389 lines
18 KiB
Python
389 lines
18 KiB
Python
import rospy
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from sensor_msgs.msg import Image
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from nav_msgs.msg import Odometry
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from std_msgs.msg import Float32MultiArray, MultiArrayDimension
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from geometry_msgs.msg import PoseStamped
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from cv_bridge import CvBridge
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import numpy as np
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import cv2
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import os
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import torch
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import argparse
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import time
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from ruamel.yaml import YAML
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from scipy.spatial.transform import Rotation as R
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from flightpolicy.yopo.yopo_policy import YopoPolicy
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from flightpolicy.yopo.primitive_utils import LatticeParam, LatticePrimitive
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try:
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from torch2trt import TRTModule
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except ImportError:
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print("tensorrt not found.")
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class YopoNet:
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def __init__(self, config, weight):
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self.config = config
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rospy.init_node('yopo_net', anonymous=False)
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# load params
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self.bridge = CvBridge()
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self.odom = Odometry()
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self.odom_ref = Odometry()
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self.height = self.config['img_height']
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self.width = self.config['img_width']
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self.depth = np.zeros((1, 1, self.config['img_height'], self.config['img_width']))
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self.goal = np.array(self.config['goal'])
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self.env = self.config['env']
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self.use_trt = self.config['use_tensorrt']
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self.verbose = self.config['verbose']
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self.visualize = self.config['visualize']
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self.Rotation_bc = R.from_euler('ZYX', [0, self.config['pitch_angle_deg'], 0], degrees=True).as_matrix()
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self.new_odom = False
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self.new_depth = False
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self.odom_ref_init = False
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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cfg = YAML().load(open(os.environ["FLIGHTMARE_PATH"] + "/flightlib/configs/traj_opt.yaml", 'r'))
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self.lattice_space = LatticeParam(cfg)
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self.lattice_primitive = LatticePrimitive(self.lattice_space)
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# eval
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self.time_forward = 0.0
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self.time_process = 0.0
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self.time_prepare = 0.0
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self.time_interpolation = 0.0
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self.count = 0
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self.count_interpolation = 0
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# Load Network
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if self.use_trt:
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self.policy = TRTModule()
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self.policy.load_state_dict(torch.load(weight))
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else:
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saved_variables = torch.load(weight, map_location=self.device)
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saved_variables["data"]["lattice_space"] = self.lattice_space
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saved_variables["data"]["lattice_primitive"] = self.lattice_primitive
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self.policy = YopoPolicy(device=self.device, **saved_variables["data"])
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self.policy.load_state_dict(saved_variables["state_dict"], strict=False)
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self.policy.to(self.device)
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self.policy.set_training_mode(False)
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torch.set_grad_enabled(False)
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self.warm_up()
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# ros publisher
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odom_topic = self.config['odom_topic']
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depth_topic = self.config['depth_topic']
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self.endstate_pub = rospy.Publisher("/yopo_net/pred_endstate", Float32MultiArray, queue_size=1)
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self.all_endstate_pub = rospy.Publisher("/yopo_net/pred_endstates", Float32MultiArray, queue_size=1)
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self.goal_pub = rospy.Publisher("/yopo_net/goal", Float32MultiArray, queue_size=1)
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# ros subscriber
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self.odom_sub = rospy.Subscriber(odom_topic, Odometry, self.callback_odometry, queue_size=1, tcp_nodelay=True)
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self.odom_ref_sub = rospy.Subscriber("/juliett/state_ref/odom", Odometry, self.callback_odometry_ref,
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queue_size=1, tcp_nodelay=True)
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self.depth_sub = rospy.Subscriber(depth_topic, Image, self.callback_depth, queue_size=1, tcp_nodelay=True)
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self.goal_sub = rospy.Subscriber("/move_base_simple/goal", PoseStamped, self.callback_set_goal, queue_size=1)
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self.timer_net = rospy.Timer(rospy.Duration(1. / self.config['network_frequency']), self.test_policy)
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print("YOPO Net Node Ready!")
