当我启动PyCharm for remote python interpreter时,它总是执行"Uploading PyCharm helpers",即使远程机器IP是相同的并且已经包含以前上传的帮助程序.行为是否正确？
在PyCharm(社区版2016.2.3)中,使用anaconda2 + ubuntu 14.04,import matplotlib在调试模式期间导致信号11错误.在发布模式下执行脚本时没有问题.
python代码:
import matplotlib as pt
调试控制台:
连接到pydev调试器(build 162.1967.10)GLib-GIO-Message:使用'memory'GSettings后端.您的设置不会保存或与其他应用程序共享.后端Qt4Agg是交互式后端.打开交互模式.
进程以退出代码139结束(由信号11中断:SIGSEGV)
我的Python代码给出了这个错误：
from cv_bridge import CvBridge, CvBridgeError
导入错误：libcv_bridge.so：无法打开共享对象文件：没有这样的文件或目录。
但是，如果我做 ldd，一切似乎都很好。怎么了？（ROS indigo、ubuntu 14.04 LTS、PyCharm）
user@user-VirtualBox:/opt/ros/indigo/lib$ ldd libcv_bridge.so
linux-vdso.so.1 => (0x00007ffc9439d000)
libopencv_imgproc.so.2.4 => /usr/lib/x86_64-linux-gnu/libopencv_imgproc.so.2.4 (0x00007fcac6b34000)
libopencv_highgui.so.2.4 => /usr/lib/x86_64-linux-gnu/libopencv_highgui.so.2.4 (0x00007fcac68e9000)
libopencv_core.so.2.4 => /usr/lib/x86_64-linux-gnu/libopencv_core.so.2.4 (0x00007fcac64b1000)
libopencv_contrib.so.2.4 => /usr/lib/x86_64-linux-gnu/libopencv_contrib.so.2.4 (0x00007fcac61ce000)
libstdc++.so.6 => /usr/lib/x86_64-linux-gnu/libstdc++.so.6 (0x00007fcac5eca000)
libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007fcac5cb3000)
libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007fcac58ee000)
libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007fcac56d0000)
libtbb.so.2 => /usr/lib/libtbb.so.2 (0x00007fcac549b000)
libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007fcac5195000)
libGL.so.1 => /var/lib/VBoxGuestAdditions/lib/libGL.so.1 (0x00007fcac4f5d000)
libjpeg.so.8 => /usr/lib/x86_64-linux-gnu/libjpeg.so.8 (0x00007fcac4d07000)
libpng12.so.0 => /lib/x86_64-linux-gnu/libpng12.so.0 (0x00007fcac4ae1000)
libtiff.so.5 => /usr/lib/x86_64-linux-gnu/libtiff.so.5 (0x00007fcac486f000)
libjasper.so.1 => /usr/lib/x86_64-linux-gnu/libjasper.so.1 (0x00007fcac4617000)
libIlmImf.so.6 => …考虑代码:
#tensorflow graph
input = tf.some_place_holder
func1 = tf.some_function .....(input)
func2 = tf.some_function .....(func1)
....
#code 1
res1, res2 = sess.run([ func1, func2 ],feed_dict_input)
#code 2
res1 = sess.run([ func1 ],feed_dict_input)
res2 = sess.run([ func2 ],feed_dict_input)
如果我运行代码2,func1会运行两次吗？即func1首先运行以获取res1并再次运行获取res2.
tensorflow是否足够智能以计算依赖关系func1和func2,以便将函数称为minium时间？