-------------------
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
def save_model_to_cv_dnn(netmodel, frozen_out_path, frozen_graph_filename):
full_model = tf.function(lambda x: netmodel(x)).get_concrete_function(tf.TensorSpec(netmodel.inputs[0].shape, netmodel.inputs[0].dtype))
# Get frozen ConcreteFunction
frozen_func = convert_variables_to_constants_v2(full_model)
frozen_func.graph.as_graph_def()
layers = [op.name for op in frozen_func.graph.get_operations()]
print("-" * 60)
print("Frozen model layers: ")
for layer in layers:
print(layer)
print("-" * 60)
print("Frozen model inputs: ")
print(frozen_func.inputs) # 模型输入
print("Frozen model outputs: ")
print(frozen_func.outputs) # 模型输出
# 存储PB模型
# Save frozen graph to disk
tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
logdir=frozen_out_path,
name=f"{frozen_graph_filename}.pb",
as_text=False)
# Save its text representation
tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
logdir=frozen_out_path,
name=f"{frozen_graph_filename}.pbtxt",
as_text=True)
path of the directory where you want to save your model
model.save('./data/tf_model_savedmodel', save_format="tf") print('export saved model.') model_loaded = tf.keras.models.load_model('./data/tf_model_savedmodel') yout = model_loaded.predict(x_test) ylab = yout[:, 0] > 0.5 print(ylab.shape) print(y_test.shape) model_loaded.evaluate(x = x_test,y = y_test)
frozen_out_path = './' # 存储模型的路径
name of the .pb file
frozen_graph_filename = "frozen_graph1" # 模型名称
save_model_to_cv_dnn(model,frozen_out_path,frozen_graph_filename)
Original: https://blog.csdn.net/u010795146/article/details/122561465
Author: 这个不开车的老司机
Title: tensorflow2.x保存pb模型，用于opencv3.4.16 dnn模块调用
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