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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