run([sequence, context])
assert_equal(expected_sequence, actual_sequence)
assert_equal(expected_context, actual_context)
def train(data_dicts,
class_num,
input_size,
lr,
n_epochs,
num_clones,
iters_cnt,
val_every,
model_init_fn,
save_cback,
atrous_rates=[6, 12, 18],
fine_tune_batch_norm=True,
output_stride=16):
tf.logging.set_verbosity(tf.logging.INFO)
# Set up deployment (i.e., multi-GPUs and/or multi-replicas).
config = model_deploy.DeploymentConfig(num_clones=num_clones,
clone_on_cpu=clone_on_cpu,
replica_id=task,
num_replicas=num_replicas,
num_ps_tasks=num_ps_tasks)
with tf.Graph().as_default():
with tf.device(config.inputs_device()):
samples = get(data_dicts['train'],
input_size,
is_training=True,
model_variant=model_variant)
samples_val = get(data_dicts['val'],
input_size,
is_training=True,
model_variant=model_variant)
inputs_queue = prefetch_queue.prefetch_queue(samples,
capacity=128 *
config.num_clones,
dynamic_pad=True)
inputs_queue_val = prefetch_queue.prefetch_queue(samples_val,
capacity=128 *
config.num_clones,
dynamic_pad=True)
coord = tf.train.Coordinator()
# Create the global step on the device storing the variables.
with tf.device(config.variables_device()):
global_step = tf.train.create_global_step()
# Define the model and create clones.
model_fn = _build_deeplab
model_args = (inputs_queue, {
'semantic': class_num
}, input_size, atrous_rates, output_stride, fine_tune_batch_norm)
clones = model_deploy.create_clones(config,
model_fn,
args=model_args)
# Gather update_ops from the first clone. These contain, for example,
# the updates for the batch_norm variables created by model_fn.
first_clone_scope = config.clone_scope(0)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS,
first_clone_scope)
# Build the optimizer based on the device specification.
with tf.device(config.optimizer_device()):
learning_rate = lr
optimizer = tf.train.AdamOptimizer(learning_rate)
with tf.device(config.variables_device()):
total_loss, grads_and_vars = model_deploy.optimize_clones(
clones, optimizer)
total_loss = tf.check_numerics(total_loss, 'Loss is inf or nan.')
model_fn_val = _build_deeplab_val
model_args_val = (inputs_queue_val, {
'semantic': class_num
}, input_size, atrous_rates, output_stride)
val_clones, val_losses = create_val_clones(num_clones,
config,
model_fn_val,
args=model_args_val)
val_total_loss = get_clones_val_losses(val_clones, None,
val_losses)
# Modify the gradients for biases and last layer variables.
last_layers = model.get_extra_layer_scopes()
grad_mult = train_utils.get_model_gradient_multipliers(
last_layers, last_layer_gradient_multiplier)
if grad_mult:
grads_and_vars = slim.learning.multiply_gradients(
grads_and_vars, grad_mult)
# Create gradient update op.
grad_updates = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops)
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
coord.clear_stop()
sess = tf.Session(config=config)
graph = ops.get_default_graph()
with graph.as_default():
with ops.name_scope('init_ops'):
init_op = variables.global_variables_initializer()
ready_op = variables.report_uninitialized_variables()
local_init_op = control_flow_ops.group(
variables.local_variables_initializer(),
lookup_ops.tables_initializer())
# graph.finalize()
sess.run([init_op, ready_op, local_init_op])
queue_runners = graph.get_collection(ops.GraphKeys.QUEUE_RUNNERS)
threads = []
for qr in queue_runners:
threads.extend(
qr.create_threads(sess, coord=coord, daemon=True, start=True))
