From 31b903845a66b201c69084220ccea1ad0f98dfd0 Mon Sep 17 00:00:00 2001 From: Rohit Kumar Srivastava Date: Fri, 23 Aug 2019 22:04:12 +0000 Subject: [PATCH] removing tests not required for vector testing --- python/mxnet/test_utils.py | 10 +- tests/nightly/test_large_vector.py | 157 +++-------------------------- 2 files changed, 16 insertions(+), 151 deletions(-) diff --git a/python/mxnet/test_utils.py b/python/mxnet/test_utils.py index 30d78d2e1593..bb730fd3a007 100644 --- a/python/mxnet/test_utils.py +++ b/python/mxnet/test_utils.py @@ -264,15 +264,13 @@ def assign_each2(input1, input2, function): # For testing Large Tensors having total size > 2^32 elements def create_2d_tensor(rows, columns, dtype=np.int64): - a = nd.arange(0, rows, dtype=dtype).reshape(rows, 1) - b = nd.broadcast_to(a, shape=(a.shape[0], columns)) - return nd.array(b, dtype=dtype) + a = mx.nd.arange(0, rows, dtype=dtype).reshape(rows, 1) + b = mx.nd.broadcast_to(a, shape=(a.shape[0], columns)) + return b # For testing Large Vectors having total size > 2^32 elements def create_vector(size, dtype=np.int64): - a = nd.arange(0, size, dtype=dtype) - # Implicitly calling nd.waitall() - assert a[0] == 0 + a = mx.nd.arange(0, size, dtype=dtype) return a def rand_sparse_ndarray(shape, stype, density=None, dtype=None, distribution=None, diff --git a/tests/nightly/test_large_vector.py b/tests/nightly/test_large_vector.py index 779afd5cb9b2..bb6f01c77772 100644 --- a/tests/nightly/test_large_vector.py +++ b/tests/nightly/test_large_vector.py @@ -18,32 +18,22 @@ import numpy as np import mxnet as mx -from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, default_context, create_vector +from mxnet.test_utils import rand_ndarray, assert_almost_equal, rand_coord_2d, create_vector from mxnet import gluon, nd from tests.python.unittest.common import with_seed # dimension constants LARGE_X = 5000000000 MEDIUM_X = 1000000000 -LARGE_Y = 100000 -SMALL_Y = 1 def test_slice(): a = nd.ones(LARGE_X) res = nd.slice(a, begin=(LARGE_X - MEDIUM_X), end=LARGE_X) + assert a[0] == 1 assert res.shape[0] == MEDIUM_X -def test_gluon_embedding(): - m = gluon.nn.Embedding(1, LARGE_Y) - m.initialize() - a = nd.zeros((LARGE_Y, 1)) - b = m(a) - assert b.shape == (LARGE_Y, 1, LARGE_Y) - assert b.asnumpy().size == LARGE_X*2 - - def test_ndarray_zeros(): a = nd.zeros(shape=LARGE_X) assert a[-1] == 0 @@ -93,36 +83,21 @@ def test_elementwise(): def test_reduce(): - a = nd.ones(shape=(LARGE_X, SMALL_Y)) + a = nd.ones(shape=(LARGE_X, 1)) assert nd.sum(a).asnumpy() == a.shape[0] * a.shape[1] -def test_broadcast(): - a = nd.ones(shape=(LARGE_X, SMALL_Y*2)) - b = nd.arange(0, LARGE_X).reshape(LARGE_X, 1) - res = nd.broadcast_to(b, shape=(b.shape[0], SMALL_Y*2)) - assert np.sum(res[-1].asnumpy() == LARGE_X) == res.shape[1] - res = mx.nd.broadcast_like(b, a) - assert np.sum(res[-1].asnumpy() == LARGE_X) == res.shape[1] - - def test_clip(): - a = nd.arange(0, LARGE_X) + a = create_vector(LARGE_X) res = nd.clip(a, a_min=100, a_max=1000) assert np.sum(res[-1].asnumpy() == 1000) == 1 def test_argmin(): - a = nd.arange(0, LARGE_X) + a = create_vector(LARGE_X) + assert a[0] == 0 idx = mx.nd.argmin(a, axis=0) - assert idx.shape[0] == SMALL_Y - - -def test_tile(): - a = nd.arange(0, LARGE_X) - b = nd.tile(a, reps=(1,2)) - assert b[0][LARGE_X] == b[0][0] - assert b[0][LARGE_X-1] == b[0][-1] + assert idx.shape[0] == 1 def test_take(): @@ -169,114 +144,6 @@ def test_Dense(ctx=mx.cpu(0)): assert res.shape == (LARGE_X, 2) -def test_pick(): - a = mx.nd.ones(shape=(LARGE_X, 2)) - b = mx.nd.ones(shape=LARGE_X) - res = mx.nd.pick(a, b) - assert res.shape == b.shape - - -def test_depthtospace(): - def numpy_depth_to_space(x, blocksize): - b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3] - tmp = np.reshape(x, [b, blocksize, blocksize, c // (blocksize**2), h, w]) - tmp = np.transpose(tmp, [0, 3, 4, 1, 5, 2]) - y = np.reshape(tmp, [b, c // (blocksize**2), h * blocksize, w * blocksize]) - return y - - shape_inp = (LARGE_X, 4, 1, 1) - data = rand_ndarray(shape_inp, 'default') - data_np = data.asnumpy() - expected = numpy_depth_to_space(data_np, 2) - output = mx.nd.depth_to_space(data, 2) - assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3) - - -def test_spacetodepth(): - def numpy_space_to_depth(x, blocksize): - b, c, h, w = x.shape[0], x.shape[1], x.shape[2], x.shape[3] - tmp = np.reshape(x, [b, c, h // blocksize, blocksize, w // blocksize, blocksize]) - tmp = np.transpose(tmp, [0, 3, 5, 1, 2, 4]) - y = np.reshape(tmp, [b, c * (blocksize**2), h // blocksize, w // blocksize]) - return y - - shape_inp = (LARGE_X, 1, 2, 2) - data = rand_ndarray(shape_inp, 'default') - data_np = data.asnumpy() - expected = numpy_space_to_depth(data_np, 2) - output = mx.nd.space_to_depth(data, 2) - assert_almost_equal(output.asnumpy(), expected, atol=1e-3, rtol=1e-3) - -@with_seed() -def test_diag(): - a_np = np.random.random((LARGE_X, 2)).astype(np.float32) - a = mx.nd.array(a_np) - - # k == 0 - r = mx.nd.diag(a) - assert_almost_equal(r.asnumpy(), np.diag(a_np)) - - # k == 1 - k = 1 - r = mx.nd.diag(a, k=k) - assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k)) - - # k == -1 - k = -1 - r = mx.nd.diag(a, k=k) - assert_almost_equal(r.asnumpy(), np.diag(a_np, k=k)) - - -@with_seed() -def test_ravel_multi_index(): - x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, SMALL_Y, 4) - x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, SMALL_Y, 3) - x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, SMALL_Y, 2) - indices_2d = [[x1, x2, x3], [y1, y2, y3]] - idx = mx.nd.ravel_multi_index(mx.nd.array(indices_2d, dtype=np.int64), shape=(LARGE_X, 5)) - idx_numpy = np.ravel_multi_index(indices_2d, (LARGE_X, 5)) - assert np.sum(1 for i in range(idx.size) if idx[i] == idx_numpy[i]) == 3 - - -@with_seed() -def test_unravel_index(): - x1, y1 = rand_coord_2d((LARGE_X - 100), LARGE_X, SMALL_Y, 4) - x2, y2 = rand_coord_2d((LARGE_X - 200), LARGE_X, SMALL_Y, 3) - x3, y3 = rand_coord_2d((LARGE_X - 300), LARGE_X, SMALL_Y, 2) - original_2d_indices = [[x1, x2, x3], [y1, y2, y3]] - idx_numpy = np.ravel_multi_index(original_2d_indices, (LARGE_X, 5)) - indices_2d = mx.nd.unravel_index(mx.nd.array(idx_numpy, dtype=np.int64), shape=(LARGE_X, 5)) - assert (indices_2d.asnumpy() == np.array(original_2d_indices)).all() - - -def test_transpose(): - b = nd.arange(0, LARGE_X, dtype=np.int64).reshape(1, LARGE_X) - t = b.T - assert t.shape == (LARGE_X, 1) - assert t[-1, 0].asnumpy() == (LARGE_X - 1) - - -def test_swapaxes(): - b = nd.arange(0, LARGE_X, dtype=np.int64).reshape(LARGE_X, 1) - t = nd.swapaxes(b, dim1=0, dim2=1) - assert t.shape == (1, LARGE_X) - assert t[0, -1].asnumpy() == (LARGE_X - 1) - - -def test_flip(): - b = nd.arange(0, LARGE_X, dtype=np.int64).reshape(1, LARGE_X) - t = nd.flip(b, axis=1) - assert t.shape == (1, LARGE_X) - assert t[-1, -1].asnumpy() == 0 - - -def test_softmax(): - input_data = nd.ones((2, LARGE_X)) - output = nd.softmax(input_data, axis=0) - assert output[0][0] == 0.5 - assert output[-1][-1] == 0.5 - - def test_argsort(): b = create_vector(size=LARGE_X) s = nd.argsort(b, axis=0, is_ascend=False, dtype=np.int64) @@ -287,19 +154,19 @@ def test_argsort(): def test_sort(): b = create_vector(size=LARGE_X) s = nd.sort(b, axis=0, is_ascend=False) - assert np.sum(s[-1][SMALL_Y//2:SMALL_Y].asnumpy() == 0).all() + assert np.sum(s[-1].asnumpy() == 0).all() s = nd.sort(b, is_ascend=True) assert np.sum(s[0].asnumpy() == 0).all() def test_topk(): b = create_vector(size=LARGE_X) - k = nd.topk(b, k=10, axis=0, dtype=np.int64) - assert np.sum(k.asnumpy() == (LARGE_X - 1)) == SMALL_Y + ind = nd.topk(b, k=10, axis=0, dtype=np.int64) + assert np.sum(ind.asnumpy() == (LARGE_X - 1)) == 1 ind, val = mx.nd.topk(b, k=3, axis=0, dtype=np.int64, ret_typ="both", is_ascend=False) assert np.all(ind == val) - l = nd.topk(b, k=1, axis=0, dtype=np.int64, ret_typ="value") - assert l.sum() == (LARGE_X - 1) + val = nd.topk(b, k=1, axis=0, dtype=np.int64, ret_typ="value") + assert val.sum() == (LARGE_X - 1) if __name__ == '__main__':