@szha @eric-haibin-lin

Thanks for the contribution! I have not done reading all code yet, just one quick comment

@canerturkmen Thanks for the contribution, could you take a look at the test failure?

@canerturkmen Thanks for the contribution, could you take a look at the test failure?

Thanks for the comment @roywei. I understand it was an unrelated random fail of another op's test.

@eric-haibin-lin thanks for your comment Haibin! I think this version should do it (with more verbose indexing of the inputs and outputs).

I believe the test fail is unrelated to the contribution (for commit hash a47a7f0)

FAIL: test_random_size_crop (test_image.TestImage)

----------------------------------------------------------------------

Traceback (most recent call last):

  File "/work/mxnet/tests/python/unittest/common.py", line 177, in test_new

    orig_test(*args, **kwargs)

  File "/work/mxnet/tests/python/unittest/test_image.py", line 368, in test_random_size_crop

    assert ratio[0] <= float(new_w)/new_h <= ratio[1]

AssertionError: 

-------------------- >> begin captured logging << --------------------

common: INFO: Setting test np/mx/python random seeds, use MXNET_TEST_SEED=869412463 to reproduce.

--------------------- >> end captured logging << ---------------------

Thanks for the update. I triggered the CI again and hopefully it passes this time. Are you going to contribute an end2end example for point process model? Or do you have a link to an example for point process?

Thanks @eric-haibin-lin! The model will be part of a separate library which will use the operator.

Apologies for a careless mistake in CONTRIBUTORS.

As far as the example goes, this is rather a "likelihood" that can be reused in point process models, but the operator itself can be used directly to compute the density of a simple Hawkes process model. There's a link in the doc string to a tutorial. I can update with a link later when the model itself is open-sourced (WIP).

The test fail is not related to the operator.

Triggered the test again. This test seems to be disabled on mxnet master. If it does not pass this time, we need to sync with the master branch

Looks like it's another unrelated test fail this time. Anyway, I synced with the latest master. Fingers crossed.

======================================================================
FAIL: Run LeNet-5 inference comparison between MXNet and TensorRT.
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/usr/local/lib/python3.6/dist-packages/nose/case.py", line 198, in runTest
    self.test(*self.arg)
  File "/work/mxnet/tests/python/tensorrt/test_tensorrt_lenet5.py", line 100, in test_tensorrt_inference
    MXNet = %f, TensorRT = %f""" % (mx_pct, trt_pct)
AssertionError: Diff. between MXNet & TensorRT accuracy too high:
           MXNet = 99.130000, TensorRT = 99.140000

Thanks for your contribution! Look forward to your model when it is open sourced