def robot_khop_model(): # input/output = num of sensors
num_sensors = 9
input_shape = (84, 84 * 4, 3)
sensor_matrix1 = Input(shape=(num_sensors + 1, num_sensors + 1))
sensor_matrix2 = Input(shape=(num_sensors + 1, num_sensors + 1))
#sensor_matrix3 = Input(shape=(num_sensors, num_sensors))
r_input = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input1 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input2 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input3 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input4 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input5 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input6 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input7 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input8 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input9 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_cnn = sensor_cnn(input_shape, repetitions=[2, 2, 2, 2])
robot_cnn = s_cnn(r_input)
extract_cnn1 = s_cnn(s_input1)
extract_cnn2 = s_cnn(s_input2)
extract_cnn3 = s_cnn(s_input3)
extract_cnn4 = s_cnn(s_input4)
extract_cnn5 = s_cnn(s_input5)
extract_cnn6 = s_cnn(s_input6)
extract_cnn7 = s_cnn(s_input7)
extract_cnn8 = s_cnn(s_input8)
extract_cnn9 = s_cnn(s_input9)
extract_cnn = Concatenate(axis=1)([
extract_cnn1, extract_cnn2, extract_cnn3, extract_cnn4, extract_cnn5,
extract_cnn6, extract_cnn7, extract_cnn8, extract_cnn9, robot_cnn
])
#extract_cnn = np.reshape(extract_cnn, (-1,))
G_h1 = GraphConv(256, 'relu')([extract_cnn, sensor_matrix1])
G_h2 = GraphConv(256, 'relu')([extract_cnn, sensor_matrix2])
G_1 = Concatenate(axis=-1)([G_h1, G_h2])
G_2h1 = GraphConv(256, 'relu')([G_1, sensor_matrix1])
G_2h2 = GraphConv(256, 'relu')([G_1, sensor_matrix2])
G_2 = Concatenate(axis=-1)([G_2h1, G_2h2])
gnn_output = tf.split(G_2, num_sensors + 1, 1)
r_output = Dense(64, activation='relu',
name='policy_mlp')(Flatten()(gnn_output[-1]))
output1 = Dense(2, activation='linear', name='robot_loss')(r_output)
model = Model(inputs=[
s_input1, s_input2, s_input3, s_input4, s_input5, s_input6, s_input7,
s_input8, s_input9, r_input, sensor_matrix1, sensor_matrix2
],
outputs=[output1])
return model
def train_model(num_sensors=9,
num_hop=4,
input_shape=(84, 84 * 4, 3),
gnn_unit=128): # input/output = num of sensors
input_data, output_data = [], []
s_cnn = sensor_cnn(input_shape, repetitions=[2, 2, 2, 2])
for i in range(num_sensors):
exec(
's_input{} = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))'
.format(i))
exec('extract_cnn{} = s_cnn(s_input{})'.format(i, i))
exec('input_data.append(s_input{})'.format(i))
for j in range(num_hop):
exec(
'sensor_matrix{} = Input(shape=(num_sensors, num_sensors))'.format(
j))
exec('input_data.append(sensor_matrix{})'.format(j))
exec('extract_cnn = extract_cnn0')
for i in range(1, num_sensors):
exec('extract_cnn = Concatenate(axis=1)([extract_cnn, extract_cnn{}])'.
