Add nazoru-input in mozc-nazoru/

mozc-nazoru/src/nazoru/predictor.py

@@ -0,0 +1,64 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright 2018 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+
+import tensorflow as tf
+from . import lib
+from . import utils
+import numpy as np
+
+def _load_graph(model_file):
+  graph = tf.Graph()
+  graph_def = tf.GraphDef()
+  with open(model_file, "rb") as f:
+    graph_def.ParseFromString(f.read())
+  with graph.as_default():
+    tf.import_graph_def(graph_def)
+  return graph
+
+class NazoruPredictor():
+  def __init__(self, model_file):
+    graph = _load_graph(model_file)
+    self._graph = graph
+    self._input_operation = graph.get_operation_by_name(
+        'import/' + lib.INPUT_NODE_NAME)
+    self._output_operation = graph.get_operation_by_name(
+        'import/' + lib.OUTPUT_NODE_NAME)
+
+  def _predict(self, data):
+    with utils.Measure('inputs'):
+      inputs = lib.keydowns2image(data, True, True, 16, 2)
+      inputs = np.expand_dims(inputs, axis=0)
+    with utils.Measure('sess.run'):
+      with tf.Session(graph=self._graph) as sess:
+        result = sess.run(self._output_operation.outputs[0],
+                          {self._input_operation.outputs[0]: inputs})[0]
+    return result
+  def predict_top_n(self, data, n):
+    """Predict the charactor drawn by |data|.
+
+    Args:
+      data: [(key, time)] |time| is elapsed time since the first character in ms.
+      n: integer of the number of the return value.
+    Returns:
+      ans: [(kana, key, probability)] sorted by the probability.
+    """
+    result = self._predict(data)
+    ans = []
+    for i in result.argsort()[::-1][:n]:
+      ans.append((lib.KANAS[i], lib.KEYS[i], result[i]))
+    return ans