def arch_vgg16(self,
X,
num_classes,
dropout_keep_prob=0.8,
is_train=False,
embedding_size=128):
arg_scope = vgg_arg_scope()
with slim.arg_scope(arg_scope):
net_vis, end_points, _ = vgg_16_conv(X, is_training=is_train)
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1,
padding='SAME'):
with tf.variable_scope('Logits_out'):
net_vis = slim.avg_pool2d(net_vis,
net_vis.get_shape()[1:3],
padding='VALID',
scope='AvgPool_1a_out')
# 1 x 1 x 512
net_vis = slim.dropout(net_vis,
dropout_keep_prob,
scope='Dropout_1b_out')
net_vis = slim.flatten(net_vis, scope='PreLogitsFlatten_out')
net_vis = slim.fully_connected(net_vis,
embedding_size,
activation_fn=tf.nn.relu,
scope='Logits_out0')
net = slim.fully_connected(net_vis,
num_classes,
activation_fn=None,
scope='Logits_out1')
return net, net_vis
def arch_multi_vgg16_conv(self,
X1,
X2,
X3,
num_classes,
dropout_keep_prob=0.8,
is_train=False):
arg_scope = vgg_arg_scope()
with slim.arg_scope(arg_scope):
with tf.variable_scope('arch_multi_vgg16_conv_1'):
net_vis1, end_points1 = vgg_16_conv(X1, is_training=is_train)
with tf.variable_scope('arch_multi_vgg16_conv_2'):
net_vis2, end_points2 = vgg_16_conv(X2, is_training=is_train)
with tf.variable_scope('arch_multi_vgg16_conv_3'):
net_vis3, end_points3 = vgg_16_conv(X3, is_training=is_train)
# net_vis3, end_points3 = alexnet_v2(X3, is_training=is_train)
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1,
padding='SAME'):
with tf.variable_scope('Logits_out'):
net_vis1 = slim.avg_pool2d(net_vis1,
net_vis1.get_shape()[1:3],
padding='VALID',
scope='AvgPool_1a_out')
net_vis2 = slim.avg_pool2d(net_vis2,
net_vis2.get_shape()[1:3],
padding='VALID',
scope='AvgPool_2a_out')
net_vis3 = slim.avg_pool2d(net_vis3,
net_vis3.get_shape()[1:3],
padding='VALID',
scope='AvgPool_3a_out')
net_vis = tf.concat([net_vis1, net_vis2, net_vis3], 3)
# 加入一个全连接
# net = slim.flatten(net_vis, scope='PreLogitsFlatten_out')
# net = slim.fully_connected(net, 256, activation_fn=tf.nn.relu, scope='Logits_out0')
# net = slim.fully_connected(net, num_classes, activation_fn=None,scope='Logits_out1')
net = slim.conv2d(net_vis,
num_classes, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='fc8')
net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
return net, net_vis
def arch_vgg16_multi_conv(self,
X,
num_classes,
dropout_keep_prob=0.8,
is_train=False,
embedding_size=64):
arg_scope = vgg_arg_scope()
with slim.arg_scope(arg_scope):
_, end_points, net_c = vgg_16_conv(X, is_training=is_train)
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1,
padding='SAME'):
with tf.variable_scope('Logits_out'):
#net_1 = slim.max_pool2d(net_c[-5], [32,32], stride=32, padding='VALID', scope='net_c_1')
#net_1 = slim.conv2d(net_1, net_1.get_shape()[3], [1, 1], scope='net_1')
#net_2 = slim.max_pool2d(net_c[-4], [16,16], stride=16, padding='VALID', scope='net_c_1')
#net_2 = slim.conv2d(net_2, net_2.get_shape()[3], [1, 1], scope='net_2')
#net_3 = slim.max_pool2d(net_c[-3], [8,8], stride=8, padding='VALID', scope='net_c_1')
#net_3 = slim.conv2d(net_3, net_3.get_shape()[3], [1, 1], scope='net_3')
net_4 = slim.max_pool2d(net_c[-2], [4, 4],
stride=4,
padding='VALID',
scope='net_c_1')
net_4 = slim.conv2d(net_4,
net_4.get_shape()[3], [1, 1],
scope='net_4')
net_5 = slim.max_pool2d(net_c[-1], [2, 2],
stride=2,
padding='VALID',
scope='net_c_1')
net_5 = slim.conv2d(net_5,
net_5.get_shape()[3], [1, 1],
scope='net_5')
# net_vis = tf.concat([net_1, net_2, net_3, net_4, net_5],3)
net_vis = tf.concat([net_4, net_5], 3)
net_vis = slim.avg_pool2d(net_vis,
net_vis.get_shape()[1:3],
padding='VALID',
scope='AvgPool_1a_out')
# 1 x 1 x 512
net_vis = slim.dropout(net_vis,
dropout_keep_prob,
scope='Dropout_1b_out')
net_vis = slim.flatten(net_vis, scope='PreLogitsFlatten_out')
net_vis = slim.fully_connected(net_vis,
embedding_size,
activation_fn=tf.nn.relu,
scope='Logits_out0')
net = slim.fully_connected(net_vis,
num_classes,
activation_fn=None,
scope='Logits_out1')
return net, net_vis