format(i))
for j in range(num_hop):
exec(
"G_h{} = GraphAttention(gnn_unit, activation='selu', dropout_rate=0)([extract_cnn, sensor_matrix{}])"
.format(j, j))
exec('G_1 = G_h0')
for j in range(1, num_hop):
exec('G_1 = Concatenate(axis=-1)([G_1, G_h{}])'.format(j))
for j in range(num_hop):
exec(
"G_2h{} = GraphAttention(gnn_unit, activation='selu', dropout_rate=0)([G_1, sensor_matrix{}])"
.format(j, j))
exec('G_2 = G_2h0')
for j in range(1, num_hop):
exec('G_2 = Concatenate(axis=-1)([G_2, G_2h{}])'.format(j))
exec('gnn_output = tf.split(G_2, num_sensors, 1)')
for i in range(num_sensors):
exec(
"mlp_input{}=Concatenate(axis = -1)([gnn_output[i], extract_cnn{}])"
.format(i, i))
mlp_layer = mlp_model()
for i in range(num_sensors):
exec('output{} = mlp_layer(Flatten()(mlp_input{}))'.format(i, i))
exec('output_data.append(output{})'.format(i))
model = Model(inputs=input_data, outputs=output_data)
return model
def load_model(num_sensors,
input_shape=(84, 84 * 4, 3),
gnn_layers=1,
gnn_unit=128,
is_robot=0):
input_data, output_data = [], []
#tf.compat.v1.enable_eager_execution()
s_cnn = sensor_cnn(input_shape, repetitions=[2, 2, 2, 2])
if is_robot:
sensor_matrix = Input(shape=(num_sensors + 1, num_sensors + 1))
else:
sensor_matrix = Input(shape=(num_sensors, num_sensors))
for i in range(num_sensors):
exec(
's_input{} = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))'
.format(i))
exec('extract_cnn{} = s_cnn(s_input{})'.format(i, i))
exec('input_data.append(s_input{})'.format(i))
input_data.append(sensor_matrix)
exec('extract_cnn = extract_cnn0')
for i in range(1, num_sensors):
exec('extract_cnn = Concatenate(axis=1)([extract_cnn, extract_cnn{}])'.
format(i))
for j in range(1, gnn_layers + 1):
if j == 1:
exec(
"G_h{} = GCNConv(gnn_unit, activation='relu')([extract_cnn, sensor_matrix])"
.format(j))
else:
exec(
"G_h{} = GCNConv(gnn_unit, activation='relu')([G_h{}, sensor_matrix])"
.format(j, j - 1))
exec('gnn_output = tf.split(G_h{}, num_sensors, 1)'.format(gnn_layers))
mlp_layer = mlp_model()
for i in range(num_sensors):
exec('output{} = mlp_layer(Flatten()(gnn_output[i]))'.format(i))
exec('output_data.append(output{})'.format(i))
model = Model(inputs=input_data, outputs=output_data)
return model
def khop_model_distribute(num_sensors=10): # input/output = num of sensors
gnn_unit = 128
sensor_matrix1 = Input(shape=(num_sensors, num_sensors))
sensor_matrix2 = Input(shape=(num_sensors, num_sensors))
sensor_matrix3 = Input(shape=(num_sensors, num_sensors))
sensor_matrix4 = Input(shape=(num_sensors, num_sensors))
#sensor_matrix3 = Input(shape=(num_sensors, num_sensors))
s_input1 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input2 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input3 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input4 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input5 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input6 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input7 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input8 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input9 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input10 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_cnn = sensor_cnn(input_shape, repetitions=[2, 2, 2, 2])
extract_cnn1 = s_cnn(s_input1)
extract_cnn2 = s_cnn(s_input2)
extract_cnn3 = s_cnn(s_input3)
extract_cnn4 = s_cnn(s_input4)
extract_cnn5 = s_cnn(s_input5)
extract_cnn6 = s_cnn(s_input6)
extract_cnn7 = s_cnn(s_input7)
extract_cnn8 = s_cnn(s_input8)
extract_cnn9 = s_cnn(s_input9)
extract_cnn10 = s_cnn(s_input10)
extract_cnn = Concatenate(axis=1)([
