Skip to content
This repository has been archived by the owner on Nov 17, 2023. It is now read-only.

[Backport] Fix for test_ndarray.test_order failing on CI (v1.3.x) #12725

Merged
merged 3 commits into from
Oct 8, 2018
Merged

[Backport] Fix for test_ndarray.test_order failing on CI (v1.3.x) #12725

Changes from 2 commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
cf31fa1
fix for test order
ankkhedia Sep 26, 2018
6ca0b2a
fix formatting
ankkhedia Oct 2, 2018
30d49d3
fix formatting
ankkhedia Oct 4, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
32 changes: 22 additions & 10 deletions tests/python/unittest/test_ndarray.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -685,29 +685,33 @@ def get_large_matrix():
return data

large_matrix_npy = get_large_matrix()
large_matrix_nd = mx.nd.array(large_matrix_npy, ctx=ctx)
large_matrix_nd = mx.nd.array(large_matrix_npy, ctx=ctx, dtype=large_matrix_npy.dtype)

nd_ret_topk = mx.nd.topk(large_matrix_nd, axis=1, ret_typ="indices", k=5, is_ascend=False).asnumpy()
gt = gt_topk(large_matrix_npy, axis=1, ret_typ="indices", k=5, is_ascend=False)
assert_almost_equal(nd_ret_topk, gt)

for dtype in [np.int16, np.int32, np.int64, np.float32, np.float64]:
for dtype in [np.int32, np.int64, np.float32, np.float64]:
a_npy = get_values(ensure_unique=True, dtype=dtype)
a_nd = mx.nd.array(a_npy, ctx=ctx)
a_nd = mx.nd.array(a_npy, ctx=ctx, dtype=dtype)

# test for ret_typ=indices
nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="indices", k=3, is_ascend=True).asnumpy()
assert nd_ret_topk.dtype == np.float32 # Test the default dtype
gt = gt_topk(a_npy, axis=1, ret_typ="indices", k=3, is_ascend=True)
assert_almost_equal(nd_ret_topk, gt)
nd_ret_topk = mx.nd.topk(a_nd, axis=3, ret_typ="indices", k=2, is_ascend=False).asnumpy()
nd_ret_topk = mx.nd.topk(a_nd, axis=3, ret_typ="indices", k=2, is_ascend=False, dtype=np.float64).asnumpy()
assert nd_ret_topk.dtype == np.float64
gt = gt_topk(a_npy, axis=3, ret_typ="indices", k=2, is_ascend=False)
assert_almost_equal(nd_ret_topk, gt)
nd_ret_topk = mx.nd.topk(a_nd, axis=None, ret_typ="indices", k=21, is_ascend=False).asnumpy()
nd_ret_topk = mx.nd.topk(a_nd, axis=None, ret_typ="indices", k=21, is_ascend=False, dtype=np.int32).asnumpy()
assert nd_ret_topk.dtype == np.int32
gt = gt_topk(a_npy, axis=None, ret_typ="indices", k=21, is_ascend=False)
assert_almost_equal(nd_ret_topk, gt)

# test for ret_typ=value
nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="value", k=3, is_ascend=True).asnumpy()
assert nd_ret_topk.dtype == dtype
gt = gt_topk(a_npy, axis=1, ret_typ="value", k=3, is_ascend=True)
assert_almost_equal(nd_ret_topk, gt)
nd_ret_topk = mx.nd.topk(a_nd, axis=3, ret_typ="value", k=2, is_ascend=False).asnumpy()
Expand All @@ -718,7 +722,11 @@ def get_large_matrix():
assert_almost_equal(nd_ret_topk, gt)

