| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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 mxnet as mx |
| import numpy as np |
| from distutils.version import LooseVersion |
| import os |
| import pickle as pkl |
| import unittest |
| from nose.tools import raises |
| from common import setup_module, with_seed, assertRaises, TemporaryDirectory, teardown |
| from mxnet.test_utils import almost_equal |
| from mxnet.test_utils import assert_almost_equal, assert_exception |
| from mxnet.test_utils import default_context |
| from mxnet.test_utils import np_reduce |
| from mxnet.test_utils import same |
| from mxnet.test_utils import random_sample, rand_shape_nd |
| from mxnet import runtime |
| from numpy.testing import assert_allclose |
| import mxnet.autograd |
| |
| |
| def check_with_uniform(uf, arg_shapes, dim=None, npuf=None, rmin=-10, type_list=[np.float32]): |
| """check function consistency with uniform random numbers""" |
| if isinstance(arg_shapes, int): |
| assert dim |
| shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim)) |
| arg_shapes = [shape] * arg_shapes |
| for dtype in type_list: |
| ndarray_arg = [] |
| numpy_arg = [] |
| for s in arg_shapes: |
| npy = np.random.uniform(rmin, 10, s).astype(dtype) |
| narr = mx.nd.array(npy, dtype=dtype) |
| ndarray_arg.append(narr) |
| numpy_arg.append(npy) |
| out1 = uf(*ndarray_arg) |
| if npuf is None: |
| out2 = uf(*numpy_arg).astype(dtype) |
| else: |
| out2 = npuf(*numpy_arg).astype(dtype) |
| |
| assert out1.shape == out2.shape |
| if isinstance(out1, mx.nd.NDArray): |
| out1 = out1.asnumpy() |
| if dtype == np.float16: |
| assert_almost_equal(out1, out2, rtol=2e-3, atol=1e-5) |
| else: |
| assert_almost_equal(out1, out2, atol=1e-5) |
| |
| |
| def random_ndarray(dim): |
| shape = tuple(np.random.randint(1, int(1000**(1.0/dim)), size=dim)) |
| data = mx.nd.array(np.random.uniform(-10, 10, shape)) |
| return data |
| |
| |
| @with_seed() |
| def test_ndarray_setitem(): |
| shape = (3, 4, 2) |
| |
| # scalar assignment |
| x = mx.nd.zeros(shape) |
| x[:] = 1 |
| x_np = np.ones(shape, dtype=x.dtype) |
| assert same(x.asnumpy(), x_np) |
| |
| # ndarray assignment |
| x = mx.nd.zeros(shape) |
| x[:] = mx.nd.ones(shape) |
| x_np = np.ones(shape, dtype=x.dtype) |
| assert same(x.asnumpy(), x_np) |
| |
| # numpy assignment |
| x = mx.nd.zeros(shape) |
| x[:] = np.ones(shape) |
| x_np = np.ones(shape, dtype=x.dtype) |
| assert same(x.asnumpy(), x_np) |
| |
| # indexing sub-arrays |
| x = mx.nd.zeros(shape) |
| x[1] = 1 |
| x_np = np.zeros(shape, dtype=x.dtype) |
| x_np[1] = 1 |
| assert same(x.asnumpy(), x_np) |
| x[-1] = 1 |
| x_np[-1] = 1 |
| assert same(x.asnumpy(), x_np) |
| |
| # short all-dim indexing |
| x = mx.nd.zeros(shape) |
| val = mx.nd.ones((3, 2)) |
| x[:, 1:3, 1] = val |
| x_np = np.zeros(shape, dtype=x.dtype) |
| x_np[:, 1:3, 1] = val.asnumpy() |
| assert same(x.asnumpy(), x_np) |
| x[:, 1:3, -1] = val |
| x_np[:, 1:3, -1] = val.asnumpy() |
| assert same(x.asnumpy(), x_np) |
| |
| x = mx.nd.zeros(shape) |
| x[:, 1:3, 1:2] = 1 |
| x_np = np.zeros(shape, dtype=x.dtype) |
| x_np[:, 1:3, 1:2] = 1 |
| assert same(x.asnumpy(), x_np) |
| x[:, -3:-1, -2:-1] = 1 |
| x_np[:, -3:-1, -2:-1] = 1 |
| assert same(x.asnumpy(), x_np) |
| |
| # numpy assignment for empty axis |
| for trivial_shape in [(), (1,), (1, 1), (1, 1, 1)]: |
| if trivial_shape == tuple(): |
| with mx.np_shape(): |
| x = mx.nd.zeros(trivial_shape) |
| else: |
| x = mx.nd.zeros(trivial_shape) |
| x[:] = np.ones(trivial_shape) |
| x_np = np.ones(trivial_shape, dtype=x.dtype) |
| assert x.shape == trivial_shape |
| assert same(x.asnumpy(), x_np) |
| |
| |
| @with_seed() |
| def test_ndarray_elementwise(): |
| nrepeat = 10 |
| maxdim = 4 |
| all_type = [np.float32, np.float64, np.float16, np.uint8, np.int8, np.int32, np.int64] |
| real_type = [np.float32, np.float64, np.float16] |
| for repeat in range(nrepeat): |
| for dim in range(1, maxdim): |
| check_with_uniform(lambda x, y: x + y, 2, dim, type_list=all_type) |
| check_with_uniform(lambda x, y: x - y, 2, dim, type_list=all_type) |
| check_with_uniform(lambda x, y: x * y, 2, dim, type_list=all_type) |
| check_with_uniform(lambda x, y: x / y, 2, dim, type_list=real_type) |
| check_with_uniform(lambda x, y: x / y, 2, dim, rmin=1, type_list=all_type) |
| check_with_uniform(mx.nd.sqrt, 1, dim, np.sqrt, rmin=0) |
| check_with_uniform(mx.nd.square, 1, dim, np.square, rmin=0) |
| check_with_uniform(lambda x: mx.nd.norm(x).asscalar(), 1, dim, np.linalg.norm) |
| |
| |
| @with_seed() |
| def test_ndarray_elementwisesum(): |
| ones = mx.nd.ones((10,), dtype=np.int32) |
| res = mx.nd.ElementWiseSum(ones, ones*2, ones*4, ones*8) |
| assert same(res.asnumpy(), ones.asnumpy()*15) |
| |
| |
| @with_seed() |
| def test_ndarray_negate(): |
| npy = np.random.uniform(-10, 10, (2,3,4)) |
| arr = mx.nd.array(npy) |
| assert_almost_equal(npy, arr.asnumpy()) |
| assert_almost_equal(-npy, (-arr).asnumpy()) |
| |
| # a final check to make sure the negation (-) is not implemented |
| # as inplace operation, so the contents of arr does not change after |
| # we compute (-arr) |
| assert_almost_equal(npy, arr.asnumpy()) |
| |
| |
| @with_seed() |
| def test_ndarray_reshape(): |
| tensor = (mx.nd.arange(30) + 1).reshape(2, 3, 5) |
| true_res = mx.nd.arange(30) + 1 |
| assert same(tensor.reshape((-1,)).asnumpy(), true_res.asnumpy()) |
| assert same(tensor.reshape((2, -1)).asnumpy(), true_res.reshape(2, 15).asnumpy()) |
| assert same(tensor.reshape((0, -1)).asnumpy(), true_res.reshape(2, 15).asnumpy()) |
| assert same(tensor.reshape((-1, 2)).asnumpy(), true_res.reshape(15, 2).asnumpy()) |
| assert same(tensor.reshape(6, 5).asnumpy(), true_res.reshape(6, 5).asnumpy()) |
| assert same(tensor.reshape(-1, 2).asnumpy(), true_res.reshape(15, 2).asnumpy()) |
| assert same(tensor.reshape(-1).asnumpy(), true_res.asnumpy()) |
| assert same(tensor.reshape(30).asnumpy(), true_res.asnumpy()) |
| assert same(tensor.reshape(0, -1).asnumpy(), true_res.reshape(2, 15).asnumpy()) |
| assert same(tensor.reshape(-1, 6).asnumpy(), true_res.reshape(5, 6).asnumpy()) |
| assert same(tensor.reshape(-2,).asnumpy(), true_res.reshape(2, 3, 5).asnumpy()) |
| assert same(tensor.reshape(-3, -1).asnumpy(), true_res.reshape(6, 5).asnumpy()) |
| assert same(tensor.reshape(-1, 15).reshape(0, -4, 3, -1).asnumpy(), true_res.reshape(2, 3, 5).asnumpy()) |
| assert same(tensor.reshape(-1, 0).asnumpy(), true_res.reshape(10, 3).asnumpy()) |
| assert same(tensor.reshape(-1, 0, reverse=True).asnumpy(), true_res.reshape(6, 5).asnumpy()) |
| |
| |
| @with_seed() |
| def test_ndarray_choose(): |
| shape = (100, 20) |
| npy = np.arange(np.prod(shape)).reshape(shape) |
| arr = mx.nd.array(npy) |
| nrepeat = 3 |
| for repeat in range(nrepeat): |
| indices = np.random.randint(shape[1], size=shape[0]) |
| assert same(npy[np.arange(shape[0]), indices], |
| mx.nd.choose_element_0index(arr, mx.nd.array(indices)).asnumpy()) |
| |
| |
| @with_seed() |
| def test_ndarray_fill(): |
| shape = (100, 20) |
| npy = np.arange(np.prod(shape)).reshape(shape) |
| arr = mx.nd.array(npy) |
| new_npy = npy.copy() |
| nrepeat = 3 |
| for repeat in range(nrepeat): |
| indices = np.random.randint(shape[1], size=shape[0]) |
| val = np.random.randint(shape[1], size=shape[0]) |
| new_npy[:] = npy |
| new_npy[np.arange(shape[0]), indices] = val |
| assert same(new_npy, |
| mx.nd.fill_element_0index(arr, mx.nd.array(val), mx.nd.array(indices)).asnumpy()) |
| |
| |
| @with_seed() |
| def test_ndarray_onehot(): |
| shape = (100, 20) |
| npy = np.arange(np.prod(shape)).reshape(shape) |
| arr = mx.nd.array(npy) |
| nrepeat = 3 |
| for repeat in range(nrepeat): |
| indices = np.random.randint(shape[1], size=shape[0]) |
| npy[:] = 0.0 |
