def localization_net_alpha(inputs, num_transformer, num_theta_params):
"""
Utilize inception_v2 as the localization net of spatial transformer
"""
# outputs 7*7*1024: default final_endpoint='Mixed_5c' before full connection layer
with tf.variable_scope('inception_net'):
net, _ = inception_v2.inception_v2_base(inputs)
# fc layer using [1, 1] convolution kernel: 1*1*1024
with tf.variable_scope('logits'):
net = slim.conv2d(net, 128, [1, 1], scope='conv2d_a_1x1')
kernel_size = inception_v2._reduced_kernel_size_for_small_input(
net, [7, 7])
net = slim.conv2d(net,
128,
kernel_size,
padding='VALID',
scope='conv2d_b_{}x{}'.format(*kernel_size))
init_biase = tf.constant_initializer([2.0, .0, 2.0, .0] *
num_transformer)
logits = slim.conv2d(
net,
num_transformer * num_theta_params, [1, 1],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
biases_initializer=init_biase,
normalizer_fn=None,
activation_fn=tf.nn.tanh,
scope='conv2d_c_1x1')
return tf.squeeze(logits, [1, 2])
def _inception_logits(inputs, num_outputs, dropout_keep_prob, activ_fn=None):
with tf.variable_scope('logits'):
kernel_size = inception_v2._reduced_kernel_size_for_small_input(inputs, [7, 7])
# shape ?*1*1*?
net = slim.avg_pool2d(inputs, kernel_size, padding='VALID')
# drop out neuron before fc conv
net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='dropout')
# [1, 1] fc conv
logits = slim.conv2d(net, num_outputs, [1, 1], normalizer_fn=None, activation_fn=activ_fn,
scope='conv2_a_1x1')
return tf.squeeze(logits, [1, 2])
def localization_net_beta(inputs, num_transformer, num_theta_parmas):
with tf.variable_scope('inception_net'):
net, _ = inception_v2.inception_v2_base(inputs)
with tf.variable_scope('logits'):
with tf.variable_scope('branch_0'):
branch0 = slim.conv2d(net, 128, [1, 1], scope='conv2d_a_1x1')
branch0 = slim.conv2d(branch0,
144, [3, 3],
stride=2,
scope='conv2d_b_3x3')
with tf.variable_scope('branch_1'):
branch1 = slim.conv2d(net, 144, [1, 1], scope='conv2d_a_1x1')
branch1 = slim.max_pool2d(branch1, [3, 3],
stride=2,
padding='SAME',
scope='max_pool_b_3x3')
net = tf.concat([branch0, branch1], axis=-1)
kernel_size = inception_v2._reduced_kernel_size_for_small_input(
net, [7, 7])
net = slim.avg_pool2d(net,
kernel_size,
padding='VALID',
scope='avg_pool_a_{}x{}'.format(*kernel_size))
init_biase = tf.constant_initializer([2.0, .0, 2.0, .0] *
num_transformer)
logits = slim.conv2d(
net,
num_transformer * num_theta_parmas, [1, 1],
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
biases_initializer=init_biase,
normalizer_fn=None,
activation_fn=tf.nn.tanh,
scope='conv2d_b_1x1')
return tf.squeeze(logits, [1, 2])
