def generator_AB(self, inputs, is_training, reuse=False):
with tf.variable_scope('Generator_AB', reuse=reuse):
with tf.variable_scope('g_ab_hidden1'):
layer1_1 = lrelu(
bn(
conv2d(inputs,
64, [3, 3],
initializer='random',
name='conv_1'), is_training))
with tf.variable_scope('g_ab_hidden2'):
layer2_1 = max_pool(layer1_1, name='pool1')
layer2_2 = lrelu(
bn(
conv2d(layer2_1,
128, [3, 3],
initializer='random',
name='conv_2'), is_training))
with tf.variable_scope('g_ab_hidden3'):
layer3_1 = max_pool(layer2_2, name='pool2')
layer3_2 = lrelu(
bn(
conv2d(layer3_1,
256, [3, 3],
initializer='random',
name='conv_3'), is_training))
with tf.variable_scope('g_ab_hidden4'):
layer4_1 = conv2d_t(layer3_2, [None, 14, 14, 128], [2, 2],
initializer='random',
name='convT_4')
layer4_2 = tf.concat([layer4_1, layer2_2], axis=3)
layer4_3 = lrelu(
bn(
conv2d(layer4_2,
128, [3, 3],
initializer='random',
name='conv_4'), is_training))
with tf.variable_scope('g_ab_hidden5'):
layer5_1 = conv2d_t(layer4_3, [None, 28, 28, 64], [2, 2],
initializer='random',
name='convT5')
layer5_2 = tf.concat([layer1_1, layer5_1], axis=3)
layer5_3 = conv2d(layer5_2,
1, [3, 3],
initializer='random',
name='conv5')
layer5_4 = conv2d(layer5_3,
1, [1, 1],
initializer='random',
name='conv6')
gen_ab = tf.nn.sigmoid(layer5_4)
return gen_ab, layer5_4
def generator(self, inputs, is_training, reuse=False):
with tf.variable_scope('generator', reuse=reuse):
g_hidden1 = fully_connect(inputs, 1024, name='g_hidden1')
g_hidden2 = lrelu(bn(fully_connect(g_hidden1, 7*7*128), is_training))
g_reshape = tf.reshape(g_hidden2, [-1, 7, 7, 128])
g_hidden3 = lrelu(bn(conv2d_t(g_reshape, [None, 14, 14, 64], [4, 4], name='g_hidden3'), is_training))
g_hidden4 = conv2d_t(g_hidden3, [None, 28, 28, 1], [4, 4], name='g_hidden4')
g_logtis = sigmoid(g_hidden4)
return g_hidden4, g_logtis
def discriminator(self, inputs, is_training, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
d_hidden1 = conv2d(inputs, 64, [4, 4], strides=[1, 2, 2, 1], name='d_hidden1')
d_hidden2 = lrelu(bn(conv2d(d_hidden1, 128, [4, 4], strides=[1, 2, 2, 1], name='d_hidden2'), is_training))
d_flatten = tf.reshape(d_hidden2, [-1, 7*7*128])
d_hidden3 = lrelu(bn(fully_connect(d_flatten, 1024, name='d_hidden3'), is_training))
d_hidden4 = fully_connect(d_hidden3, 1, name='d_hidden4')
d_logits = sigmoid(d_hidden4)
return d_hidden4, d_logits
def discriminator_B(self, inputs, is_training, reuse=False):
with tf.variable_scope('discirminator_B', reuse=reuse):
layer1 = lrelu(
bn(
conv2d(inputs,
64, [4, 4],
strides=[1, 2, 2, 1],
initializer='random',
name='d_B_hiddne1'), is_training))
layer2 = lrelu(
bn(
conv2d(layer1,
128, [4, 4],
strides=[1, 2, 2, 1],
initializer='random',
name='d_B_hidden2'), is_training))
flatten = tf.reshape(layer2, [-1, 7 * 7 * 128])
layer3 = lrelu(
bn(fully_connect(flatten, 1024, name='d_B_hidden3'),
is_training))
layer4 = fully_connect(layer3, 1, name='d_B_hidden')
logits = sigmoid(layer4)
return logits, layer4
def make_model(self, inputs, is_training):
with tf.variable_scope('Darknet19'):
net = lrelu(bn(conv2d(inputs, 32, [3, 3], name='conv1'), is_training))
net = max_pool(net, name='pool1')
net = lrelu(bn(conv2d(net, 64, [3, 3], name='conv2'), is_training))
net = max_pool(net, name='pool2')
net = lrelu(bn(conv2d(net, 128, [3, 3], name='conv3'), is_training))
net = lrelu(bn(conv2d(net, 64, [1, 1], name='conv4'), is_training))
net = lrelu(bn(conv2d(net, 128, [3, 3], name='conv5'), is_training))
net = max_pool(net, name='pool3')
net = lrelu(bn(conv2d(net, 256, [3, 3], name='conv6'), is_training))
net = lrelu(bn(conv2d(net, 128, [1, 1], name='conv7'), is_training))
net = lrelu(bn(conv2d(net, 256, [3, 3], name='conv8'), is_training))
net = max_pool(net, name='pool4')
net = lrelu(bn(conv2d(net, 512, [3, 3], name='conv9'), is_training))
net = lrelu(bn(conv2d(net, 256, [1, 1], name='conv10'), is_training))
net = lrelu(bn(conv2d(net, 512, [3, 3], name='conv11'), is_training))
net = lrelu(bn(conv2d(net, 256, [3, 3], name='conv12'), is_training))
skip = lrelu(bn(conv2d(net, 512, [3, 3], name='conv13'), is_training))
net = max_pool(net, name='pool5')
net = lrelu(bn(conv2d(net, 1024, [3, 3], name='conv14'), is_training))
net = lrelu(bn(conv2d(net, 512, [1, 1], name='conv15'), is_training))
net = lrelu(bn(conv2d(net, 1024, [3, 3], name='conv16'), is_training))
net = lrelu(bn(conv2d(net, 512, [1, 1], name='conv17'), is_training))
net = lrelu(bn(conv2d(net, 1024, [3, 3], name='conv18'), is_training))
with tf.variable_scope('Detection'):
net = lrelu(bn(conv2d(net, 1024, [3, 3], name='conv19'), is_training))
net = lrelu(bn(conv2d(net, 1024, [3, 3], name='conv20'), is_training))
passthrough = lrelu(bn(conv2d(skip, 64, [1, 1], name='conv21_passthrough'), is_training))
passthrough = tf.space_to_depth(passthrough, block_size=2)
concated = tf.concat([passthrough, net], axis=3)
net = lrelu(bn(conv2d(concated, 1024, [3, 3], name='conv22'), is_training))
out_depth = self.N_ANCHORS * (5 + self.N_CLASSES)
net = conv2d(net, out_depth, [1, 1], name='conv23')
return net