def iv_net(x, training=True):
with tf.variable_scope('iv_net', reuse=tf.AUTO_REUSE):
x = ms.fc(x, units=40 * 40 * 32)
x = ms.bn(x, training=training)
x = tf.reshape(x, [-1, 40, 40, 32])
x = fn.id_blk(x, 32, [3, 3])
x = fn.id_blk(x, 32, [3, 3])
x = ms.max_pool(x, [2, 2], 2)
x = ms.conv(x, 64, [3, 3])
x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = fn.id_blk(x, 64, [3, 3])
x = fn.id_blk(x, 64, [3, 3])
x = ms.max_pool(x, [2, 2], 2)
x = ms.conv(x, 128, [3, 3])
x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = fn.id_blk(x, 128, [3, 3])
x = fn.id_blk(x, 128, [3, 3])
x = ms.max_pool(x, [2, 2], 2)
x = ms.conv(x, 256, [3, 3])
x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = fn.id_blk(x, 256, [3, 3])
x = fn.id_blk(x, 256, [3, 3])
x = tf.reshape(x, [-1, 5 * 5 * 256])
x = ms.fc(x, units=5)
x = ms.activation(x, relu=False)
return x
x = sess.graph.get_tensor_by_name(x_tensor_name)
# y = sess.graph.get_tensor_by_name(y_tensor_name)
# connect = sess.graph.get_operation_by_name('fw_net/identical_conv3/relu_3').outputs[0]
connect = sess.graph.get_operation_by_name(
'fw_net/t_conv3/relu').outputs[0]
with tf.name_scope('trans_part'):
with tf.variable_scope('trans_part'):
# with tf.variable_scope('t_conv3'):
# t = ms.t_conv2d(connect, 32, [2, 2], 2)
# # x = ms.bn(x)
# t = ms.activation(t, relu=True)
with tf.variable_scope('identical_conv4'):
t = ms.conv(connect, 32, [3, 3])
# x = ms.bn(x, training=training)
t = ms.activation(t, relu=True)
t = ms.conv(t, 32, [3, 3])
# x = ms.bn(x, training=training)
t = ms.activation(t, relu=True)
t = ms.conv(t, 32, [3, 3])
# x = ms.bn(x, training=training)
t = ms.activation(t, relu=True)
t = ms.conv(t, 32, [3, 3])
# x = ms.bn(x, training=training)
t = ms.activation(t, relu=True)
def fw_net(x, training=True):
with tf.name_scope('fw_net'):
with tf.variable_scope('fw_net'):
with tf.name_scope('fc1'):
with tf.variable_scope('fc1'):
x = ms.fc(x, 5 * 5 * 256)
# x = ms.bn(x)
x = ms.activation(x, relu=True)
x = tf.reshape(x, [-1, 5, 5, 256])
with tf.name_scope('identical_conv1'):
with tf.variable_scope('identical_conv1'):
x = ms.conv(x, 256, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 256, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 256, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 256, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 256, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
with tf.name_scope('t_conv1'):
with tf.variable_scope('t_conv1'):
x = ms.t_conv2d(x, 128, [2, 2], 2)
# x = ms.bn(x)
x = ms.activation(x, relu=True)
with tf.name_scope('identical_conv2'):
with tf.variable_scope('identical_conv2'):
x = ms.conv(x, 128, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 128, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 128, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 128, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 128, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
with tf.name_scope('t_conv2'):
with tf.variable_scope('t_conv2'):
x = ms.t_conv2d(x, 64, [2, 2], 2)
# x = ms.bn(x)
x = ms.activation(x, relu=True)
with tf.name_scope('identical_conv3'):
with tf.variable_scope('identical_conv3'):
x = ms.conv(x, 64, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 64, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 64, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 64, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 64, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
with tf.name_scope('t_conv3'):
with tf.variable_scope('t_conv3'):
x = ms.t_conv2d(x, 32, [2, 2], 2)
# x = ms.bn(x)
x = ms.activation(x, relu=True)
with tf.name_scope('identical_conv4'):
with tf.variable_scope('identical_conv4'):
x = ms.conv(x, 32, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 32, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 32, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 32, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = ms.conv(x, 32, [3, 3])
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=True)
x = tf.reshape(x, [-1, 40 * 40 * 32])
with tf.name_scope('fc2'):
with tf.variable_scope('fc2'):
x = ms.fc(x, units=603)
# x = ms.bn(x, training=training)
x = ms.activation(x, relu=False)
return x
x = sess.graph.get_tensor_by_name(x_tensor_name)
# y = sess.graph.get_tensor_by_name(y_tensor_name)
connect = sess.graph.get_operation_by_name('fw_net/fw_net/identical_conv3/identical_conv3/relu_4').outputs[0]
with tf.name_scope('trans_part'):
with tf.variable_scope('trans_part'):
with tf.name_scope('t_conv3'):
with tf.variable_scope('t_conv3'):
t = ms.t_conv2d(connect, 32, [2, 2], 2)
# x = ms.bn(x)
t = ms.activation(t, relu=True)
with tf.name_scope('identical_conv4'):
with tf.variable_scope('identical_conv4'):
t = ms.conv(t, 32, [3, 3])
# x = ms.bn(x, training=training)
t = ms.activation(t, relu=True)
t = ms.conv(t, 32, [3, 3])
# x = ms.bn(x, training=training)
t = ms.activation(t, relu=True)
t = ms.conv(t, 32, [3, 3])
# x = ms.bn(x, training=training)
t = ms.activation(t, relu=True)
t = ms.conv(t, 32, [3, 3])
# x = ms.bn(x, training=training)
t = ms.activation(t, relu=True)