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modify print

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TJU-Lu 2025-10-28 16:30:53 +08:00
parent 6e4e75df0d
commit 3826bf7d34
1 changed files with 15 additions and 16 deletions
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31
YOPO/test_yopo_ros.py
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@ -2,7 +2,6 @@ import rospy
import std_msgs.msg
from nav_msgs.msg import Odometry
from geometry_msgs.msg import PoseStamped
from cv_bridge import CvBridge
from threading import Lock
from sensor_msgs.msg import PointCloud2, PointField, Image
from sensor_msgs import point_cloud2
@ -46,7 +45,6 @@ class YopoNet:
self.device = "cuda" if torch.cuda.is_available() else "cpu"
# variables
# self.bridge = CvBridge()
self.odom = Odometry()
self.odom_init = False
self.last_yaw = 0.0
@ -73,6 +71,7 @@ class YopoNet:
self.time_interpolation = 0.0
self.time_visualize = 0.0
self.count = 0
self.depth_fps = 30 # used only as processing time tolerance for printing logs
# Load Network
if self.use_trt:
@ -334,22 +333,22 @@ class YopoNet:
self.count = self.count + 1
total_time = (time5 - time0) * 1000
tolerance = 30.0
tolerance = 1000.0 / self.depth_fps
if total_time > tolerance:
rospy.logwarn(f"Warn: Processing time {(time5 - time0) * 1000:.2f} ms exceeds {tolerance} ms, may cause message lag!")
print(f"Current Time Consuming:"
f"depth-interpolation: {1000 * (time1 - time0):.2f}ms;"
f"data-prepare: {1000 * (time2 - time1):.2f}ms; "
f"network-inference: {1000 * (time3 - time2):.2f}ms; "
f"post-process: {1000 * (time4 - time3):.2f}ms;"
f"visualize-trajectory: {1000 * (time5 - time4):.2f}ms")
rospy.logwarn(f"Warn: Processing time {(time5 - time0) * 1000:.2f} ms exceeds {tolerance:.2f} ms, may cause message lag!")
print(f"\033[34mCurrent Time Consuming:\033[0m "
f"depth-interpolation: \033[32m{1000 * (time1 - time0):.2f} ms\033[0m; "
f"data-prepare: \033[32m{1000 * (time2 - time1):.2f} ms\033[0m; "
f"network-inference: \033[32m{1000 * (time3 - time2):.2f} ms\033[0m; "
f"post-process: \033[32m{1000 * (time4 - time3):.2f} ms\033[0m; "
f"visualize-trajectory: \033[32m{1000 * (time5 - time4):.2f} ms\033[0m")
if self.verbose or (total_time > tolerance):
print(f"Average Time Consuming:"
f"depth-interpolation: {1000 * self.time_interpolation / self.count:.2f}ms;"
f"data-prepare: {1000 * self.time_prepare / self.count:.2f}ms; "
f"network-inference: {1000 * self.time_forward / self.count:.2f}ms; "
f"post-process: {1000 * self.time_process / self.count:.2f}ms;"
f"visualize-trajectory: {1000 * self.time_visualize / self.count:.2f}ms")
print(f"\033[34mAverage Time Consuming:\033[0m "
f"depth-interpolation: \033[32m{1000 * self.time_interpolation / self.count:.2f} ms\033[0m; "
f"data-prepare: \033[32m{1000 * self.time_prepare / self.count:.2f} ms\033[0m; "
f"network-inference: \033[32m{1000 * self.time_forward / self.count:.2f} ms\033[0m; "
f"post-process: \033[32m{1000 * self.time_process / self.count:.2f} ms\033[0m; "
f"visualize-trajectory: \033[32m{1000 * self.time_visualize / self.count:.2f} ms\033[0m")
def warm_up(self):
depth = torch.zeros((1, 1, self.height, self.width), dtype=torch.float32, device=self.device)