# # for i in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
# # print(i)
# vary_23 = [v for v in tf.global_variables() if v.name == 'xception_65/middle_flow/block1/unit_8/xception_module/separable_conv3_depthwise/BatchNorm/moving_mean:0'][0]
#
# beta_23 = [v for v in tf.global_variables() if v.name == 'xception_65/middle_flow/block1/unit_8/xception_module/separable_conv3_depthwise/BatchNorm/gamma:0'][0]
# for i in range(1000):
# train_loss = sess.run(train_tensor)
# print(train_loss)
# vary, beta = sess.run([vary_23, beta_23])
# print('mean', vary[0:3])
# print('beta', beta[0:3])
# if (i + 1) % 10 == 0:
# for i in range(10):
# val_loss = sess.run(val_total_loss)
# vary, beta = sess.run([vary_23, beta_23])
# print('mean val', vary[0:3])
# print('beta', beta[0:3])
# print('VAl_loss', val_loss)
model_init_fn(sess)
saver = tf.train.Saver()
eval_planner = EvalPlanner(n_epochs, val_every)
progress = sly.progress_counter_train(n_epochs, iters_cnt['train'])
best_val_loss = float('inf')
epoch_flt = 0
for epoch in range(n_epochs):
logger.info("Before new epoch", extra={'epoch': epoch_flt})
for train_it in range(iters_cnt['train']):
total_loss = sess.run(train_tensor)
metrics_values_train = {
'loss': total_loss,
}
progress.iter_done_report()
epoch_flt = epoch_float(epoch, train_it + 1,
iters_cnt['train'])
sly.report_metrics_training(epoch_flt, metrics_values_train)
if eval_planner.need_validation(epoch_flt):
logger.info("Before validation",
extra={'epoch': epoch_flt})
overall_val_loss = 0
for val_it in range(iters_cnt['val']):
overall_val_loss += sess.run(val_total_loss)
logger.info("Validation in progress",
extra={
'epoch': epoch_flt,
'val_iter': val_it,
'val_iters': iters_cnt['val']
})
metrics_values_val = {
'loss': overall_val_loss / iters_cnt['val'],
}
sly.report_metrics_validation(epoch_flt,
metrics_values_val)
logger.info("Validation has been finished",
extra={'epoch': epoch_flt})
eval_planner.validation_performed()
val_loss = metrics_values_val['loss']
model_is_best = val_loss < best_val_loss
if model_is_best:
best_val_loss = val_loss
logger.info(
'It\'s been determined that current model is the best one for a while.'
)
save_cback(saver,
sess,
model_is_best,
opt_data={
'epoch': epoch_flt,
'val_metrics': metrics_values_val,
})
logger.info("Epoch was finished", extra={'epoch': epoch_flt})
def _initialized_session(self, config=None):
sess = session_lib.Session(config=config)
sess.run(variables_lib.global_variables_initializer())
sess.run(lookup_ops.tables_initializer())
return sess
def train(datasets_dicts,
epochs,
val_every,
iters_cnt,
validate_with_eval_model,
pipeline_config,
num_clones=1,
save_cback=None):
logger.info('Start train')
configs = configs_from_pipeline(pipeline_config)
model_config = configs['model']
train_config = configs['train_config']
create_model_fn = functools.partial(model_builder.build,
model_config=model_config,
is_training=True)
detection_model = create_model_fn()
def get_next(dataset):
return dataset_util.make_initializable_iterator(
build_dataset(dataset)).get_next()
create_tensor_dict_fn = functools.partial(get_next,
datasets_dicts['train'])
create_tensor_dict_fn_val = functools.partial(get_next,
datasets_dicts['val'])
data_augmentation_options = [
preprocessor_builder.build(step)
for step in train_config.data_augmentation_options
]
with tf.Graph().as_default():
# Build a configuration specifying multi-GPU and multi-replicas.
deploy_config = model_deploy.DeploymentConfig(
num_clones=4,
clone_on_cpu=False,
replica_id=0,
num_replicas=1,
num_ps_tasks=0,
worker_job_name='lonely_worker')
