看来您在第1版中修剪模型的方式很好；根据您的错误消息，无法保存生成的修剪模型，因为它不是“可跟踪的”，这是使用保存模型的必要条件tf.saved_model.save。生成可跟踪对象的一种方法是从tf.Module类继承，如使用SavedModel格式和具体函数的指南中所述。下面是一个示例，尝试保存一个tf.function对象（由于该对象不可跟踪而失败），从继承tf.module并保存生成的对象：
（使用Python版本3.7.6，TensorFlow版本2.1.0和NumPy版本1.18.1）
import tensorflow as tf, numpy as np
# Define a random TensorFlow function and generate a reference output
conv_filter = tf.random.normal([1, 2, 4, 2], seed=1254)
@tf.function
def conv_model(x):
return tf.nn.Conv2d(x, conv_filter, 1, "SAME")
input_tensor = tf.ones([1, 2, 3, 4])
output_tensor = conv_model(input_tensor)
print("Original model outputs:", output_tensor, sep="\n")
# Try saving the model: it won't work because a tf.function is not trackable
export_dir = "./tmp/"
try: tf.saved_model.save(conv_model, export_dir)
except ValueError: print(
"Can't save {} object because it's not trackable".format(type(conv_model)))
# Now define a trackable object by inheriting from the tf.Module class
class MyModule(tf.Module):
@tf.function
def __call__(self, x): return conv_model(x)
# Instantiate the trackable object, and call once to trace-compile a graph
module_func = MyModule()
module_func(input_tensor)
tf.saved_model.save(module_func, export_dir)
# Restore the model and verify that the outputs are consistent
restored_model = tf.saved_model.load(export_dir)
restored_output_tensor = restored_model(input_tensor)
print("Restored model outputs:", restored_output_tensor, sep="\n")
if np.array_equal(output_tensor.numpy(), restored_output_tensor.numpy()):
print("Outputs are consistent :)")
else: print("Outputs are NOT consistent :(")
控制台输出：
Original model outputs:
tf.Tensor(
[[[[-2.3629642 1.2904963 ]
[-2.3629642 1.2904963 ]
[-0.02110204 1.3400152 ]]
[[-2.3629642 1.2904963 ]
[-2.3629642 1.2904963 ]
[-0.02110204 1.3400152 ]]]], shape=(1, 2, 3, 2), dtype=float32)
Can't save <class 'tensorflow.python.eager.def_function.Function'> object
because it's not trackable
Restored model outputs:
tf.Tensor(
[[[[-2.3629642 1.2904963 ]
[-2.3629642 1.2904963 ]
[-0.02110204 1.3400152 ]]
[[-2.3629642 1.2904963 ]
[-2.3629642 1.2904963 ]
[-0.02110204 1.3400152 ]]]], shape=(1, 2, 3, 2), dtype=float32)
Outputs are consistent :)
svmod = tf.saved_model.load(fn) #version 1
svmod2 = svmod.prune(Feeds=['foo:0'], fetches=['bar:0'])
class Exportable(tf.Module):
@tf.function
def __call__(self, model_inputs): return svmod2(model_inputs)
svmod2_export = Exportable()
svmod2_export(typical_input) # call once with typical input to trace-compile
tf.saved_model.save(svmod2_export, '/tmp/saved_model/')
如果您不想继承自tf.Module，则可以替换实例代码，实例化一个tf.Module对象并添加tf.function方法/可调用属性，如下所示：
to_export = tf.Module()
to_export.call = tf.function(conv_model)
to_export.call(input_tensor)
tf.saved_model.save(to_export, export_dir)
restored_module = tf.saved_model.load(export_dir)
restored_func = restored_module.call
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