在Tensorflow2中将图形冻结为pb [英] Freezing graph to pb in Tensorflow2

问题描述

我们通过冻结图来保存它们，从而从TF1部署了许多模型:

We deploy lot of our models from TF1 by saving them through graph freezing:

tf.train.write_graph(self.session.graph_def, some_path)

# get graph definitions with weights
output_graph_def = tf.graph_util.convert_variables_to_constants(
        self.session,  # The session is used to retrieve the weights
        self.session.graph.as_graph_def(),  # The graph_def is used to retrieve the nodes
        output_nodes,  # The output node names are used to select the usefull nodes
)

# optimize graph
if optimize:
    output_graph_def = optimize_for_inference_lib.optimize_for_inference(
            output_graph_def, input_nodes, output_nodes, tf.float32.as_datatype_enum
    )

with open(path, "wb") as f:
    f.write(output_graph_def.SerializeToString())

然后通过以下方式加载它们:

and then loading them through:

with tf.Graph().as_default() as graph:
    with graph.device("/" + args[name].processing_unit):
        tf.import_graph_def(graph_def, name="")
            for key, value in inputs.items():
                self.input[key] = graph.get_tensor_by_name(value + ":0")

我们想以类似的方式保存TF2模型.一个protobuf文件，其中将包含图形和权重.我该如何实现?

We would like to save TF2 models in similar way. One protobuf file which will include graph and weights. How can I achieve this?

我知道有一些保存方法:

I know that there are some methods for saving:

• keras.experimental.export_saved_model(model, 'path_to_saved_model')

这是实验性的，会创建多个文件:(.

Which is experimental and creates multiple files :(.

model.save('path_to_my_model.h5')

保存h5格式的:(.

tf.saved_model.save(self.model, "test_x_model")

再次保存多个文件:(.

Which agains save multiple files :(.

推荐答案

我使用TF2转换模型，例如:

I use TF2 to convert model like:

1. 将keras.callbacks.ModelCheckpoint(save_weights_only=True)传递到model.fit并在训练时保存checkpoint；
2. 训练后，self.model.load_weights(self.checkpoint_path)加载checkpoint，并转换为h5:self.model.save(h5_path, overwrite=True, include_optimizer=False);
3. 将h5转换为pb:

1. pass keras.callbacks.ModelCheckpoint(save_weights_only=True) to model.fit and save checkpoint while training;
2. After training, self.model.load_weights(self.checkpoint_path) load checkpoint, and convert to h5: self.model.save(h5_path, overwrite=True, include_optimizer=False);
3. convert h5 to pb:

import logging
import tensorflow as tf
from tensorflow.compat.v1 import graph_util
from tensorflow.python.keras import backend as K
from tensorflow import keras

# necessary !!!
tf.compat.v1.disable_eager_execution()

h5_path = '/path/to/model.h5'
model = keras.models.load_model(h5_path)
model.summary()
# save pb
with K.get_session() as sess:
    output_names = [out.op.name for out in model.outputs]
    input_graph_def = sess.graph.as_graph_def()
    for node in input_graph_def.node:
        node.device = ""
    graph = graph_util.remove_training_nodes(input_graph_def)
    graph_frozen = graph_util.convert_variables_to_constants(sess, graph, output_names)
    tf.io.write_graph(graph_frozen, '/path/to/pb/model.pb', as_text=False)
logging.info("save pb successfully！")

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