# Place the global step on the device storing the variables.
with tf.device(deploy_config.variables_device()):
global_step = slim.create_global_step()
with tf.device(deploy_config.inputs_device()):
coord = coordinator.Coordinator()
input_queue = create_input_queue(
train_config.batch_size, create_tensor_dict_fn,
train_config.batch_queue_capacity,
train_config.num_batch_queue_threads,
train_config.prefetch_queue_capacity,
data_augmentation_options)
input_queue_val = create_input_queue(
train_config.batch_size, create_tensor_dict_fn_val,
train_config.batch_queue_capacity,
train_config.num_batch_queue_threads,
train_config.prefetch_queue_capacity,
data_augmentation_options)
# create validation graph
create_model_fn_val = functools.partial(
model_builder.build,
model_config=model_config,
is_training=not validate_with_eval_model)
with tf.device(deploy_config.optimizer_device()):
training_optimizer, optimizer_summary_vars = optimizer_builder.build(
train_config.optimizer)
for var in optimizer_summary_vars:
tf.summary.scalar(var.op.name, var, family='LearningRate')
train_losses = []
grads_and_vars = []
with slim.arg_scope([slim.model_variable, slim.variable],
device='/device:CPU:0'):
for curr_dev_id in range(num_clones):
with tf.device('/gpu:{}'.format(curr_dev_id)):
with tf.name_scope(
'clone_{}'.format(curr_dev_id)) as scope:
with tf.variable_scope(
tf.get_variable_scope(),
reuse=True if curr_dev_id > 0 else None):
losses = _create_losses_val(
input_queue, create_model_fn, train_config)
clones_loss = tf.add_n(losses)
clones_loss = tf.divide(clones_loss,
1.0 * num_clones)
grads = training_optimizer.compute_gradients(
clones_loss)
train_losses.append(clones_loss)
grads_and_vars.append(grads)
if curr_dev_id == 0:
update_ops = tf.get_collection(
tf.GraphKeys.UPDATE_OPS)
val_total_loss = get_val_loss(num_clones, input_queue_val,
create_model_fn_val, train_config)
with tf.device(deploy_config.optimizer_device()):
total_loss = tf.add_n(train_losses)
grads_and_vars = model_deploy._sum_clones_gradients(grads_and_vars)
total_loss = tf.check_numerics(total_loss,
'LossTensor is inf or nan.')
# Optionally multiply bias gradients by train_config.bias_grad_multiplier.
if train_config.bias_grad_multiplier:
biases_regex_list = ['.*/biases']
grads_and_vars = variables_helper.multiply_gradients_matching_regex(
grads_and_vars,
biases_regex_list,
multiplier=train_config.bias_grad_multiplier)
# Optionally freeze some layers by setting their gradients to be zero.
if train_config.freeze_variables:
grads_and_vars = variables_helper.freeze_gradients_matching_regex(
grads_and_vars, train_config.freeze_variables)
# Optionally clip gradients
if train_config.gradient_clipping_by_norm > 0:
with tf.name_scope('clip_grads'):
grads_and_vars = slim.learning.clip_gradient_norms(
grads_and_vars, train_config.gradient_clipping_by_norm)
# Create gradient updates.
grad_updates = training_optimizer.apply_gradients(
grads_and_vars, global_step=global_step)
update_ops.append(grad_updates)
update_op = tf.group(*update_ops, name='update_barrier')
with tf.control_dependencies([update_op]):
train_tensor = tf.identity(total_loss, name='train_op')
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
coord.clear_stop()
sess = tf.Session(config=config)
saver = tf.train.Saver()
graph = ops.get_default_graph()
with graph.as_default():
with ops.name_scope('init_ops'):
init_op = variables.global_variables_initializer()
ready_op = variables.report_uninitialized_variables()
local_init_op = control_flow_ops.group(
variables.local_variables_initializer(),
lookup_ops.tables_initializer())
# graph.finalize()
sess.run([init_op, ready_op, local_init_op])
queue_runners = graph.get_collection(ops.GraphKeys.QUEUE_RUNNERS)
threads = []
for qr in queue_runners:
threads.extend(
qr.create_threads(sess, coord=coord, daemon=True, start=True))
logger.info('Start restore')
if train_config.fine_tune_checkpoint:
var_map = detection_model.restore_map(
fine_tune_checkpoint_type=train_config.
fine_tune_checkpoint_type,
load_all_detection_checkpoint_vars=(
train_config.load_all_detection_checkpoint_vars))
available_var_map = (
variables_helper.get_variables_available_in_checkpoint(
var_map, train_config.fine_tune_checkpoint))
if 'global_step' in available_var_map:
del available_var_map['global_step']
init_saver = tf.train.Saver(available_var_map)
logger.info('Restoring model weights from previous checkpoint.')
init_saver.restore(sess, train_config.fine_tune_checkpoint)
logger.info('Model restored.')
