恢复使用 tf.estimator 训练的模型并通过 feed_dict 提供输入 [英] Restoring a model trained with tf.estimator and feeding input through feed_dict

问题描述

我用 tf.estimator 训练了一个 resnet，模型是在训练过程中保存的.保存的文件由.data、.index 和.meta 组成.我想重新加载这个模型并获得新图像的预测.在训练期间使用 tf.data.Dataset 将数据输入模型.我密切关注 此处 的 resnet 实现.

I trained a resnet with tf.estimator, the model was saved during the training process. The saved files consist of .data, .index and .meta. I'd like to load this model back and get predictions for new images. The data was fed to the model during training using tf.data.Dataset. I have closely followed the resnet implementation given here.

我想使用 feed_dict 恢复模型并将输入提供给节点.

I would like to restore the model and feed inputs to the nodes using a feed_dict.

第一次尝试

  #rebuild input pipeline
  images, labels = input_fn(data_dir, batch_size=32, num_epochs=1)

  #rebuild graph
  prediction= imagenet_model_fn(images,labels,{'batch_size':32,'data_format':'channels_first','resnet_size':18},mode = tf.estimator.ModeKeys.EVAL).predictions 

  saver  = tf.train.Saver()
  with tf.Session() as sess:
    ckpt = tf.train.get_checkpoint_state(r'./model')
    saver.restore(sess, ckpt.model_checkpoint_path)
    while True:
    try:
        pred,im= sess.run([prediction,images])
        print(pred)
    except tf.errors.OutOfRangeError:
      break

我使用 classifier.evaluate 在同一模型上提供了一个数据集，但上述方法给出了错误的预测.该模型为所有图像提供相同的类别和概率，1.0.

I fed a dataset which was evaluated on the same model using classifier.evaluate, but the above method gives wrong predictions. The model gives same class and probability, 1.0, for all images.

第二次尝试

saver = tf.train.import_meta_graph(r'.\resnet\model\model-3220.meta')
sess = tf.Session()
saver.restore(sess,tf.train.latest_checkpoint(r'.\resnet\model'))
graph = tf.get_default_graph()
inputImage = graph.get_tensor_by_name('image:0')
logits= graph.get_tensor_by_name('logits:0')

#Get prediction
print(sess.run(logits,feed_dict={inputImage:newimage}))

与 classifier.evaluate 相比，这也给出了错误的预测.我什至可以在没有 feed_dict 的情况下运行 sess.run(logits)！

This also gives wrong predictions compared to classifier.evaluate. I can even run sess.run(logits) without a feed_dict!

第三次尝试

def serving_input_fn():
  receiver_tensor = {'feature': tf.placeholder(shape=[None, 384, 256, 3], dtype=tf.float32)}
  features = {'feature': receiver_tensor['images']}
return tf.estimator.export.ServingInputReceiver(features, receiver_tensor)

它失败了

Traceback (most recent call last):
  File "imagenet_main.py", line 213, in <module>
    tf.app.run(argv=[sys.argv[0]] + unparsed)
  File "C:\Users\Photogauge\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\platform\app.py", line 124, in run
    _sys.exit(main(argv))
  File "imagenet_main.py", line 204, in main
    resnet.resnet_main(FLAGS, imagenet_model_fn, input_fn)
  File "C:\Users\Photogauge\Desktop\iprings_images\models-master\models-master\official\resnet\resnet.py", line 527, in resnet_main
    classifier.export_savedmodel(export_dir_base=r"C:\Users\Photogauge\Desktop\iprings_images\models-master\models-master\official\resnet\export", serving_input_receiver_fn=serving_input_fn)
  File "C:\Users\Photogauge\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\estimator\estimator.py", line 528, in export_savedmodel
    config=self.config)
  File "C:\Users\Photogauge\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\estimator\estimator.py", line 725, in _call_model_fn
    model_fn_results = self._model_fn(features=features, **kwargs)
  File "imagenet_main.py", line 200, in imagenet_model_fn
    loss_filter_fn=None)
  File "C:\Users\Photogauge\Desktop\iprings_images\models-master\models-master\official\resnet\resnet.py", line 433, in resnet_model_fn
    tf.argmax(labels, axis=1), predictions['classes'])
  File "C:\Users\Photogauge\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\deprecation.py", line 316, in new_func
    return func(*args, **kwargs)
  File "C:\Users\Photogauge\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\math_ops.py", line 208, in argmax
    return gen_math_ops.arg_max(input, axis, name=name, output_type=output_type)
  File "C:\Users\Photogauge\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\gen_math_ops.py", line 508, in arg_max
    name=name)
  File "C:\Users\Photogauge\Anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 528, in _apply_op_helper
    (input_name, err))
ValueError: Tried to convert 'input' to a tensor and failed. Error: None values not supported.

