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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
import tensorflow as tf

from datasets import dataset_factory
from nets import nets_factory
from preprocessing import preprocessing_factory

slim = tf.contrib.slim

tf.app.flags.DEFINE_integer(
    'batch_size', 100, 'The number of samples in each batch.')

tf.app.flags.DEFINE_integer(
    'max_num_batches', None,
    'Max number of batches to evaluate by default use all.')

tf.app.flags.DEFINE_string(
    'master', '', 'The address of the TensorFlow master to use.')

tf.app.flags.DEFINE_string(
    'checkpoint_path', '/tmp/tfmodel/',
    'The directory where the model was written to or an absolute path to a '
    'checkpoint file.')

tf.app.flags.DEFINE_string(
    'eval_dir', '/tmp/tfmodel/', 'Directory where the results are saved to.')

tf.app.flags.DEFINE_integer(
    'num_preprocessing_threads', 4,
    'The number of threads used to create the batches.')

tf.app.flags.DEFINE_string(
    'dataset_name', 'imagenet', 'The name of the dataset to load.')

tf.app.flags.DEFINE_string(
    'dataset_split_name', 'test', 'The name of the train/test split.')

tf.app.flags.DEFINE_string(
    'dataset_dir', None, 'The directory where the dataset files are stored.')

tf.app.flags.DEFINE_integer(
    'labels_offset', 0,
    'An offset for the labels in the dataset. This flag is primarily used to '
    'evaluate the VGG and ResNet architectures which do not use a background '
    'class for the ImageNet dataset.')

tf.app.flags.DEFINE_string(
    'model_name', 'inception_v3', 'The name of the architecture to evaluate.')

tf.app.flags.DEFINE_string(
    'preprocessing_name', None, 'The name of the preprocessing to use. If left '
    'as `None`, then the model_name flag is used.')

tf.app.flags.DEFINE_float(
    'moving_average_decay', None,
    'The decay to use for the moving average.'
    'If left as None, then moving averages are not used.')

tf.app.flags.DEFINE_integer(
    'eval_image_size', None, 'Eval image size')

FLAGS = tf.app.flags.FLAGS


def main(_):
  if not FLAGS.dataset_dir:
    raise ValueError('You must supply the dataset directory with --dataset_dir')

  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.Graph().as_default():
    tf_global_step = slim.get_or_create_global_step()

    ######################
    # Select the dataset #
    ######################
    dataset = dataset_factory.get_dataset(
        FLAGS.dataset_name, FLAGS.dataset_split_name, FLAGS.dataset_dir)

    ####################
    # Select the model #
    ####################
    network_fn = nets_factory.get_network_fn(
        FLAGS.model_name,
        num_classes=(dataset.num_classes - FLAGS.labels_offset),
        is_training=False)

    ##############################################################
    # Create a dataset provider that loads data from the dataset #
    ##############################################################
    provider = slim.dataset_data_provider.DatasetDataProvider(
        dataset,
        shuffle=False,
        common_queue_capacity=2 * FLAGS.batch_size,
        common_queue_min=FLAGS.batch_size)
    [image, label] = provider.get(['image', 'label'])
    label -= FLAGS.labels_offset

    #####################################
    # Select the preprocessing function #
    #####################################
    preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name
    image_preprocessing_fn = preprocessing_factory.get_preprocessing(
        preprocessing_name,
        is_training=False)

    eval_image_size = FLAGS.eval_image_size or network_fn.default_image_size

    image = image_preprocessing_fn(image, eval_image_size, eval_image_size)

    images, labels = tf.train.batch(
        [image, label],
        batch_size=FLAGS.batch_size,
        num_threads=FLAGS.num_preprocessing_threads,
        capacity=5 * FLAGS.batch_size)

    ####################
    # Define the model #
    ####################
    logits, _ = network_fn(images)

    if FLAGS.moving_average_decay:
      variable_averages = tf.train.ExponentialMovingAverage(
          FLAGS.moving_average_decay, tf_global_step)
      variables_to_restore = variable_averages.variables_to_restore(
          slim.get_model_variables())
      variables_to_restore[tf_global_step.op.name] = tf_global_step
    else:
      variables_to_restore = slim.get_variables_to_restore()

    predictions = tf.argmax(logits, 1)
    labels = tf.squeeze(labels)

    # Define the metrics:
    names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({
        'Accuracy': slim.metrics.streaming_accuracy(predictions, labels),
        'Recall_5': slim.metrics.streaming_recall_at_k(
            logits, labels, 5),
    })

    # Print the summaries to screen.
    for name, value in names_to_values.items():
      summary_name = 'eval/%s' % name
      op = tf.summary.scalar(summary_name, value, collections=[])
      op = tf.Print(op, [value], summary_name)
      tf.add_to_collection(tf.GraphKeys.SUMMARIES, op)

    # TODO(sguada) use num_epochs=1
    if FLAGS.max_num_batches:
      num_batches = FLAGS.max_num_batches
    else:
      # This ensures that we make a single pass over all of the data.
      num_batches = math.ceil(dataset.num_samples / float(FLAGS.batch_size))

    if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
      checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
    else:
      checkpoint_path = FLAGS.checkpoint_path

    tf.logging.info('Evaluating %s' % checkpoint_path)

    slim.evaluation.evaluate_once(
        master=FLAGS.master,
        checkpoint_path=checkpoint_path,
        logdir=FLAGS.eval_dir,
        num_evals=num_batches,
        eval_op=list(names_to_updates.values()),
        variables_to_restore=variables_to_restore)


if __name__ == '__main__':
  tf.app.run()

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/evaluation.py

嗨，大家好！

我想通过使用 tensorflow slim 获得批量图像的分类预测结果。

很长时间以来我尝试了很多方法，但我未能将“预测”打印或写入 aaa.txt 等文件。怎么办？

先感谢您。

4

1 回答 1

1

这是一个常见的问题。您遇到问题的原因是因为上图中定义的每个运算符都有两种状态。在 python 代码执行时，您定义运算符并指定它们的输入张量和输出张量——您正在构建图形本身。另一种状态是您感兴趣的状态，它是数据在通过您的图表的任何给定通道上进入操作时的值。

在 theslim.evaluation.evaluate_once和其他此类方法中，图形会运行一批，并且所有定义的操作都会在处理器通过图形定义时执行。

最简单的事情（如果您想要的只是打印）是熟悉tf.Print操作符，它允许您在评估张量（或 n 个张量）时观察它们的内容。这里要注意的是，您需要确保图形Print在其定义中包含用于 的运算符，以便打印实际上知道每次传递的张量值是什么。

如果您需要自定义报告，另一种方法是创建一个自定义PyOp，它位于图形定义中作为无操作（输入张量 == 输出张量，或节点没有输出），具体取决于您的图形的定义方式。一旦你在 py 代码中 - 你可以在 python 上下文中访问输入张量作为 numpy 数组（我认为）。这是我不久前编写的一个小实用程序，slim用于在每次运行后将 base64 图像以及正确/不正确的预测类转储到报告（html 文件）中。警告不要脸的自我插件：https ://github.com/sabhiram/tf-eval-reporter

于 2018-02-06T22:14:30.500 回答