eval_planner = EvalPlanner(epochs, val_every)
progress = sly.progress_counter_train(epochs, iters_cnt['train'])
best_val_loss = float('inf')
epoch_flt = 0
for epoch in range(epochs):
logger.info("Before new epoch", extra={'epoch': epoch_flt})
for train_it in range(iters_cnt['train']):
total_loss, np_global_step = sess.run(
[train_tensor, global_step])
metrics_values_train = {
'loss': total_loss,
}
progress.iter_done_report()
epoch_flt = epoch_float(epoch, train_it + 1,
iters_cnt['train'])
sly.report_metrics_training(epoch_flt, metrics_values_train)
if eval_planner.need_validation(epoch_flt):
logger.info("Before validation",
extra={'epoch': epoch_flt})
overall_val_loss = 0
for val_it in range(iters_cnt['val']):
overall_val_loss += sess.run(val_total_loss)
logger.info("Validation in progress",
extra={
'epoch': epoch_flt,
'val_iter': val_it,
'val_iters': iters_cnt['val']
})
metrics_values_val = {
'loss': overall_val_loss / iters_cnt['val'],
}
sly.report_metrics_validation(epoch_flt,
metrics_values_val)
logger.info("Validation has been finished",
extra={'epoch': epoch_flt})
eval_planner.validation_performed()
val_loss = metrics_values_val['loss']
model_is_best = val_loss < best_val_loss
if model_is_best:
best_val_loss = val_loss
logger.info(
'It\'s been determined that current model is the best one for a while.'
)
save_cback(saver,
sess,
model_is_best,
opt_data={
'epoch': epoch_flt,
'val_metrics': metrics_values_val,
})
logger.info("Epoch was finished", extra={'epoch': epoch_flt})
coord.request_stop()
coord.join(threads)
def main_op():
init_local = variables.local_variables_initializer()
init_tables = lookup_ops.tables_initializer()
return control_flow_ops.group(init_local, init_tables)
def _initialized_session():
sess = session.Session()
sess.run(variables_lib.global_variables_initializer())
sess.run(lookup_ops.tables_initializer())
return sess
def train(train_op,
logdir,
train_step_fn=train_step,
train_step_kwargs=_USE_DEFAULT,
log_every_n_steps=1,
graph=None,
master='',
is_chief=True,
global_step=None,
number_of_steps=None,
init_op=_USE_DEFAULT,
init_feed_dict=None,
local_init_op=_USE_DEFAULT,
init_fn=None,
ready_op=_USE_DEFAULT,
summary_op=_USE_DEFAULT,
save_summaries_secs=600,
summary_writer=_USE_DEFAULT,
startup_delay_steps=0,
saver=None,
save_interval_secs=600,
sync_optimizer=None,
session_config=None,
session_wrapper=None,
trace_every_n_steps=None,
ignore_live_threads=False):
"""Runs a training loop using a TensorFlow supervisor.
When the sync_optimizer is supplied, gradient updates are applied
synchronously. Otherwise, gradient updates are applied asynchronous.
Args:
train_op: A `Tensor` that, when executed, will apply the gradients and
return the loss value.
logdir: The directory where training logs are written to. If None, model
checkpoints and summaries will not be written.
train_step_fn: The function to call in order to execute a single gradient
step. The function must have take exactly four arguments: the current
session, the `train_op` `Tensor`, a global step `Tensor` and a dictionary.
train_step_kwargs: A dictionary which is passed to the `train_step_fn`. By
default, two `Boolean`, scalar ops called "should_stop" and "should_log"
are provided.
log_every_n_steps: The frequency, in terms of global steps, that the loss
and global step and logged.
graph: The graph to pass to the supervisor. If no graph is supplied the
default graph is used.
master: The address of the tensorflow master.
is_chief: Specifies whether or not the training is being run by the primary
replica during replica training.
global_step: The `Tensor` representing the global step. If left as `None`,
then slim.variables.get_or_create_global_step() is used.
number_of_steps: The max number of gradient steps to take during training,
as measured by 'global_step': training will stop if global_step is
greater than 'number_of_steps'. If the value is left as None, training
proceeds indefinitely.
init_op: The initialization operation. If left to its default value, then
the session is initialized by calling `tf.global_variables_initializer()`.
init_feed_dict: A feed dictionary to use when executing the `init_op`.
local_init_op: The local initialization operation. If left to its default
value, then the session is initialized by calling
`tf.local_variables_initializer()` and `tf.tables_initializer()`.
init_fn: An optional callable to be executed after `init_op` is called. The
callable must accept one argument, the session being initialized.
ready_op: Operation to check if the model is ready to use. If left to its
default value, then the session checks for readiness by calling
`tf.report_uninitialized_variables()`.
summary_op: The summary operation.
save_summaries_secs: How often, in seconds, to save summaries.
summary_writer: `SummaryWriter` to use. Can be `None`
to indicate that no summaries should be written. If unset, we
create a SummaryWriter.
startup_delay_steps: The number of steps to wait for before beginning. Note
that this must be 0 if a sync_optimizer is supplied.
saver: Saver to save checkpoints. If None, a default one will be created
and used.
save_interval_secs: How often, in seconds, to save the model to `logdir`.
sync_optimizer: an instance of tf.train.SyncReplicasOptimizer, or a list of
them. If the argument is supplied, gradient updates will be synchronous.
