def freeze_model(self) -> Tuple[Text, Text]:
"""Freeze model and convert them into tflite and tf graph."""
with tf.Graph().as_default(), tf.Session() as sess:
inputs = tf.placeholder(tf.float32,
name='input',
shape=self.inputs_shape)
outputs = self.build_model(inputs)
if self.ckpt_path:
# Load the true weights if available.
inference.restore_ckpt(sess, self.ckpt_path,
self.model_config.moving_average_decay,
self.export_ckpt)
else:
# Load random weights if not checkpoint is not available.
self.build_and_save_model()
checkpoint = tf.train.latest_checkpoint(self.logdir)
logging.info('Loading checkpoint: %s', checkpoint)
saver = tf.train.Saver()
saver.restore(sess, checkpoint)
output_node_names = [node.op.name for node in outputs]
graphdef = tf.graph_util.convert_variables_to_constants(
sess, sess.graph_def, output_node_names)
tf_graph = os.path.join(self.logdir,
self.model_name + '_frozen.pb')
tf.io.gfile.GFile(tf_graph,
'wb').write(graphdef.SerializeToString())
return graphdef
def build_and_save_model(self):
"""build and save the model into self.logdir."""
with tf.Graph().as_default(), tf.Session() as sess:
# Build model with inputs and labels.
inputs = tf.placeholder(tf.float32,
name='input',
shape=self.inputs_shape)
outputs = self.build_model(inputs)
# Run the model
inputs_val = np.random.rand(*self.inputs_shape).astype(float)
labels_val = np.zeros(self.labels_shape).astype(np.int64)
labels_val[:, 0] = 1
if self.ckpt_path:
# Load the true weights if available.
inference.restore_ckpt(sess, self.ckpt_path,
self.model_config.moving_average_decay,
self.export_ckpt)
else:
sess.run(tf.global_variables_initializer())
# Run a single train step.
sess.run(outputs, feed_dict={inputs: inputs_val})
all_saver = tf.train.Saver(save_relative_paths=True)
all_saver.save(sess, os.path.join(self.logdir, self.model_name))
tf_graph = os.path.join(self.logdir, self.model_name + '_train.pb')
with tf.io.gfile.GFile(tf_graph, 'wb') as f:
f.write(sess.graph_def.SerializeToString())
def freeze_model(self) -> Tuple[Text, Text]:
"""Freeze model and convert them into tflite and tf graph."""
with tf.Graph().as_default(), tf.Session() as sess:
inputs = tf.placeholder(tf.float32,
name='input',
shape=self.inputs_shape)
outputs = self.build_model(inputs)
if self.ckpt_path:
# Load the true weights if available.
inference.restore_ckpt(sess, self.ckpt_path,
self.model_config.moving_average_decay,
self.export_ckpt)
else:
# Load random weights if not checkpoint is not available.
self.build_and_save_model()
checkpoint = tf.train.latest_checkpoint(self.logdir)
logging.info('Loading checkpoint: %s', checkpoint)
saver = tf.train.Saver()
saver.restore(sess, checkpoint)
# export frozen graph.
output_node_names = [node.op.name for node in outputs]
graphdef = tf.graph_util.convert_variables_to_constants(
sess, sess.graph_def, output_node_names)
tf_graph = os.path.join(self.logdir,
self.model_name + '_frozen.pb')
tf.io.gfile.GFile(tf_graph,
'wb').write(graphdef.SerializeToString())
# export savaed model.
output_dict = {
'class_predict_%d' % i: outputs[i]
for i in range(5)
}
output_dict.update(
{'box_predict_%d' % i: outputs[5 + i]
for i in range(5)})
signature_def_map = {
'serving_default':
tf.saved_model.predict_signature_def(
{'input': inputs},
output_dict,
)
}
output_dir = os.path.join(self.logdir, 'savedmodel')
b = tf.saved_model.Builder(output_dir)
b.add_meta_graph_and_variables(sess,
tags=['serve'],
signature_def_map=signature_def_map,
assets_collection=tf.get_collection(
tf.GraphKeys.ASSET_FILEPATHS),
clear_devices=True)
b.save()
logging.info('Model saved at %s', output_dir)
return graphdef
def eval_ckpt(self):
"""build and save the model into self.logdir."""
with tf.Graph().as_default(), tf.Session() as sess:
# Build model with inputs and labels.
inputs = tf.placeholder(tf.float32, name='input', shape=self.inputs_shape)
self.build_model(inputs, is_training=False)
inference.restore_ckpt(sess, self.ckpt_path,
self.model_config.moving_average_decay,
self.export_ckpt)
