def create_structure(tf, x, input_data, input_size, dropout, config):
branches = []
network_manager = Network(config, dropout)
"""conv1"""
x = network_manager.conv_block(x, 5, 2, 32, padding_in='VALID')
print(x)
"""conv2"""
x = network_manager.conv_block(x, 5, 2, 64, padding_in='VALID')
print(x)
print(x)
"""conv3"""
x = network_manager.conv_block(x, 3, 2, 64, padding_in='VALID')
print(x)
"""conv4"""
x = network_manager.conv_block(x, 3, 2, 128, padding_in='VALID')
print(x)
"""mp3 (default values)"""
""" reshape """
x = tf.reshape(x, [-1, int(np.prod(x.get_shape()[1:]))], name='reshape')
print(x)
""" fc1 """
x = network_manager.fc_block(x, 1024)
print(x)
""" fc2 """
x = network_manager.fc_block(x, 512)
"""Process Control"""
# control = tf.reshape(control, [-1, int(np.prod(control.get_shape()[1:]))],name = 'reshape_control')
# print control
""" fc3 """
with tf.name_scope("Speed"):
speed = input_data[config.inputs_names.index(
"Speed")] # get the speed from input data
speed = network_manager.fc(speed, 128)
speed = network_manager.activation(speed)
speed = network_manager.fc(speed, 128)
speed = network_manager.activation(speed)
"""Start BRANCHING"""
for i in range(0, len(config.branch_config)):
with tf.name_scope("Branch_" + str(i) + "_" +
config.branch_config[i][0]):
if config.branch_config[i][0] == "Gas":
branch_output = network_manager.fc_block(
tf.concat([x, speed], 1), 512)
else:
branch_output = network_manager.fc_block(x, 512)
branches.append(
network_manager.fc(branch_output,
len(config.branch_config[i])))
print(branch_output)
""" fc3 """
weights = network_manager.get_weigths_dict()
features = network_manager.get_feat_tensors_dict()
return branches, None, features, weights
def create_structure(tf, input_image, input_data, input_size, dropout, config):
branches = []
x = input_image
network_manager = Network(config, dropout, tf.shape(x))
"""conv1"""
xc = network_manager.conv_block(x, 5, 2, 32, padding_in='VALID')
print(xc)
res1 = network_manager.fc(
tf.reshape(xc, [-1, int(np.prod(xc.get_shape()[1:]))],
name='reshape_res1'), 512)
"""conv2"""
xc = network_manager.conv_block(xc, 3, 2, 64, padding_in='VALID')
print(xc)
res2 = network_manager.fc(
tf.reshape(xc, [-1, int(np.prod(xc.get_shape()[1:]))],
name='reshape_res2'), 512)
"""conv3"""
xc = network_manager.conv_block(xc, 3, 2, 128, padding_in='VALID')
print(xc)
res3 = network_manager.fc(
tf.reshape(xc, [-1, int(np.prod(xc.get_shape()[1:]))],
name='reshape_res3'), 512)
"""conv4"""
xc = network_manager.conv_block(xc, 3, 2, 256, padding_in='VALID')
print(xc)
res4 = network_manager.fc(
tf.reshape(xc, [-1, int(np.prod(xc.get_shape()[1:]))],
name='reshape_res4'), 512)
"""mp3 (default values)"""
""" reshape """
x = tf.reshape(xc, [-1, int(np.prod(xc.get_shape()[1:]))], name='reshape')
print(x)
""" fc1 """
x = network_manager.fc(x, 512)
print(x)
# Sum residuals
x = network_manager.activation(x + res1 + res2 + res3 + res4)
""" fc2 """
x = network_manager.fc_block(x, 512)
"""Process Control"""
# control = tf.reshape(control, [-1, int(np.prod(control.get_shape()[1:]))],name = 'reshape_control')
# print control
""" Speed (measurements)"""
with tf.name_scope("Speed"):
speed = input_data[config.inputs_names.index(
"Speed")] # get the speed from input data
speed = network_manager.fc_block(speed, 128)
speed = network_manager.fc_block(speed, 128)
""" Joint sensory """
j = tf.concat([x, speed], 1)
j = network_manager.fc_block(j, 512)
"""Start BRANCHING"""
for i in range(0, len(config.branch_config)):
with tf.name_scope("Branch_" + str(i)):
if config.branch_config[i][0] == "Speed":
# we only use the image as input to speed prediction
branch_output = network_manager.fc_block(x, 256)
branch_output = network_manager.fc_block(branch_output, 256)
else:
branch_output = network_manager.fc_block(j, 256)
branch_output = network_manager.fc_block(branch_output, 256)
branches.append(
network_manager.fc(branch_output,
len(config.branch_config[i])))
print(branch_output)
weights = network_manager.get_weigths_dict()
features = network_manager.get_feat_tensors_dict()
vis_images = network_manager.get_vbp_images(xc)
print(vis_images)
print(vis_images.get_shape())
# vis_images = tf.div(vis_images -tf.reduce_min(vis_images),tf.reduce_max(vis_images) -tf.reduce_min(vis_images))
return branches, vis_images, features, weights
