tensorflow-slim的运用

1.github网址

a.https://github.com/tensorflow/models/tree/master/research/slim

b.https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim

2.slim预测

# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Generic evaluation script that evaluates a model using a given dataset."""

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', 10, '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/train_logs/',
    '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', 'flowers', 'The name of the dataset to load.')

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

tf.app.flags.DEFINE_string(
    'dataset_dir', './tmp/data', '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

import os
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)




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



    saver=tf.train.Saver()

    with tf.Session() as sess:

        checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
        # sess.run(tf.global_variables_initializer())
        saver.restore(sess,checkpoint_path)
        # logit, pre, labels = sess.run([logits,predictions,labels])
        # print(logit, pre, labels)

        coord = tf.train.Coordinator()

        threads = tf.train.start_queue_runners(coord=coord)
        
        logit = sess.run(labels)
        print(logit)





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

3.多线程预测

https://blog.youkuaiyun.com/dcrmg/article/details/79776876

# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Generic evaluation script that evaluates a model using a given dataset."""

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', 10, '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/train_logs/',
    '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', 'flowers', 'The name of the dataset to load.')

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

tf.app.flags.DEFINE_string(
    'dataset_dir', './tmp/data', '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

import os
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)


    #####################################
    # 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


    images = []
    labels = []
    dirs = 'D:\yang\code\classify\slim\\tmp\data\\flower_photos\daisy\\'
    for name in os.listdir(dirs):
        images.append(dirs + name)
        labels.append(name)
    train_input_queue = tf.train.slice_input_producer(
        [images, labels], num_epochs=1,
        shuffle=False)
    file_content = tf.read_file(train_input_queue[0])
    image = tf.image.decode_jpeg(file_content, channels=3)

    image = image_preprocessing_fn(image, eval_image_size, eval_image_size)
    image=tf.reshape(image,(299,299,3))

    label = train_input_queue[1]
    image_batch, label_batch = tf.train.batch([image, label], batch_size=1, num_threads=1, capacity=64)

    logits, _ = network_fn(image_batch)




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



    saver=tf.train.Saver()

    with tf.Session() as sess:
        tf.local_variables_initializer().run()
        threads = tf.train.start_queue_runners(sess=sess)
        checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
        # sess.run(tf.global_variables_initializer())
        saver.restore(sess,checkpoint_path)

        for i in range(10):
            logit ,label= sess.run([logits,label_batch])
            print(logit,str(label[0])[2:-1])





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