Source code for storm_kit.geom.nn_model.robot_self_collision

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import torch
from torch import nn
from torch.nn import Sequential as Seq, Linear as Lin, ReLU, ELU, ReLU6
from .network_macros import MLPRegression, scale_to_base, scale_to_net
from ...util_file import get_weights_path, join_path


[docs]class RobotSelfCollisionNet(): """This class loads a network to predict the signed distance given a robot joint config.""" def __init__(self, n_joints=0): """initialize class Args: n_joints (int, optional): Number of joints, same as number of channels for nn input. Defaults to 0. """ super().__init__() act_fn = ReLU6 in_channels = n_joints out_channels = 1 dropout_ratio = 0.1 mlp_layers = [256, 64] self.model = MLPRegression(in_channels, out_channels, mlp_layers, dropout_ratio, batch_norm=False, act_fn=act_fn, layer_norm=False, nerf=True)
[docs] def load_weights(self, f_name, tensor_args): """Loads pretrained network weights if available. Args: f_name (str): file name, this is relative to weights folder in this repo. tensor_args (Dict): device and dtype for pytorch tensors """ try: chk = torch.load(join_path(get_weights_path(), f_name)) self.model.load_state_dict(chk["model_state_dict"]) self.norm_dict = chk["norm"] for k in self.norm_dict.keys(): self.norm_dict[k]['mean'] = self.norm_dict[k]['mean'].to(**tensor_args) self.norm_dict[k]['std'] = self.norm_dict[k]['std'].to(**tensor_args) except Exception: print('WARNING: Weights not loaded') self.model = self.model.to(**tensor_args) self.tensor_args = tensor_args self.model.eval()
[docs] def compute_signed_distance(self, q): """Compute the signed distance given the joint config. Args: q (tensor): input batch of joint configs [b, n_joints] Returns: [tensor]: largest signed distance between any two non-consecutive links of the robot. """ with torch.no_grad(): q_scale = scale_to_net(q, self.norm_dict,'x') dist = self.model.forward(q_scale) dist_scale = scale_to_base(dist, self.norm_dict, 'y') return dist_scale
[docs] def check_collision(self, q): """Check collision given joint config. Requires classifier like training. Args: q (tensor): input batch of joint configs [b, n_joints] Returns: [tensor]: probability of collision of links, from sigmoid value. """ with torch.no_grad(): q_scale = scale_to_net(q, self.norm_dict,'x') dist = torch.sigmoid(self.model.forward(q_scale)) return dist