| npy[np.arange(shape[0]), indices] = 1.0 |
| mx.nd.onehot_encode(mx.nd.array(indices), out=arr) |
| assert same(npy, arr.asnumpy()) |
| |
| |
| def test_init_from_scalar(): |
| npy = np.ones([]) |
| arr = mx.nd.array(npy) |
| assert arr.shape == () |
| assert same(npy, arr.asnumpy()) |
| |
| |
| @with_seed() |
| def test_ndarray_copy(): |
| c = mx.nd.array(np.random.uniform(-10, 10, (10, 10))) |
| d = c.copyto(mx.Context('cpu', 0)) |
| assert np.sum(np.abs(c.asnumpy() != d.asnumpy())) == 0.0 |
| |
| |
| @with_seed() |
| def test_ndarray_scalar(): |
| c = mx.nd.empty((10,10)) |
| d = mx.nd.empty((10,10)) |
| c[:] = 0.5 |
| d[:] = 1.0 |
| d -= c * 2 / 3 * 6.0 |
| c += 0.5 |
| assert(np.sum(c.asnumpy()) - 100 < 1e-5) |
| assert(np.sum(d.asnumpy()) + 100 < 1e-5) |
| c[:] = 2 |
| assert(np.sum(c.asnumpy()) - 200 < 1e-5) |
| d = -c + 2 |
| assert(np.sum(d.asnumpy()) < 1e-5) |
| |
| |
| @with_seed() |
| def test_ndarray_pickle(): |
| maxdim = 5 |
| for dim in range(1, maxdim): |
| a = random_ndarray(dim) |
| b = mx.nd.empty(a.shape) |
| a[:] = np.random.uniform(-10, 10, a.shape) |
| b[:] = np.random.uniform(-10, 10, a.shape) |
| a = a + b |
| data = pkl.dumps(a) |
| a2 = pkl.loads(data) |
| assert np.sum(a.asnumpy() != a2.asnumpy()) == 0 |
| |
| |
| @with_seed() |
| def test_ndarray_saveload(): |
| nrepeat = 10 |
| fname = 'tmp_list.bin' |
| for repeat in range(nrepeat): |
| data = [] |
| # test save/load as list |
| for i in range(10): |
| data.append(random_ndarray(np.random.randint(1, 5))) |
| mx.nd.save(fname, data) |
| data2 = mx.nd.load(fname) |
| assert len(data) == len(data2) |
| for x, y in zip(data, data2): |
| assert np.sum(x.asnumpy() != y.asnumpy()) == 0 |
| # test save/load as dict |
| dmap = {'ndarray xx %s' % i : x for i, x in enumerate(data)} |
| mx.nd.save(fname, dmap) |
| dmap2 = mx.nd.load(fname) |
| assert len(dmap2) == len(dmap) |
| for k, x in dmap.items(): |
| y = dmap2[k] |
| assert np.sum(x.asnumpy() != y.asnumpy()) == 0 |
| # test save/load as ndarray |
| # we expect the single ndarray to be converted into a list containing the ndarray |
| single_ndarray = data[0] |
| mx.nd.save(fname, single_ndarray) |
| single_ndarray_loaded = mx.nd.load(fname) |
| assert len(single_ndarray_loaded) == 1 |
| single_ndarray_loaded = single_ndarray_loaded[0] |
| assert np.sum(single_ndarray.asnumpy() != single_ndarray_loaded.asnumpy()) == 0 |
| os.remove(fname) |
| |
| |
| @with_seed() |
| def test_ndarray_legacy_load(): |
| data = [] |
| for i in range(6): |
| data.append(mx.nd.arange(128)) |
| path = os.path.dirname(os.path.realpath(__file__)) |
| legacy_data = mx.nd.load(os.path.join(path, 'legacy_ndarray.v0')) |
| assert len(data) == len(legacy_data) |
| for i in range(len(data)): |
| assert same(data[i].asnumpy(), legacy_data[i].asnumpy()) |
| |
| |
| @with_seed() |
| def test_buffer_load(): |
| nrepeat = 10 |
| with TemporaryDirectory(prefix='test_buffer_load_') as tmpdir: |
| for repeat in range(nrepeat): |
| # test load_buffer as list |
| data = [] |
| for i in range(10): |
| data.append(random_ndarray(np.random.randint(1, 5))) |
| fname = os.path.join(tmpdir, 'list_{0}.param'.format(repeat)) |
| mx.nd.save(fname, data) |
| with open(fname, 'rb') as dfile: |
| buf_data = dfile.read() |
| data2 = mx.nd.load_frombuffer(buf_data) |
| assert len(data) == len(data2) |
| for x, y in zip(data, data2): |
| assert np.sum(x.asnumpy() != y.asnumpy()) == 0 |
| # test garbage values |
| assertRaises(mx.base.MXNetError, mx.nd.load_frombuffer, buf_data[:-10]) |
| # test load_buffer as dict |
| dmap = {'ndarray xx %s' % i : x for i, x in enumerate(data)} |
| fname = os.path.join(tmpdir, 'dict_{0}.param'.format(repeat)) |
| mx.nd.save(fname, dmap) |
| with open(fname, 'rb') as dfile: |
| buf_dmap = dfile.read() |
| dmap2 = mx.nd.load_frombuffer(buf_dmap) |
| assert len(dmap2) == len(dmap) |
| for k, x in dmap.items(): |
| y = dmap2[k] |
| assert np.sum(x.asnumpy() != y.asnumpy()) == 0 |
| # test garbage values |
| assertRaises(mx.base.MXNetError, mx.nd.load_frombuffer, buf_dmap[:-10]) |
| |
| # we expect the single ndarray to be converted into a list containing the ndarray |
| single_ndarray = data[0] |
| fname = os.path.join(tmpdir, 'single_{0}.param'.format(repeat)) |
| mx.nd.save(fname, single_ndarray) |
| with open(fname, 'rb') as dfile: |
| buf_single_ndarray = dfile.read() |
| single_ndarray_loaded = mx.nd.load_frombuffer(buf_single_ndarray) |
| assert len(single_ndarray_loaded) == 1 |
| single_ndarray_loaded = single_ndarray_loaded[0] |
| assert np.sum(single_ndarray.asnumpy() != single_ndarray_loaded.asnumpy()) == 0 |
| # test garbage values |
| assertRaises(mx.base.MXNetError, mx.nd.load_frombuffer, buf_single_ndarray[:-10]) |
| |
| |
| @with_seed() |
| def test_ndarray_slice(): |
| shape = (10,) |
| A = mx.nd.array(np.random.uniform(-10, 10, shape)) |
| A2 = A.asnumpy() |
| assert same(A[3:8].asnumpy(), A2[3:8]) |
| A2[3:8] *= 10 |
| A[3:8] = A2[3:8] |
| assert same(A[3:8].asnumpy(), A2[3:8]) |
| |
| shape = (3,4,5,6,7) |
| A = mx.nd.random.uniform(shape=shape) |
| A2 = A.asnumpy() |
| |
| assert same(A[1,3:4,:,1:5].asnumpy(), A2[1,3:4,:,1:5]) |
| |
| assert A[1,2,3,4,5].asscalar() == A2[1,2,3,4,5] |
| assert A[-1,-2,-3,-4,-5].asscalar() == A2[-1,-2,-3,-4,-5] |
| |
| a = mx.nd.array([[0, 1], [2, 3]]) |
| assert (a[[1, 1, 0], [0, 1, 0]].asnumpy() == [2, 3, 0]).all() |
| assert (a[mx.nd.array([1, 1, 0]), mx.nd.array([0, 1, 0])].asnumpy() == [2, 3, 0]).all() |
| |
| shape = (4, 4) |
| A = mx.nd.random.uniform(shape=shape) |
| A2 = A.asnumpy() |
| for i in range(-4, 0): |
| assert A[i, i].asscalar() == A2[i, i] |
| assert same(A[:, i].asnumpy(), A2[:, i]) |
| assert same(A[i, :].asnumpy(), A2[i, :]) |
| |
| |
| @with_seed() |
| def test_ndarray_crop(): |
| # get crop |
| x = mx.nd.ones((2, 3, 4)) |
| y = mx.nd.crop(x, begin=(0, 0, 0), end=(2, 1, 3)) |
| assert same(y.asnumpy(), np.ones((2, 1, 3), dtype=y.dtype)) |
| |
| # crop assign |
| z = mx.nd.zeros((2, 1, 3)) |
| mx.nd._internal._crop_assign(x, z, begin=(0, 0, 0), |
| end=(2, 1, 3), out=x) |
| np_x = np.ones(x.shape, dtype=x.dtype) |
| np_x[0:2, 0:1, 0:3] = 0 |
| assert same(x.asnumpy(), np_x) |
| |
| # crop assign with scalar |
| x = mx.nd.ones((2, 3, 4)) |
| mx.nd._internal._crop_assign_scalar(x, scalar=5, |
| begin=(0, 0, 0), |
| end=(2, 1, 3), out=x) |
| np_x = np.ones(x.shape, dtype=x.dtype) |
| np_x[0:2, 0:1, 0:3] = 5 |
| assert same(x.asnumpy(), np_x) |
| |
| |
| @with_seed() |
| def test_ndarray_concatenate(): |
| axis = 1 |
| shapes = [(2, 3, 4, 2), (2, 2, 4, 2), (2, 1, 4, 2)] |
| arrays_np = [np.random.uniform(-10, 10, s).astype(np.float32) for s in shapes] |
| arrays_nd = [mx.nd.array(x) for x in arrays_np] |
| |
| array_nd = mx.nd.concatenate(arrays_nd, axis=axis) |
| array_np = np.concatenate(arrays_np, axis=axis) |
| |
| assert same(array_np, array_nd.asnumpy()) |
| |
| |
| @with_seed() |
| def test_clip(): |
| shape = (10,) |
| A = mx.random.uniform(-10, 10, shape) |
| B = mx.nd.clip(A, -2, 2) |
| B1 = B.asnumpy() |
| for i in range(shape[0]): |
| assert B1[i] >= -2 |
| assert B1[i] <= 2 |
| |
| |
| @with_seed() |
| def test_dot(): |
| # Non-zero atol required, as exposed by seed 828791701 |
| atol = 1e-5 |
| # Test normal dot |
| a = np.random.uniform(-3, 3, (3, 4)) |
| b = np.random.uniform(-3, 3, (4, 5)) |
| c = np.dot(a, b) |
| A = mx.nd.array(a) |
| B = mx.nd.array(b) |
| C = mx.nd.dot(A, B) |
| assert_almost_equal(c, C.asnumpy(), atol=atol) |
| # Test dot with transpose kargs |
| a = np.random.uniform(-3, 3, (3, 4)) |
| b = np.random.uniform(-3, 3, (3, 5)) |
| c = np.dot(a.T, b) |
| A = mx.nd.array(a) |
| B = mx.nd.array(b) |
| C = mx.nd.dot(A, B, transpose_a=True) |
| assert_almost_equal(c, C.asnumpy(), atol=atol) |
| # Test dot with transpose kargs |
| a = np.random.uniform(-3, 3, (3, 4)) |
