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 = resnet._get_variable('beta',
params_shape,
initializer=tf.zeros_initializer)
gamma = resnet._get_variable('gamma',
params_shape,
initializer=tf.ones_initializer)
moving_mean = resnet._get_variable('moving_mean',
params_shape,
initializer=tf.zeros_initializer,
trainable=False)
moving_variance = resnet._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 conv(x, c):
ksize = c['ksize']
stride = c['stride']
filters_out = c['conv_filters_out']
filters_in = x.get_shape()[-1]
shape = [ksize, ksize, ksize, filters_in, filters_out]
initializer = tf.truncated_normal_initializer(stddev=CONV_WEIGHT_STDDEV)
weights = resnet._get_variable('weights',
shape=shape,
dtype='float',
initializer=initializer,
weight_decay=CONV_WEIGHT_DECAY)
return tf.nn.conv3d(x, weights, [1, 1, stride, stride, 1], padding='SAME')