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rospy.spin()
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# the first frame
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def callback_odometry(self, data):
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self.odom = data
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if not self.odom_ref_init:
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self.new_odom = True
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# the following frame (The planner is planning from the desired state, instead of the actual state)
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def callback_odometry_ref(self, data):
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if not self.odom_ref_init:
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print("odom ref init")
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self.odom_ref_init = True
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self.odom_ref = data
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self.new_odom = True
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def process_odom(self):
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# Rwb
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Rotation_wb = R.from_quat([self.odom.pose.pose.orientation.x,
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self.odom.pose.pose.orientation.y,
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self.odom.pose.pose.orientation.z,
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self.odom.pose.pose.orientation.w]).as_matrix()
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self.Rotation_wc = np.dot(Rotation_wb, self.Rotation_bc)
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if self.odom_ref_init:
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odom_data = self.odom_ref
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# vel_b
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vel_w = np.array([odom_data.twist.twist.linear.x,
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odom_data.twist.twist.linear.y,
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odom_data.twist.twist.linear.z])
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vel_b = np.dot(np.linalg.inv(self.Rotation_wc), vel_w)
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# acc_b
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acc_w = np.array([odom_data.twist.twist.angular.x, # acc stored in angular in our ref_state topic
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odom_data.twist.twist.angular.y,
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odom_data.twist.twist.angular.z])
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acc_b = np.dot(np.linalg.inv(self.Rotation_wc), acc_w)
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else:
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odom_data = self.odom
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vel_b = np.array([0.0, 0.0, 0.0])
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acc_b = np.array([0.0, 0.0, 0.0])
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# pose
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pos = np.array([odom_data.pose.pose.position.x,
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odom_data.pose.pose.position.y,
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odom_data.pose.pose.position.z])
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# goal_dir
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goal_w = (self.goal - pos) / np.linalg.norm(self.goal - pos)
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goal_b = np.dot(np.linalg.inv(self.Rotation_wc), goal_w)
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vel_acc = np.concatenate((vel_b, acc_b), axis=0)
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vel_acc_norm = self.normalize_obs(vel_acc[np.newaxis, :])
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obs_norm = np.hstack((vel_acc_norm, goal_b[np.newaxis, :]))
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return obs_norm
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def callback_depth(self, data):
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max_dis = 20.0
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min_dis = 0.03
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if self.env == '435':
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scale = 0.001
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elif self.env == 'flightmare':
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scale = 1.0
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try:
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depth_ = self.bridge.imgmsg_to_cv2(data, "32FC1")
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except:
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print("CV_bridge ERROR: The ROS path is not included in Python Path!")
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if depth_.shape[0] != self.height or depth_.shape[1] != self.width:
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depth_ = cv2.resize(depth_, (self.width, self.height), interpolation=cv2.INTER_NEAREST)
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depth_ = np.minimum(depth_ * scale, max_dis) / max_dis
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# interpolated the nan value (experiment shows that treating nan directly as 0 produces similar results)
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start = time.time()
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nan_mask = np.isnan(depth_) | (depth_ < min_dis)
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interpolated_image = cv2.inpaint(np.uint8(depth_ * 255), np.uint8(nan_mask), 1, cv2.INPAINT_NS)
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interpolated_image = interpolated_image.astype(np.float32) / 255.0
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depth_ = interpolated_image.reshape([1, 1, self.height, self.width])
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if self.verbose:
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self.time_interpolation = self.time_interpolation + (time.time() - start)
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self.count_interpolation = self.count_interpolation + 1
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print("interpolation time:", self.time_interpolation / self.count_interpolation)
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# cv2.imshow("1", depth_[0][0])
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# cv2.waitKey(1)
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self.new_depth = True
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self.depth = depth_.astype(np.float32)
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def callback_set_goal(self, data):
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self.goal = np.asarray([data.pose.position.x, data.pose.position.y, 2])
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print("New goal:", self.goal)
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def test_policy(self, _timer):
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if self.new_depth and self.new_odom:
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self.new_odom = False
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self.new_depth = False
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obs = self.process_odom()
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odom_sec = self.odom.header.stamp.to_sec()
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# input prepare
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time0 = time.time()
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depth = torch.from_numpy(self.depth).to(self.device, non_blocking=True) # (non_blocking: copying speed 3x)
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obs_norm_input = self.prepare_input_observation(obs)
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obs_norm_input = obs_norm_input.to(self.device, non_blocking=True)
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# torch.cuda.synchronize()
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# forward
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if self.use_trt: # TensorRT (inference speed increased by 10x)
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time1 = time.time()
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trt_output = self.policy(depth, obs_norm_input)
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time2 = time.time()
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endstate_pred, score_pred = self.trt_process(trt_output, return_all_preds=self.visualize)
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endstate_pred = endstate_pred.squeeze()
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time3 = time.time()
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else:
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endstate_pred, score_pred = self.policy.predict(depth, obs_norm_input, return_all_preds=self.visualize)
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endstate_pred = endstate_pred.cpu().numpy().squeeze()
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score_pred = score_pred.cpu().numpy()
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# Transform the prediction(body frame) to the world frame with the attitude in inference
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# Replacing PyTorch calculations on CUDA with NumPy calculations on the CPU (speed increased by 10x)
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endstate_b = endstate_pred