| b = np.random.uniform(-3, 3, (5, 4)) |
| c = np.dot(a, b.T) |
| A = mx.nd.array(a) |
| B = mx.nd.array(b) |
| C = mx.nd.dot(A, B, transpose_b=True) |
| assert_almost_equal(c, C.asnumpy(), atol=atol) |
| # Test dot with transpose kargs |
| a = np.random.uniform(-3, 3, (4, 3)) |
| b = np.random.uniform(-3, 3, (5, 4)) |
| c = np.dot(a.T, b.T) |
| A = mx.nd.array(a) |
| B = mx.nd.array(b) |
| C = mx.nd.dot(A, B, transpose_a=True, transpose_b=True) |
| assert_almost_equal(c, C.asnumpy(), atol=atol) |
| |
| |
| @with_seed() |
| def test_reduce(): |
| sample_num = 200 |
| def test_reduce_inner(numpy_reduce_func, nd_reduce_func, multi_axes): |
| for i in range(sample_num): |
| ndim = np.random.randint(1, 6) |
| shape = np.random.randint(1, 11, size=ndim) |
| dat = np.random.rand(*shape) - 0.5 |
| keepdims = np.random.randint(0, 2) |
| if multi_axes: |
| axis_flags = np.random.randint(0, 2, size=ndim) |
| axes = [] |
| for (axis, flag) in enumerate(axis_flags): |
| if flag: |
| axes.append(axis) |
| if 0 == len(axes): |
| axes = tuple(range(ndim)) |
| else: |
| axes = tuple(axes) |
| else: |
| axes = np.random.randint(0, ndim) |
| numpy_ret = numpy_reduce_func(dat, axis=axes, keepdims=keepdims) |
| |
| ndarray_ret = nd_reduce_func(mx.nd.array(dat), axis=axes, keepdims=keepdims) |
| if type(ndarray_ret) is mx.ndarray.NDArray: |
| ndarray_ret = ndarray_ret.asnumpy() |
| assert (ndarray_ret.shape == numpy_ret.shape) or \ |
| (ndarray_ret.shape == (1,) and numpy_ret.shape == ()), "nd:%s, numpy:%s" \ |
| %(ndarray_ret.shape, numpy_ret.shape) |
| err = np.square(ndarray_ret - numpy_ret).mean() |
| assert err < 1E-4 |
| test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.sum), |
| mx.nd.sum, True) |
| test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.max), |
| mx.nd.max, True) |
| test_reduce_inner(lambda data, axis, keepdims:np_reduce(data, axis, keepdims, np.min), |
| mx.nd.min, True) |
| # argmax and argmin are sensitive to the precision of the calculation (repro seed 1985162693). |
| # Force numpy to match mxnet's float32. |
| test_reduce_inner(lambda data, axis, |
| keepdims:np_reduce(np.float32(data), axis, keepdims, np.argmax), |
| mx.nd.argmax, False) |
| test_reduce_inner(lambda data, axis, |
| keepdims:np_reduce(np.float32(data), axis, keepdims, np.argmin), |
| mx.nd.argmin, False) |
| |
| |
| @with_seed() |
| def test_broadcast(): |
| sample_num = 1000 |
| def test_broadcast_to(): |
| for _ in range(sample_num): |
| ndim = np.random.randint(1, 6) |
| target_shape = np.random.randint(1, 11, size=ndim) |
| shape = target_shape.copy() |
| axis_flags = np.random.randint(0, 2, size=ndim) |
| for (axis, flag) in enumerate(axis_flags): |
| if flag: |
| shape[axis] = 1 |
| dat = np.random.rand(*shape) - 0.5 |
| numpy_ret = dat |
| ndarray_ret = mx.nd.array(dat).broadcast_to(shape=target_shape) |
| if type(ndarray_ret) is mx.ndarray.NDArray: |
| ndarray_ret = ndarray_ret.asnumpy() |
| assert (ndarray_ret.shape == target_shape).all() |
| err = np.square(ndarray_ret - numpy_ret).mean() |
| assert err < 1E-8 |
| |
| def test_broadcast_like(): |
| for _ in range(sample_num): |
| ndim = np.random.randint(1, 6) |
| target_shape = np.random.randint(1, 11, size=ndim) |
| target = mx.nd.ones(shape=tuple(target_shape)) |
| shape = target_shape.copy() |
| axis_flags = np.random.randint(0, 2, size=ndim) |
| for (axis, flag) in enumerate(axis_flags): |
| if flag: |
| shape[axis] = 1 |
| dat = np.random.rand(*shape) - 0.5 |
| numpy_ret = dat |
| ndarray_ret = mx.nd.array(dat).broadcast_like(target) |
| if type(ndarray_ret) is mx.ndarray.NDArray: |
| ndarray_ret = ndarray_ret.asnumpy() |
| assert (ndarray_ret.shape == target_shape).all() |
| err = np.square(ndarray_ret - numpy_ret).mean() |
| assert err < 1E-8 |
| |
| def test_broadcast_like_axis(): |
| testcases = [ |
| # Lhs shape, rhs shape, lhs axis, rhs axis, result |
| [(1, 2, 1, 3), (5, 6, 7, 8), (0,2), (1,3), (6, 2, 8, 3)], |
| [(1,), (5,), (0,), (-1,), (5,)], |
| [(1, 7, 9, 1, 1), (9,), (-2,), (0,), (1, 7, 9, 9, 1)], |
| [(1, 7, 9, 1, 1), (9, 1), (-2, -1), (-2, -1), (1, 7, 9, 9, 1)], |
| [(2, 1), (1, 7, 9, 1, 1), (1,), (-3,), (2, 9)] |
| ] |
| |
| for test_data in testcases: |
| lhs = mx.nd.random.uniform(shape=test_data[0]) |
| rhs = mx.nd.random.uniform(shape=test_data[1]) |
| output = mx.nd.broadcast_like(lhs, rhs, lhs_axes=test_data[2], rhs_axes=test_data[3]) |
| |
| assert_exception(mx.nd.broadcast_like, mx.base.MXNetError, lhs, rhs, lhs_axes=(), rhs_axes=()) |
| assert output.shape == test_data[4] |
| |
| test_broadcast_to() |
| test_broadcast_like() |
| test_broadcast_like_axis() |
| |
| |
| @with_seed() |
| def test_broadcast_binary(): |
| N = 100 |
| def check_broadcast_binary(fn): |
| for _ in range(N): |
| ndim = np.random.randint(1, 6) |
| oshape = np.random.randint(1, 6, size=(ndim,)) |
| bdim = np.random.randint(1, ndim+1) |
| lshape = list(oshape) |
| rshape = list(oshape[ndim-bdim:]) |
| for i in range(bdim): |
| sep = np.random.uniform(0, 1) |
| if sep < 0.33: |
| lshape[ndim-i-1] = 1 |
| elif sep < 0.66: |
| rshape[bdim-i-1] = 1 |
| lhs = np.random.normal(0, 1, size=lshape) |
| rhs = np.random.normal(0, 1, size=rshape) |
| assert_allclose(fn(lhs, rhs), |
| fn(mx.nd.array(lhs), mx.nd.array(rhs)).asnumpy(), |
| rtol=1e-4, atol=1e-4) |
| |
| check_broadcast_binary(lambda x, y: x + y) |
| check_broadcast_binary(lambda x, y: x - y) |
| check_broadcast_binary(lambda x, y: x * y) |
| check_broadcast_binary(lambda x, y: x / y) |
| # The following ops are sensitive to the precision of the calculation. |
| # Force numpy to match mxnet's float32. |
| check_broadcast_binary(lambda x, y: x.astype(np.float32) > y.astype(np.float32)) |
| check_broadcast_binary(lambda x, y: x.astype(np.float32) < y.astype(np.float32)) |
| check_broadcast_binary(lambda x, y: x.astype(np.float32) >= y.astype(np.float32)) |
| check_broadcast_binary(lambda x, y: x.astype(np.float32) <= y.astype(np.float32)) |
| check_broadcast_binary(lambda x, y: x.astype(np.float32) == y.astype(np.float32)) |
| |
| |
| @with_seed() |
| def test_moveaxis(): |
| X = mx.nd.array([[[1, 2, 3], [4, 5, 6]], |
| [[7, 8, 9], [10, 11, 12]]]) |
| res = mx.nd.moveaxis(X, 0, 2).asnumpy() |
| true_res = mx.nd.array([[[ 1., 7.], |
| [ 2., 8.], |
| [ 3., 9.]], |
| [[ 4., 10.], |
| [ 5., 11.], |
| [ 6., 12.]]]) |
| assert same(res, true_res.asnumpy()) |
| assert mx.nd.moveaxis(X, 2, 0).shape == (3, 2, 2) |
| |
| def test_move_to_end(): |
| x = mx.nd.random.normal(0, 1, (5, 6, 7)) |
| for source, expected in [(0, (6, 7, 5)), |
| (1, (5, 7, 6)), |
| (2, (5, 6, 7)), |
| (-1, (5, 6, 7))]: |
| actual = mx.nd.moveaxis(x, source, -1).shape |
| assert actual == expected |
| |
| def test_move_new_position(): |
| x = mx.nd.random.normal(0, 1, (1, 2, 3, 4)) |
| for source, destination, expected in [ |
| (0, 1, (2, 1, 3, 4)), |
| (1, 2, (1, 3, 2, 4)), |
| (1, -1, (1, 3, 4, 2)), |
| ]: |
| actual = mx.nd.moveaxis(x, source, destination).shape |
| assert actual == expected |
| |
| def test_preserve_order(): |
| x = mx.nd.zeros((1, 2, 3, 4)) |
| for source, destination in [ |
| (0, 0), |
| (3, -1), |
| (-1, 3), |
| ([0, -1], [0, -1]), |
| ([2, 0], [2, 0]), |
| (range(4), range(4)), |
| ]: |
| actual = mx.nd.moveaxis(x, source, destination).shape |
| assert actual == (1, 2, 3, 4) |
| |
| def test_move_multiples(): |
| x = mx.nd.zeros((4, 1, 2, 3)) |
| for source, destination, expected in [ |
| ([0, 1], [2, 3], (2, 3, 4, 1)), |
| ([2, 3], [0, 1], (2, 3, 4, 1)), |
| ([0, 1, 2], [2, 3, 0], (2, 3, 4, 1)), |
| ([3, 0], [1, 0], (4, 3, 1, 2)), |
| ([0, 3], [0, 1], (4, 3, 1, 2)), |
| ]: |
| actual = mx.nd.moveaxis(x, source, destination).shape |
| assert actual == expected |
| |
| def test_errors(): |
| x = mx.nd.random.normal(0, 1, (1, 2, 3)) |
