def build_baseline_network(self):
'''
@brief:
Build the baseline network, and fetch the baseline variable list
'''
adj_matrix, node_attr, xml_str = model_gen.get_initial_settings()
xml_str = etree.tostring(xml_str, pretty_print=True)
self._node_info = gen_gnn_param.gen_gnn_param(
self.args.task_name,
adj_matrix,
node_attr,
gnn_node_option=self.args.gnn_node_option,
root_connection_option=self.args.root_connection_option,
gnn_output_option=self.args.gnn_output_option,
gnn_embedding_option='parameter'
)
self._node_info = gnn_util.construct_ob_size_dict(self._node_info, 64)
self._node_info = \
gnn_util.get_inverse_type_offset(self._node_info, 'node')
self._node_info = \
gnn_util.get_inverse_type_offset(self._node_info, 'output')
self._node_info = gnn_util.get_receive_send_idx(self._node_info)
self.init_node_info = self._node_info
self.baseline_network = pruning_network.GGNN(
args=self.args,
session=self.session,
name_scope=self.name_scope + '_baseline',
initial_node_info=self.init_node_info,
bayesian_op=self.args.bayesian_pruning
)
self.dropout_mask_shape = self.baseline_network.dropout_mask_shape
self.dropout_mask_placeholder = self.baseline_network.test_dropout_mask
# step 2: get the placeholders for the network
# self.target_return_placeholder = \
# self.baseline_network.get_target_return_placeholder()
self.vf_loss = self.baseline_network.get_vf_loss()
self.tvars = self.baseline_network._trainable_var_list
# define the loss and gradient
self.optimizer = tf.train.AdamOptimizer(self.args.lr)
self.grads = tf.gradients(self.vf_loss, self.tvars)
self.gradient_placeholder = []
for i_id in range(len(self.tvars)):
self.gradient_placeholder.append(
tf.placeholder(tf.float32, shape=self.tvars[i_id].get_shape())
)
self.update_op = self.optimizer.apply_gradients(
zip(self.gradient_placeholder, self.tvars)
)
def get_feed_dict(self, new_species):
adj_matrix, node_attr, xml_str = new_species['adj_matrix'], \
new_species['node_attr'], new_species['xml_str']
node_info = gen_gnn_param.gen_gnn_param(
self.args.task_name,
adj_matrix,
node_attr,
gnn_node_option=self.args.gnn_node_option,
root_connection_option=self.args.root_connection_option,
gnn_output_option=self.args.gnn_output_option,
gnn_embedding_option='parameter'
)
node_info = gnn_util.construct_ob_size_dict(node_info, 64)
node_info = gnn_util.get_inverse_type_offset(node_info, 'node')
node_info = gnn_util.get_inverse_type_offset(node_info, 'output')
node_info = gnn_util.get_receive_send_idx(node_info)
dummy_obs = np.zeros([1, 6 * node_info['num_nodes'] + 6])
_, graph_parameters, receive_idx, send_idx, \
node_type_idx, inverse_node_type_idx, _, _, _ = \
graph_data_util.construct_graph_input_feeddict(
node_info, dummy_obs, -1, -1, -1, -1, -1, -1, -1
)
feed_dict = {
self._receive_idx: receive_idx,
# self._send_idx: send_idx,
# self._node_type_idx: node_type_idx,
self._inverse_node_type_idx: inverse_node_type_idx,
self._batch_size_int: 1,
# self._input_parameters: graph_parameters,
# self._target_returns: self.data_dict[i_species_id]['LastRwd']
}
for i_edge in node_info['edge_type_list']:
feed_dict[self._send_idx[i_edge]] = send_idx[i_edge]
# append the node type idx
for i_node_type in node_info['node_type_dict']:
feed_dict[self._node_type_idx[i_node_type]] = \
node_type_idx[i_node_type]
for i_node_type in node_info['node_type_dict']:
feed_dict[self._input_parameters[i_node_type]] = \
graph_parameters[i_node_type]
feed_dict[self._num_nodes_ph] = adj_matrix.shape[0]
return feed_dict
def _parse_mujoco_template(self):
'''
@brief:
In this function, we construct the dict for node information.
The structure is _node_info
@attribute:
1. general informatin about the graph
@self._node_info['tree']
@self._node_info['debug_info']
@self._node_info['relation_matrix']
2. information about input output
@self._node_info['input_dict']:
self._node_info['input_dict'][id_of_node] is a list of
ob positions
@self._node_info['output_list']
3. information about the node
@self._node_info['node_type_dict']:
self._node_info['node_type_dict']['body'] is a list of
node id
@self._node_info['num_nodes']
4. information about the edge
@self._node_info['edge_type_list'] = self._edge_type_list
the list of edge ids
@self._node_info['num_edges']
@self._node_info['num_edge_type']
6. information about the index
@self._node_info['node_in_graph_list']
The order of nodes if placed by types ('joint', 'body')
@self._node_info['inverse_node_list']
The inverse of 'node_in_graph_list'
@self._node_info['receive_idx'] = receive_idx
@self._node_info['receive_idx_raw'] = receive_idx_raw
@self._node_info['send_idx'] = send_idx
7. information about the embedding size and ob size
@self._node_info['para_size_dict']
@self._node_info['ob_size_dict']
self._node_info['ob_size_dict']['root'] = 10
self._node_info['ob_size_dict']['joint'] = 6
'''
# step 0: parse the mujoco xml
if 'evo' in self.args.task:
self._node_info = gen_gnn_param.gen_gnn_param(
self._task_name,
self.adj_matrix,
self.node_attr,
gnn_node_option=self._gnn_node_option,
root_connection_option=self._root_connection_option,
gnn_output_option=self._gnn_output_option,
gnn_embedding_option=self._gnn_embedding_option)
else:
self._node_info = mujoco_parser.parse_mujoco_graph(
self._task_name,
gnn_node_option=self._gnn_node_option,
root_connection_option=self._root_connection_option,
gnn_output_option=self._gnn_output_option,
gnn_embedding_option=self._gnn_embedding_option)
# step 1: check that the input and output size is matched
gnn_util.io_size_check(self._input_size, self._output_size,
self._node_info, self._is_baseline)
# step 2: check for ob size for each node type, construct the node dict
self._node_info = gnn_util.construct_ob_size_dict(
self._node_info, self._input_feat_dim)
# step 3: get the inverse node offsets (used to construct gather idx)
self._node_info = gnn_util.get_inverse_type_offset(
self._node_info, 'node')
# step 4: get the inverse node offsets (used to gather output idx)
self._node_info = gnn_util.get_inverse_type_offset(
self._node_info, 'output')
# step 5: register existing edge and get the receive and send index
self._node_info = gnn_util.get_receive_send_idx(self._node_info)