Python tensorflow2.0使用检查点恢复模型

transformer.py：

import tensorflow as tf
import numpy as np
from modules.encoder import Encoder
from modules.decoder import Decoder

class Transformer(tf.keras.Model):
    def __init__(self, num_layers=4, d_model=512, num_heads=8, dff=2048, pe_max_len=8000,
                 target_vocab_size=8000, rate=0.1,config=None,logger=None):
        super(Transformer, self).__init__()

        if config is not None:
            num_enc_layers = config.model.N_encoder
            if logger is not None:
                logger.info('config.model.N_encoder: '+str(num_enc_layers))
            num_dec_layers = config.model.N_decoder
            if logger is not None:
                logger.info('config.model.N_decoder: '+str(num_dec_layers))
            d_model = config.model.d_model
            if logger is not None:
                logger.info('config.model.d_model:   '+str(d_model))
            num_heads = config.model.n_heads
            if logger is not None:
                logger.info('config.model.n_heads:   '+str(num_heads))
            dff = config.model.d_ff
            if logger is not None:
                logger.info('config.model.d_ff:      '+str(dff))
            pe_max_len = config.model.pe_max_len
            if logger is not None:
                logger.info('config.model.pe_max_len:'+str(pe_max_len))
            target_vocab_size = config.model.vocab_size
            if logger is not None:
                logger.info('config.model.vocab_size:'+str(target_vocab_size))
            rate = config.model.dropout
            if logger is not None:
                logger.info('config.model.dropout:   '+str(rate))
        else:
            print('use default params')
            num_enc_layers = num_layers
            num_dec_layers = num_layers

        self.encoder = Encoder(num_enc_layers, d_model, num_heads, dff,
                                   pe_max_len,'encoder', rate)

        self.decoder = Decoder(num_dec_layers, d_model, num_heads, dff,
                               target_vocab_size, 'decoder',pe_max_len,rate)

        # self.final_layer = tf.keras.layers.Dense(target_vocab_size)

    def call(self, inputs, training, enc_padding_mask,
             look_ahead_mask, dec_padding_mask):

        inp = tf.cast(inputs[0],tf.float32)
        tar = tf.cast(inputs[1],tf.int32)

        enc_output = self.encoder((inp, enc_padding_mask),training)  # (batch_size, inp_seq_len, d_model)

        # dec_output.shape == (batch_size, tar_seq_len, d_model)
        dec_output, attention_weights = self.decoder(
            (tar, enc_output,  look_ahead_mask, dec_padding_mask),training)

        # final_output = self.final_layer(dec_output)  # (batch_size, tar_seq_len, target_vocab_size)
        final_output = dec_output

        return final_output, attention_weights



if __name__=='__main__':
    sample_transformer = Transformer(
        num_layers=2, d_model=512, num_heads=4, dff=2048,
        pe_max_len=8500, target_vocab_size=32)

    temp_input = tf.random.uniform((64, 62))
    temp_target = tf.random.uniform((64, 26))
    # temp_input = tf.keras.layers.Input((64,62),dtype=tf.float32)
    # temp_target = tf.keras.layers.Input((16,),dtype=tf.float32)
    # 如果想对inputs浓缩，那么几个mask也要建立Input？
    fn_out, _ = sample_transformer(inputs=(temp_input, temp_target), training=False,
                                   enc_padding_mask=None,
                                   look_ahead_mask=None,
                                   dec_padding_mask=None)

    sample_transformer.summary()# 如果在调用call之前进行summary会提示model not build

    '''
    Model: "transformer"
    _________________________________________________________________
    Layer (type)                 Output Shape              Param #   
    =================================================================
    encoder (Encoder)            multiple                  10656768  
    _________________________________________________________________
    decoder (Decoder)            multiple                  12504064  
    _________________________________________________________________
    dense_32 (Dense)             multiple                  4104000   
    =================================================================
    Total params: 27,264,832
    Trainable params: 27,264,832
    Non-trainable params: 0
    _________________________________________________________________