我用于训练和构建模型的代码如下:

The code I used for training and building the model is as below:

解析数据集的规范:

def parse_record(raw_record, is_training):
  keys_to_features = {
      'image/encoded':
          tf.FixedLenFeature((), tf.string, default_value=''),
      'image/class/label':
          tf.FixedLenFeature([], dtype=tf.int64, default_value=-1),
  }
  parsed = tf.parse_single_example(raw_record, keys_to_features)
  image = tf.image.decode_image(
      tf.reshape(parsed['image/encoded'], shape=[]),3)
  image = tf.image.convert_image_dtype(image, dtype=tf.float32)
  label = tf.cast(
      tf.reshape(parsed['image/class/label'], shape=[]),
      dtype=tf.int32)
  return image, tf.one_hot(label,2)

以下函数解析数据并创建用于训练的批次

The following function parses the data and creates batches for training

def input_fn(is_training, data_dir, batch_size, num_epochs=1):
  dataset = tf.data.Dataset.from_tensor_slices(
      filenames(is_training, data_dir))
  if is_training:
     dataset = dataset.shuffle(buffer_size=_FILE_SHUFFLE_BUFFER)
  dataset = dataset.flat_map(tf.data.TFRecordDataset)
  dataset = dataset.map(lambda value: parse_record(value, is_training),
                        num_parallel_calls=5)
  dataset = dataset.prefetch(batch_size)
  if is_training:
      dataset = dataset.shuffle(buffer_size=_SHUFFLE_BUFFER)
  dataset = dataset.repeat(num_epochs)
  dataset = dataset.batch(batch_size)

  iterator = dataset.make_one_shot_iterator()
  images, labels = iterator.get_next()
  return images, labels

如下创建一个分类器用于训练集的训练和验证集的评估

A classifier is created as below for training on train set and evaluation on validation set

classifier = tf.estimator.Estimator(
      model_fn=model_function, model_dir=flags.model_dir, config=run_config,
      params={
          'resnet_size': flags.resnet_size,
          'data_format': flags.data_format,
          'batch_size': flags.batch_size,
      })

    #Training cycle
     classifier.train(
         input_fn=lambda: input_function(
             training_phase=True, flags.data_dir, flags.batch_size, flags.epochs_per_eval),
         hooks=[logging_hook])
    # Evaluate the model 
    eval_results = classifier.evaluate(input_fn=lambda: input_function(
        training_phase=False, flags.data_dir, flags.batch_size))

这就是我尝试从模型加载和获取预测的方式.

This is how I tried to load and get predictions from the model.

恢复已保存模型并对其进行推理的正确方法是什么.我想直接提供图像而不使用 tf.data.Dataset.

What is the right way to restore a saved model and perform inference on it. I want to feed images directly without using tf.data.Dataset.

更新

1. ckpt 的值是运行后 ckpt = tf.train.get_checkpoint_state(r'./model') 是

1. The value of ckpt is after running ckpt = tf.train.get_checkpoint_state(r'./model') is

model_checkpoint_path: "./model\model.ckpt-5980"all_model_checkpoint_paths: "./model\model.ckpt-5060"all_model_checkpoint_paths: "./model\model.ckpt-5061"all_model_checkpoint_paths: "./model\model.ckpt-5520"all_model_checkpoint_paths: "./model\model.ckpt-5521"all_model_checkpoint_paths: "./model\model.ckpt-5980"

model_checkpoint_path: "./model\model.ckpt-5980" all_model_checkpoint_paths: "./model\model.ckpt-5060" all_model_checkpoint_paths: "./model\model.ckpt-5061" all_model_checkpoint_paths: "./model\model.ckpt-5520" all_model_checkpoint_paths: "./model\model.ckpt-5521" all_model_checkpoint_paths: "./model\model.ckpt-5980"

当我尝试 `saver.restore(sess, tf.train.latest_checkpoint(r'.\resnet\model')) 时，输出是一样的

The output is same when I try `saver.restore(sess, tf.train.latest_checkpoint(r'.\resnet\model'))

将完整路径传递给 saver.restore 给出相同的输出在所有情况下，相同的模型，model.ckpt-5980 被恢复

Passing in full path to saver.restore gives the same output In all cases the same model, model.ckpt-5980 was restored

推荐答案

如果您有模型 pb 或 pb.txt，那么推理很容易.使用预测器模块，我们可以进行推理.查看这里 了解更多信息.对于图像数据，它将类似于以下示例.希望这会有所帮助！！

If you have model pb or pb.txt then inference is easy. Using predictor module, we can do an inference. Check out here for more information. For image data it will be something to similar to below example. Hope this helps !!

示例代码:

import numpy as np
import matplotlib.pyplot as plt

def extract_data(index=0, filepath='data/cifar-10-batches-bin/data_batch_5.bin'):
    bytestream = open(filepath, mode='rb')
    label_bytes_length = 1
    image_bytes_length = (32 ** 2) * 3
    record_bytes_length = label_bytes_length + image_bytes_length
    bytestream.seek(record_bytes_length * index, 0)
    label_bytes = bytestream.read(label_bytes_length)
    image_bytes = bytestream.read(image_bytes_length)
    label = np.frombuffer(label_bytes, dtype=np.uint8)  
    image = np.frombuffer(image_bytes, dtype=np.uint8)
    image = np.reshape(image, [3, 32, 32])
    image = np.transpose(image, [1, 2, 0])
    image = image.astype(np.float32)
   result = {
     'image': image,
     'label': label,
   }
   bytestream.close()
   return result


    predictor_fn = tf.contrib.predictor.from_saved_model(
  export_dir = saved_model_dir, signature_def_key='predictions')
    N = 1000
    labels = []
    images = []
    for i in range(N):
       result = extract_data(i)
       images.append(result['image'])
       labels.append(result['label'][0])
    output = predictor_fn(
      {
        'images': images,
      }
    )

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