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https://github.com/google/mozc-devices.git
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Update TF version
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@@ -15,8 +15,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from tensorflow.contrib import slim
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from tensorflow.contrib.slim.python.slim.learning import train_step
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import tf_slim as slim
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import graph_util
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from tensorflow.python.platform import gfile
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@@ -31,7 +30,7 @@ import nazoru.core as nazoru
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import numpy as np
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import os
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import sys
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import tensorflow as tf
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import tensorflow.compat.v1 as tf
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import zipfile
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FLAGS = None
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@@ -139,6 +138,7 @@ def main(_):
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nazoru.DepthSepConv(kernel=[3, 3], stride=1, depth=128),
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]
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tf.disable_eager_execution()
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with tf.Graph().as_default():
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tf.logging.set_verbosity(tf.logging.INFO)
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@@ -176,7 +176,7 @@ def main(_):
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train_op = slim.learning.create_train_op(train_total_loss, optimizer)
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def train_step_fn(sess, *args, **kwargs):
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total_loss, should_stop = train_step(sess, *args, **kwargs)
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total_loss, should_stop = slim.learning.train_step(sess, *args, **kwargs)
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if train_step_fn.step % FLAGS.n_steps_to_log == 0:
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val_acc = sess.run(val_accuracy)
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tf_logging.info('step: %d, validation accuracy: %.3f' % (
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@@ -236,19 +236,19 @@ if __name__ == '__main__':
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parser.add_argument(
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'--output_graph',
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type=str,
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default='nazoru.pb',
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default='nazoru_custom.pb',
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help='Where to save the trained graph.'
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)
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parser.add_argument(
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'--optimized_output_graph',
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type=str,
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default='optimized_nazoru.pb',
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default='optimized_nazoru_custom.pb',
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help='Where to save the trained graph optimized for inference.'
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)
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parser.add_argument(
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'--saved_model_dir',
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type=str,
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default='nazoru_saved_model',
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default='nazoru_saved_model_custom',
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help='Where to save the exported graph.'
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)
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parser.add_argument(
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@@ -59,12 +59,10 @@ setup(
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# data_files=[('/etc/systemd/system', ['data/nazoru.service'])],
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install_requires=[
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'np_utils',
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'cairocffi<=1.0.0',
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'h5py',
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'cairocffi',
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'pillow',
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'tensorflow~=1.15.4',
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'markdown<=3.0.1',
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'tensorflow~=2.5.1',
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'tf_slim~=1.1.0',
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'enum34;python_version<"3.4"',
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'pyserial',
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'evdev;platform_system=="Linux"',
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@@ -20,8 +20,8 @@ predict input characters from visualized trace.
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"""
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from collections import namedtuple
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from tensorflow.contrib import slim
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import tensorflow as tf
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import tf_slim as slim
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import tensorflow.compat.v1 as tf
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Conv = namedtuple('Conv', ['kernel', 'stride', 'depth'])
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DepthSepConv = namedtuple('DepthSepConv', ['kernel', 'stride', 'depth'])
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@@ -161,7 +161,7 @@ def nazorunet(inputs,
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is_training=True,
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min_depth=8,
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depth_multiplier=1.0,
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prediction_fn=tf.contrib.layers.softmax,
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prediction_fn=slim.layers.softmax,
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spatial_squeeze=True,
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reuse=None,
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scope='NazoruNet',
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@@ -23,7 +23,7 @@ import numpy as np
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def _load_graph(model_file):
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graph = tf.Graph()
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graph_def = tf.GraphDef()
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graph_def = tf.compat.v1.GraphDef()
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with open(model_file, "rb") as f:
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graph_def.ParseFromString(f.read())
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with graph.as_default():
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@@ -44,7 +44,7 @@ class NazoruPredictor():
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inputs = lib.keydowns2image(data, True, True, 16, 2)
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inputs = np.expand_dims(inputs, axis=0)
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with utils.Measure('sess.run'):
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with tf.Session(graph=self._graph) as sess:
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with tf.compat.v1.Session(graph=self._graph) as sess:
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result = sess.run(self._output_operation.outputs[0],
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{self._input_operation.outputs[0]: inputs})[0]
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return result
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