| assert_exception(mx.nd.moveaxis, ValueError, x, 3, 0) |
| assert_exception(mx.nd.moveaxis, ValueError, x, -4, 0) |
| assert_exception(mx.nd.moveaxis, ValueError, x, 0, 5) |
| assert_exception(mx.nd.moveaxis, ValueError, x, [0, 0], [0, 1]) |
| assert_exception(mx.nd.moveaxis, ValueError, x, [0, 1], [1, 1]) |
| assert_exception(mx.nd.moveaxis, ValueError, x, 0, [0, 1]) |
| assert_exception(mx.nd.moveaxis, ValueError, x, [0, 1], [0]) |
| |
| test_move_to_end() |
| test_move_new_position() |
| test_preserve_order() |
| test_move_multiples() |
| test_errors() |
| |
| |
| @with_seed() |
| def test_arange(): |
| for i in range(5): |
| start = np.random.rand() * 10 |
| stop = start + np.random.rand() * 100 |
| step = np.random.rand() * 4 |
| repeat = int(np.random.rand() * 5) + 1 |
| gt = np.arange(start=start, stop=stop, step=step) |
| gt = np.broadcast_to(gt.reshape((gt.shape[0], 1)), shape=(gt.shape[0], repeat)).ravel() |
| pred = mx.nd.arange(start=start, stop=stop, step=step, repeat=repeat).asnumpy() |
| assert_almost_equal(pred, gt) |
| gt = np.arange(start=0, stop=10000**2, step=10001, dtype=np.int32) |
| pred = mx.nd.arange(start=0, stop=10000**2, step=10001, |
| dtype="int32").asnumpy() |
| assert_almost_equal(pred, gt) |
| |
| |
| @with_seed() |
| def test_linspace(): |
| for i in range(5): |
| start = np.random.rand() * 100 |
| stop = np.random.rand() * 100 |
| num = np.random.randint(20) |
| gt = np.linspace(start, stop, num) |
| pred = mx.nd.linspace(start, stop, num).asnumpy() |
| assert_almost_equal(pred, gt) |
| gt = np.linspace(start, stop, num, endpoint=False) |
| pred = mx.nd.linspace(start, stop, num, endpoint=False).asnumpy() |
| assert_almost_equal(pred, gt) |
| gt = np.linspace(start, stop, num, dtype="int32") |
| pred = mx.nd.linspace(start, stop, num, dtype="int32").asnumpy() |
| assert_almost_equal(pred, gt) |
| |
| |
| @with_seed() |
| def test_order(): |
| ctx = default_context() |
| dat_size = 5 |
| is_large_tensor_enabled = runtime.Features().is_enabled('INT64_TENSOR_SIZE') |
| def gt_topk(dat, axis, ret_typ, k, is_ascend): |
| if ret_typ == "indices": |
| if is_ascend: |
| indices = np.arange(k) |
| else: |
| indices = np.arange(-1, -k-1, -1) |
| ret = np.take(dat.argsort(axis=axis), axis=axis, indices=indices, mode='wrap') |
| elif ret_typ == "value": |
| if is_ascend: |
| indices = np.arange(k) |
| else: |
| indices = np.arange(-1, -k-1, -1) |
| ret = np.take(np.sort(dat, axis=axis), axis=axis, indices=indices, mode='wrap') |
| else: |
| assert dat.shape == (dat_size, dat_size, dat_size, dat_size) |
| assert axis is None or axis ==1 |
| ret = np.zeros(dat.shape) |
| if is_ascend: |
| indices = np.arange(k) |
| else: |
| indices = np.arange(-1, -k-1, -1) |
| gt_argsort = np.take(dat.argsort(axis=axis), axis=axis, indices=indices, mode='wrap') |
| if axis is None: |
| ret.ravel()[gt_argsort] = 1 |
| else: |
| for i in range(dat_size): |
| for j in range(dat_size): |
| for k in range(dat_size): |
| ret[i, gt_argsort[i, :, j, k], j, k] = 1 |
| return ret |
| |
| # Produce input data for the tests, including ensuring unique values if desired. |
| # Numpy's argsort does not consistently return lowest-index-first for matching |
| # values, making it hard to generate a numpy 'golden copy' to compare against |
| # the mxnet operator. The 'mask' function is particularly hard to test given that |
| # equal values might span the 'k' boundary. Issue exposed with seed 1405838964. |
| def get_values(ensure_unique, dtype): |
| if dtype == np.int16 or dtype == np.int32 or dtype == np.int64: |
| return np.arange(dat_size ** 4, dtype=dtype).reshape((dat_size, dat_size, dat_size, dat_size)) |
| elif dtype == np.float32 or dtype == np.float64: |
| while True: |
| data = np.random.normal(size=(dat_size, dat_size, dat_size, dat_size)).astype(dtype) |
| if not ensure_unique: |
| return data |
| num_unique_values = len(set(data.flatten())) |
| if data.size == num_unique_values: |
| return data |
| else: |
| raise NotImplementedError |
| |
| # Produce a large matrix (256, 300096) as the input data, to cover the case which |
| # has a large size of matrix (exceed the express range by float precisly), but |
| # the number of elements in each dimension could be expressed by float precisly. |
| def get_large_matrix(): |
| data = np.array([np.arange(300096).astype(np.float32)]) |
| data = np.repeat(data, 100, axis=0) |
| np.apply_along_axis(np.random.shuffle, 1, data) |
| return data |
| |
| large_matrix_npy = get_large_matrix() |
| 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.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, 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() |
| # Test the default dtype |
| assert nd_ret_topk.dtype == np.float32 |
| 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, 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, 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() |
| gt = gt_topk(a_npy, axis=3, ret_typ="value", k=2, is_ascend=False) |
| assert_almost_equal(nd_ret_topk, gt) |
| nd_ret_topk = mx.nd.topk(a_nd, axis=None, ret_typ="value", k=21, is_ascend=False).asnumpy() |
| gt = gt_topk(a_npy, axis=None, ret_typ="value", k=21, is_ascend=False) |
| assert_almost_equal(nd_ret_topk, gt) |
| |
| # test for ret_typ=mask |
| 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() |
| gt = gt_topk(a_npy, axis=1, ret_typ="mask", k=2, is_ascend=False) |
| assert_almost_equal(nd_ret_topk, gt) |
| 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, 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 |
| nd_ret_topk_val, _ = mx.nd.topk(a_nd, axis=1, ret_typ="both", k=3, is_ascend=True) |
| nd_ret_topk_val = nd_ret_topk_val.asnumpy() |
| assert_almost_equal(nd_ret_topk_val, gt_val) |
| |
| # test for sort |
| nd_ret_sort = mx.nd.sort(a_nd, axis=1, is_ascend=True).asnumpy() |
| gt = gt_topk(a_npy, axis=1, ret_typ="value", k=dat_size, is_ascend=True) |
| assert_almost_equal(nd_ret_sort, gt) |
| nd_ret_sort = mx.nd.sort(a_nd, axis=None, is_ascend=False).asnumpy() |
| gt = gt_topk(a_npy, axis=None, ret_typ="value", |
| k=dat_size*dat_size*dat_size*dat_size, is_ascend=False) |
| assert_almost_equal(nd_ret_sort, gt) |
| |
| # test for argsort |
| for idtype in [np.int32, np.float16, np.float32, np.float64]: |
| nd_ret_argsort = mx.nd.argsort(a_nd, axis=3, is_ascend=True, dtype=idtype).asnumpy() |
| assert nd_ret_argsort.dtype == idtype |
| 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) |
| |
| # 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). |
| a_npy = get_values(ensure_unique=False, dtype=dtype) |
| 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() |
| 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() |
| gt = gt_topk(a_npy, axis=3, ret_typ="value", k=2, is_ascend=False) |
| assert_almost_equal(nd_ret_topk, gt) |
| nd_ret_topk = mx.nd.topk(a_nd, axis=None, ret_typ="value", k=21, is_ascend=False).asnumpy() |
| gt = gt_topk(a_npy, axis=None, ret_typ="value", k=21, is_ascend=False) |
| assert_almost_equal(nd_ret_topk, gt) |
| |
| # test for sort |
| nd_ret_sort = mx.nd.sort(a_nd, axis=1, is_ascend=True).asnumpy() |
| gt = gt_topk(a_npy, axis=1, ret_typ="value", k=dat_size, is_ascend=True) |
| assert_almost_equal(nd_ret_sort, gt) |
| nd_ret_sort = mx.nd.sort(a_nd, axis=None, is_ascend=False).asnumpy() |
| gt = gt_topk(a_npy, axis=None, ret_typ="value", |
| k=dat_size*dat_size*dat_size*dat_size, is_ascend=False) |
| assert_almost_equal(nd_ret_sort, gt) |
| |
| a = mx.nd.arange(0, 1024, step=1, repeat=1, dtype=np.int32) |
| assert_almost_equal(a.topk(k=1024, dtype=np.int32).asnumpy(), a.asnumpy()[::-1]) |
| a.attach_grad() |
| |
| k = 10 |
| with mx.autograd.record(): |
| b = mx.nd.topk(a, k=k, ret_typ='value') |
| b.backward(mx.nd.ones((k,), dtype=np.int32)) |
