def setup(self, flags):
# Model output directory
out_dir = flags.out_dir
if out_dir and not tf.gfile.Exists(out_dir):
tf.gfile.MakeDirs(out_dir)
# Load hparams.
default_hparams = create_hparams(flags)
loaded_hparams = False
if flags.ckpt: # Try to load hparams from the same directory as ckpt
ckpt_dir = os.path.dirname(flags.ckpt)
ckpt_hparams_file = os.path.join(ckpt_dir, "hparams")
if tf.gfile.Exists(ckpt_hparams_file) or flags.hparams_path:
# Note: for some reason this will create an empty "best_bleu" directory and copy vocab files
hparams = create_or_load_hparams(ckpt_dir,
default_hparams,
flags.hparams_path,
save_hparams=False)
loaded_hparams = True
assert loaded_hparams
# GPU device
config_proto = utils.get_config_proto(
allow_soft_placement=True,
num_intra_threads=hparams.num_intra_threads,
num_inter_threads=hparams.num_inter_threads)
utils.print_out("# Devices visible to TensorFlow: %s" %
repr(tf.Session(config=config_proto).list_devices()))
# Inference indices (inference_indices is broken, but without setting it to None we'll crash)
hparams.inference_indices = None
# Create the graph
model_creator = get_model_creator(hparams)
infer_model = model_helper.create_infer_model(model_creator,
hparams,
scope=None)
sess, loaded_infer_model = start_sess_and_load_model(
infer_model, flags.ckpt, hparams)
# Parameters needed by TF GNMT
self.hparams = hparams
self.infer_model = infer_model
self.sess = sess
self.loaded_infer_model = loaded_infer_model
def _createTestInferenceCheckpint(self, hparams, name):
#Prepare
hparams.vocab_prefix = ('nmt/testdata/test_infer_vocab')
hparams.src_vocab_file = hparams.vocab_prefix + '.' + hparams.src
hparams.tgt_vocab_file = hparams.vocab_prefix + '.' + hparams.tgt
out_dir = os.path.join(tf.test.get_temp_dir(), name)
os.makedirs(out_dir)
hparams.out_dir = out_dir
#create check point
model_creator = inference.get_model_creator(hparams)
infer_model = model_helper.create_infer_model(model_creator, hparams)
with self.test_session(graph=infer_model.graph) as sess:
loaded_model, global_step = model_helper.create_or_load_model(
infer_model.model, out_dir, sess, 'infer_name')
ckpt_path = loaded_model.saver.save(sess,
os.path.join(
out_dir,
'translation.ckpt'),
global_step=global_step)
return ckpt_path
print(ckpt_path2)
hparams1 = create_or_load_hparams(out_dir1,
default_hparams1,
None,
save_hparams=0)
hparams2 = create_or_load_hparams(out_dir2,
default_hparams2,
None,
save_hparams=0)
hparams1.inference_indices = None
hparams2.inference_indices = None
model_creator1 = get_model_creator(hparams1)
model_creator2 = get_model_creator(hparams2)
infer_model1 = create_infer_model(model_creator1, hparams1, None)
infer_model2 = create_infer_model(model_creator2, hparams2, None)
sess1, loaded_infer_model1 = start_sess_and_load_model(infer_model1,
ckpt_path1)
sess2, loaded_infer_model2 = start_sess_and_load_model(infer_model2,
ckpt_path2)
jieba.load_userdict("字典.txt")
def is_contains_chinese(strs):
for _char in strs:
def setup(self, flags):
# Model output directory
out_dir = flags.out_dir
if out_dir and not tf.gfile.Exists(out_dir):
tf.gfile.MakeDirs(out_dir)
# Load hparams.
default_hparams = create_hparams(flags)
loaded_hparams = False
if flags.ckpt: # Try to load hparams from the same directory as ckpt
ckpt_dir = os.path.dirname(flags.ckpt)
ckpt_hparams_file = os.path.join(ckpt_dir, "hparams")
if tf.gfile.Exists(ckpt_hparams_file) or flags.hparams_path:
# Note: for some reason this will create an empty "best_bleu" directory and copy vocab files
hparams = create_or_load_hparams(ckpt_dir, default_hparams, flags.hparams_path, save_hparams=False)
loaded_hparams = True
assert loaded_hparams
# GPU device
config_proto = utils.get_config_proto(
allow_soft_placement=True,
num_intra_threads=hparams.num_intra_threads,
num_inter_threads=hparams.num_inter_threads)
utils.print_out(
"# Devices visible to TensorFlow: %s"
% repr(tf.Session(config=config_proto).list_devices()))
# Inference indices (inference_indices is broken, but without setting it to None we'll crash)
hparams.inference_indices = None
# Create the graph
model_creator = get_model_creator(hparams)
infer_model = model_helper.create_infer_model(model_creator, hparams, scope=None)
sess, loaded_infer_model = start_sess_and_load_model(infer_model, flags.ckpt,
hparams)
# FIXME (bryce): Set to False to disable inference from frozen graph and run fast again
if True:
frozen_graph = None
with infer_model.graph.as_default():
output_node_names = ['hash_table_Lookup_1/LookupTableFindV2']
other_node_names = ['MakeIterator', 'IteratorToStringHandle', 'init_all_tables', 'NoOp', 'dynamic_seq2seq/decoder/NoOp']
frozen_graph = tf.graph_util.convert_variables_to_constants(sess,
tf.get_default_graph().as_graph_def(),
output_node_names=output_node_names + other_node_names)
# FIXME (bryce): Uncomment this block to enable tensorRT convert
from tensorflow.python.compiler.tensorrt import trt_convert as trt
converter = trt.TrtGraphConverter(input_graph_def=frozen_graph, nodes_blacklist=(output_node_names),
is_dynamic_op=True, max_batch_size=hparams.infer_batch_size,
max_beam_size=hparams.beam_width, max_src_seq_len=hparams.src_max_len)
frozen_graph = converter.convert()
with tf.Graph().as_default():
tf.graph_util.import_graph_def(frozen_graph, name="")
sess = tf.Session(graph=tf.get_default_graph(),
config=utils.get_config_proto(
num_intra_threads=hparams.num_intra_threads,
num_inter_threads=hparams.num_inter_threads)
)
iterator = iterator_utils.BatchedInput(
initializer=tf.get_default_graph().get_operation_by_name(infer_model.iterator.initializer.name),
source=tf.get_default_graph().get_tensor_by_name(infer_model.iterator.source.name),
target_input=None,
target_output=None,
source_sequence_length=tf.get_default_graph().get_tensor_by_name(infer_model.iterator.source_sequence_length.name),
target_sequence_length=None)
infer_model = model_helper.InferModel(
graph=tf.get_default_graph(),
model=infer_model.model,
src_placeholder=tf.get_default_graph().get_tensor_by_name(infer_model.src_placeholder.name),
batch_size_placeholder=tf.get_default_graph().get_tensor_by_name(infer_model.batch_size_placeholder.name),
iterator=iterator)
# Parameters needed by TF GNMT
self.hparams = hparams
self.infer_model = infer_model
self.sess = sess
self.loaded_infer_model = loaded_infer_model