def _build_layers_v2(self, input_dict, num_outputs, options):
# Hard deprecate this class. All Models should use the ModelV2
# API from here on.
deprecation_warning(
"Model->VisionNetwork", "ModelV2->VisionNetwork", error=False)
inputs = input_dict["obs"]
filters = options.get("conv_filters")
if not filters:
filters = _get_filter_config(inputs.shape.as_list()[1:])
activation = get_activation_fn(options.get("conv_activation"))
with tf.name_scope("vision_net"):
for i, (out_size, kernel, stride) in enumerate(filters[:-1], 1):
inputs = tf.layers.conv2d(
inputs,
out_size,
kernel,
stride,
activation=activation,
padding="same",
name="conv{}".format(i))
out_size, kernel, stride = filters[-1]
# skip final linear layer
if options.get("no_final_linear"):
fc_out = tf.layers.conv2d(
inputs,
num_outputs,
kernel,
stride,
activation=activation,
padding="valid",
name="fc_out")
return flatten(fc_out), flatten(fc_out)
fc1 = tf.layers.conv2d(
inputs,
out_size,
kernel,
stride,
activation=activation,
padding="valid",
name="fc1")
fc2 = tf.layers.conv2d(
fc1,
num_outputs, [1, 1],
activation=None,
padding="same",
name="fc2")
return flatten(fc2), flatten(fc1)
def _build_layers_v2(self, input_dict, num_outputs, options):
inputs = input_dict["obs"]
hiddens = [32, 32]
with tf.name_scope("custom_net"):
inputs = slim.conv2d(inputs,
6, [3, 3],
1,
activation_fn=tf.nn.relu,
scope="conv")
last_layer = flatten(inputs)
i = 1
for size in hiddens:
label = "fc{}".format(i)
last_layer = slim.fully_connected(
last_layer,
size,
weights_initializer=normc_initializer(1.0),
activation_fn=tf.nn.relu,
scope=label)
i += 1
output = slim.fully_connected(
last_layer,
num_outputs,
weights_initializer=normc_initializer(0.01),
activation_fn=None,
scope="fc_out")
return output, last_layer
def forward(self, input_dict, state, seq_lens):
obs = input_dict['obs']['board']
obs = tf.expand_dims(obs, -1) if self.is_conv else flatten(obs)
action_mask = tf.maximum(tf.log(input_dict['obs']['action_mask']),
tf.float32.min)
model_out, self._value_out = self.base_model(obs)
return action_mask + model_out, state
def _build_layers_v2(self, input_dict, num_outputs, options):
inputs = input_dict["obs"]
filters = options.get("conv_filters")
if not filters:
filters = _get_filter_config(inputs.shape.as_list()[1:])
activation = get_activation_fn(options.get("conv_activation"))
with tf.name_scope("vision_net"):
for i, (out_size, kernel, stride) in enumerate(filters[:-1], 1):
inputs = tf.layers.conv2d(
inputs,
out_size,
kernel,
stride,
activation=activation,
padding="same",
name="conv{}".format(i))
out_size, kernel, stride = filters[-1]
# skip final linear layer
if options.get("no_final_linear"):
fc_out = tf.layers.conv2d(
inputs,
num_outputs,
kernel,
stride,
activation=activation,
padding="valid",
name="fc_out")
return flatten(fc_out), flatten(fc_out)
fc1 = tf.layers.conv2d(
inputs,
out_size,
kernel,
stride,
activation=activation,
padding="valid",
name="fc1")
fc2 = tf.layers.conv2d(
fc1,
num_outputs, [1, 1],
activation=None,
padding="same",
name="fc2")
return flatten(fc2), flatten(fc1)
def forward(self, input_dict, state, seq_lens):
obs = input_dict['obs']['board']
if self.is_conv:
paddings = [[0, 0], [0, 1], [0, 0]]
obs = tf.expand_dims(
tf.pad(obs, paddings, mode='CONSTANT', constant_values=3), -1)
else:
obs = flatten(obs)
action_mask = tf.maximum(tf.log(input_dict['obs']['action_mask']),
tf.float32.min)
model_out, self._value_out = self.base_model(obs)
return action_mask + model_out, state
def forward(self, input_dict, state, seq_lens):
obs = flatten(input_dict['obs']['board'])
action_mask = tf.maximum(tf.log(input_dict['obs']['action_mask']),
tf.float32.min)
model_out, _ = self.mlp({'obs': obs})
return action_mask + model_out, state
def __init__(self, obs_space, action_space, num_outputs, *args, **kwargs):
super(RobotModel, self).__init__(obs_space, action_space, num_outputs,
*args, **kwargs)
obs_space = {
"maps": Box((48, 48)),
"robot_theta": Box((1, )),
"robot_velocity": Box((3, )),
"target": Box((2, )),
"ckpts": Box((4, 2)),
}
maps_input = tf.keras.layers.Input(shape=obs_space["maps"].shape,
dtype="float32",
name="map")
target_input = tf.keras.layers.Input(shape=obs_space["target"].shape,
dtype="float32",
name="target")
robot_theta_input = tf.keras.layers.Input(
shape=obs_space["robot_theta"].shape,
dtype="float32",
name="robot_theta")
robot_velocity_input = tf.keras.layers.Input(
shape=obs_space["robot_velocity"].shape,
dtype="float32",
name="robot_velocity")
ckpt_input = tf.keras.layers.Input(shape=obs_space["ckpts"].shape,
dtype="float32",
name="ckpts")
inputs = [
maps_input,
target_input,
robot_theta_input,
robot_velocity_input,
ckpt_input,
]
x = (maps_input - MAP_MEAN) / MAP_STD
x = tf.keras.backend.expand_dims(x, -1)
# Convolutional block
x = tf.keras.layers.Conv2D(16, (3, 3),
strides=(1, 1),
activation="relu",
padding="same",
name="conv1")(x)
x = tf.keras.layers.BatchNormalization(momentum=0.999)(x)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Conv2D(32, (3, 3),
strides=(2, 2),
activation="relu",
padding="same",
name="conv2")(x)
x = tf.keras.layers.Conv2D(32, (3, 3),
strides=(1, 1),
activation="relu",
padding="same",
name="conv3")(x)
x = tf.keras.layers.BatchNormalization(momentum=0.999)(x)
x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
x = tf.keras.layers.Conv2D(32, (3, 3),
strides=(2, 2),
activation="relu",
padding="same",
name="conv4")(x)
x = tf.keras.layers.BatchNormalization(momentum=0.999)(x)
x = flatten(x)
metrics = x
# Concatenate all inputs together
sensors = [
target_input / DIST_STD,
robot_theta_input / DIST_STD,
robot_velocity_input / DIST_STD,
flatten(ckpt_input) / DIST_STD,
]
x = tf.keras.layers.Concatenate(axis=-1,
name="sensor_concat")(sensors + [x])
x = tf.keras.layers.Dense(num_outputs - 14)(x)
x = tf.keras.layers.BatchNormalization(center=False,
scale=False,
momentum=0.999)(x)
output_layer = tf.keras.layers.Concatenate(
axis=-1, name="robot_concat")(sensors + [x])
self.base_model = tf.keras.Model(inputs, [output_layer, metrics])
self.register_variables(self.base_model.variables)
