传输学习与tf.estimator.Estimator框架

Z时代
2024-01-10
分类：问答

我正在尝试使用我自己的数据集和类在imagenet pretrained传输学习的Inception-resnet v2模型。我的原始代码库是一个tf.slim样本的修改，我找不到了，现在我试图用tf.estimator.*框架重写相同的代码。传输学习与tf.estimator.Estimator框架

但是，我正在运行，只能加载一些的权重从预训练检查点，初始化其余层与他们的默认初始值设定项。

研究这个问题，我发现this GitHub issue和this question，都提到需要在我的model_fn中使用tf.train.init_from_checkpoint。我试过了，但由于缺乏两个例子，我想我错了。

这是我的小例子：

import sys 
import os 
os.environ['CUDA_VISIBLE_DEVICES'] = '-1' 
import tensorflow as tf 
import numpy as np 
import inception_resnet_v2 
NUM_CLASSES = 900 
IMAGE_SIZE = 299 
def input_fn(mode, num_classes, batch_size=1): 
    # some code that loads images, reshapes them to 299x299x3 and batches them 
    return tf.constant(np.zeros([batch_size, 299, 299, 3], np.float32)), tf.one_hot(tf.constant(np.zeros([batch_size], np.int32)), NUM_CLASSES) 
def model_fn(images, labels, num_classes, mode): 
    with tf.contrib.slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()): 
    logits, end_points = inception_resnet_v2.inception_resnet_v2(images, 
              num_classes, 
              is_training=(mode==tf.estimator.ModeKeys.TRAIN)) 
    predictions = { 
     'classes': tf.argmax(input=logits, axis=1), 
     'probabilities': tf.nn.softmax(logits, name='softmax_tensor') 
    } 
    if mode == tf.estimator.ModeKeys.PREDICT: 
    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions) 
    exclude = ['InceptionResnetV2/Logits', 'InceptionResnetV2/AuxLogits'] 
    variables_to_restore = tf.contrib.slim.get_variables_to_restore(exclude=exclude) 
    scopes = { os.path.dirname(v.name) for v in variables_to_restore } 
    tf.train.init_from_checkpoint('inception_resnet_v2_2016_08_30.ckpt', 
           {s+'/':s+'/' for s in scopes}) 
    tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits) 
    total_loss = tf.losses.get_total_loss() #obtain the regularization losses as well 
    # Configure the training op 
    if mode == tf.estimator.ModeKeys.TRAIN: 
    global_step = tf.train.get_or_create_global_step() 
    optimizer = tf.train.AdamOptimizer(learning_rate=0.00002) 
    train_op = optimizer.minimize(total_loss, global_step) 
    else: 
    train_op = None 
    return tf.estimator.EstimatorSpec(
    mode=mode, 
    predictions=predictions, 
    loss=total_loss, 
    train_op=train_op) 
def main(unused_argv): 
    # Create the Estimator 
    classifier = tf.estimator.Estimator(
     model_fn=lambda features, labels, mode: model_fn(features, labels, NUM_CLASSES, mode), 
     model_dir='model/MCVE') 
    # Train the model 
    classifier.train(
     input_fn=lambda: input_fn(tf.estimator.ModeKeys.TRAIN, NUM_CLASSES, batch_size=1), 
     steps=1000) 
    # Evaluate the model and print results 
    eval_results = classifier.evaluate(
     input_fn=lambda: input_fn(tf.estimator.ModeKeys.EVAL, NUM_CLASSES, batch_size=1)) 
    print() 
    print('Evaluation results:\n %s' % eval_results) 
if __name__ == '__main__': 
    tf.app.run(main=main, argv=[sys.argv[0]])

其中inception_resnet_v2为the model implementation in Tensorflow's models repository。

如果我运行这个脚本，我会从init_from_checkpoint得到一堆信息日志，但是在会话创建时，它似乎尝试从检查点加载Logits权重，并因形状不兼容而失败。这是完整的回溯：

