Remote Debugging with Eclipse

I am not familiar with Eclipse, as I prefer to use CLion. However, for projects that do not use CMake build system, I will have to use Eclipse.

In this post, I will discuss how to remote-debug with Eclipse. The setup is as follows:

target (local): running the application from, say terminal
host (Eclipse): debug the program as it is running

First, open up the project with Eclipse. Make sure that Eclipse can build the project.

Next, setup gdbserver on the target:
$ gdbserver :7777 EXECUTABLE ARG1 ARG2 ...

Here, 7777 is the port we will use for remote-debugging, EXECUTABLE is the binary file we are going to debug as it is running, and ARG1, ARG2, ... are appropriate arguments for this program.

Next, we setup Eclipse debugging.
From the menu, select Run --> Debug Configurations... --> C/C++ Remote Application (double click) --> Using GDB (DSF) Auto Remote Debugging Launcher (Select other) --> GDB (DSF) Manual Remote Debugging Launcher --> OK. Basically, we have selected "manual" remote debugging configuration here.

Make sure Project and C/C++ Application fields are properly filled, i.e., you should be able to select the project from the drop down menu if the project import/build is successful, and choose the EXECUTABLE for C/C++ Application.

In the Debugger tab --> Connection tab, change Port Number to 7777.

Finally, click Debug button. You now should be able to remote debug with Eclipse.

Happy hacking!

Debugging Python Module Built with Pybind11

Pybind11 is a wonderful library to build native c++ functions callable within Python. Let's see how we can debug a module built with Pybind11.

For this project, we will use the very simple Pybind11 example module. Let's get started.

Clone the repository
$ git clone https://github.com/pybind/python_example.git
$ cd python_example

Install the package
$ python setup.py install

Run gdb on Python
$ gdb -q python

Break into the desired function, such as
(gdb) b add
(gdb) b src/main.cpp:4

Run the command
(gdb) r tests/test.py

That is it!

Network Visualization with Intermediate Layer Shapes

Network visualization is very important. Looking at the network graph is much easier than reading through the network description. In this post, I will discuss how to create visual graph of a (Pytorch) network using Netron and include intermediate layer shapes as well.

Netron is an excellent tool for network visualization. At the moment, however, Netron does not support Pytorch natively (experimental feature but not stable). The best thing is to convert Pytorch model to ONNX and then use Netron to graph it.

The following is an example code that graphs ResNet50.
import torch
from torchvision import models
import onnx
from onnx import shape_inference

DEVICE = 'cuda:1'
PATH = 'resnet.onnx'
model = models.resnet50().to(DEVICE)
model.eval()
dummy_input = torch.randn(1,3,224,224).to(DEVICE)
torch.onnx.export(model, dummy_input, PATH, verbose=False)
onnx.save(shape_inference.infer_shapes(onnx.load(PATH)), PATH) # this is required for displaying intermediate shapes



To graph it, simply download Netron and run
$ netron resnet.onnx

That's it!