If left as `None`, gradient updates will be asynchronous.
session_config: An instance of `tf.ConfigProto` that will be used to
configure the `Session`. If left as `None`, the default will be used.
session_wrapper: A function that takes a `tf.Session` object as the only
argument and returns a wrapped session object that has the same methods
that the original object has, or `None`. Iff not `None`, the wrapped
object will be used for training.
trace_every_n_steps: produce and save a `Timeline` in Chrome trace format
and add it to the summaries every `trace_every_n_steps`. If None, no trace
information will be produced or saved.
ignore_live_threads: If `True` ignores threads that remain running after
a grace period when stopping the supervisor, instead of raising a
RuntimeError.
Returns:
the value of the loss function after training.
Raises:
ValueError: if `train_op` is empty or if `startup_delay_steps` is
non-zero when `sync_optimizer` is supplied, if `number_of_steps` is
negative, or if `trace_every_n_steps` is not `None` and no `logdir` is
provided.
"""
if train_op is None:
raise ValueError('train_op cannot be None.')
if logdir is None:
if summary_op != _USE_DEFAULT:
raise ValueError('Cannot provide summary_op because logdir=None')
if saver is not None:
raise ValueError('Cannot provide saver because logdir=None')
if trace_every_n_steps is not None:
raise ValueError('Cannot provide trace_every_n_steps because '
'logdir=None')
if isinstance(sync_optimizer,
sync_replicas_optimizer.SyncReplicasOptimizer):
sync_optimizer = [sync_optimizer]
if sync_optimizer is not None and startup_delay_steps > 0:
raise ValueError(
'startup_delay_steps must be zero when sync_optimizer is supplied.'
)
if number_of_steps is not None and number_of_steps <= 0:
raise ValueError(
'`number_of_steps` must be either None or a positive number.')
graph = graph or ops.get_default_graph()
with graph.as_default():
if global_step is None:
global_step = training_util.get_or_create_global_step()
saver = saver or tf_saver.Saver()
if sync_optimizer is not None:
for opt in sync_optimizer:
if not isinstance(
opt, sync_replicas_optimizer.SyncReplicasOptimizer):
raise ValueError(
'`sync_optimizer` must be a tf.train.SyncReplicasOptimizer.'
)
with ops.name_scope('init_ops'):
if init_op == _USE_DEFAULT:
init_op = variables.global_variables_initializer()
if ready_op == _USE_DEFAULT:
ready_op = variables.report_uninitialized_variables()
if local_init_op == _USE_DEFAULT:
local_init_op = control_flow_ops.group(
variables.local_variables_initializer(),
lookup_ops.tables_initializer())
if sync_optimizer is not None and isinstance(sync_optimizer, list):
with ops.control_dependencies(
[local_init_op] if local_init_op is not None else []):
if is_chief:
local_init_op = control_flow_ops.group(
*[opt.chief_init_op for opt in sync_optimizer])
else:
local_init_op = control_flow_ops.group(
*
[opt.local_step_init_op for opt in sync_optimizer])
ready_for_local_init_op = control_flow_ops.group(
*[opt.ready_for_local_init_op for opt in sync_optimizer])
else:
ready_for_local_init_op = None
if summary_op == _USE_DEFAULT:
summary_op = summary.merge_all()
if summary_writer == _USE_DEFAULT:
summary_writer = supervisor.Supervisor.USE_DEFAULT
if is_chief and sync_optimizer is not None:
# Need to create these BEFORE the supervisor finalizes the graph:
init_tokens_op = [
opt.get_init_tokens_op() for opt in sync_optimizer
]
chief_queue_runner = [
opt.get_chief_queue_runner() for opt in sync_optimizer
]
if train_step_kwargs == _USE_DEFAULT:
with ops.name_scope('train_step'):
train_step_kwargs = {}
if number_of_steps:
should_stop_op = math_ops.greater_equal(
global_step, number_of_steps)
else:
should_stop_op = constant_op.constant(False)
