def make_torch_type_for_GCN(nodes_pos, edges_indices, edges_thickness,
node_adj):
# GCNの為の引数を作成
T = make_T_matrix(edges_indices)
edge_adj = make_edge_adj(edges_indices, T)
D_e = make_D_matrix(edge_adj)
D_v = make_D_matrix(node_adj)
# GCNへの変換
node = torch.from_numpy(nodes_pos).clone().double()
node = node.unsqueeze(0)
edge = torch.from_numpy(edges_thickness).clone().double()
edge = edge.unsqueeze(0).unsqueeze(2)
node_adj = torch.from_numpy(node_adj).clone().double()
node_adj = node_adj.unsqueeze(0)
edge_adj = torch.from_numpy(edge_adj).clone().double()
edge_adj = edge_adj.unsqueeze(0)
D_v = torch.from_numpy(D_v).clone().double()
D_v = D_v.unsqueeze(0)
D_e = torch.from_numpy(D_e).clone().double()
D_e = D_e.unsqueeze(0)
T = torch.from_numpy(T).clone().double()
T = T.unsqueeze(0)
return node, edge, node_adj, edge_adj, D_v, D_e, T
def select_action(first_node_num):
nodes_pos, edges_indices, edges_thickness, node_adj = env.extract_node_edge_info(
)
node_num = nodes_pos.shape[0]
# ラベル作成
label = np.zeros((node_num, 1))
label[:first_node_num] = 1
nodes_pos = np.concatenate([nodes_pos, label], 1)
# GCNの為の引数を作成
T = make_T_matrix(edges_indices)
edge_adj = make_edge_adj(edges_indices, T)
D_e = make_D_matrix(edge_adj)
D_v = make_D_matrix(node_adj)
# GCNへの変換
node = torch.from_numpy(nodes_pos).clone().double()
node = node.unsqueeze(0)
edge = torch.from_numpy(edges_thickness).clone().double()
edge = edge.unsqueeze(0).unsqueeze(2)
node_adj = torch.from_numpy(node_adj).clone().double()
node_adj = node_adj.unsqueeze(0)
edge_adj = torch.from_numpy(edge_adj).clone().double()
edge_adj = edge_adj.unsqueeze(0)
D_v = torch.from_numpy(D_v).clone().double()
D_v = D_v.unsqueeze(0)
D_e = torch.from_numpy(D_e).clone().double()
D_e = D_e.unsqueeze(0)
T = torch.from_numpy(T).clone().double()
T = T.unsqueeze(0)
# action求め
emb_graph, state_value = GCN(node, edge, node_adj, edge_adj, D_v, D_e, T)
# 新規ノードの座標決め
coord = X_Y(emb_graph)
coord_x_tdist = tdist.Normal(coord[0][0].item(), coord[0][2].item())
coord_y_tdist = tdist.Normal(coord[0][1].item(), coord[0][3].item())
coord_x_action = coord_x_tdist.sample()
coord_y_action = coord_y_tdist.sample()
# 0-1に収める
coord_x_action = torch.clamp(coord_x_action, min=0, max=1)
coord_y_action = torch.clamp(coord_y_action, min=0, max=1)
# グラフ追加を止めるかどうか
stop_prob = Stop(emb_graph)
stop_categ = Categorical(stop_prob)
stop = stop_categ.sample()
# ノード1を求める
node1_prob = Select_node1(emb_graph)
node1_categ = Categorical(node1_prob)
node1 = node1_categ.sample()
# 新規ノード
new_node = torch.Tensor([coord_x_action, coord_y_action, 0]).double()
new_node = torch.reshape(new_node, (1, 1, 3))
node_cat = torch.cat([node, new_node], 1)
# H1を除いたnode_catの作成
non_node1_node_cat = torch.cat(
[node_cat[:, 0:node1, :], node_cat[:, node1 + 1:, :]], 1)
# H1の情報抽出
H1 = emb_graph[0][node1]
H1_cat = H1.repeat(non_node1_node_cat.shape[1], 1)
H1_cat = H1_cat.unsqueeze(0)
# HとH1のノード情報をconcat
emb_graph_cat = torch.cat([non_node1_node_cat, H1_cat], 2)
# ノード2を求める
node2_prob = Select_node2(emb_graph_cat)
node2_categ = Categorical(node2_prob)
node2_temp = node2_categ.sample()
if node2_temp >= node1:
node2 = node2_temp + 1 # node1分の調整
else:
node2 = node2_temp
H2 = node_cat[0][node2] # node_posよりH2の特徴を抽出
# エッジの太さ決め
# H1とH2のノード情報をconcat
H1 = H1.unsqueeze(0)
H2 = H2.unsqueeze(0)
emb_graph_cat2 = torch.cat([H1, H2], 2)
edge_thickness = Edge_thickness(emb_graph_cat2)
edge_thickness_tdist = tdist.Normal(edge_thickness[0][0].item(),
edge_thickness[0][1].item())
edge_thickness_action = edge_thickness_tdist.sample()
edge_thickness_action = torch.clamp(edge_thickness_action, min=0, max=1)
action = {}
action["which_node"] = np.array([node1.item(), node2.item()])
action["new_node"] = np.array([[coord_x_action, coord_y_action]])
action["edge_thickness"] = np.array([edge_thickness_action])
action["end"] = stop.item()
# save to action buffer
GCN.saved_actions.append(Saved_Action(action, state_value))
Stop.saved_actions.append(Saved_prob_Action(stop_categ.log_prob(stop)))
Select_node1.saved_actions.append(
Saved_prob_Action(node1_categ.log_prob(node1)))
Select_node2.saved_actions.append(
Saved_prob_Action(
node2_categ.log_prob(node2_temp))) # node2_tempを利用していることに注意
X_Y.saved_actions.append(Saved_mean_std_Action(coord[0][:2], coord[0][2:]))
Edge_thickness.saved_actions.append(
Saved_mean_std_Action(edge_thickness[0][0], edge_thickness[0][1]))
GCN.node_nums.append(node_num)
return action