tensorflow - 如何在tensorflow中使用预训练的ResNet50？

Question

我想用tensorflow设计一个基于预训练网络的网络，以Reset50为例。 ry released a model但是，我不知道如何使用它来通过他们的检查点构建我的模型？ resnet的定义可以在resnet.py中找到。谁能帮我？非常感谢!

def inference(x, is_training,
          num_classes=1000,
          num_blocks=[3, 4, 6, 3],  # defaults to 50-layer network
          use_bias=False, # defaults to using batch norm
          bottleneck=True):
c = Config()
c['bottleneck'] = bottleneck
c['is_training'] = tf.convert_to_tensor(is_training,
                                        dtype='bool',
                                        name='is_training')
c['ksize'] = 3
c['stride'] = 1
c['use_bias'] = use_bias
c['fc_units_out'] = num_classes
c['num_blocks'] = num_blocks
c['stack_stride'] = 2

with tf.variable_scope('scale1'):
    c['conv_filters_out'] = 64
    c['ksize'] = 7
    c['stride'] = 2
    x = conv(x, c)
    x = bn(x, c)
    x = activation(x)

with tf.variable_scope('scale2'):
    x = _max_pool(x, ksize=3, stride=2)
    c['num_blocks'] = num_blocks[0]
    c['stack_stride'] = 1
    c['block_filters_internal'] = 64
    x = stack(x, c)

with tf.variable_scope('scale3'):
    c['num_blocks'] = num_blocks[1]
    c['block_filters_internal'] = 128
    assert c['stack_stride'] == 2
    x = stack(x, c)

with tf.variable_scope('scale4'):
    c['num_blocks'] = num_blocks[2]
    c['block_filters_internal'] = 256
    x = stack(x, c)

with tf.variable_scope('scale5'):
    c['num_blocks'] = num_blocks[3]
    c['block_filters_internal'] = 512
    x = stack(x, c)

# post-net
x = tf.reduce_mean(x, reduction_indices=[1, 2], name="avg_pool")

if num_classes != None:
    with tf.variable_scope('fc'):
        x = fc(x, c)

return x
def stack(x, c):
for n in range(c['num_blocks']):
    s = c['stack_stride'] if n == 0 else 1
    c['block_stride'] = s
    with tf.variable_scope('block%d' % (n + 1)):
        x = block(x, c)
return x


def block(x, c):
filters_in = x.get_shape()[-1]

m = 4 if c['bottleneck'] else 1
filters_out = m * c['block_filters_internal']

shortcut = x  # branch 1

c['conv_filters_out'] = c['block_filters_internal']

if c['bottleneck']:
    with tf.variable_scope('a'):
        c['ksize'] = 1
        c['stride'] = c['block_stride']
        x = conv(x, c)
        x = bn(x, c)
        x = activation(x)

    with tf.variable_scope('b'):
        x = conv(x, c)
        x = bn(x, c)
        x = activation(x)

    with tf.variable_scope('c'):
        c['conv_filters_out'] = filters_out
        c['ksize'] = 1
        assert c['stride'] == 1
        x = conv(x, c)
        x = bn(x, c)
else:
    with tf.variable_scope('A'):
        c['stride'] = c['block_stride']
        assert c['ksize'] == 3
        x = conv(x, c)
        x = bn(x, c)
        x = activation(x)

    with tf.variable_scope('B'):
        c['conv_filters_out'] = filters_out
        assert c['ksize'] == 3
        assert c['stride'] == 1
        x = conv(x, c)
        x = bn(x, c)

with tf.variable_scope('shortcut'):
    if filters_out != filters_in or c['block_stride'] != 1:
        c['ksize'] = 1
        c['stride'] = c['block_stride']
        c['conv_filters_out'] = filters_out
        shortcut = conv(shortcut, c)
        shortcut = bn(shortcut, c)

return activation(x + shortcut)


def bn(x, c):
x_shape = x.get_shape()
params_shape = x_shape[-1:]

if c['use_bias']:
    bias = _get_variable('bias', params_shape,
                         initializer=tf.zeros_initializer)
    return x + bias


axis = list(range(len(x_shape) - 1))

beta = _get_variable('beta',
                     params_shape,
                     initializer=tf.zeros_initializer)
gamma = _get_variable('gamma',
                      params_shape,
                      initializer=tf.ones_initializer)

moving_mean = _get_variable('moving_mean',
                            params_shape,
                            initializer=tf.zeros_initializer,
                            trainable=False)
moving_variance = _get_variable('moving_variance',
                                params_shape,
                                initializer=tf.ones_initializer,
                                trainable=False)

