def transition(x, K, wd, is_training):
with tf.variable_scope('conv'):
current = common.batchNormalization(x, is_training=is_training)
current = tf.nn.relu(current)
shape = current.get_shape().as_list()
dim = math.floor(shape[3] * 0.5)
current = common.spatialConvolution(current, 1, 1, dim, wd=wd)
current = common.avgPool(current, 2, 2)
return current
def getModel(x, num_output, K, stages, wd, is_training, transfer_mode=False):
with tf.variable_scope('conv1'):
x = common.spatialConvolution(x, 7, 2, 2 * K, wd=wd)
x = common.batchNormalization(x, is_training=is_training)
x = tf.nn.relu(x)
x = common.maxPool(x, 3, 2)
with tf.variable_scope('block1'):
x = block(x, stages[0], K, is_training=is_training, wd=wd)
with tf.variable_scope('trans1'):
x = transition(x, K, wd=wd, is_training=is_training)
with tf.variable_scope('block2'):
x = block(x, stages[1], K, is_training=is_training, wd=wd)
with tf.variable_scope('trans2'):
x = transition(x, K, wd=wd, is_training=is_training)
with tf.variable_scope('block3'):
x = block(x, stages[2], K, is_training=is_training, wd=wd)
with tf.variable_scope('trans3'):
x = transition(x, K, wd=wd, is_training=is_training)
with tf.variable_scope('block4'):
x = block(x, stages[3], K, is_training=is_training, wd=wd)
x = common.avgPool(x, 7, 1, padding='VALID')
x = common.flatten(x)
if not transfer_mode:
with tf.variable_scope('output'):
x = common.fullyConnected(x, num_output, wd=wd)
else:
with tf.variable_scope('transfer_output'):
x = common.fullyConnected(x, num_output, wd=wd)
return x
def inference(x, num_output, wd, is_training, transfer_mode= False):
with tf.variable_scope('block1'):
network = block(x, [11, 4, 96], wd, is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('block2'):
network = block(network, [5, 1, 256], wd, is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('block3'):
network = block(network, [3, 1, 384], wd, is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('block4'):
network = block(network, [3, 1, 1024], wd, is_training)
network = common.avgPool(network, 7, 1)
network = common.flatten(network)
if not transfer_mode:
with tf.variable_scope('output'):
network = common.fullyConnected(network, num_output, wd= wd)
else:
with tf.variable_scope('transfer_output'):
network = common.fullyConnected(network, num_output, wd= wd)
return network
def inference(x,
num_output,
wd,
dropout_rate,
is_training,
transfer_mode=False):
with tf.variable_scope('features'):
with tf.variable_scope('conv1'):
network = common.spatialConvolution(x, 7, 2, 64, wd=wd)
network = common.batchNormalization(network,
is_training=is_training)
network = tf.nn.relu(network)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('conv2'):
network = common.spatialConvolution(network, 1, 1, 64, wd=wd)
network = common.batchNormalization(network,
is_training=is_training)
network = tf.nn.relu(network)
with tf.variable_scope('conv3'):
network = common.spatialConvolution(network, 3, 1, 192, wd=wd)
network = common.batchNormalization(network,
is_training=is_training)
network = tf.nn.relu(network)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('inception3a'):
network = inception(network,
64, [96, 128], [16, 32],
32,
wd=wd,
is_training=is_training)
with tf.variable_scope('inception3b'):
network = inception(network,
128, [128, 192], [32, 96],
64,
wd=wd,
is_training=is_training)
network = common.maxPool(network, 3, 2)
with tf.variable_scope('inception4a'):
network = inception(network,
192, [96, 208], [16, 48],
64,
wd=wd,
is_training=is_training)
with tf.variable_scope('inception4b'):
network = inception(network,
160, [112, 224], [24, 64],
64,
wd=wd,
is_training=is_training)
with tf.variable_scope('inception4c'):
network = inception(network,
128, [128, 256], [24, 64],
64,
wd=wd,
is_training=is_training)
with tf.variable_scope('inception4d'):
network = inception(network,
112, [144, 288], [32, 64],
64,
wd=wd,
is_training=is_training)
with tf.variable_scope('mainb'):
with tf.variable_scope('inception4e'):
main_branch = inception(network,
256, [160, 320], [32, 128],
128,
wd=wd,
is_training=is_training)
main_branch = common.maxPool(main_branch, 3, 2)
with tf.variable_scope('inception5a'):
main_branch = inception(main_branch,
256, [160, 320], [32, 128],
128,
wd=wd,
is_training=is_training)
with tf.variable_scope('inception5b'):
main_branch = inception(main_branch,
384, [192, 384], [48, 128],
128,
wd=wd,
is_training=is_training)
main_branch = common.avgPool(main_branch, 7, 1)
main_branch = common.flatten(main_branch)
main_branch = tf.nn.dropout(main_branch, dropout_rate)
if not transfer_mode:
with tf.variable_scope('output'):
main_branch = common.fullyConnected(main_branch,
num_output,
wd=wd)
else:
with tf.variable_scope('transfer_output'):
main_branch = common.fullyConnected(main_branch,
num_output,
wd=wd)
with tf.variable_scope('auxb'):
aux_classifier = common.avgPool(network, 5, 3)
with tf.variable_scope('conv1'):
aux_classifier = common.spatialConvolution(aux_classifier,
1,
1,
128,
wd=wd)
aux_classifier = common.batchNormalization(aux_classifier,
is_training=is_training)
aux_classifier = tf.nn.relu(aux_classifier)
aux_classifier = common.flatten(aux_classifier)
with tf.variable_scope('fc1'):
aux_classifier = common.fullyConnected(aux_classifier, 1024, wd=wd)
aux_classifier = tf.nn.dropout(aux_classifier, dropout_rate)
if not transfer_mode:
with tf.variable_scope('output'):
aux_classifier = common.fullyConnected(aux_classifier,
num_output,
wd=wd)
else:
with tf.variable_scope('transfer_output'):
aux_classifier = common.fullyConnected(aux_classifier,
num_output,
wd=wd)
return tf.concat([main_branch, aux_classifier], 1)