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endstate_w = np.zeros_like(endstate_b)
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traj_num = self.lattice_space.horizon_num * self.lattice_space.vertical_num if self.visualize else 1
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Pb, Vb, Ab = [np.zeros((3, traj_num)) for _ in range(3)]
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for i in range(3):
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Pb[i] = endstate_b[3 * i]
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Vb[i] = endstate_b[3 * i + 1]
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Ab[i] = endstate_b[3 * i + 2]
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# pos_actual = np.array([self.odom.pose.pose.position.x,
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# self.odom.pose.pose.position.y,
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# self.odom.pose.pose.position.z])
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Pw = np.dot(self.Rotation_wc, Pb) # + np.tile(pos_actual, (15, 1)).T
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Vw = np.dot(self.Rotation_wc, Vb)
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Aw = np.dot(self.Rotation_wc, Ab)
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for i in range(3):
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endstate_w[3 * i] = Pw[i]
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endstate_w[3 * i + 1] = Vw[i]
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endstate_w[3 * i + 2] = Aw[i]
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if self.verbose:
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self.time_prepare = self.time_prepare + (time1 - time0)
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self.time_forward = self.time_forward + (time2 - time1)
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self.time_process = self.time_process + (time3 - time2)
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self.count = self.count + 1
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print("time forward:", self.time_forward / self.count, "process:", self.time_process / self.count,
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"prepare:", self.time_prepare / self.count)
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# publish
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if not self.visualize:
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endstate_pred_to_pub = Float32MultiArray(data=endstate_w.reshape(-1))
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endstate_pred_to_pub.layout.data_offset = int(1000 * odom_sec) % 1000000 # 预测时用的里程计时间戳(ms)
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self.endstate_pub.publish(endstate_pred_to_pub)
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else:
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action_id = np.argmin(score_pred)
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best_endstate_pred = endstate_w[:, action_id].reshape(-1)
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endstate_pred_to_pub = Float32MultiArray(data=best_endstate_pred)
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endstate_pred_to_pub.layout.data_offset = int(1000 * odom_sec) % 1000000 # 预测时用的里程计时间戳(ms)
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self.endstate_pub.publish(endstate_pred_to_pub)
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# visualization
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endstate_score_preds = np.concatenate((endstate_w, score_pred), axis=0)
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all_endstate_pred = Float32MultiArray(data=endstate_score_preds.T.reshape(-1))
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all_endstate_pred.layout.dim.append(MultiArrayDimension())
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all_endstate_pred.layout.dim[0].size = endstate_score_preds.shape[1]
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all_endstate_pred.layout.dim[0].label = "primitive_num"
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all_endstate_pred.layout.dim.append(MultiArrayDimension())
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all_endstate_pred.layout.dim[1].size = endstate_score_preds.shape[0]
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all_endstate_pred.layout.dim[1].label = "endstate_and_score_num"
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self.all_endstate_pub.publish(all_endstate_pred)
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self.goal_pub.publish(Float32MultiArray(data=self.goal))
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else:
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if not self.new_odom: # start a new round
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self.odom_ref_init = False
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def trt_process(self, input_tensor: torch.Tensor, return_all_preds=False) -> torch.Tensor:
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batch_size = input_tensor.shape[0]
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input_tensor = input_tensor.cpu().numpy()
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input_tensor = input_tensor.reshape(batch_size, 10,
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self.lattice_space.horizon_num * self.lattice_space.vertical_num)
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endstate_pred = input_tensor[:, 0:9, :]
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score_pred = input_tensor[:, 9, :]
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if not return_all_preds:
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endstate_prediction = np.zeros((batch_size, 9))
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score_prediction = np.zeros((batch_size, 1))
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for i in range(0, batch_size):
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action_id = np.argmin(score_pred[i])
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lattice_id = self.lattice_space.horizon_num * self.lattice_space.vertical_num - 1 - action_id
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endstate_prediction[i] = self.pred_to_endstate(np.expand_dims(endstate_pred[i, :, action_id], axis=0), lattice_id)
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score_prediction[i] = score_pred[i, action_id]
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else:
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endstate_prediction = np.zeros_like(endstate_pred)
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score_prediction = score_pred
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for i in range(0, self.lattice_space.horizon_num * self.lattice_space.vertical_num):
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lattice_id = self.lattice_space.horizon_num * self.lattice_space.vertical_num - 1 - i
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endstate = self.pred_to_endstate(endstate_pred[:, :, i], lattice_id)
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endstate_prediction[:, :, i] = endstate
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return endstate_prediction, score_prediction
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def prepare_input_observation(self, obs):
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"""
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convert the observation from body frame to primitive frame,
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and then concatenate it with the depth features (to ensure the translational invariance)
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"""
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obs_return = np.ones(
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(obs.shape[0], self.lattice_space.vertical_num, self.lattice_space.horizon_num, obs.shape[1]),
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dtype=np.float32)
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id = 0
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v_b = obs[:, 0:3]
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a_b = obs[:, 3:6]
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g_b = obs[:, 6:9]
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for i in range(self.lattice_space.vertical_num - 1, -1, -1):
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for j in range(self.lattice_space.horizon_num - 1, -1, -1):
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Rbp = self.lattice_primitive.getRotation(id)
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v_p = np.dot(Rbp.T, v_b.T).T
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a_p = np.dot(Rbp.T, a_b.T).T
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g_p = np.dot(Rbp.T, g_b.T).T
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obs_return[:, i, j, 0:3] = v_p
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obs_return[:, i, j, 3:6] = a_p
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obs_return[:, i, j, 6:9] = g_p
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# obs_return[:, i, j, 0:6] = self.normalize_obs(obs_return[:, i, j, 0:6])
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id = id + 1
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obs_return = np.transpose(obs_return, [0, 3, 1, 2])
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return torch.from_numpy(obs_return)
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def pred_to_endstate(self, endstate_pred: np.ndarray, id: int):
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"""
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Transform the predicted state to the body frame.