# These ops will only be preformed when training.
mean, variance = tf.nn.moments(x, axis)
update_moving_mean = moving_averages.assign_moving_average(moving_mean,
                                                           mean, BN_DECAY)
update_moving_variance = moving_averages.assign_moving_average(
    moving_variance, variance, BN_DECAY)
tf.add_to_collection(UPDATE_OPS_COLLECTION, update_moving_mean)
tf.add_to_collection(UPDATE_OPS_COLLECTION, update_moving_variance)

mean, variance = control_flow_ops.cond(
    c['is_training'], lambda: (mean, variance),
    lambda: (moving_mean, moving_variance))

x = tf.nn.batch_normalization(x, mean, variance, beta, gamma, BN_EPSILON)
#x.set_shape(inputs.get_shape()) ??

return x


def fc(x, c):
num_units_in = x.get_shape()[1]
num_units_out = c['fc_units_out']
weights_initializer = tf.truncated_normal_initializer(
    stddev=FC_WEIGHT_STDDEV)

weights = _get_variable('weights',
                        shape=[num_units_in, num_units_out],
                        initializer=weights_initializer,
                        weight_decay=FC_WEIGHT_STDDEV)
biases = _get_variable('biases',
                       shape=[num_units_out],
                       initializer=tf.zeros_initializer)
x = tf.nn.xw_plus_b(x, weights, biases)
return x


def _get_variable(name,
              shape,
              initializer,
              weight_decay=0.0,
              dtype='float',
              trainable=True):
"A little wrapper around tf.get_variable to do weight decay and add to"
"resnet collection"
if weight_decay > 0:
    regularizer = tf.contrib.layers.l2_regularizer(weight_decay)
else:
    regularizer = None
collections = [tf.GraphKeys.VARIABLES, RESNET_VARIABLES]
return tf.get_variable(name,
                       shape=shape,
                       initializer=initializer,
                       dtype=dtype,
                       regularizer=regularizer,
                       collections=collections,
                       trainable=trainable)


def conv(x, c):
ksize = c['ksize']
stride = c['stride']
filters_out = c['conv_filters_out']

filters_in = x.get_shape()[-1]
shape = [ksize, ksize, filters_in, filters_out]
initializer = tf.truncated_normal_initializer(stddev=CONV_WEIGHT_STDDEV)
weights = _get_variable('weights',
                        shape=shape,
                        dtype='float',
                        initializer=initializer,
                        weight_decay=CONV_WEIGHT_DECAY)
return tf.nn.conv2d(x, weights, [1, stride, stride, 1], padding='SAME')


def _max_pool(x, ksize=3, stride=2):
return tf.nn.max_pool(x,
                      ksize=[1, ksize, ksize, 1],
                      strides=[1, stride, stride, 1],
                      padding='SAME')

Answer 1

基本上，您应该使用为模型提供的代码。您可以使用它们创建图形，然后提供检查点文件，请参阅下面的 ResNet50 情况下如何执行此操作:

from tensorflow.contrib.slim.nets import resnet_v1
import tensorflow as tf
import tensorflow.contrib.slim as slim

# Create graph
inputs = tf.placeholder(tf.float32, shape=[batch_size, height, width, channels])
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
    net, end_points = resnet_v1.resnet_v1_50(inputs, is_training=False)

saver = tf.train.Saver()    

with tf.Session() as sess:
    saver.restore(sess, '.resnet_v1_50.ckpt')
    representation_tensor = sess.graph.get_tensor_by_name('resnet_v1_50/pool5:0') # if you don't know names like these, consider referring to corresponding model file or generate .pbtxt file as mentioned in  @civilman628 's answer in link below
    img = ...  #load image here with size [1, 224,224, 3]
    features = sess.run(representation_tensor, {'Placeholder:0': x})

有关更多详细信息，请参阅我 (@parthg) 在与此相关的 Tensorflow Github 问题上的回答:#7172