extract_cnn1, extract_cnn2, extract_cnn3, extract_cnn4, extract_cnn5,
extract_cnn6, extract_cnn7, extract_cnn8, extract_cnn9, extract_cnn10
])
#extract_cnn = np.reshape(extract_cnn, (-1,))
G_h1 = GraphConv(gnn_unit, 'selu')([extract_cnn, sensor_matrix1])
G_h2 = GraphConv(gnn_unit, 'selu')([extract_cnn, sensor_matrix2])
G_h3 = GraphConv(gnn_unit, 'selu')([extract_cnn, sensor_matrix3])
G_h4 = GraphConv(gnn_unit, 'selu')([extract_cnn, sensor_matrix4])
G_1 = Concatenate(axis=-1)([G_h1, G_h2, G_h3, G_h4])
G_2h1 = GraphConv(gnn_unit, 'selu')([G_1, sensor_matrix1])
G_2h2 = GraphConv(gnn_unit, 'selu')([G_1, sensor_matrix2])
G_2h3 = GraphConv(gnn_unit, 'selu')([G_1, sensor_matrix3])
G_2h4 = GraphConv(gnn_unit, 'selu')([G_1, sensor_matrix4])
G_2 = Concatenate(axis=-1)([G_2h1, G_2h2, G_2h3, G_2h4])
#G_3h1 = GraphConv(gnn_unit, 'selu')([G_2, sensor_matrix1])
#G_3h2 = GraphConv(gnn_unit, 'selu')([G_2, sensor_matrix2])
#G_3h3 = GraphConv(gnn_unit, 'selu')([G_2, sensor_matrix3])
#G_3h4 = GraphConv(gnn_unit, 'selu')([G_2, sensor_matrix4])
#G_3 = Concatenate(axis=-1)([G_3h1, G_3h2, G_3h3, G_3h4])
#G_4h1 = GraphConv(gnn_unit, 'selu')([G_3, sensor_matrix1])
#G_4h2 = GraphConv(gnn_unit, 'selu')([G_3, sensor_matrix2])
#G_4h3 = GraphConv(gnn_unit, 'selu')([G_3, sensor_matrix3])
#G_4h4 = GraphConv(gnn_unit, 'selu')([G_3, sensor_matrix4])
#G_4 = Concatenate(axis=-1)([G_4h1, G_4h2, G_4h3, G_4h4])
gnn_output = tf.split(G_2, num_sensors, 1)
mlp_layer = mlp_model()
output1 = mlp_layer(Flatten()(gnn_output[0]))
output2 = mlp_layer(Flatten()(gnn_output[1]))
output3 = mlp_layer(Flatten()(gnn_output[2]))
output4 = mlp_layer(Flatten()(gnn_output[3]))
output5 = mlp_layer(Flatten()(gnn_output[4]))
output6 = mlp_layer(Flatten()(gnn_output[5]))
output7 = mlp_layer(Flatten()(gnn_output[6]))
output8 = mlp_layer(Flatten()(gnn_output[7]))
output9 = mlp_layer(Flatten()(gnn_output[8]))
output10 = mlp_layer(Flatten()(gnn_output[9]))
model = Model(inputs=[
s_input1, s_input2, s_input3, s_input4, s_input5, s_input6, s_input7,
s_input8, s_input9, s_input10, sensor_matrix1, sensor_matrix2,
sensor_matrix3, sensor_matrix4
],
outputs=[
output1, output2, output3, output4, output5, output6,
output7, output8, output9, output10
])
return model
def khop_model_share(): # input/output = num of sensors
sensor_matrix1 = Input(shape=(num_sensors, num_sensors))
sensor_matrix2 = Input(shape=(num_sensors, num_sensors))
#sensor_matrix3 = Input(shape=(num_sensors, num_sensors))
s_input1 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input2 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input3 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input4 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input5 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input6 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input7 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input8 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input9 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_cnn = sensor_cnn(input_shape, repetitions = [2,2,2,2])
extract_cnn1 = s_cnn(s_input1)
extract_cnn2 = s_cnn(s_input2)
extract_cnn3 = s_cnn(s_input3)
extract_cnn4 = s_cnn(s_input4)
extract_cnn5 = s_cnn(s_input5)
extract_cnn6 = s_cnn(s_input6)
extract_cnn7 = s_cnn(s_input7)
extract_cnn8 = s_cnn(s_input8)
extract_cnn9 = s_cnn(s_input9)
extract_cnn = Concatenate(axis=1)([extract_cnn1, extract_cnn2, extract_cnn3,
extract_cnn4, extract_cnn5, extract_cnn6,
extract_cnn7, extract_cnn8, extract_cnn9])
#extract_cnn = np.reshape(extract_cnn, (-1,))
G_h1 = GraphConv(256, 'relu')([extract_cnn, sensor_matrix1])
