def gravnet_model(Inputs, feature_dropout=-1.):
nregressions = 5
# I_data = tf.keras.Input(shape=(num_features,), dtype="float32")
# I_splits = tf.keras.Input(shape=(1,), dtype="int32")
I_data = Inputs[0]
I_splits = tf.cast(Inputs[1], tf.int32)
x_data, x_row_splits = RaggedConstructTensor(name="construct_ragged")([I_data, I_splits])
input_features = x_data # these are going to be passed through for the loss
x_basic = BatchNormalization(momentum=0.6)(x_data) # mask_and_norm is just batch norm now
x = x_basic
n_filters = 0
n_gravnet_layers = 3
feat = [x]
for i in range(n_gravnet_layers):
n_filters = 128
n_propagate = [128,64,32,16,16,8,8,4,4]
n_neighbours = 128
n_dim=4
if i == n_gravnet_layers - 1:
n_dim=2
x = Concatenate()([x_basic,x])
x,coords = FusedRaggedGravNetLinParse(n_neighbours=n_neighbours,
n_dimensions=n_dim,
n_filters=n_filters,
n_propagate=n_propagate,
name='gravnet_' + str(i))([x, x_row_splits])
#tf.print('minmax coords',tf.reduce_min(coords),tf.reduce_max(coords))
x = BatchNormalization(momentum=0.6)(x)
x = RaggedGlobalExchange(name="global_exchange_bottom_"+str(i))([x, x_row_splits])
x = Dense(96, activation='elu',name="dense_bottom_"+str(i)+"_a")(x)
x = Dense(96, activation='elu',name="dense_bottom_"+str(i)+"_b")(x)
x = Dense(96, activation='elu',name="dense_bottom_"+str(i)+"_c")(x)
x = BatchNormalization(momentum=0.6)(x)
feat.append(x)
x = Concatenate(name="concat_gravout")(feat)
x = Dense(128, activation='elu',name="dense_last_a")(x)
x = Dense(128, activation='elu',name="dense_last_a1")(x)
x = BatchNormalization(momentum=0.6)(x)
x = Dense(64, activation='elu',name="dense_last_b")(x)
x = BatchNormalization(momentum=0.6)(x)
x = Dense(64, activation='elu',name="dense_last_c")(x)
predictions = create_default_outputs(input_features, x, x_row_splits, energy_block=True)
# outputs = tf.tuple([predictions, x_row_splits])
return Model(inputs=Inputs, outputs=[predictions, predictions])
def gravnet_model(Inputs, feature_dropout=-1.):
nregressions = 5
# I_data = tf.keras.Input(shape=(num_features,), dtype="float32")
# I_splits = tf.keras.Input(shape=(1,), dtype="int32")
I_data = Inputs[0]
I_splits = tf.cast(Inputs[1], tf.int32)
x_data, x_row_splits = RaggedConstructTensor(name="construct_ragged")(
[I_data, I_splits])
input_features = x_data # these are going to be passed through for the loss
x_basic = BatchNormalization(momentum=0.6)(
x_data) # mask_and_norm is just batch norm now
x = x_basic
n_filters = 0
n_gravnet_layers = 7
feat = []
for i in range(n_gravnet_layers):
n_filters = 128
n_propagate = 96
n_neighbours = 200
x = RaggedGlobalExchange(name="global_exchange_" +
str(i))([x, x_row_splits])
x = Dense(64, activation='elu', name="dense_" + str(i) + "_a")(x)
x = Dense(64, activation='elu', name="dense_" + str(i) + "_b")(x)
x = Dense(64, activation='elu', name="dense_" + str(i) + "_c")(x)
x = BatchNormalization(momentum=0.6)(x)
x = FusedRaggedGravNet_simple(n_neighbours=n_neighbours,
n_dimensions=4,
n_filters=n_filters,
n_propagate=n_propagate,
name='gravnet_' +
str(i))([x, x_row_splits])
x = BatchNormalization(momentum=0.6)(x)
feat.append(
Dense(48, activation='elu', name="dense_compress_" + str(i))(x))
x = Concatenate(name="concat_gravout")(feat)
x = Dense(128, activation='elu', name="dense_last_a")(x)
x = Dense(64, activation='elu', name="dense_last_b")(x)
x = Dense(64, activation='elu', name="dense_last_c")(x)
predictions = create_default_outputs(input_features,
x,
x_row_splits,
energy_block=False)
# outputs = tf.tuple([predictions, x_row_splits])
return Model(inputs=Inputs, outputs=[predictions, predictions])
def gravnet_model(Inputs, feature_dropout=-1.):
nregressions = 5
# I_data = tf.keras.Input(shape=(num_features,), dtype="float32")
# I_splits = tf.keras.Input(shape=(1,), dtype="int32")
I_data = Inputs[0]
I_splits = tf.cast(Inputs[1], tf.int32)
x_data, x_row_splits = RaggedConstructTensor(name="construct_ragged")(
[I_data, I_splits])
input_features = x_data # these are going to be passed through for the loss
x_basic = BatchNormalization(momentum=0.6)(
x_data) # mask_and_norm is just batch norm now
x = x_basic
x = RaggedGlobalExchange(name="global_exchange")([x, x_row_splits])
x = Dense(64, activation='elu', name="dense_start")(x)
n_filters = 0
n_gravnet_layers = 4
feat = [x_basic, x]
for i in range(n_gravnet_layers):
n_filters = 196
n_propagate = 128
n_propagate_2 = [64, 32, 16, 8, 4, 2]
n_neighbours = 32
n_dim = 8
#if n_dim < 2:
# n_dim = 2
x, coords, neighbor_indices, neighbor_dist = RaggedGravNet(
n_neighbours=n_neighbours,
n_dimensions=n_dim,
n_filters=n_filters,
n_propagate=n_propagate,
name='gravnet_' + str(i))([x, x_row_splits])
x = DistanceWeightedMessagePassing(n_propagate_2)(
[x, neighbor_indices, neighbor_dist])
x = BatchNormalization(momentum=0.6)(x)
x = Dense(128, activation='elu',
name="dense_bottom_" + str(i) + "_a")(x)
x = BatchNormalization(momentum=0.6, name="bn_a_" + str(i))(x)
x = Dense(96, activation='elu',
name="dense_bottom_" + str(i) + "_b")(x)
x = RaggedGlobalExchange(name="global_exchange_bot_" +
str(i))([x, x_row_splits])
x = Dense(96, activation='elu',
name="dense_bottom_" + str(i) + "_c")(x)
x = BatchNormalization(momentum=0.6, name="bn_b_" + str(i))(x)
feat.append(x)
x = Concatenate(name="concat_gravout")(feat)
x = Dense(128, activation='elu', name="dense_last_a")(x)
x = BatchNormalization(momentum=0.6, name="bn_last_a")(x)
x = Dense(128, activation='elu', name="dense_last_a1")(x)
x = BatchNormalization(momentum=0.6, name="bn_last_a1")(x)
x = Dense(128, activation='elu', name="dense_last_a2")(x)
x = BatchNormalization(momentum=0.6, name="bn_last_a2")(x)
x = Dense(64, activation='elu', name="dense_last_b")(x)
x = Dense(64, activation='elu', name="dense_last_c")(x)
predictions = create_default_outputs(input_features,
x,
x_row_splits,
energy_block=False,
n_ccoords=n_ccoords,
scale_exp_e=False)
# outputs = tf.tuple([predictions, x_row_splits])
return Model(inputs=Inputs, outputs=[predictions, predictions])