train_step_kwargs['should_stop'] = should_stop_op
if log_every_n_steps > 0:
train_step_kwargs['should_log'] = math_ops.equal(
math_ops.mod(global_step, log_every_n_steps), 0)
if is_chief and trace_every_n_steps is not None:
train_step_kwargs['should_trace'] = math_ops.equal(
math_ops.mod(global_step, trace_every_n_steps), 0)
train_step_kwargs['logdir'] = logdir
sv = supervisor.Supervisor(graph=graph,
is_chief=is_chief,
logdir=logdir,
init_op=init_op,
init_feed_dict=init_feed_dict,
local_init_op=local_init_op,
ready_for_local_init_op=ready_for_local_init_op,
ready_op=ready_op,
summary_op=summary_op,
summary_writer=summary_writer,
global_step=global_step,
saver=saver,
save_summaries_secs=save_summaries_secs,
save_model_secs=save_interval_secs,
init_fn=init_fn)
if summary_writer is not None:
train_step_kwargs['summary_writer'] = sv.summary_writer
total_loss = None
should_retry = True
while should_retry:
try:
should_retry = False
with sv.managed_session(master,
start_standard_services=False,
config=session_config) as sess:
logging.info('Starting Session.')
if session_wrapper is not None:
logging.info('Wrapping session with wrapper function: %s',
session_wrapper)
sess = session_wrapper(sess)
if is_chief:
if logdir:
sv.start_standard_services(sess)
elif startup_delay_steps > 0:
# (use sys.maxsize because sys.maxint doesn't exist in Python 3)
_wait_for_step(
sess, global_step,
min(startup_delay_steps, number_of_steps
or sys.maxsize))
threads = sv.start_queue_runners(sess)
logging.info('Starting Queues.')
if is_chief and sync_optimizer is not None:
sv.start_queue_runners(sess, chief_queue_runner)
sess.run(init_tokens_op)
try:
while not sv.should_stop():
total_loss, should_stop = train_step_fn(
sess, train_op, global_step, train_step_kwargs)
if should_stop:
logging.info('Stopping Training.')
sv.request_stop()
break
except errors.OutOfRangeError as e:
# OutOfRangeError is thrown when epoch limit per
# tf.train.limit_epochs is reached.
logging.info(
'Caught OutOfRangeError. Stopping Training. %s', e)
if logdir and sv.is_chief:
logging.info('Finished training! Saving model to disk.')
sv.saver.save(sess,
sv.save_path,
global_step=sv.global_step)
sv.stop(threads,
close_summary_writer=True,
ignore_live_threads=ignore_live_threads)
except errors.AbortedError:
# Always re-run on AbortedError as it indicates a restart of one of the
# distributed tensorflow servers.
logging.info('Retrying training!')
should_retry = True
return total_loss
def test_dense_features_not_trainable(self):
# Inputs.
vocabulary_size = 3
sparse_input = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
# example 2, ids []
# example 3, ids [1]
indices=((0, 0), (1, 0), (1, 4), (3, 0)),
values=(2, 0, 1, 1),
dense_shape=(4, 5))
# Embedding variable.
embedding_dimension = 2
embedding_values = (
(1., 2.), # id 0
(3., 5.), # id 1
(7., 11.) # id 2
)
def _initializer(shape, dtype, partition_info=None):
self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
self.assertEqual(dtypes.float32, dtype)
self.assertIsNone(partition_info)
return embedding_values
# Expected lookup result, using combiner='mean'.
expected_lookups = (
# example 0, ids [2], embedding = [7, 11]
(7., 11.),
# example 1, ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
(2., 3.5),
# example 2, ids [], embedding = [0, 0]
(0., 0.),
# example 3, ids [1], embedding = [3, 5]
(3., 5.),
)
# Build columns.
categorical_column = fc.categorical_column_with_identity(
key='aaa', num_buckets=vocabulary_size)
embedding_column = fc.embedding_column(categorical_column,
dimension=embedding_dimension,
initializer=_initializer,
trainable=False)
# Provide sparse input and get dense result.
dense_features = df.DenseFeatures((embedding_column, ))({
'aaa':
sparse_input
})