G_h2 = GraphConv(256, 'relu')([extract_cnn, sensor_matrix2])
G_1 = Concatenate(axis=-1)([G_h1, G_h2])
G_2h1 = GraphConv(256, 'relu')([G_1, sensor_matrix1])
G_2h2 = GraphConv(256, 'relu')([G_1, sensor_matrix2])
G_2 = Concatenate(axis=-1)([G_2h1, G_2h2])
gnn_output = tf.split(G_2, num_sensors, 1)
output1 = Dense(32, activation='relu')(Flatten()(gnn_output[0]))
output1 = Dense(2, activation='linear', name='sensor_1')(output1)
output2 = Dense(32, activation='relu')(Flatten()(gnn_output[1]))
output2 = Dense(2, activation='linear', name='sensor_2')(output2)
output3 = Dense(32, activation='relu')(Flatten()(gnn_output[2]))
output3 = Dense(2, activation='linear', name='sensor_3')(output3)
output4 = Dense(32, activation='relu')(Flatten()(gnn_output[3]))
output4 = Dense(2, activation='linear', name='sensor_4')(output4)
output5 = Dense(32, activation='relu')(Flatten()(gnn_output[4]))
output5 = Dense(2, activation='linear', name='sensor_5')(output5)
output6 = Dense(32, activation='relu')(Flatten()(gnn_output[5]))
output6 = Dense(2, activation='linear', name='sensor_6')(output6)
output7 = Dense(32, activation='relu')(Flatten()(gnn_output[6]))
output7 = Dense(2, activation='linear', name='sensor_7')(output7)
output8 = Dense(32, activation='relu')(Flatten()(gnn_output[7]))
output8 = Dense(2, activation='linear', name='sensor_8')(output8)
output9 = Dense(32, activation='relu')(Flatten()(gnn_output[8]))
output9 = Dense(2, activation='linear', name='sensor_9')(output9)
model = Model(inputs=[s_input1, s_input2, s_input3, s_input4,
s_input5, s_input6, s_input7, s_input8, s_input9,
sensor_matrix1, sensor_matrix2],
outputs= [output1,output2,output3,output4,
output5,output6,output7,output8,output9])
return model
def att_model_distribute12(num_sensors=12): # input/output = num of sensors
gnn_unit = 128
sensor_matrix1 = Input(shape=(num_sensors, num_sensors))
sensor_matrix2 = Input(shape=(num_sensors, num_sensors))
sensor_matrix3 = Input(shape=(num_sensors, num_sensors))
sensor_matrix4 = Input(shape=(num_sensors, num_sensors))
#sensor_matrix3 = Input(shape=(num_sensors, num_sensors))
s_input1 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input2 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input3 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input4 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input5 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input6 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input7 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input8 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input9 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input10 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input11 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_input12 = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))
s_cnn = sensor_cnn(input_shape, repetitions=[2, 2, 2, 2])
extract_cnn1 = s_cnn(s_input1)
extract_cnn2 = s_cnn(s_input2)
extract_cnn3 = s_cnn(s_input3)
extract_cnn4 = s_cnn(s_input4)
extract_cnn5 = s_cnn(s_input5)
extract_cnn6 = s_cnn(s_input6)
extract_cnn7 = s_cnn(s_input7)
extract_cnn8 = s_cnn(s_input8)
extract_cnn9 = s_cnn(s_input9)
extract_cnn10 = s_cnn(s_input10)
extract_cnn11 = s_cnn(s_input11)
extract_cnn12 = s_cnn(s_input12)
extract_cnn = Concatenate(axis=1)([
extract_cnn1, extract_cnn2, extract_cnn3, extract_cnn4, extract_cnn5,
extract_cnn6, extract_cnn7, extract_cnn8, extract_cnn9, extract_cnn10,
extract_cnn11, extract_cnn12
])
if num_sensors > 12:
for i in range(13, num_sensors + 1):
exec(
's_input{} = Input(shape=(input_shape[0], input_shape[1], input_shape[2]))'
.format(i))
