what's the test data?

[hitxiaoting]

Hi,

I tried to test the trained model, but I can't find the right test data in the config.py (TEST_DATA = '/datasets/liver/lspig_affine_test_3d.h5'). It seems that you used the preprocessed test data? can you provide the test data?

[hitxiaoting]

2. Why are the init_dice and dice unchanged during the first 100 ep? Is there something wrong with my training?

there is the log: Number of data in combine-train is 1025 Number of data in sliver-val is 19 Number of data in lits-val is 131 Number of data in lspig-val is 34 start training! 1 0.7367 0.7367 2 0.7424 0.7424 3 0.8181 0.8181 4 0.8623 0.8623 5 0.7958 0.7958 6 0.8404 0.8404 7 0.8432 0.8432 8 0.8039 0.8039 9 0.7727 0.7727 10 0.6873 0.6873 11 0.8171 0.8171 12 0.7196 0.7196 13 0.7786 0.7786 14 0.7673 0.7673 15 0.4266 0.4266 16 0.6368 0.6368 17 0.7970 0.7970 18 0.7799 0.7799 19 0.8095 0.8095 20 0.7448 0.7448 21 0.5320 0.5320 22 0.7305 0.7305 23 0.4475 0.4475 24 0.7091 0.7091 25 0.6955 0.6955 26 0.8044 0.8044 27 0.8705 0.8705 28 0.8099 0.8099 29 0.8085 0.8085 30 0.7590 0.7590 31 0.8467 0.8467 32 0.7659 0.7659 33 0.4205 0.4205 34 0.8080 0.8080 35 0.8509 0.8509 36 0.6484 0.6484 37 0.8460 0.8460 38 0.7490 0.7490 39 0.7785 0.7785 40 0.8021 0.8021 41 0.5664 0.5664 42 0.8024 0.8024 43 0.7085 0.7085 44 0.8203 0.8203 45 0.7671 0.7671 46 0.8082 0.8082 47 0.8068 0.8068 48 0.7761 0.7761 49 0.6964 0.6964 50 0.9018 0.9018 51 0.7247 0.7247 52 0.7643 0.7643 53 0.7813 0.7813 54 0.7564 0.7564 55 0.7361 0.7361 56 0.7870 0.7870 57 0.7829 0.7829 58 0.8236 0.8236 59 0.8188 0.8188 60 0.6168 0.6168 61 0.7109 0.7109 62 0.7636 0.7636 63 0.7866 0.7866 64 0.7220 0.7220 65 0.7810 0.7810 66 0.5895 0.5895 67 0.7467 0.7467 68 0.6633 0.6633 69 0.8002 0.8002 70 0.7011 0.7011 71 0.8119 0.8119 72 0.7412 0.7412 73 0.7666 0.7666 74 0.7417 0.7417 75 0.7154 0.7154 76 0.7312 0.7312 77 0.7254 0.7254 78 0.8062 0.8062 79 0.7590 0.7590 80 0.7032 0.7032 81 0.8269 0.8269 82 0.7603 0.7603 83 0.7347 0.7347 84 0.8269 0.8269 85 0.7470 0.7470 86 0.8150 0.8150 87 0.7413 0.7413 88 0.7485 0.7485 89 0.7555 0.7555 90 0.7575 0.7575 91 0.7978 0.7978 92 0.7066 0.7066 93 0.7734 0.7734 94 0.7871 0.7871 95 0.8375 0.8375 96 0.7074 0.7074 97 0.7436 0.7436 98 0.7733 0.7733 99 0.7960 0.7960 ------------------------Saving model------------------ ------------------------Model saved!------------------ ep-100- 7: -- critic: 225.4139, alpha: 0.3679, reg: 1.5779, reward: 0.000, score: 0.924, init_score: 0.924 ep-100-15: -- critic: 163.2971, alpha: 0.3679, reg: 1.5373, reward: 0.000, score: 0.924, init_score: 0.924 ep-110- 7: -- critic: 194.0040, alpha: 0.3681, reg: 1.5689, reward: 0.000, score: 0.805, init_score: 0.805 ep-110-15: -- critic: 185.1218, alpha: 0.3681, reg: 1.5527, reward: 0.000, score: 0.805, init_score: 0.805 ep-120- 7: -- critic: 163.0904, alpha: 0.3682, reg: 1.5526, reward: 0.000, score: 0.820, init_score: 0.820 ep-120-15: -- critic: 201.7190, alpha: 0.3683, reg: 1.5674, reward: 0.000, score: 0.820, init_score: 0.820

[Algolzw]

Hi,

I tried to test the trained model, but I can't find the right test data in the config.py (TEST_DATA = '/datasets/liver/lspig_affine_test_3d.h5'). It seems that you used the preprocessed test data? can you provide the test data?

1. For the first question, we just perform a very simple affine transformation to pre-align test images, please refer to this code.

2. The training log is fine because we have set the "bootstrap epoch" to 100 in the config file [EP_BOOTSTRAP]. That means the agent would like to randomly run 100 epochs without learning it. And the learning process will start after BOOTSTRAP epochs.

[Baichenjia]

Hi, Algolzw @Algolzw . I also have some problem about the right test data.

If I set the test data to be e.g., TEST_DATA = 'datasets/lspig_val.h5', then I have error like KeyError: "Unable to open object (object 'moving_0' doesn't exist)"

It seems that training data cannot be used for testing since the BrainData class in dataloader.py have different key settings compared to the training data. BrainData class is written for some specificed datasets. Should I writte a new BrainData class for testing?

Thank you very much!

[Algolzw]

@Baichenjia

In our work, the testing data are preprocessed and saved in a specific format. You absolutely can save your preprocessed data in your format and rebuild a new dataloader for it.

Good luck!

[Baichenjia]

Thanks for your reply.

Another quick question is why the dice score seems also high even before traning (i.e., 0.7~0.8)?

[Algolzw]

That is caused by the large initial overlapping between fixed and moving 3D images (as shown in Fig.4 in our paper).

[rraayyii]

In your training log, why is the score from epoch 100 higher than that from epoch 120?

Also, why is the reward metric always zero?