python – TensorFlow：运行training_op永远挂起

samples_all,labels_all = getsamples()

上面的代码加载数据集.
samples_all是包含图像路径的列表.图像的大小为240 * 320 * 3. labels_all是一个包含密集类的列表.有101个班级.
???我进入sess.run函数,发现它进入_do_call函数,并执行fn(* args).但是,它永远不会返回,也不会捕获任何异常.

import pickle
import re
import random
import numpy as np
import tensorflow as tf
from tensorflow.contrib import *
_R_MEAN = 123.68
_G_MEAN = 116.78
_B_MEAN = 103.94
def vgg16Net(inputs,num_classes=1000,is_training=True,dropout_keep_prob=0.5,spatial_squeeze=True,scope='vgg_16'):
    with tf.variable_scope(scope,'vgg_16',[inputs]) as sc:
        end_points_collection = sc.name + '_end_points'
        # Collect outputs for conv2d,fully_connected and max_pool2d
        with framework.arg_scope([slim.conv2d,slim.fully_connected,slim.max_pool2d],outputs_collections=end_points_collection):
          net = layers.repeat(inputs,2,layers.conv2d,64,[3,3],scope='conv1')
          net = layers.max_pool2d(net,[2,2],scope='pool1')
          net = layers.repeat(net,128,scope='conv2')
          net = layers.max_pool2d(net,scope='pool2')
          net = layers.repeat(net,3,256,scope='conv3')
          net = layers.max_pool2d(net,scope='pool3')
          net = layers.repeat(net,512,scope='conv4')
          net = layers.max_pool2d(net,scope='pool4')
          net = layers.repeat(net,scope='conv5')
          net = layers.max_pool2d(net,scope='pool5')
          # Use conv2d instead of fully_connected layers.
          net = layers.conv2d(net,4096,[7,10],padding='VALID',scope='fc6')
          net = layers.dropout(net,dropout_keep_prob,is_training=is_training,scope='dropout6')
          net = layers.conv2d(net,[1,1],scope='fc7')
          net = layers.dropout(net,scope='dropout7')
          net = layers.conv2d(net,num_classes,activation_fn=None,normalizer_fn=None,scope='fc8')
          # Convert end_points_collection into a end_point dict.
          end_points = layers.utils.convert_collection_to_dict(end_points_collection)
          if spatial_squeeze:
            net = tf.squeeze(net,name='fc8/squeezed')
            end_points[sc.name + '/fc8'] = net
          return net,end_points

def getsamples():
    rootpath = 'C:UsersmxDesktopnextLevelUCF-101'
    with open('res.pickle','rb') as f:
        pathdict = pickle.load(f)

    with open('trainlist01.txt','r') as f:
        lines=f.readlines()
    samples_all=[]
    labels_all=[]
    for line in lines:
        [videogroup,video,label]=re.split('/| |n',line)[0:3]
        samples = [rootpath + '' + videogroup + '' + video[:-4] + '' + i for i in pathdict[videogroup]]
        samples_all.extend(samples)
        labels_all.extend([label]*len(samples))
    return samples_all,labels_all



samples_all,labels_all = getsamples()

numOfSamples = len(samples_all)
labels_one_hot_all = np.zeros((len(labels_all),101))
index_offset = np.arange(len(labels_all))*101
ind = index_offset + np.array(labels_all,np.int32) - 1
labels_one_hot_all.flat[ind]=1
#samples_all = tf.constant(samples_all)
#labels_all = tf.constant(labels_all)
#samples_all_p = tf.placeholder(dtype = tf.string,shape = (numOfSamples,) )
#labels_one_hot_all_p = tf.placeholder(dtype = tf.float32,101))
#samples_all_v = tf.Variable(np.asarray(['']*numOfSamples),name = 'sample',trainable = False)
#labels_one_hot_all_v = tf.Variable(np.zeros_like(labels_one_hot_all,dtype=np.float32),name = 'label',trainable = False)
[sample,label] = tf.train.slice_input_producer([samples_all,labels_one_hot_all])
imagecontent = tf.read_file(sample)
image = tf.image.decode_jpeg(imagecontent,channels=3)
image = tf.cast(image,dtype = tf.float32)
channels = tf.split(2,image)
channels[0] -= _R_MEAN
channels[1] -= _G_MEAN
channels[2] -= _B_MEAN
image=tf.concat(2,channels)
image=tf.reshape(image,[240,320,3])
images,labels = tf.train.batch([image,label],16,32)
net,end = vgg16Net(images,num_classes = 101,is_training=True)



losses.softmax_cross_entropy(net,labels)
total_loss = losses.get_total_loss()

global_step = tf.Variable(0,trainable = False,name = 'global_step')
starter_learning_rate = 0.1
learning_rate = tf.train.exponential_decay(starter_learning_rate,global_step,100000,0.96,staircase=True)

train_var = framework.get_variables_to_restore(exclude = ['vgg_16/conv1','vgg_16/conv2','vgg_16/conv3','vgg_16/conv4','vgg_16/conv5','global_step','sample','label']);
init_var = framework.get_variables_to_restore(exclude = ['vgg_16/fc6','vgg_16/fc7','vgg_16/fc8','label'])
init_op,feed_init = framework.assign_from_checkpoint('./vgg_16.ckpt',init_var)

optimizer = tf.train.GradientDescentOptimizer(learning_rate)
training_op = optimizer.minimize(total_loss,var_list=train_var)


#with tf.name_scope('accuracy'):
#    with tf.name_scope('correct_prediction'):
#      correct_prediction = tf.equal(tf.argmax(net,1),tf.argmax(labels,1))
#    with tf.name_scope('accuracy'):
#      accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

#summaries
model_store_dir = 'C:UsersmxDesktopnextLevelnextLevelnextLevellog1'
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
#summaries.add(tf.summary.scalar('accuracy',accuracy))
for end_point in end:
    x=end[end_point]
    summaries.add(tf.summary.histogram('activations/' + end_point,x))
    summaries.add(tf.summary.scalar('sparsity/' + end_point,tf.nn.zero_fraction(x)))

for loss in tf.get_collection(tf.GraphKeys.LOSSES):
    summaries.add(tf.summary.scalar('losses/%s' % loss.op.name,loss))

for variable in framework.get_model_variables():
    summaries.add(tf.summary.histogram(variable.op.name,variable))

summaries.add(tf.summary.scalar('learning_rate',learning_rate))

summaries.add(tf.summary.scalar('total_loss',total_loss))

#summaries |= set(tf.get_collection(tf.GraphKeys.SUMMARIES))

summary_op = tf.summary.merge(list(summaries))

summary_writer = tf.summary.FileWriter(model_store_dir)

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    sess.run(init_op,feed_dict=feed_init)
    for i in range(100000):
        if i % 100 == 99:
            run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
            run_metadata = tf.RunMetadata()
            s,_ = sess.run([summary_op,training_op],options=run_options,run_metadata=run_metadata)
            summary_writer.add_run_metadata(run_metadata,'step%03d' % i)
            summary_writer.add_summary(s,i)
            print('Adding run metadata for',i)
        else:
            training_op.run()
            s=summary_op.run()
            summary_writer.add_summary(s,i)
            s,training_op])