# Assert expected embedding variable and lookups.
global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
self.assertCountEqual(
('dense_features/aaa_embedding/embedding_weights:0', ),
tuple([v.name for v in global_vars]))
self.assertCountEqual([],
ops.get_collection(
ops.GraphKeys.TRAINABLE_VARIABLES))
self.evaluate(variables_lib.global_variables_initializer())
self.evaluate(lookup_ops.tables_initializer())
self.assertAllEqual(embedding_values, self.evaluate(global_vars[0]))
self.assertAllEqual(expected_lookups, self.evaluate(dense_features))
def _test_dense_features(self, trainable=True):
# Inputs.
vocabulary_size = 3
sparse_input_a = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 4)),
values=(2, 0, 1),
dense_shape=(2, 5))
sparse_input_b = sparse_tensor.SparseTensorValue(
# example 0, ids [0]
# example 1, ids []
indices=((0, 0), ),
values=(0, ),
dense_shape=(2, 5))
sparse_input_c = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 1), (1, 1), (1, 3)),
values=(2, 0, 1),
dense_shape=(2, 5))
sparse_input_d = sparse_tensor.SparseTensorValue(
# example 0, ids [2]
# example 1, ids []
indices=((0, 1), ),
values=(2, ),
dense_shape=(2, 5))
# Embedding variable.
embedding_dimension = 2
embedding_values = (
(1., 2.), # id 0
(3., 5.), # id 1
(7., 11.) # id 2
)
def _initializer(shape, dtype, partition_info=None):
self.assertAllEqual((vocabulary_size, embedding_dimension), shape)
self.assertEqual(dtypes.float32, dtype)
self.assertIsNone(partition_info)
return embedding_values
# Expected lookup result, using combiner='mean'.
expected_lookups = (
# example 0:
# A ids [2], embedding = [7, 11]
# B ids [0], embedding = [1, 2]
# C ids [2], embedding = [7, 11]
# D ids [2], embedding = [7, 11]
(7., 11., 1., 2., 7., 11., 7., 11.),
# example 1:
# A ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
# B ids [], embedding = [0, 0]
# C ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5]
# D ids [], embedding = [0, 0]
(2., 3.5, 0., 0., 2., 3.5, 0., 0.),
)
# Build columns.
categorical_column_a = fc.categorical_column_with_identity(
key='aaa', num_buckets=vocabulary_size)
categorical_column_b = fc.categorical_column_with_identity(
key='bbb', num_buckets=vocabulary_size)
categorical_column_c = fc.categorical_column_with_identity(
key='ccc', num_buckets=vocabulary_size)
categorical_column_d = fc.categorical_column_with_identity(
key='ddd', num_buckets=vocabulary_size)
embedding_column_a, embedding_column_b = fc.shared_embedding_columns_v2(
[categorical_column_a, categorical_column_b],
dimension=embedding_dimension,
initializer=_initializer,
trainable=trainable)
embedding_column_c, embedding_column_d = fc.shared_embedding_columns_v2(
[categorical_column_c, categorical_column_d],
dimension=embedding_dimension,
initializer=_initializer,
trainable=trainable)
features = {
'aaa': sparse_input_a,
'bbb': sparse_input_b,
'ccc': sparse_input_c,
'ddd': sparse_input_d
}
# Provide sparse input and get dense result.
dense_features = df.DenseFeatures(
feature_columns=(embedding_column_b, embedding_column_a,
embedding_column_c, embedding_column_d))(features)
# Assert expected embedding variable and lookups.
global_vars = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
self.assertCountEqual(
['aaa_bbb_shared_embedding:0', 'ccc_ddd_shared_embedding:0'],
tuple([v.name for v in global_vars]))
for v in global_vars:
self.assertIsInstance(v, variables_lib.Variable)
trainable_vars = ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)
if trainable:
self.assertCountEqual(
['aaa_bbb_shared_embedding:0', 'ccc_ddd_shared_embedding:0'],
tuple([v.name for v in trainable_vars]))
else:
self.assertCountEqual([], tuple([v.name for v in trainable_vars]))
shared_embedding_vars = global_vars
self.evaluate(variables_lib.global_variables_initializer())
self.evaluate(lookup_ops.tables_initializer())
self.assertAllEqual(embedding_values,
self.evaluate(shared_embedding_vars[0]))
self.assertAllEqual(expected_lookups, self.evaluate(dense_features))