| a_grad = a.grad.asnumpy() |
| for i in range(-1, - k - 1, -1): |
| assert a_grad[i] == 1 |
| |
| # test topk gradient with a small shape |
| for dtype in [np.int32, np.int64, np.float32, np.float64]: |
| a = mx.nd.arange(0, 1000, step=1, repeat=1, dtype=dtype) |
| a.attach_grad() |
| k = 10 |
| ograd = mx.nd.arange(0, k, dtype=dtype) |
| with mx.autograd.record(): |
| b = mx.nd.topk(a, k=k, ret_typ='value') |
| b.backward(ograd) |
| a_grad = a.grad.asnumpy() |
| ograd_npy = ograd.asnumpy() |
| for i in range(-1, - k - 1, -1): |
| assert a_grad[i] == ograd_npy[-i - 1] |
| |
| # 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.int32, np.int64, np.float32, np.float64]: |
| a_npy = get_values(ensure_unique=False, dtype=dtype) |
| 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() |
| 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() |
| gt = gt_topk(a_npy, axis=3, ret_typ="value", k=2, is_ascend=False) |
| assert_almost_equal(nd_ret_topk, gt) |
| nd_ret_topk = mx.nd.topk(a_nd, axis=None, ret_typ="value", k=21, is_ascend=False).asnumpy() |
| gt = gt_topk(a_npy, axis=None, ret_typ="value", k=21, is_ascend=False) |
| assert_almost_equal(nd_ret_topk, gt) |
| |
| # test for sort |
| nd_ret_sort = mx.nd.sort(a_nd, axis=1, is_ascend=True).asnumpy() |
| gt = gt_topk(a_npy, axis=1, ret_typ="value", k=dat_size, is_ascend=True) |
| assert_almost_equal(nd_ret_sort, gt) |
| nd_ret_sort = mx.nd.sort(a_nd, axis=None, is_ascend=False).asnumpy() |
| gt = gt_topk(a_npy, axis=None, ret_typ="value", |
| k=dat_size*dat_size*dat_size*dat_size, is_ascend=False) |
| assert_almost_equal(nd_ret_sort, gt) |
| |
| |
| @with_seed() |
| def test_ndarray_equal(): |
| x = mx.nd.zeros((2, 3)) |
| y = mx.nd.ones((2, 3)) |
| z = x == y |
| assert (z.asnumpy() == np.zeros((2, 3))).all() |
| z = 0 == x |
| assert (z.asnumpy() == np.ones((2, 3))).all() |
| |
| |
| @with_seed() |
| def test_ndarray_not_equal(): |
| x = mx.nd.zeros((2, 3)) |
| y = mx.nd.ones((2, 3)) |
| z = x != y |
| assert (z.asnumpy() == np.ones((2, 3))).all() |
| z = 0 != x |
| assert (z.asnumpy() == np.zeros((2, 3))).all() |
| |
| |
| @with_seed() |
| def test_ndarray_greater(): |
| x = mx.nd.zeros((2, 3)) |
| y = mx.nd.ones((2, 3)) |
| z = x > y |
| assert (z.asnumpy() == np.zeros((2, 3))).all() |
| z = y > 0 |
| assert (z.asnumpy() == np.ones((2, 3))).all() |
| z = 0 > y |
| assert (z.asnumpy() == np.zeros((2, 3))).all() |
| |
| |
| @with_seed() |
| def test_ndarray_greater_equal(): |
| x = mx.nd.zeros((2, 3)) |
| y = mx.nd.ones((2, 3)) |
| z = x >= y |
| assert (z.asnumpy() == np.zeros((2, 3))).all() |
| z = y >= 0 |
| assert (z.asnumpy() == np.ones((2, 3))).all() |
| z = 0 >= y |
| assert (z.asnumpy() == np.zeros((2, 3))).all() |
| z = y >= 1 |
| assert (z.asnumpy() == np.ones((2, 3))).all() |
| |
| |
| @with_seed() |
| def test_ndarray_lesser(): |
| x = mx.nd.zeros((2, 3)) |
| y = mx.nd.ones((2, 3)) |
| z = y < x |
| assert (z.asnumpy() == np.zeros((2, 3))).all() |
| z = 0 < y |
| assert (z.asnumpy() == np.ones((2, 3))).all() |
| z = y < 0 |
| assert (z.asnumpy() == np.zeros((2, 3))).all() |
| |
| |
| @with_seed() |
| def test_ndarray_lesser_equal(): |
| x = mx.nd.zeros((2, 3)) |
| y = mx.nd.ones((2, 3)) |
| z = y <= x |
| assert (z.asnumpy() == np.zeros((2, 3))).all() |
| z = 0 <= y |
| assert (z.asnumpy() == np.ones((2, 3))).all() |
| z = y <= 0 |
| assert (z.asnumpy() == np.zeros((2, 3))).all() |
| z = 1 <= y |
| assert (z.asnumpy() == np.ones((2, 3))).all() |
| |
| |
| @with_seed() |
| def test_take(): |
| for data_ndim in range(2, 5): |
| for idx_ndim in range(1, 4): |
| data_shape = () |
| for _ in range(data_ndim): |
| data_shape += (np.random.randint(low=3, high=6), ) |
| data_real = np.random.normal(size=data_shape).astype('float32') |
| idx_shape = () |
| for _ in range(idx_ndim): |
| idx_shape += (np.random.randint(low=3, high=5), ) |
| idx_real = np.random.randint(low=0, high=data_shape[0], size=idx_shape) |
| data_real_mx = mx.nd.array(data_real) |
| idx_real_mx = mx.nd.array(idx_real) |
| result = mx.nd.take(data_real_mx, idx_real_mx) |
| assert_almost_equal(result.asnumpy(), data_real[idx_real]) |
| |
| |
| @with_seed() |
| def test_iter(): |
| x = mx.nd.array([1, 2, 3]) |
| y = [] |
| for a in x: |
| y.append(a) |
| |
| for i in range(x.size): |
| assert same(y[i].asnumpy(), x[i].asnumpy()) |
| |
| @with_seed() |
| def test_cached(): |
| sym = mx.sym.Convolution(kernel=(3, 3), num_filter=10) + 2 |
| op = mx.nd.CachedOp(sym) |
| data = mx.nd.ones((3, 4, 10, 10)) |
| weight = mx.nd.ones((10, 4, 3, 3)) |
| bias = mx.nd.ones((10,)) |
| o1 = op(data, weight, bias) |
| bias[:] = 2 |
| o2 = op(data, weight, bias) |
| assert_almost_equal(o2.asnumpy(), o1.asnumpy()+1) |
| o2[:] = 0 |
| op(data, weight, bias, out=o2) |
| assert_almost_equal(o2.asnumpy(), o1.asnumpy()+1) |
| |
| weight.attach_grad() |
| bias.attach_grad() |
| with mx.autograd.record(): |
| bias = bias + 1 |
| o = op(data, weight, bias) |
| o = o * 2 |
| o.backward() |
| |
| with mx.autograd.record(): |
| bias = bias + 1 |
| o = op(data, weight, bias) |
| o = o * 2 |
| o.backward(retain_graph=True) |
| o.backward() |
| |
| # try a different shape |
| data = mx.nd.ones((5, 2, 10, 10)) |
| weight = mx.nd.ones((10, 2, 3, 3)) |
| bias = mx.nd.ones((10,)) |
| data.attach_grad() |
| |
| with mx.autograd.record(): |
| bias = bias + 1 |
| o = op(data, weight, bias) |
| o = o * 2 |
| o.backward() |
| |
| |
| @with_seed() |
| def test_output(): |
| shape = (2,2) |
| ones = mx.nd.ones(shape) |
| zeros = mx.nd.zeros(shape) |
| out = mx.nd.zeros(shape) |
| mx.nd.ones(shape, out=out) |
| assert_almost_equal(out.asnumpy(), ones.asnumpy()) |
| mx.nd.zeros(shape, out=out) |
| assert_almost_equal(out.asnumpy(), zeros.asnumpy()) |
| mx.nd.full(shape, 2, out=out) |
| assert_almost_equal(out.asnumpy(), ones.asnumpy() * 2) |
| arange_out = mx.nd.arange(0, 20, dtype='int64') |
| assert_almost_equal(arange_out.asnumpy(), np.arange(0, 20)) |
| N_array = np.random.randint(1, high=8, size=10) |
| M_array = np.random.randint(1, high=8, size=10) |
| k_array = np.random.randint(-10, high=10, size=10) |
| for i in range(10): |
| N = N_array[i] |
| M = M_array[i] |
| k = k_array[i] |
| assert_almost_equal(np.eye(N, M, k), mx.nd.eye(N, M, k).asnumpy()) |
| assert_almost_equal(np.eye(N, k=k), mx.nd.eye(N, k=k).asnumpy()) |
| |
| |
| @with_seed() |
| def test_ndarray_fluent(): |
| has_grad = set(['flatten', 'expand_dims', 'flip', 'tile', 'transpose', 'sum', 'nansum', 'prod', |
| 'nanprod', 'mean', 'max', 'min', 'reshape', 'broadcast_to', 'split', 'split_v2', |
| 'broadcast_axes', 'pad', 'swapaxes', 'slice', 'slice_axis', 'slice_like', |
| 'take', 'one_hot', 'pick', 'sort', 'topk', 'argsort', 'argmax', 'argmin', |
| 'clip', 'abs', 'sign', 'sin', 'cos', 'tan', 'arcsin', 'arccos', 'arctan', |
| 'degrees', 'radians', 'sinh', 'cosh', 'tanh', 'arcsinh', 'arccosh', 'arctanh', |
| 'exp', 'expm1', 'log', 'log10', 'log2', 'log1p', 'sqrt', 'rsqrt', 'square', |
| 'reshape_like', 'cbrt', 'rcbrt', 'relu', 'sigmoid', 'softmax', 'log_softmax', |
| 'softmin', 'reciprocal']) |
| def check_fluent_regular(func, kwargs, shape=(5, 17, 1), equal_nan=False): |
| with mx.name.NameManager(): |
| data = mx.nd.random_uniform(shape=shape, ctx=default_context()) |
| regular = getattr(mx.ndarray, func)(data, **kwargs) |
| fluent = getattr(data, func)(**kwargs) |
| if isinstance(regular, list): |
| for r, f in zip(regular, fluent): |
| assert almost_equal(r.asnumpy(), f.asnumpy(), equal_nan=equal_nan) |
| else: |
| assert almost_equal(regular.asnumpy(), fluent.asnumpy(), equal_nan=equal_nan) |
| |
| for func in ['flatten', 'norm', 'round', 'rint', 'fix', 'floor', 'ceil', 'trunc', 'zeros_like', |
| 'ones_like', 'abs', 'sign', 'sin', 'cos', 'degrees', 'radians', 'exp', 'expm1', |