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"""
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delta_yaw = endstate_pred[:, 0] * self.lattice_primitive.yaw_diff
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delta_pitch = endstate_pred[:, 1] * self.lattice_primitive.pitch_diff
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radio = endstate_pred[:, 2] * self.lattice_space.radio_range + self.lattice_space.radio_range
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yaw, pitch = self.lattice_primitive.getAngleLattice(id)
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endstate_x = np.cos(pitch + delta_pitch) * np.cos(yaw + delta_yaw) * radio
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endstate_y = np.cos(pitch + delta_pitch) * np.sin(yaw + delta_yaw) * radio
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endstate_z = np.sin(pitch + delta_pitch) * radio
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endstate_p = np.stack((endstate_x, endstate_y, endstate_z), axis=1)
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endstate_vp = endstate_pred[:, 3:6] * self.lattice_space.vel_max
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endstate_ap = endstate_pred[:, 6:9] * self.lattice_space.acc_max
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Rbp = self.lattice_primitive.getRotation(id)
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endstate_vb = np.matmul(Rbp, endstate_vp.T).T
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endstate_ab = np.matmul(Rbp, endstate_ap.T).T
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endstate = np.concatenate((endstate_p, endstate_vb, endstate_ab), axis=1)
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endstate[:, [0, 1, 2, 3, 4, 5, 6, 7, 8]] = endstate[:, [0, 3, 6, 1, 4, 7, 2, 5, 8]]
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return endstate
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def normalize_obs(self, vel_acc):
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vel_norm = vel_acc[:, 0:3] / self.lattice_space.vel_max
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acc_norm = vel_acc[:, 3:6] / self.lattice_space.acc_max
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return np.hstack((vel_norm, acc_norm))
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def warm_up(self):
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depth = np.zeros(shape=[1, 1, self.height, self.width], dtype=np.float32)
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obs = np.zeros(shape=[1, 9], dtype=np.float32)
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obs_input = self.prepare_input_observation(obs)
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if self.use_trt:
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trt_output = self.policy(torch.from_numpy(depth).to(self.device), obs_input.to(self.device))
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self.trt_process(trt_output, return_all_preds=True)
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else:
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self.policy.predict(torch.from_numpy(depth).to(self.device), obs_input.to(self.device),
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return_all_preds=True)
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def parser():
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parser = argparse.ArgumentParser()
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parser.add_argument("--use_tensorrt", type=int, default=0, help="use tensorrt or not")
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parser.add_argument("--trial", type=int, default=1, help="trial number")
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parser.add_argument("--epoch", type=int, default=0, help="epoch number")
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parser.add_argument("--iter", type=int, default=0, help="iter number")
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return parser
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# In realworld flight: visualize=False; use_tensorrt=True, and ensure the pitch_angle consistent with your platform
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# When modifying the pitch_angle, there's no need to re-collect and re-train, as all predictions are in the camera coordinate system
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def main():
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args = parser().parse_args()
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rsg_root = os.path.dirname(os.path.abspath(__file__))
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if args.use_tensorrt:
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weight = "yopo_trt.pth"
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else:
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weight = rsg_root + "/saved/YOPO_{}/Policy/epoch{}_iter{}.pth".format(args.trial, args.epoch, args.iter)
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settings = {'use_tensorrt': args.use_tensorrt,
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'network_frequency': 30,
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'img_height': 96,
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'img_width': 160,
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'goal': [20, 20, 2], # the goal
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'env': 'flightmare', # use Realsense D435 or Flightmare Simulator ('435' or 'flightmare')
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'pitch_angle_deg': -5, # pitch of camera, ensure consistent with the simulator or your platform (no need to re-collect and re-train when modifying)
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'odom_topic': '/juliett/ground_truth/odom',
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'depth_topic': '/depth_image',
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'verbose': False, # print the latency?
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'visualize': True # visualize all predictions? set False in real flight
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}
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YopoNet(settings, weight)
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if __name__ == "__main__":
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main()
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