def build_net(images,
n_class=None,
is_training=True,
reuse=False,
alpha=1,
scope='mobile_id'):
with tf.variable_scope(scope, reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm], is_training=is_training):
net = conv2d_same(images, 58, 3, stride=2, scope='conv1')
# net = slim.max_pool2d(net, 3, stride=2, padding='SAME', scope='pool1')
net = shuffle_block(net, 2, 'stage2_block1')
for i in range(3):
net = shuffle_block(net, 1, 'stage2_block%d' % (i + 2))
net = shuffle_block(net, 2, 'stage3_block1')
for i in range(7):
net = shuffle_block(net, 1, 'stage3_block%d' % (i + 2))
net = shuffle_block(net, 2, 'stage4_block1')
for i in range(3):
net = shuffle_block(net, 1, 'stage4_block%d' % (i + 2))
net = conv2d_same(net, 1024, 1, stride=1, scope='conv5')
# net = slim.avg_pool2d(net, 7, stride=1, padding='VALID', scope='global_pool')
net = slim.flatten(net)
net = slim.fully_connected(
net,
128,
# weights_initializer=tf.truncated_normal_initializer(stddev=0.001),
normalizer_fn=slim.batch_norm,
# normalizer_params={'param_initializers': {'gamma': tf.constant_initializer(0.1)}},
activation_fn=None,
scope='fc1')
if isinstance(n_class, int):
net = slim.flatten(net)
net = slim.fully_connected(net,
n_class,
activation_fn=None,
scope='logits')
return net
def build_net(images,
n_class=None,
is_training=True,
reuse=False,
scope='resnet_50_half'):
with tf.variable_scope(scope, reuse=reuse):
with slim.arg_scope([slim.batch_norm], is_training=is_training):
net_spec = [[[i, i, i * 4] for i in [32, 64, 128, 256]],
[3, 4, 6, 3]]
with slim.arg_scope([slim.conv2d],
normalizer_fn=None,
activation_fn=None):
net = conv2d_same(images, 32, 5, stride=2, scope='conv1')
for i, spec in enumerate(zip(*net_spec)):
stride = 2 if i != 0 else 1
block_spec, n_block = spec
for j in range(n_block):
net = pre_bottleneck_block(net,
block_spec,
stride=stride,
scope='res_%d_%d' %
(i + 1, j + 1))
stride = 1
net = slim.batch_norm(net,
activation_fn=tf.nn.relu,
scope='postnorm')
net = slim.conv2d(net, 256, 1, stride=1, scope='last_conv')
net = slim.flatten(net)
net = slim.fully_connected(
net,
128,
# weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
activation_fn=None,
scope='fc5')
if isinstance(n_class, int):
net = slim.flatten(net)
net = slim.fully_connected(net,
n_class,
activation_fn=None,
scope='logits')
# add summarys
slim.summarize_collection(tf.GraphKeys.MODEL_VARIABLES)
return net
def build_net(images,
n_class=None,
is_training=True,
reuse=False,
scope='resnet_152_half'):
with tf.variable_scope(scope, reuse=reuse):
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
net_spec = [[[i, i] for i in [32, 64, 128, 256]], [2, 2, 2, 2]]
net = conv2d_same(images, 32, 3, stride=2, scope='conv1')
for i, spec in enumerate(zip(*net_spec)):
stride = 2 if i != 0 else 1
block_spec, n_block = spec
for j in range(n_block):
net = pre_basic_block(net,
block_spec,
stride=stride,
scope='res_%d_%d' % (i + 1, j + 1))
stride = 1
net = slim.batch_norm(net,
activation_fn=tf.nn.relu,
scope='postnorm')
net = slim.flatten(net)
# net = slim.dropout(net, 0.4)
net = slim.fully_connected(
net,
128,
# weights_initializer=tf.truncated_normal_initializer(stddev=0.001),
normalizer_fn=slim.batch_norm,
activation_fn=None,
scope='fc5')
if isinstance(n_class, int):
net = slim.flatten(net)
net = slim.fully_connected(net,
n_class,
activation_fn=None,
scope='logits')
return net
def build_net(images,
n_class=None,
is_training=True,
reuse=False,
alpha=1.25,
scope='mobile_id'):
with tf.variable_scope(scope, reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm], is_training=is_training):
