Accuracy in cubes

[Levy-Yuwei]

Hi, I've trained a model with your code in cubes dataset and saved checkpoints every 10 epoches. However, when I test on these checkpoints using test_job_1.py , the output accuracy seems to be around 0.9 from epoch 10 to epoch 790. This is quite different from the result in the paper, which is 94.39%.

The only modifications I made are:

1. move the # Create the checkpoint subfolder if nonexistent. part below # If nonexistent, create the subfolder of the log folder associated with # the current training job. Otherwise, verify if a saved checkpoint can # be found, and in that case assume that training should continued from # it. because it originally makes self.__found_job_folder to be always True even when you create a new train task.

2. add 'checkpoint_epoch_frequency':10 in TRAINING_PARAMS

The environment I run your code is: Ubuntu 18.04.1 LTS NVIDIA 2080TI torch related packages: torch 1.4.0 torch-cluster 1.4.5 torch-geometric 1.3.2 torch-scatter 1.4.0 torch-sparse 0.4.3 torch-spline-conv 1.1.1 torchvision 0.5.0

The accuracy for each 10 epoch is as follow: 10 0.7784522003034902 20 0.881638846737481 30 0.8755690440060698 40 0.834597875569044 50 0.8452200303490136 60 0.8603945371775418 70 0.8877086494688923 80 0.8179059180576631 90 0.8983308042488619 100 0.9089529590288316 110 0.8937784522003035 120 0.9165402124430956 130 0.8998482549317147 140 0.8831562974203339 150 0.8725341426403642 160 0.8877086494688923 170 0.8801213960546282 180 0.834597875569044 190 0.8937784522003035 200 0.8968133535660091 210 0.8937784522003035 220 0.8907435508345979 230 0.9013657056145675 240 0.8968133535660091 250 0.8861911987860395 260 0.8968133535660091 270 0.9089529590288316 280 0.8877086494688923 290 0.8998482549317147 300 0.9028831562974203 310 0.8998482549317147 320 0.8998482549317147 330 0.8952959028831563 340 0.8937784522003035 350 0.9059180576631259 360 0.8877086494688923 370 0.9013657056145675 380 0.8968133535660091 390 0.9074355083459787 400 0.8907435508345979 410 0.8922610015174507 420 0.9028831562974203 430 0.8937784522003035 440 0.8998482549317147 450 0.9013657056145675 460 0.8998482549317147 470 0.9059180576631259 480 0.8725341426403642 490 0.8952959028831563 500 0.8892261001517451 510 0.8983308042488619 520 0.8861911987860395 530 0.8998482549317147 540 0.91350531107739 550 0.8968133535660091 560 0.8952959028831563 570 0.9028831562974203 580 0.866464339908953 590 0.8998482549317147 600 0.8983308042488619 610 0.8907435508345979 620 0.8725341426403642 630 0.9028831562974203 640 0.8573596358118362 650 0.8679817905918058 660 0.9044006069802731 670 0.8755690440060698 680 0.9028831562974203 690 0.9059180576631259 700 0.9089529590288316 710 0.9013657056145675 720 0.8482549317147192 730 0.8952959028831563 740 0.8998482549317147 750 0.8755690440060698 760 0.8937784522003035 770 0.9059180576631259 780 0.8892261001517451 790 0.9074355083459787

Maybe I've missed something, could you pls help me with it?

Best.