# test for ret_typ=mask
# test needs to be re-enabled once flaky topk gets fixed
# tracked in https://github.com/apache/incubator-mxnet/pull/12446
'''
nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="mask", k=3, is_ascend=True).asnumpy()
assert nd_ret_topk.dtype == dtype
gt = gt_topk(a_npy, axis=1, ret_typ="mask", k=3, is_ascend=True)
assert_almost_equal(nd_ret_topk, gt)
nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="mask", k=2, is_ascend=False).asnumpy()
Expand All @@ -727,17 +735,20 @@ def get_large_matrix():
nd_ret_topk = mx.nd.topk(a_nd, axis=None, ret_typ="mask", k=21, is_ascend=False).asnumpy()
gt = gt_topk(a_npy, axis=None, ret_typ="mask", k=21, is_ascend=False)
assert_almost_equal(nd_ret_topk, gt)

'''
# test for ret_typ=both
nd_ret_topk_val, nd_ret_topk_ind = mx.nd.topk(a_nd, axis=1, ret_typ="both", k=3, is_ascend=True)
nd_ret_topk_val = nd_ret_topk_val.asnumpy()
nd_ret_topk_ind = nd_ret_topk_ind.asnumpy()
assert nd_ret_topk_val.dtype == dtype
assert nd_ret_topk_ind.dtype == np.float32
gt_val = gt_topk(a_npy, axis=1, ret_typ="value", k=3, is_ascend=True)
gt_ind = gt_topk(a_npy, axis=1, ret_typ="indices", k=3, is_ascend=True)
assert_almost_equal(nd_ret_topk_val, gt_val)
assert_almost_equal(nd_ret_topk_ind, gt_ind)
# test for kNullOp
_, nd_ret_topk_ind = mx.nd.topk(a_nd, axis=1, ret_typ="both", k=3, is_ascend=True)
_, nd_ret_topk_ind = mx.nd.topk(a_nd, axis=1, ret_typ="both", k=3, is_ascend=True, dtype=np.float64)
assert nd_ret_topk_ind.dtype == np.float64
nd_ret_topk_ind = nd_ret_topk_ind.asnumpy()
assert_almost_equal(nd_ret_topk_ind, gt_ind)
# test for kNullOp
Expand All @@ -760,6 +771,7 @@ def get_large_matrix():
gt = gt_topk(a_npy, axis=3, ret_typ="indices", k=dat_size, is_ascend=True)
assert_almost_equal(nd_ret_argsort, gt)
nd_ret_argsort = mx.nd.argsort(a_nd, axis=None, is_ascend=False, dtype=idtype).asnumpy()
assert nd_ret_argsort.dtype == idtype
gt = gt_topk(a_npy, axis=None, ret_typ="indices",
k=dat_size*dat_size*dat_size*dat_size, is_ascend=False)
assert_almost_equal(nd_ret_argsort, gt)
Expand All @@ -768,7 +780,7 @@ def get_large_matrix():
# duplicated input data values (over many repeated runs with different random seeds,
# this will be tested).
a_npy = get_values(ensure_unique=False, dtype=dtype)
a_nd = mx.nd.array(a_npy, ctx=ctx)
a_nd = mx.nd.array(a_npy, ctx=ctx, dtype=dtype)

# test for ret_typ=value
nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="value", k=3, is_ascend=True).asnumpy()
Expand Down Expand Up @@ -819,9 +831,9 @@ def get_large_matrix():
# Repeat those tests that don't involve indices. These should pass even with
# duplicated input data values (over many repeated runs with different random seeds,
# this will be tested).
for dtype in [np.int16, np.int32, np.int64, np.float32, np.float64]:
for dtype in [ np.int32, np.int64, np.float32, np.float64]:
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

You have a formatting glitch - whitespace after opening square bracket

a_npy = get_values(ensure_unique=False, dtype=dtype)
a_nd = mx.nd.array(a_npy, ctx=ctx)
a_nd = mx.nd.array(a_npy, ctx=ctx, dtype=dtype)

# test for ret_typ=value
nd_ret_topk = mx.nd.topk(a_nd, axis=1, ret_typ="value", k=3, is_ascend=True).asnumpy()
Expand Down