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| import torch | |
| class Normalization(object): | |
| def __init__(self, actor, normalize_position=False, device='cpu'): | |
| self.normalize_position = normalize_position | |
| self.device = device | |
| graph = actor.graph | |
| fleet = actor.fleet | |
| batch_size = graph.distance_matrix.shape[0] | |
| num_nodes = graph.distance_matrix.shape[1] | |
| num_cars = fleet.start_time.shape[1] | |
| self.greatest_drive_time = graph.time_matrix.reshape(batch_size, -1).max(dim=1)[0] | |
| self.greatest_distance = graph.distance_matrix.reshape(batch_size, -1).max(dim=1)[0] | |
| a = fleet.start_time.reshape(batch_size, -1) | |
| b = graph.start_time.reshape(batch_size, -1) | |
| self.earliest_start_time = torch.cat([a, b], dim=1).min(dim=1)[0] | |
| self.mean_positions = graph.node_positions.mean(dim=1) | |
| self.std_positions = torch.std(graph.node_positions, dim=1) | |
| def normalize(self, actor): | |
| batch_size = actor.graph.distance_matrix.shape[0] | |
| num_nodes = actor.graph.distance_matrix.shape[1] | |
| num_cars = actor.fleet.start_time.shape[1] | |
| d = self.greatest_distance.reshape(batch_size, 1, 1).repeat(1, num_nodes, num_nodes) | |
| actor.graph.distance_matrix = actor.graph.distance_matrix / d | |
| t = self.greatest_drive_time.reshape(batch_size, 1, 1).repeat(1, num_nodes, num_nodes) | |
| actor.graph.time_matrix = actor.graph.time_matrix / t | |
| s = self.earliest_start_time.reshape(batch_size, 1, 1).repeat(1, num_nodes, 1) | |
| t = self.greatest_drive_time.reshape(batch_size, 1, 1).repeat(1, num_nodes, 1) | |
| actor.graph.start_time = (actor.graph.start_time - s) / t | |
| actor.graph.end_time = (actor.graph.end_time - s) / t | |
| t = self.greatest_drive_time.reshape(batch_size) | |
| actor.fleet.late_time = actor.fleet.late_time / t | |
| s = actor.fleet.arrival_times.shape | |
| t = self.greatest_drive_time.reshape(batch_size, 1, 1).repeat(1, s[1], s[2]) | |
| actor.fleet.arrival_times = actor.fleet.arrival_times / t | |
| if self.normalize_position: | |
| m = self.mean_positions.reshape(batch_size, 1, self.mean_positions.shape[-1]).repeat(1, num_nodes, 1) | |
| st = self.std_positions.reshape(batch_size, 1, self.std_positions.shape[-1]).repeat(1, num_nodes, 1) | |
| actor.graph.node_positions = (actor.graph.node_positions - m) / st | |
| def inverse_normalize(self, actor): | |
| batch_size = actor.graph.distance_matrix.shape[0] | |
| num_nodes = actor.graph.distance_matrix.shape[1] | |
| num_cars = actor.fleet.start_time.shape[1] | |
| d = self.greatest_distance.reshape(batch_size, 1, 1).repeat(1, num_nodes, num_nodes) | |
| actor.graph.distance_matrix = actor.graph.distance_matrix * d | |
| t = self.greatest_drive_time.reshape(batch_size, 1, 1).repeat(1, num_nodes, num_nodes) | |
| actor.graph.time_matrix = actor.graph.time_matrix * t | |
| s = self.earliest_start_time.reshape(batch_size, 1, 1).repeat(1, num_nodes, 1) | |
| t = self.greatest_drive_time.reshape(batch_size, 1, 1).repeat(1, num_nodes, 1) | |
| actor.graph.start_time = actor.graph.start_time * t + s | |
| actor.graph.end_time = actor.graph.end_time * t + s | |
| t = self.greatest_drive_time.reshape(batch_size) | |
| actor.fleet.late_time = actor.fleet.late_time * t | |
| s = actor.fleet.arrival_times.shape | |
| t = self.greatest_drive_time.reshape(batch_size, 1, 1).repeat(1, s[1], s[2]) | |
| actor.fleet.arrival_times = actor.fleet.arrival_times * t | |
| if self.normalize_position: | |
| m = self.mean_positions.reshape(batch_size, 1, self.mean_positions.shape[-1]).repeat(1, num_nodes, 1) | |
| st = self.std_positions.reshape(batch_size, 1, self.std_positions.shape[-1]).repeat(1, num_nodes, 1) | |
| actor.graph.node_positions = actor.graph.node_positions * st + m | |