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h5_to_pb.py
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h5_to_pb.py
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import tensorflow as tf
# from tensorflow import keras
from tensorflow.keras.models import load_model
from tensorflow.keras import backend as K
from tensorflow.python.framework import graph_io
def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
from tensorflow.python.framework.graph_util import convert_variables_to_constants
graph = session.graph
with graph.as_default():
freeze_var_names = list(set(v.op.name for v in tf.global_variables()).difference(keep_var_names or []))
output_names = output_names or []
output_names += [v.op.name for v in tf.global_variables()]
input_graph_def = graph.as_graph_def()
if clear_devices:
for node in input_graph_def.node:
node.device = ""
frozen_graph = convert_variables_to_constants(session, input_graph_def,
output_names, freeze_var_names)
return frozen_graph
"""----------------------------------配置路径-----------------------------------"""
epochs=20
# h5_model_path='./my_model_ep{}.h5'.format(epochs)
h5_model_path = './my_model.h5'
output_path='.'
# pb_model_name='my_model_ep{}.pb'.format(epochs)
pb_model_name='my_model.pb'
"""----------------------------------导入keras模型------------------------------"""
K.set_learning_phase(0)
net_model = load_model(h5_model_path)
print('input is :', net_model.input.name)
print('output is:', net_model.output.name)
"""----------------------------------保存为.pb格式------------------------------"""
sess = K.get_session()
frozen_graph = freeze_session(K.get_session(), output_names=[net_model.output.op.name])
graph_io.write_graph(frozen_graph, output_path, pb_model_name, as_text=False)