def _callback(result_future):
exception = result_future.exception()
if exception:
print exception
else:
results = tf.contrib.util.make_ndarray(result_future.result().outputs['outputs'])
for result in results[0]:
print ndarray_to_text(result)
event.set()
def main(_):
start = stopwatch()
initialize_globals()
if len(FLAGS.one_shot_infer):
#load the frozen graph as in train(...) or as in https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc
with tf.gfile.FastGFile("../../models/output_graph.pb", 'rb') as fin:
graph_def = tf.GraphDef()
graph_def.ParseFromString(fin.read())
with tf.Graph().as_default() as pretrained_model:
tf.import_graph_def(graph_def, name="pretrained_")
"""
for op in pretrained_model.get_operations():
print(op.name)
"""
# print("------------***-------------")
# https://stackoverflow.com/questions/36883949/in-tensorflow-get-the-names-of-all-the-tensors-in-a-graph?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
lstTensors = [op.values() for op in pretrained_model.get_operations()]
input_node = lstTensors[0]
input_lengths = lstTensors[1]
output_node = lstTensors[-1]
"""
print("input node name: ")
print(input_node[0].name)
print("input node shape: ")
print(input_node[0].shape)#V IMP: shape of input node is [x,y,z] where x = batch_size. For one shot infer, batch_size = 1
print("input lengths name: ")
print(input_lengths[0].name)
print("input lengths shape: ")
print(input_lengths[0].shape) #V IMP: shape of input_lengths node is [x,y] where x = batch_size. For one shot infer, batch_size = 1
print("output node name: ")
print(output_node[0].name)
print("output node shape: ")
print(output_node[0].shape)
"""
# do_single_file_inference(FLAGS.one_shot_infer)
# print("n_input = "+repr(n_input))
# print("n_context = "+repr(n_context))
mfcc = audiofile_to_input_vector(FLAGS.one_shot_infer, n_input,
n_context)
# print(mfcc.shape)
# output_node = pretrained_model.get_tensor_by_name(pretrained_model.get_operations()[-1].name)
batch_size = 1
with tf.Session(graph=pretrained_model) as sess:
output = sess.run(
output_node,
feed_dict={
input_node:
[mfcc.reshape((batch_size, mfcc.shape[0], mfcc.shape[1]))],
input_lengths:
[np.array(len(mfcc)).reshape((batch_size, ))]
})
# print(output)
text = ndarray_to_text(output[0][0][0], alphabet)
print("\n\nResult:")
print(text)
else:
print(
"Correct usage: python3 _this.py --one_shot_infer <<path-of-input-wav-file>>"
)
delta = stopwatch(start)
print("Net execution time including loading of the graph = " +
format_duration(delta))