import os
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2 as Net
import tensorflow as tf
from tensorflow.python.framework import graph_io
from tensorflow.keras.models import load_model
import tensorflow.contrib.tensorrt as trt
save_pb_dir = './models'
model_fname = './models/model.h5'
model = Net(weights='imagenet')
os.makedirs(save_pb_dir, exist_ok=True)
# Save the h5 file to path specified.
model.save(model_fname)
# Clear any previous session.
tf.keras.backend.clear_session()
def freeze_graph(graph,
session,
output,
save_pb_dir='.',
save_pb_name='frozen_model.pb',
save_pb_as_text=False):
with graph.as_default():
graphdef_inf = tf.graph_util.remove_training_nodes(
graph.as_graph_def())
graphdef_frozen = tf.graph_util.convert_variables_to_constants(
import os
from docopt import docopt
import json
from pathlib import Path
import tensorflow as tf
from tensorflow.python.framework import graph_io
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2 as Net
model = Net(weights='imagenet')
os.makedirs('./model', exist_ok=True)
# Save the h5 file to path specified.
model.save("./model/model.h5")
in_model = './model/model.h5'
output = './model'
output_path = Path(output)
output_meta = Path('%s/%s.metadata' %
(output_path.parent.as_posix(), output_path.stem))
# Reset session
tf.keras.backend.clear_session()
tf.keras.backend.set_learning_phase(0)
model = tf.keras.models.load_model(in_model, compile=False)
session = tf.compat.v1.keras.backend.get_session()
input_names = sorted([layer.op.name for layer in model.inputs])
output_names = sorted([layer.op.name for layer in model.outputs])