| 'square', 'reciprocal', 'argmax_channel', 'shape_array', 'size_array']: |
| check_fluent_regular(func, {}) |
| |
| for func in ['arccosh', 'arcsin', 'arccos', 'arctan', 'tan', 'sinh', 'cosh', 'tanh', |
| 'arcsinh', 'arctanh', 'log', 'log10', 'log2', 'log1p', 'sqrt', 'rsqrt', |
| 'cbrt', 'rcbrt', 'relu', 'sigmoid', 'softmax', 'log_softmax', 'softmin']: |
| check_fluent_regular(func, {}, equal_nan=True) |
| |
| for func in ['expand_dims', 'flip', 'sort', 'topk', 'argsort', 'argmax', 'argmin']: |
| check_fluent_regular(func, {'axis': 1}) |
| |
| check_fluent_regular('one_hot', {'depth': 15}) |
| check_fluent_regular('tile', {'reps': (1,2)}) |
| check_fluent_regular('repeat', {'repeats': 3}) |
| check_fluent_regular('transpose', {'axes': (1,0,2)}) |
| check_fluent_regular('split', {'axis': 2, 'num_outputs': 3}, shape=(5, 17, 6)) |
| check_fluent_regular('split_v2', {'axis': 2, 'indices_or_sections': 3}, shape=(5, 17, 6)) |
| check_fluent_regular('split_v2', {'axis': 2, 'indices_or_sections': (1, 3, 5)}, shape=(5, 17, 6)) |
| check_fluent_regular('slice', {'begin': (2, 5, 1), 'end': (4, 7, 6)}, shape=(5, 17, 6)) |
| check_fluent_regular('slice_axis', {'axis': 1, 'begin': 5, 'end': 7}) |
| check_fluent_regular('slice_like', {'axes': (0, -2), 'shape_like': mx.nd.zeros((3, 3))}) |
| check_fluent_regular('take', {'indices': mx.nd.array([2, 3])}) |
| check_fluent_regular('pick', {'axis': 1, 'index': mx.nd.array([[2], [3], [5], [6], [11]])}) |
| check_fluent_regular('clip', {'a_min': 0.25, 'a_max': 0.75}) |
| check_fluent_regular('broadcast_axes', {'axis': (2,), 'size': (5,)}) |
| check_fluent_regular('pad', {'mode': 'constant', 'pad_width': (0,0,0,0,3,0,0,4)}, shape=(5, 17, 2, 3)) |
| check_fluent_regular('reshape_like', {'rhs': mx.nd.ones((30, 17))}, shape=(5, 17, 2, 3)) |
| |
| for func in ['sum', 'nansum', 'prod', 'nanprod', 'mean', 'max', 'min', 'norm']: |
| check_fluent_regular(func, {'axis': (1, 2)}) |
| |
| check_fluent_regular('reshape', {'shape': (17, 1, 5)}) |
| check_fluent_regular('broadcast_to', {'shape': (5, 17, 47)}) |
| check_fluent_regular('squeeze', {'axis': (1, 3)}, shape=(2, 1, 3, 1, 4)) |
| |
| |
| @raises(ValueError) |
| def test_bool_ambiguous(): |
| bool(mx.nd.ones((2,3,4))) |
| |
| |
| def test_bool(): |
| assert not bool(mx.nd.array([])) |
| assert not bool(mx.nd.zeros((1,))) |
| assert bool(mx.nd.ones((1,))) |
| |
| |
| @with_seed() |
| def test_ndarray_indexing(): |
| def test_getitem(np_array, index, is_scalar=False): |
| """`is_scalar` indicates whether we should expect a scalar for the result. |
| If so, the indexed array of NDArray should call asscalar to compare |
| with numpy's indexed array.""" |
| np_index = index |
| if isinstance(index, mx.nd.NDArray): |
| np_index = index.asnumpy() |
| if isinstance(index, tuple): |
| np_index = [] |
| for idx in index: |
| if isinstance(idx, mx.nd.NDArray): |
| np_index.append(idx.asnumpy()) |
| else: |
| np_index.append(idx) |
| np_index = tuple(np_index) |
| |
| np_indexed_array = np_array[np_index] |
| mx_array = mx.nd.array(np_array, dtype=np_array.dtype) |
| mx_indexed_array = mx_array[index] |
| if is_scalar: |
| mx_indexed_array = mx_indexed_array.asscalar() |
| else: |
| mx_indexed_array = mx_indexed_array.asnumpy() |
| assert same(np_indexed_array, mx_indexed_array), 'Failed with index=%s' % str(index) |
| |
| def test_setitem(np_array, index, is_scalar): |
| def assert_same(np_array, np_index, mx_array, mx_index, mx_value, np_value=None): |
| if np_value is not None: |
| np_array[np_index] = np_value |
| elif isinstance(mx_value, mx.nd.NDArray): |
| np_array[np_index] = mx_value.asnumpy() |
| else: |
| np_array[np_index] = mx_value |
| mx_array[mx_index] = mx_value |
| assert same(np_array, mx_array.asnumpy()) |
| |
| np_index = index |
| if isinstance(index, mx.nd.NDArray): |
| np_index = index.asnumpy() |
| if isinstance(index, tuple): |
| np_index = [] |
| for idx in index: |
| if isinstance(idx, mx.nd.NDArray): |
| np_index.append(idx.asnumpy()) |
| else: |
| np_index.append(idx) |
| np_index = tuple(np_index) |
| |
| mx_array = mx.nd.array(np_array, dtype=np_array.dtype) |
| np_array = mx_array.asnumpy() |
| if is_scalar: |
| # test value is a numeric type |
| assert_same(np_array, np_index, mx_array, index, np.random.randint(low=-10000, high=0)) |
| value_nd = [np.random.randint(low=-10000, high=0)] |
| assert_same(np_array, np_index, mx_array, index, value_nd, value_nd[0]) |
| else: |
| indexed_array_shape = np_array[np_index].shape |
| np_indexed_array = np.random.randint(low=-10000, high=0, size=indexed_array_shape) |
| # test value is a numpy array without broadcast |
| assert_same(np_array, np_index, mx_array, index, np_indexed_array) |
| # test value is an numeric_type |
| assert_same(np_array, np_index, mx_array, index, np.random.randint(low=-10000, high=0)) |
| if len(indexed_array_shape) > 1: |
| # test NDArray with broadcast |
| assert_same(np_array, np_index, mx_array, index, |
| mx.nd.random.uniform(low=-10000, high=0, shape=(indexed_array_shape[-1],))) |
| # test numpy array with broadcast |
| assert_same(np_array, np_index, mx_array, index, |
| np.random.randint(low=-10000, high=0, size=(indexed_array_shape[-1],))) |
| # test list with broadcast |
| assert_same(np_array, np_index, mx_array, index, |
| [np.random.randint(low=-10000, high=0)] * indexed_array_shape[-1]) |
| |
| def test_getitem_autograd(np_array, index): |
| x = mx.nd.array(np_array, dtype=np_array.dtype) |
| x.attach_grad() |
| with mx.autograd.record(): |
| y = x[index] |
| y.backward() |
| value = mx.nd.ones_like(y) |
| x_grad = mx.nd.zeros_like(x) |
| x_grad[index] = value |
| assert same(x_grad.asnumpy(), x.grad.asnumpy()) |
| |
| def test_setitem_autograd(np_array, index): |
| x = mx.nd.array(np_array, dtype=np_array.dtype) |
| out_shape = x[index].shape |
| y = mx.nd.random.uniform(shape=out_shape) |
| y.attach_grad() |
| try: |
| with mx.autograd.record(): |
| x[index] = y |
| assert False # should not reach here |
| except mx.base.MXNetError as err: |
| assert str(err).find('Inplace operations (+=, -=, x[:]=, etc) are not supported when recording with') != -1 |
| |
| def np_int(index, int_type=np.int32): |
| def convert(num): |
| if num is None: |
| return num |
| else: |
| return int_type(num) |
| |
| if isinstance(index, slice): |
| return slice(convert(index.start), convert(index.stop), convert(index.step)) |
| elif isinstance(index, tuple): # tuple of slices and integers |
| ret = [] |
| for elem in index: |
| if isinstance(elem, slice): |
| ret.append(slice(convert(elem.start), convert(elem.stop), convert(elem.step))) |
| else: |
| ret.append(convert(elem)) |
| return tuple(ret) |
| else: |
| assert False |
| |
| shape = (8, 16, 9, 9) |
| np_array = np.arange(np.prod(shape), dtype='int32').reshape(shape) |
| # index_list is a list of tuples. The tuple's first element is the index, the second one is a boolean value |
| # indicating whether we should expect the result as a scalar compared to numpy. |
| index_list = [(0, False), (np.int32(0), False), (np.int64(0), False), |
| (5, False), (np.int32(5), False), (np.int64(5), False), |
| (-1, False), (np.int32(-1), False), (np.int64(-1), False), |
| (slice(5), False), (np_int(slice(5), np.int32), False), (np_int(slice(5), np.int64), False), |
| (slice(1, 5), False), (np_int(slice(1, 5), np.int32), False), (np_int(slice(1, 5), np.int64), False), |
| (slice(1, 5, 2), False), (np_int(slice(1, 5, 2), np.int32), False), |
| (np_int(slice(1, 5, 2), np.int64), False), |
| (slice(7, 0, -1), False), (np_int(slice(7, 0, -1)), False), |
| (np_int(slice(7, 0, -1), np.int64), False), |
| (slice(None, 6), False), (np_int(slice(None, 6)), False), |
| (np_int(slice(None, 6), np.int64), False), |
| (slice(None, 6, 3), False), (np_int(slice(None, 6, 3)), False), |
