1
我想用張量流設計一個基於預訓練網絡的網絡，以Reset50爲例。然而，我不知道如何使用它來建立我的模型與他們的檢查點？ resnet的定義可以在resnet.py中找到。誰能幫我？非常感謝你！如何在張量流中使用預先訓練的ResNet50？
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')