    '''
    # tf.keras.utils.plot_model(sample_transformer)
    print(sample_transformer.get_layer('encoder'))
    tp = sample_transformer.trainable_variables
    for i in range(20):
        print(tp[i].name)
    '''
    <modules.encoder.Encoder object at 0x00000151AD449390>
    transformer/encoder/enc_embedding/embeddings:0
    transformer/encoder/encoder_layer/multi_head_attention/dense/kernel:0
    transformer/encoder/encoder_layer/multi_head_attention/dense/bias:0
    transformer/encoder/encoder_layer/multi_head_attention/dense_1/kernel:0
    transformer/encoder/encoder_layer/multi_head_attention/dense_1/bias:0
    transformer/encoder/encoder_layer/multi_head_attention/dense_2/kernel:0
    transformer/encoder/encoder_layer/multi_head_attention/dense_2/bias:0
    transformer/encoder/encoder_layer/multi_head_attention/dense_3/kernel:0
    transformer/encoder/encoder_layer/multi_head_attention/dense_3/bias:0
    transformer/encoder/encoder_layer/sequential/dense_4/kernel:0
    transformer/encoder/encoder_layer/sequential/dense_4/bias:0
    transformer/encoder/encoder_layer/sequential/dense_5/kernel:0
    transformer/encoder/encoder_layer/sequential/dense_5/bias:0
    transformer/encoder/encoder_layer/layer_normalization/gamma:0
    transformer/encoder/encoder_layer/layer_normalization/beta:0
    transformer/encoder/encoder_layer/layer_normalization_1/gamma:0
    transformer/encoder/encoder_layer/layer_normalization_1/beta:0
    transformer/encoder/encoder_layer_1/multi_head_attention_1/dense_6/kernel:0
    transformer/encoder/encoder_layer_1/multi_head_attention_1/dense_6/bias:0
    transformer/encoder/encoder_layer_1/multi_head_attention_1/dense_7/kernel:0
    '''

    # model = tf.keras.models.Model(inputs=[temp_input,temp_target],outputs=[fn_out])
    # model.summary()
    print(fn_out.shape)  # (batch_size, tar_seq_len, target_vocab_size)

    # summary_writer = tf.keras.callbacks.TensorBoard(log_dir='modules')
    # summary_writer.set_model(model)

在培训步骤中，使用以下代码保存模型：

ckpt = tf.train.Checkpoint(transformer=model, optimizer=optimizer)
ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=3)
ckpt_save_path = ckpt_manager.save()

model_to_be_restored = Speech_transformer(config=config)
ckpt = tf.train.Checkpoint(transformer=model_to_be_restored)
ckpt.restore(tf.train.latest_checkpoint(r'logdir/logging/P_S_Transformer_debug'))

这样，我就可以成功地训练模型并保存模型

在测试步骤中，当我使用以下代码恢复模型时：

ckpt = tf.train.Checkpoint(transformer=model, optimizer=optimizer)
ckpt_manager = tf.train.CheckpointManager(ckpt, checkpoint_path, max_to_keep=3)
ckpt_save_path = ckpt_manager.save()

model_to_be_restored = Speech_transformer(config=config)
ckpt = tf.train.Checkpoint(transformer=model_to_be_restored)
ckpt.restore(tf.train.latest_checkpoint(r'logdir/logging/P_S_Transformer_debug'))

测试过程可以使用退出代码0完成，并获得我想要的结果。 但是，在这个过程中，存在以下问题：

在中忽略异常：> 回溯（最近一次呼叫最后一次）： 文件“C:\Users\LungChi\AppData\Roaming\Python36\site packages\tensorflow\u core\Python\training\tracking\util.py”，第140行，在del TypeError:“非类型”对象不可调用

进程已完成，退出代码为0

part of util.py:

    class _CheckpointRestoreCoordinatorDeleter(object):
      """Deleter to avoid overriding _CheckpointRestoreCoordinator.__del__()."""

      def __init__(self, expect_partial, object_graph_proto, matched_proto_ids,
                   unused_attributes):
        self.expect_partial = expect_partial
        self.object_graph_proto = object_graph_proto
        self.matched_proto_ids = matched_proto_ids
        self.unused_attributes = unused_attributes

      def set_expect_partial(self, expect_partial):
        self.expect_partial = expect_partial

      def __del__(self):
        if self.expect_partial:
          return
        if logging is None:
          # The logging module may have been unloaded when __del__ is called.
          log_fn = print
        else:
          log_fn = logging.warning
        printed_warning = False
        pretty_printer = _ObjectGraphProtoPrettyPrinter(self.object_graph_proto)# traceback to this line


how can I solve it? please help me, thanks vary much!!!

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语音变压器是如何定义的？另外请包括完整的回溯。我已经添加了模型，请帮助我什么是transformer库？我似乎不能用pip安装它。transformer是我自己定义的，我已经上传了it@LungChi似乎

\u ObjectGraphProtoPrettyPrinter为None

。不知道为什么会这样。那个物体应该是什么？你能链接完整的源代码吗？另外，请添加错误的完整回溯。