| (np_int(slice(None, 6, 3), np.int64), False), |
| (slice(1, None), False), (np_int(slice(1, None)), False), |
| (np_int(slice(1, None), np.int64), False), |
| (slice(1, None, 3), False), (np_int(slice(1, None, 3)), False), |
| (np_int(slice(1, None, 3), np.int64), False), |
| (slice(None, None, 2), False), (np_int(slice(None, None, 2)), False), |
| (np_int(slice(None, None, 2), np.int64), False), |
| (slice(None, None, -1), False), |
| (np_int(slice(None, None, -1)), False), (np_int(slice(None, None, -1), np.int64), False), |
| (slice(None, None, -2), False), |
| (np_int(slice(None, None, -2), np.int32), False), (np_int(slice(None, None, -2), np.int64), False), |
| ((slice(None), slice(None), 1, 8), False), |
| ((slice(None), slice(None), -1, 8), False), |
| ((slice(None), slice(None), 1, -8), False), |
| ((slice(None), slice(None), -1, -8), False), |
| (np_int((slice(None), slice(None), 1, 8)), False), |
| (np_int((slice(None), slice(None), 1, 8), np.int64), False), |
| ((slice(None), slice(None), 1, 8), False), |
| (np_int((slice(None), slice(None), -1, -8)), False), |
| (np_int((slice(None), slice(None), -1, -8), np.int64), False), |
| ((slice(None), 2, slice(1, 5), 1), False), |
| (np_int((slice(None), 2, slice(1, 5), 1)), False), |
| (np_int((slice(None), 2, slice(1, 5), 1), np.int64), False), |
| ((1, 2, 3), False), |
| (np_int((1, 2, 3)), False), |
| (np_int((1, 2, 3), np.int64), False), |
| ((-1, -2, -3), False), |
| (np_int((-1, -2, -3)), False), |
| (np_int((-1, -2, -3), np.int64), False), |
| ((1, 2, 3, 4), True), |
| (np_int((1, 2, 3, 4)), True), |
| (np_int((1, 2, 3, 4), np.int64), True), |
| ((-4, -3, -2, -1), True), |
| (np_int((-4, -3, -2, -1)), True), |
| (np_int((-4, -3, -2, -1), np.int64), True), |
| ((slice(None, None, -1), 2, slice(1, 5), 1), False), |
| (np_int((slice(None, None, -1), 2, slice(1, 5), 1)), False), |
| (np_int((slice(None, None, -1), 2, slice(1, 5), 1), np.int64), False), |
| ((slice(None, None, -1), 2, slice(1, 7, 2), 1), False), |
| (np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1)), False), |
| (np_int((slice(None, None, -1), 2, slice(1, 7, 2), 1), np.int64), False), |
| ((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)), False), |
| (np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3))), False), |
| (np_int((slice(1, 8, 2), slice(14, 2, -2), slice(3, 8), slice(0, 7, 3)), np.int64), False), |
| ((slice(1, 8, 2), 1, slice(3, 8), 2), False), |
| (np_int((slice(1, 8, 2), 1, slice(3, 8), 2)), False), |
| (np_int((slice(1, 8, 2), 1, slice(3, 8), 2), np.int64), False), |
| ([1], False), ([1, 2], False), ([2, 1, 3], False), ([7, 5, 0, 3, 6, 2, 1], False), |
| (np.array([6, 3], dtype=np.int32), False), |
| (np.array([[3, 4], [0, 6]], dtype=np.int32), False), |
| (np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32), False), |
| (np.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64), False), |
| (np.array([[2], [0], [1]], dtype=np.int32), False), |
| (np.array([[2], [0], [1]], dtype=np.int64), False), |
| (mx.nd.array([4, 7], dtype=np.int32), False), |
| (mx.nd.array([4, 7], dtype=np.int64), False), |
| (mx.nd.array([[3, 6], [2, 1]], dtype=np.int32), False), |
| (mx.nd.array([[3, 6], [2, 1]], dtype=np.int64), False), |
| (mx.nd.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int32), False), |
| (mx.nd.array([[7, 3], [2, 6], [0, 5], [4, 1]], dtype=np.int64), False), |
| ((1, [2, 3]), False), ((1, [2, 3], np.array([[3], [0]], dtype=np.int32)), False), |
| ((1, [2, 3]), False), ((1, [2, 3], np.array([[3], [0]], dtype=np.int64)), False), |
| ((1, [2], np.array([[5], [3]], dtype=np.int32), slice(None)), False), |
| ((1, [2], np.array([[5], [3]], dtype=np.int64), slice(None)), False), |
| ((1, [2, 3], np.array([[6], [0]], dtype=np.int32), slice(2, 5)), False), |
| ((1, [2, 3], np.array([[6], [0]], dtype=np.int64), slice(2, 5)), False), |
| ((1, [2, 3], np.array([[4], [7]], dtype=np.int32), slice(2, 5, 2)), False), |
| ((1, [2, 3], np.array([[4], [7]], dtype=np.int64), slice(2, 5, 2)), False), |
| ((1, [2], np.array([[3]], dtype=np.int32), slice(None, None, -1)), False), |
| ((1, [2], np.array([[3]], dtype=np.int64), slice(None, None, -1)), False), |
| ((1, [2], np.array([[3]], dtype=np.int32), np.array([[5, 7], [2, 4]], dtype=np.int64)), False), |
| ((1, [2], mx.nd.array([[4]], dtype=np.int32), mx.nd.array([[1, 3], [5, 7]], dtype='int64')), |
| False), |
| ([0], False), ([0, 1], False), ([1, 2, 3], False), ([2, 0, 5, 6], False), |
| (([1, 1], [2, 3]), False), (([1], [4], [5]), False), (([1], [4], [5], [6]), False), |
| (([[1]], [[2]]), False), (([[1]], [[2]], [[3]], [[4]]), False), |
| ((slice(0, 2), [[1], [6]], slice(0, 2), slice(0, 5, 2)), False), |
| (([[[[1]]]], [[1]], slice(0, 3), [1, 5]), False), |
| (([[[[1]]]], 3, slice(0, 3), [1, 3]), False), |
| (([[[[1]]]], 3, slice(0, 3), 0), False), |
| (([[[[1]]]], [[2], [12]], slice(0, 3), slice(None)), False), |
| (([1, 2], slice(3, 5), [2, 3], [3, 4]), False), |
| (([1, 2], slice(3, 5), (2, 3), [3, 4]), False)] |
| for index in index_list: |
| test_getitem(np_array, index[0], index[1]) |
| test_setitem(np_array, index[0], index[1]) |
| test_getitem_autograd(np_array, index[0]) |
| test_setitem_autograd(np_array, index[0]) |
| |
| |
| def test_assign_float_value_to_ndarray(): |
| """Test case from https://github.com/apache/incubator-mxnet/issues/8668""" |
| a = np.array([47.844944], dtype=np.float32) |
| b = mx.nd.zeros(1, dtype=np.float32) |
| b[0] = a |
| assert same(a, b.asnumpy()) |
| b[0] = a[0] |
| assert same(a, b.asnumpy()) |
| |
| def test_assign_large_int_to_ndarray(): |
| """Test case from https://github.com/apache/incubator-mxnet/issues/11639""" |
| a = mx.nd.zeros((4, 1), dtype=np.int32) |
| a[1,0] = int(16800001) |
| a[2,0] = int(16800002) |
| b = a.asnumpy() |
| assert same(b[1,0], 16800001) |
| a = a-1 |
| b = a.asnumpy() |
| assert same(b[1,0], 16800000) |
| |
| @with_seed() |
| def test_assign_a_row_to_ndarray(): |
| """Test case from https://github.com/apache/incubator-mxnet/issues/9976""" |
| H, W = 10, 10 |
| dtype = np.float32 |
| a_np = np.random.random((H, W)).astype(dtype) |
| a_nd = mx.nd.array(a_np) |
| |
| # assign directly |
| a_np[0] = a_np[1] |
| a_nd[0] = a_nd[1] |
| assert same(a_np, a_nd.asnumpy()) |
| |
| # assign a list |
| v = np.random.random(W).astype(dtype).tolist() |
| a_np[1] = v |
| a_nd[1] = v |
| assert same(a_np, a_nd.asnumpy()) |
| |
| # assign a np.ndarray |
| v = np.random.random(W).astype(dtype) |
| a_np[2] = v |
| a_nd[2] = v |
| assert same(a_np, a_nd.asnumpy()) |
| |
| # assign by slice |
| a_np[0, :] = a_np[1] |
| a_nd[0, :] = a_nd[1] |
| assert same(a_np, a_nd.asnumpy()) |
| |
| @with_seed() |
| def test_ndarray_astype(): |
| x = mx.nd.zeros((2, 3), dtype='int32') |
| y = x.astype('float32') |
| assert (y.dtype==np.float32) |
| # Test that a new ndarray has been allocated |
| assert (id(x) != id(y)) |
| |
| x = mx.nd.zeros((2, 3), dtype='int32') |
| y = x.astype('float32', copy=False) |
| assert (y.dtype==np.float32) |
| # Test that a new ndarray has been allocated |
| assert (id(x) != id(y)) |
| |
| x = mx.nd.zeros((2, 3), dtype='int32') |
| y = x.astype('int32') |
| assert (y.dtype==np.int32) |
| # Test that a new ndarray has been allocated |
| # even though they have same dtype |
| assert (id(x) != id(y)) |
| |
| # Test that a new ndarray has not been allocated |
| x = mx.nd.zeros((2, 3), dtype='int32') |
| y = x.astype('int32', copy=False) |
| assert (id(x) == id(y)) |
| |
| # Test the string version 'int32' |
| # has the same behaviour as the np.int32 |
| x = mx.nd.zeros((2, 3), dtype='int32') |
| y = x.astype(np.int32, copy=False) |
| assert (id(x) == id(y)) |
| |
| |
| @with_seed() |
| def test_norm(ctx=default_context()): |
| try: |
| import scipy |