Traceback (most recent call last): File "<ipython-input-6-06fadd69ae8f>", line 1, in <module> runfile('C:/Users/1/Desktop/transfer_learning_tutorial-master/MCVE.py', wdir='C:/Users/1/Desktop/transfer_learning_tutorial-master') File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile execfile(filename, namespace) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile exec(compile(f.read(), filename, 'exec'), namespace) File "C:/Users/1/Desktop/transfer_learning_tutorial-master/MCVE.py", line 77, in <module> tf.app.run(main=main, argv=[sys.argv[0]]) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\platform\app.py", line 48, in run _sys.exit(main(_sys.argv[:1] + flags_passthrough)) File "C:/Users/1/Desktop/transfer_learning_tutorial-master/MCVE.py", line 68, in main steps=1000) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\estimator\estimator.py", line 302, in train loss = self._train_model(input_fn, hooks, saving_listeners) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\estimator\estimator.py", line 780, in _train_model log_step_count_steps=self._config.log_step_count_steps) as mon_sess: File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 368, in MonitoredTrainingSession stop_grace_period_secs=stop_grace_period_secs) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 673, in __init__ stop_grace_period_secs=stop_grace_period_secs) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 493, in __init__ self._sess = _RecoverableSession(self._coordinated_creator) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 851, in __init__ _WrappedSession.__init__(self, self._create_session()) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 856, in _create_session return self._sess_creator.create_session() File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 554, in create_session self.tf_sess = self._session_creator.create_session() File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\monitored_session.py", line 428, in create_session init_fn=self._scaffold.init_fn) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\training\session_manager.py", line 279, in prepare_session sess.run(init_op, feed_dict=init_feed_dict) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 889, in run run_metadata_ptr) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1120, in _run feed_dict_tensor, options, run_metadata) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1317, in _do_run options, run_metadata) File "C:\Users\1\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1336, in _do_call raise type(e)(node_def, op, message) InvalidArgumentError: Assign requires shapes of both tensors to match. lhs shape= [900] rhs shape= [1001] [[Node: Assign_1145 = Assign[T=DT_FLOAT, _class=["loc:@InceptionResnetV2/Logits/Logits/biases"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](InceptionResnetV2/Logits/Logits/biases, checkpoint_initializer_1145)]]

我在做什么错误时使用init_from_checkpoint？我们究竟应该如何在我们的model_fn中“使用”它？为什么当我明确告诉它不要时，估计器试图从检查点加载Logits'权重？

更新：

的意见建议后，我想其他方法来调用tf.train.init_from_checkpoint。

使用`{v.name: v.name}`

如果，如评论所说，我替换{v.name:v.name for v in variables_to_restore}电话，我得到这个错误：

ValueError: Assignment map with scope only name InceptionResnetV2/Conv2d_2a_3x3 should map to scope only InceptionResnetV2/Conv2d_2a_3x3/weights:0. Should be 'scope/': 'other_scope/'.

使用`{v.name: v}`

相反，如果我尝试使用name:variable映射，我得到以下错误：

ValueError: Tensor InceptionResnetV2/Conv2d_2a_3x3/weights:0 is not found in inception_resnet_v2_2016_08_30.ckpt checkpoint {'InceptionResnetV2/Repeat_2/block8_4/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean': [256], 'InceptionResnetV2/Repeat/block35_9/Branch_0/Conv2d_1x1/BatchNorm/beta': [32], ...

错误继续列出我认为所有检查点中的变量名称（或者它可能是范围？）。

更新（2）

上方这里检查最新的错误后，我看到InceptionResnetV2/Conv2d_2a_3x3/weights是在检查点变量列表。问题在于末尾:0！我现在要验证这是否确实解决了问题并发布了答案（如果是这种情况）。

回答：

感谢@ KathyWu的评论，我找到了正确的道路，发现了问题。

事实上，我被计算scopes将包括InceptionResnetV2/范围的方式，将触发“之下”的范围所有变量的负荷（即，网络中的所有变量）。然而，用正确的词典代替它并不是微不足道的。

可能范围模式init_from_checkpoint accepts中，有一个我不得不使用是'scope_variable_name': variable之一，但不使用实际的variable.name属性。

variable.name看起来像：'some_scope/variable_name:0'。 :0不在检查点变量的名称中，因此使用scopes = {v.name:v.name for v in variables_to_restore}将引发“变量未找到”错误。

诀窍，使其工作从名字剥张量指数：

tf.train.init_from_checkpoint('inception_resnet_v2_2016_08_30.ckpt', 
           {v.name.split(':')[0]: v for v in variables_to_restore})

以上是传输学习与tf.estimator.Estimator框架的全部内容，来源链接： utcz.com/qa/263030.html

传输学习与tf.estimator.Estimator框架

更新：

使用{v.name: v.name}

使用{v.name: v}

更新（2）

回答：

其他人也看了：

使用`{v.name: v.name}`

使用`{v.name: v}`