# Copyright 1999-2020 Alibaba Group Holding Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import unittest from mars.lib.mmh3 import hash_from_buffer as mmh3_hash_from_buffer from mars.session import new_session from mars import tensor as mt from mars.tensor.utils import hash_on_axis, normalize_axis_tuple, fetch_corner_data class Test(unittest.TestCase): def testHashOnAxis(self): hash_from_buffer = lambda x: mmh3_hash_from_buffer(memoryview(x)) a = np.random.rand(10) result = hash_on_axis(a, 0, 3) expected = np.array([mmh3_hash_from_buffer(element) % 3 for element in a]) np.testing.assert_array_equal(result, expected) result = hash_on_axis(a, 0, 1) expected = np.array([0 for _ in a]) np.testing.assert_array_equal(result, expected) a = np.random.rand(10, 5) result = hash_on_axis(a, 0, 3) expected = np.array([hash_from_buffer(a[i, :]) % 3 for i in range(a.shape[0])]) np.testing.assert_array_equal(result, expected) result = hash_on_axis(a, 1, 3) expected = np.array([hash_from_buffer(a[:, i]) % 3 for i in range(a.shape[1])]) np.testing.assert_array_equal(result, expected) a = np.random.rand(10, 5, 4) result = hash_on_axis(a, 2, 3) expected = np.array([hash_from_buffer(a[:, :, i]) % 3 for i in range(a.shape[2])]) np.testing.assert_array_equal(result, expected) def testNormalizeAxisTuple(self): self.assertEqual(normalize_axis_tuple(-1, 3), (2,)) self.assertEqual(normalize_axis_tuple([0, -2], 3), (0, 1)) self.assertEqual(sorted(normalize_axis_tuple({0, -2}, 3)), [0, 1]) with self.assertRaises(ValueError) as cm: normalize_axis_tuple((1, -2), 3, argname='axes') self.assertIn('axes', str(cm.exception)) with self.assertRaises(ValueError): normalize_axis_tuple((1, -2), 3) def testFetchTensorCornerData(self): sess = new_session() print_options = np.get_printoptions() # make sure numpy default option self.assertEqual(print_options['edgeitems'], 3) self.assertEqual(print_options['threshold'], 1000) size = 12 for i in (2, 4, size - 3, size, size + 3): arr = np.random.rand(i, i, i) t = mt.tensor(arr, chunk_size=size // 2) sess.run(t, fetch=False) corner_data = fetch_corner_data(t, session=sess) corner_threshold = 1000 if t.size < 1000 else corner_data.size - 1 with np.printoptions(threshold=corner_threshold, suppress=True): # when we repr corner data, we need to limit threshold that # it's exactly less than the size repr_corner_data = repr(corner_data) with np.printoptions(suppress=True): repr_result = repr(arr) self.assertEqual(repr_corner_data, repr_result, 'failed when size == {}'.format(i))