apeforest commented on a change in pull request #15048: [MXNET-1408] Adding
test to verify Large Tensor Support for ravel and unravel
URL: https://github.com/apache/incubator-mxnet/pull/15048#discussion_r289494889
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File path: tests/nightly/test_large_array.py
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@@ -274,11 +265,26 @@ def test_diag():
assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
# random k
- k = np.random.randint(-min(LARGE_X, 64) + 1, min(h, w))
+ k = np.random.randint(-min(LARGE_X, SMALL_Y) + 1, min(LARGE_X, SMALL_Y))
r = mx.nd.diag(a, k=k)
assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k))
+def test_ravel_multi_index():
+ indices_2d = [[LARGE_X-1, LARGE_X-100, 6], [SMALL_Y-1, SMALL_Y-10, 1]]
+ idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64),
shape=(LARGE_X, SMALL_Y))
+ idx_numpy = np.ravel_multi_index(indices_2d, (LARGE_X, SMALL_Y))
+ assert np.sum(1 for i in range(idx.size) if idx[i] == idx_numpy[i]) == 3
+
+
+def test_unravel_index():
+ original_2d_indices = [[LARGE_X-1, LARGE_X-100, 6], [SMALL_Y-1,
SMALL_Y-10, 1]]
Review comment:
same here. can we generate random indices? you can pass in the 2d_array as
input so you only keep one copy
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