net = conv2d_same(images,
int(32 * alpha),
3,
stride=2,
scope='conv1')
net = mobile_block(net,
int(16 * alpha),
3,
stride=1,
t=1,
scope='mblock1_1')
net = mobile_block(net,
int(16 * alpha),
3,
stride=1,
scope='mblock1_2')
net = mobile_block(net,
int(24 * alpha),
3,
stride=2,
scope='mblock2_1')
net = mobile_block(net,
int(24 * alpha),
3,
stride=1,
scope='mblock2_2')
net = mobile_block(net,
int(24 * alpha),
3,
stride=1,
scope='mblock2_3')
net = mobile_block(net,
int(32 * alpha),
3,
stride=2,
scope='mblock3_1')
net = mobile_block(net,
int(32 * alpha),
3,
stride=1,
scope='mblock3_2')
net = mobile_block(net,
int(32 * alpha),
3,
stride=1,
scope='mblock3_3')
net = mobile_block(net,
int(32 * alpha),
3,
stride=1,
scope='mblock3_4')
net = mobile_block(net,
int(64 * alpha),
3,
stride=2,
scope='mblock4_1')
for i in range(3):
net = mobile_block(net,
int(64 * alpha),
3,
stride=1,
scope='mblock4_%d' % (i + 2))
for i in range(4):
net = mobile_block(net,
int(96 * alpha),
3,
stride=1,
scope='mblock5_%d' % (i + 1))
net = mobile_block(net,
int(160 * alpha),
3,
stride=2,
scope='mblock6_1')
for i in range(3):
net = mobile_block(net,
int(160 * alpha),
3,
stride=1,
scope='mblock6_%d' % (i + 2))
net = mobile_block(net,
int(320 * alpha),
3,
stride=1,
scope='mblock7_1')
# net = mobile_block(net, int(320*alpha), 3, stride=1, scope='mblock7_2')
net = slim.conv2d(net, 1280, 1, stride=1, scope='final_point_conv')
net = slim.flatten(net)
net = slim.fully_connected(
net,
128,
weights_initializer=tf.truncated_normal_initializer(
stddev=0.001),
normalizer_fn=slim.batch_norm,
# normalizer_params={'param_initializers': {'gamma': tf.constant_initializer(0.0001)}},
activation_fn=None,
scope='fc1')
if isinstance(n_class, int):
net = slim.flatten(net)
net = slim.fully_connected(net,
n_class,
activation_fn=None,
scope='logits')
# add summarys
slim.summarize_collection(tf.GraphKeys.MODEL_VARIABLES)
return net
def build_net(images,
n_class=None,
is_training=True,
reuse=False,
alpha=1,
scope='mobile_id'):
with tf.variable_scope(scope, reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm], is_training=is_training):
net = conv2d_same(images,
int(64 * alpha),
3,
stride=2,
scope='conv1')
net = slim.separable_conv2d(net,
None,
3,
1,
stride=1,
padding='SAME',
scope='depth_conv1')
net = mobile_block(net,
int(64 * alpha),
3,
stride=2,
t=2,
scope='mblock1_1')
for i in range(4):
net = mobile_block(net,
int(64 * alpha),
3,
stride=1,
t=2,
scope='mblock1_%d' % (i + 2))
net = mobile_block(net,
int(128 * alpha),
3,
stride=2,
t=4,
scope='mblock2_1')
for i in range(6):
net = mobile_block(net,
int(128 * alpha),
3,
stride=1,
t=2,
scope='mblock2_%d' % (i + 2))
net = mobile_block(net,
int(128 * alpha),
3,
stride=2,
t=4,
scope='mblock3_1')
for i in range(2):
net = mobile_block(net,
int(128 * alpha),
3,
stride=1,
t=2,
scope='mblock3_%d' % (i + 2))
net = slim.conv2d(net, 512, 1, stride=1, scope='final_point_conv')
net = slim.separable_conv2d(net,
None, [7, 6],
1,
stride=1,
padding='VALID',
scope='GDConv')
print(net.shape)
net = slim.flatten(net)
net = slim.fully_connected(
net,
128,
# weights_initializer=tf.truncated_normal_initializer(stddev=0.001),
normalizer_fn=slim.batch_norm,
weights_regularizer=slim.l2_regularizer(4e-4),
# normalizer_params={'param_initializers': {'gamma': tf.constant_initializer(0.1)}},
activation_fn=None,
scope='fc1')
if isinstance(n_class, int):
net = slim.flatten(net)
net = slim.fully_connected(net,
n_class,
activation_fn=None,
scope='logits')
# add summarys
slim.summarize_collection(tf.GraphKeys.MODEL_VARIABLES)
return net