| assert LooseVersion(scipy.__version__) >= LooseVersion('0.1') |
| from scipy.linalg import norm as sp_norm |
| except (AssertionError, ImportError): |
| print("Could not import scipy.linalg.norm or scipy is too old. " |
| "Falling back to numpy.linalg.norm which is not numerically stable.") |
| from numpy.linalg import norm as sp_norm |
| |
| def l1norm(input_data, axis=0, keepdims=False): |
| return np.sum(abs(input_data), axis=axis, keepdims=keepdims) |
| def l2norm(input_data, axis=0, keepdims=False): |
| return sp_norm(input_data, axis=axis, keepdims=keepdims) |
| |
| in_data_dim = random_sample([4,5,6], 1)[0] |
| for force_reduce_dim1 in [True, False]: |
| in_data_shape = rand_shape_nd(in_data_dim) |
| if force_reduce_dim1: |
| in_data_shape = in_data_shape[:3] + (1, ) + in_data_shape[4:] |
| np_arr = np.random.uniform(-1, 1, in_data_shape).astype(np.float32) |
| mx_arr = mx.nd.array(np_arr, ctx=ctx) |
| for ord in [1, 2]: |
| for keep_dims in [True, False]: |
| for i in range(4): |
| npy_out = l1norm(np_arr, i, keep_dims) if ord == 1 else l2norm( |
| np_arr, i, keep_dims) |
| mx_out = mx.nd.norm(mx_arr, ord=ord, axis=i, keepdims=keep_dims) |
| assert npy_out.shape == mx_out.shape |
| mx.test_utils.assert_almost_equal(npy_out, mx_out.asnumpy()) |
| if (i < 3): |
| npy_out = l1norm(np_arr, (i, i + 1), keep_dims) if ord == 1 else l2norm( |
| np_arr, (i, i + 1), keep_dims) |
| mx_out = mx.nd.norm(mx_arr, ord=ord, axis=(i, i + 1), keepdims=keep_dims) |
| assert npy_out.shape == mx_out.shape |
| mx.test_utils.assert_almost_equal(npy_out, mx_out.asnumpy()) |
| |
| |
| @with_seed() |
| def test_ndarray_cpu_shared_ctx(): |
| ctx = mx.Context('cpu_shared', 0) |
| res = mx.nd.zeros((1, 2, 3), ctx=ctx) |
| assert(res.context == ctx) |
| |
| @with_seed() |
| def test_dlpack(): |
| for dtype in [np.float32, np.int32]: |
| for shape in [(3, 4, 5, 6), (2, 10), (15,)]: |
| a = mx.nd.random.uniform(shape = shape) |
| a_np = a.asnumpy() |
| |
| pack = a.to_dlpack_for_read() |
| b = mx.nd.from_dlpack(pack) |
| |
| a_copy = a.copy() |
| pack2 = a_copy.to_dlpack_for_write() |
| c = mx.nd.from_dlpack(pack2) |
| |
| pack3 = mx.nd.to_dlpack_for_read(a) |
| d = mx.nd.from_dlpack(pack3) |
| |
| a_copy = a.copy() |
| pack4 = mx.nd.to_dlpack_for_write(a_copy) |
| e = mx.nd.from_dlpack(pack4) |
| |
| del a, pack, pack2, pack3, pack4 |
| |
| b_np = b.asnumpy() |
| c_np = c.asnumpy() |
| d_np = d.asnumpy() |
| e_np = e.asnumpy() |
| mx.test_utils.assert_almost_equal(a_np, b_np) |
| mx.test_utils.assert_almost_equal(a_np, c_np) |
| mx.test_utils.assert_almost_equal(a_np, d_np) |
| mx.test_utils.assert_almost_equal(a_np, e_np) |
| |
| @with_seed() |
| def test_ndarray_is_inf(): |
| random_dimensions = np.random.randint(2, 5) |
| random_shape = [np.random.randint(2, 5) for i in range(random_dimensions)] |
| data = mxnet.test_utils.rand_ndarray(random_shape,'default') |
| data[0][0] = np.inf |
| data[0][1] = -np.inf |
| data[1][0] = np.nan |
| data[1][1] = 5 |
| output = mx.nd.contrib.isinf(data) |
| expected_output = np.isinf(data.asnumpy()) |
| np.testing.assert_equal(output.asnumpy(), expected_output.astype(int)) |
| # astype since numpy functions default return type is boolean array instead of int |
| |
| @with_seed() |
| def test_ndarray_is_finite(): |
| random_dimensions = np.random.randint(2, 5) |
| random_shape = [np.random.randint(2, 5) for i in range(random_dimensions)] |
| data = mxnet.test_utils.rand_ndarray(random_shape,'default') |
| data[0][0] = np.inf |
| data[0][1] = -np.inf |
| data[1][0] = np.nan |
| data[1][1] = 5 |
| output = mx.nd.contrib.isfinite(data) |
| expected_output = np.isfinite(data.asnumpy()) |
| np.testing.assert_equal(output.asnumpy(), expected_output.astype(int)) |
| # astype since numpy functions default return type is boolean array instead of int |
| |
| @with_seed() |
| def test_ndarray_is_nan(): |
| random_dimensions = np.random.randint(2, 5) |
| random_shape = [np.random.randint(2, 5) for i in range(random_dimensions)] |
| data = mxnet.test_utils.rand_ndarray(random_shape,'default') |
| data[0][0] = np.inf |
| data[0][1] = -np.inf |
| data[1][0] = np.nan |
| data[1][1] = 5 |
| output = mx.nd.contrib.isnan(data) |
| expected_output = np.isnan(data.asnumpy()) |
| np.testing.assert_equal(output.asnumpy(), expected_output.astype(int)) |
| # astype since numpy functions default return type is boolean array instead of int |
| |
| @with_seed() |
| def test_ndarray_nan_comparison(): |
| random_dimensions = np.random.randint(2, 5) |
| random_shape = [np.random.randint(2, 5) for i in range(random_dimensions)] |
| data1 = mxnet.test_utils.rand_ndarray(random_shape,'default') |
| data2 = mxnet.test_utils.rand_ndarray(random_shape,'default') |
| data1[1][0] = np.NaN |
| data2[0][0] = np.NaN |
| |
| nd_max = mx.nd.maximum(data1, data2) |
| np_max = np.maximum(data1.asnumpy(), data2.asnumpy()) |
| np.testing.assert_equal(nd_max.asnumpy(), np_max) |
| |
| nd_min = mx.nd.minimum(data1, data2) |
| np_min = np.minimum(data1.asnumpy(), data2.asnumpy()) |
| np.testing.assert_equal(nd_min.asnumpy(), np_min) |
| |
| nd_relu = mx.nd.relu(data1) |
| np_relu = np.maximum(data1.asnumpy(), 0) |
| np.testing.assert_equal(nd_relu.asnumpy(), np_relu) |
| |
| data1.attach_grad() |
| with mx.autograd.record(): |
| y = mx.nd.relu(data1) |
| y.backward() |
| data1_grad = data1.grad.asnumpy() |
| for i in (np.isnan(data1_grad))[1][0].flatten(): |
| assert i == True |
| |
| |
| def test_zero_from_numpy(): |
| # Test zero_copy |
| arrays = [ |
| # ordinary numpy array |
| np.array([[1, 2], [3, 4], [5, 6]], dtype="float32"), |
| # 0-dim |
| np.array((1, )).reshape(()), |
| # 0-size |
| np.array(()).reshape((1, 0, 2)), |
| ] |
| for zero_copy in [False, True]: |
| for np_array in arrays: |
| mx_array = mx.nd.from_numpy(np_array, zero_copy=zero_copy) |
| mx.test_utils.assert_almost_equal(np_array, mx_array.asnumpy()) |
| np_array = arrays[0] |
| mx_array = mx.nd.from_numpy(np_array) |
| assertRaises(ValueError, np_array.__setitem__, (2, 1), 0) |
| |
| mx_array[2, 1] = 100 |
| mx.test_utils.assert_almost_equal(np_array, mx_array.asnumpy()) |
| np_array = np.array([[1, 2], [3, 4], [5, 6]]).transpose() |
| assert not np_array.flags["C_CONTIGUOUS"] |
| try: |
| mx_array = mx.nd.from_numpy(np_array) |
| except ValueError: |
| pass |
| else: |
| assert False |
| |
| |
| @with_seed() |
| def test_save_load_scalar_zero_size_ndarrays(): |
| def check_save_load(save_is_np_shape, load_is_np_shape, shapes, save_throw_exception, load_throw_exception): |
| with mx.np_shape(save_is_np_shape): |
| array_list = [np.random.randint(0, 10, size=shape) for shape in shapes] |
| array_list = [mx.nd.array(arr) for arr in array_list] |
| with TemporaryDirectory() as work_dir: |
| fname = os.path.join(work_dir, 'dataset') |
| if save_throw_exception: |
| assert_exception(mx.nd.save, mx.MXNetError, fname, array_list) |
| else: |
| mx.nd.save(fname, array_list) |
| with mx.np_shape(load_is_np_shape): |
| if load_throw_exception: |
| assert_exception(mx.nd.load, mx.MXNetError, fname) |
| else: |
| array_list_loaded = mx.nd.load(fname) |
| assert len(array_list) == len(array_list_loaded) |
| for a1, a2 in zip(array_list, array_list_loaded): |
| assert np.array_equal(a1.asnumpy(), a2.asnumpy()) |
| |
| check_save_load(False, False, [(2, 0, 1), (0,), (0, 4), (3, 0, 0, 0), (2, 1), (0, 5, 0)], False, False) |
| check_save_load(True, False, [(2, 0, 1), (0,), (0, 4), (3, 0, 0, 0), (2, 1), (0, 5, 0)], False, True) |
| check_save_load(False, True, [(2, 0, 1), (0,), (0, 4), (3, 0, 0, 0), (2, 1), (0, 5, 0)], False, True) |
| check_save_load(True, True, [(2, 0, 1), (0,), (), (), (0, 4), (), (3, 0, 0, 0), (2, 1), (0, 5, 0)], False, False) |
| |
| |
| if __name__ == '__main__': |
| import nose |
| nose.runmodule() |
