def FCN(x, is_training, dropout_rate = 0.25):
x -= VGG_MEAN
with tf.contrib.slim.arg_scope(vgg.vgg_arg_scope()):
feature_maps = vgg.vgg_16(x, num_classes=1000, is_training=is_training, dropout_keep_prob=0.5)
x = feature_maps[4] # pool5 = 7x7x512
x = tf.layers.conv2d(inputs = x, filters = 4096, kernel_size = [1, 1], strides = 1, padding = 'SAME', kernel_initializer = init_fn, name = 'conv_6')
x = tf.layers.batch_normalization(inputs = x, training = is_training, name = 'bn_6')
x = tf.nn.relu(x, name = 'relu_6')
x = tf.layers.dropout(x, dropout_rate, training = is_training, name = 'dropout_6')
x = tf.layers.conv2d(inputs = x, filters = 4096, kernel_size = [1, 1], strides = 1, padding = 'SAME', kernel_initializer = init_fn, name = 'conv_7')
x = tf.layers.batch_normalization(inputs = x, training = is_training, name = 'bn_7')
x = tf.nn.relu(x, name = 'relu_7')
x = tf.layers.dropout(x, dropout_rate, training = is_training, name = 'dropout_7')
# print(x)
shape = feature_maps[3].get_shape().as_list()
x = tf.layers.conv2d_transpose(inputs = x, filters = shape[-1], kernel_size = [3, 3], strides = 2, padding = 'SAME', kernel_initializer = init_fn, name = 'up_conv2d_1')
x = tf.layers.batch_normalization(inputs = x, training = is_training, name = 'up_bn_1')
x = tf.nn.relu(x, name = 'up_relu_1')
x += feature_maps[3]
# print(x)
shape = feature_maps[2].get_shape().as_list()
x = tf.layers.conv2d_transpose(inputs = x, filters = shape[-1], kernel_size = [3, 3], strides = 2, padding = 'SAME', kernel_initializer = init_fn, name = 'up_conv2d_2')
x = tf.layers.batch_normalization(inputs = x, training = is_training, name = 'up_bn_2')
x = tf.nn.relu(x, name = 'up_relu_2')
x += feature_maps[2]
# print(x)
shape = feature_maps[1].get_shape().as_list()
x = tf.layers.conv2d_transpose(inputs = x, filters = shape[-1], kernel_size = [16, 16], strides = 8, padding = 'SAME', kernel_initializer = init_fn, name = 'up_conv2d_3')
x = tf.layers.batch_normalization(inputs = x, training = is_training, name = 'up_bn_3')
x = tf.nn.relu(x, name = 'up_relu_3')
# print(x)
x = tf.layers.conv2d(inputs = x, filters = CLASSES, kernel_size = [1, 1], strides = 1, padding = 'SAME', kernel_initializer = init_fn, name = 'conv2d')
x = tf.layers.batch_normalization(inputs = x, training = is_training, name = 'bn')
# predictions = tf.nn.softmax(x, axis = -1, name = 'predictions')
predictions = x
return predictions
def FCN_UNet(input_var, is_training):
x = input_var - VGG_MEAN
i_x = tf.layers.conv2d(inputs = x, filters = 64, kernel_size = [3, 3], strides = 1, padding = 'SAME', kernel_initializer = init_fn, name = 'i_conv')
i_x = tf.layers.batch_normalization(inputs = i_x, training = is_training, name = 'i_bn')
i_x = tf.nn.relu(i_x, name = 'i_relu')
with tf.contrib.slim.arg_scope(vgg.vgg_arg_scope()):
feature_maps = vgg.vgg_16(x, num_classes=1000, is_training=is_training, dropout_keep_prob=0.5)
# resolution 224x224 -> feature_maps[4], feature_maps[3 - i]
# resolution 112x112 -> feature_maps[3], feature_maps[2 - i]
x = feature_maps[3]
for i in range(len(feature_maps) - 2):
prior_feature_map = feature_maps[2 - i]
shape = prior_feature_map.get_shape().as_list()
x = tf.layers.conv2d_transpose(inputs = x, filters = shape[-1], kernel_size = [3, 3], strides = 2, padding = 'SAME', kernel_initializer = init_fn, name = 'up_conv2d_{}'.format(i))
x = tf.layers.batch_normalization(inputs = x, training = is_training, name = 'up_bn_{}'.format(i))
x = tf.nn.relu(x, name = 'up_relu_{}'.format(i))
#print(x, prior_feature_map); input()
x = tf.concat((x, prior_feature_map), axis = -1)
x = tf.layers.conv2d(inputs = x, filters = shape[-1], kernel_size = [3, 3], strides = 1, padding = 'SAME', kernel_initializer = init_fn, name = 'conv_{}'.format(i))
x = tf.layers.batch_normalization(inputs = x, training = is_training, name = 'bn_{}'.format(i))
x = tf.nn.relu(x, name = 'relu_{}'.format(i))
x = tf.layers.conv2d_transpose(inputs = x, filters = shape[-1], kernel_size = [3, 3], strides = 2, padding = 'SAME', kernel_initializer = init_fn, name = 'up_conv2d')
x = tf.layers.batch_normalization(inputs = x, training = is_training, name = 'up_bn')
x = tf.nn.relu(x, name = 'up_relu')
x = tf.concat((i_x, x), axis = -1)
x = tf.layers.conv2d(inputs = x, filters = CLASSES, kernel_size = [1, 1], strides = 1, padding = 'SAME', kernel_initializer = init_fn, name = 'conv2d')
x = tf.layers.batch_normalization(inputs = x, training = is_training, name = 'bn')
logits = x
predictions = tf.nn.softmax(x, axis = -1, name = 'predictions')
return logits, predictions