exec('extract_cnn{} = s_cnn(s_input{})'.format(i, i))
exec(
'extract_cnn = Concatenate(axis=1)([extract_cnn, extract_cnn{}])'
.format(i))
#extract_cnn = np.reshape(extract_cnn, (-1,))
G_h1 = GraphAttention(gnn_unit, activation='selu',
dropout_rate=0)([extract_cnn, sensor_matrix1])
G_h2 = GraphAttention(gnn_unit, activation='selu',
dropout_rate=0)([extract_cnn, sensor_matrix2])
G_h3 = GraphAttention(gnn_unit, activation='selu',
dropout_rate=0)([extract_cnn, sensor_matrix3])
G_h4 = GraphAttention(gnn_unit, activation='selu',
dropout_rate=0)([extract_cnn, sensor_matrix4])
G_1 = Concatenate(axis=-1)([G_h1, G_h2, G_h3, G_h4])
G_2h1 = GraphAttention(gnn_unit, activation='selu',
dropout_rate=0)([G_1, sensor_matrix1])
G_2h2 = GraphAttention(gnn_unit, activation='selu',
dropout_rate=0)([G_1, sensor_matrix2])
G_2h3 = GraphAttention(gnn_unit, activation='selu',
dropout_rate=0)([G_1, sensor_matrix3])
G_2h4 = GraphAttention(gnn_unit, activation='selu',
dropout_rate=0)([G_1, sensor_matrix4])
G_2 = Concatenate(axis=-1)([G_2h1, G_2h2, G_2h3, G_2h4])
#G_3h1 = GraphAttention(gnn_unit, activation='selu', dropout_rate=0)([G_2, sensor_matrix1])
#G_3h2 = GraphAttention(gnn_unit, activation='selu', dropout_rate=0)([G_2, sensor_matrix2])
#G_3h3 = GraphAttention(gnn_unit, activation='selu', dropout_rate=0)([G_2, sensor_matrix3])
#G_3h4 = GraphAttention(gnn_unit, activation='selu', dropout_rate=0)([G_2, sensor_matrix4])
#G_3 = Concatenate(axis=-1)([G_3h1, G_3h2, G_3h3, G_3h4])
#G_4h1 = GraphAttention(gnn_unit, activation='selu', dropout_rate=0)([G_3, sensor_matrix1])
#G_4h2 = GraphAttention(gnn_unit, activation='selu', dropout_rate=0)([G_3, sensor_matrix2])
#G_4h3 = GraphAttention(gnn_unit, activation='selu', dropout_rate=0)([G_3, sensor_matrix3])
#G_4h4 = GraphAttention(gnn_unit, activation='selu', dropout_rate=0)([G_3, sensor_matrix4])
#G_4 = Concatenate(axis=-1)([G_4h1, G_4h2, G_4h3, G_4h4])
gnn_output = tf.split(G_2, num_sensors, 1)
mlp_input1 = Concatenate(axis=-1)([gnn_output[0], extract_cnn1])
mlp_input2 = Concatenate(axis=-1)([gnn_output[1], extract_cnn2])
mlp_input3 = Concatenate(axis=-1)([gnn_output[2], extract_cnn3])
mlp_input4 = Concatenate(axis=-1)([gnn_output[3], extract_cnn4])
mlp_input5 = Concatenate(axis=-1)([gnn_output[4], extract_cnn5])
mlp_input6 = Concatenate(axis=-1)([gnn_output[5], extract_cnn6])
mlp_input7 = Concatenate(axis=-1)([gnn_output[6], extract_cnn7])
mlp_input8 = Concatenate(axis=-1)([gnn_output[7], extract_cnn8])
mlp_input9 = Concatenate(axis=-1)([gnn_output[8], extract_cnn9])
mlp_input10 = Concatenate(axis=-1)([gnn_output[9], extract_cnn10])
mlp_input11 = Concatenate(axis=-1)([gnn_output[10], extract_cnn11])
mlp_input12 = Concatenate(axis=-1)([gnn_output[11], extract_cnn12])
mlp_layer = mlp_model()
output1 = mlp_layer(Flatten()(mlp_input1))
output2 = mlp_layer(Flatten()(mlp_input2))
output3 = mlp_layer(Flatten()(mlp_input3))
output4 = mlp_layer(Flatten()(mlp_input4))
output5 = mlp_layer(Flatten()(mlp_input5))
output6 = mlp_layer(Flatten()(mlp_input6))
output7 = mlp_layer(Flatten()(mlp_input7))
output8 = mlp_layer(Flatten()(mlp_input8))
output9 = mlp_layer(Flatten()(mlp_input9))
output10 = mlp_layer(Flatten()(mlp_input10))
output11 = mlp_layer(Flatten()(mlp_input11))
output12 = mlp_layer(Flatten()(mlp_input12))
model = Model(inputs=[
s_input1, s_input2, s_input3, s_input4, s_input5, s_input6, s_input7,
s_input8, s_input9, s_input10, s_input11, s_input12, sensor_matrix1,
sensor_matrix2, sensor_matrix3, sensor_matrix4
],
outputs=[
output1, output2, output3, output4, output5, output6,
output7, output8, output9, output10, output11, output12
])
return model