You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1447 lines
42 KiB
1447 lines
42 KiB
# Copyright 2020-2021 Huawei Technologies Co., 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.
|
|
# ============================================================================
|
|
"""unit tests for numpy array operations"""
|
|
|
|
import functools
|
|
|
|
import pytest
|
|
import numpy as onp
|
|
|
|
import mindspore.numpy as mnp
|
|
from mindspore.nn import Cell
|
|
|
|
from .utils import rand_int, run_non_kw_test, check_all_results, match_array, \
|
|
rand_bool, match_res, run_multi_test, to_tensor
|
|
|
|
|
|
class Cases():
|
|
def __init__(self):
|
|
self.all_shapes = [
|
|
1, 2, (1,), (2,), (1, 2, 3), [1], [2], [1, 2, 3]
|
|
]
|
|
self.onp_dtypes = [onp.int32, 'int32', int,
|
|
onp.float32, 'float32', float,
|
|
onp.uint32, 'uint32',
|
|
onp.bool_, 'bool', bool]
|
|
|
|
self.mnp_dtypes = [mnp.int32, 'int32', int,
|
|
mnp.float32, 'float32', float,
|
|
mnp.uint32, 'uint32',
|
|
mnp.bool_, 'bool', bool]
|
|
|
|
self.array_sets = [1, 1.1, True, [1, 0, True], [1, 1.0, 2], (1,),
|
|
[(1, 2, 3), (4, 5, 6)], onp.random.random( # pylint: disable=no-member
|
|
(100, 100)).astype(onp.float32),
|
|
onp.random.random((100, 100)).astype(onp.bool)]
|
|
|
|
self.arrs = [
|
|
rand_int(2),
|
|
rand_int(2, 3),
|
|
rand_int(2, 3, 4),
|
|
rand_int(2, 3, 4, 5),
|
|
]
|
|
|
|
# scalars expanded across the 0th dimension
|
|
self.scalars = [
|
|
rand_int(),
|
|
rand_int(1),
|
|
rand_int(1, 1),
|
|
rand_int(1, 1, 1),
|
|
]
|
|
|
|
# arrays of the same size expanded across the 0th dimension
|
|
self.expanded_arrs = [
|
|
rand_int(2, 3),
|
|
rand_int(1, 2, 3),
|
|
rand_int(1, 1, 2, 3),
|
|
rand_int(1, 1, 1, 2, 3),
|
|
]
|
|
|
|
# arrays with dimensions of size 1
|
|
self.nested_arrs = [
|
|
rand_int(1),
|
|
rand_int(1, 2),
|
|
rand_int(3, 1, 8),
|
|
rand_int(1, 3, 9, 1),
|
|
]
|
|
|
|
# arrays which can be broadcast
|
|
self.broadcastables = [
|
|
rand_int(5),
|
|
rand_int(6, 1),
|
|
rand_int(7, 1, 5),
|
|
rand_int(8, 1, 6, 1)
|
|
]
|
|
|
|
# boolean arrays which can be broadcast
|
|
self.bool_broadcastables = [
|
|
rand_bool(),
|
|
rand_bool(1),
|
|
rand_bool(5),
|
|
rand_bool(6, 1),
|
|
rand_bool(7, 1, 5),
|
|
rand_bool(8, 1, 6, 1),
|
|
]
|
|
|
|
self.mnp_prototypes = [
|
|
mnp.ones((2, 3, 4)),
|
|
[mnp.ones(3), (1, 2, 3), onp.ones(3), [4, 5, 6]],
|
|
([(1, 2), mnp.ones(2)], (onp.ones(2), [3, 4])),
|
|
]
|
|
|
|
self.onp_prototypes = [
|
|
onp.ones((2, 3, 4)),
|
|
[onp.ones(3), (1, 2, 3), onp.ones(3), [4, 5, 6]],
|
|
([(1, 2), onp.ones(2)], (onp.ones(2), [3, 4])),
|
|
]
|
|
|
|
|
|
# Test np.transpose and np.ndarray.transpose
|
|
def mnp_transpose(input_tensor):
|
|
a = mnp.transpose(input_tensor, (0, 2, 1))
|
|
b = mnp.transpose(input_tensor, [2, 1, 0])
|
|
c = mnp.transpose(input_tensor, (1, 0, 2))
|
|
d = mnp.transpose(input_tensor)
|
|
return a, b, c, d
|
|
|
|
|
|
def onp_transpose(input_array):
|
|
a = onp.transpose(input_array, (0, 2, 1))
|
|
b = onp.transpose(input_array, [2, 1, 0])
|
|
c = onp.transpose(input_array, (1, 0, 2))
|
|
d = onp.transpose(input_array)
|
|
return a, b, c, d
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_transpose():
|
|
onp_array = onp.random.random((3, 4, 5)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_transposed = onp_transpose(onp_array)
|
|
m_transposed = mnp_transpose(mnp_array)
|
|
check_all_results(o_transposed, m_transposed)
|
|
|
|
|
|
# Test np.expand_dims
|
|
def mnp_expand_dims(input_tensor):
|
|
a = mnp.expand_dims(input_tensor, 0)
|
|
b = mnp.expand_dims(input_tensor, -1)
|
|
c = mnp.expand_dims(input_tensor, axis=2)
|
|
d = mnp.expand_dims(input_tensor, axis=-2)
|
|
return a, b, c, d
|
|
|
|
|
|
def onp_expand_dims(input_array):
|
|
a = onp.expand_dims(input_array, 0)
|
|
b = onp.expand_dims(input_array, -1)
|
|
c = onp.expand_dims(input_array, axis=2)
|
|
d = onp.expand_dims(input_array, axis=-2)
|
|
return a, b, c, d
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_expand_dims():
|
|
onp_array = onp.random.random((3, 4, 5)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_expanded = onp_expand_dims(onp_array)
|
|
m_expanded = mnp_expand_dims(mnp_array)
|
|
check_all_results(o_expanded, m_expanded)
|
|
|
|
|
|
# Test np.squeeze
|
|
def mnp_squeeze(input_tensor):
|
|
a = mnp.squeeze(input_tensor)
|
|
b = mnp.squeeze(input_tensor, 0)
|
|
c = mnp.squeeze(input_tensor, axis=None)
|
|
d = mnp.squeeze(input_tensor, axis=-3)
|
|
e = mnp.squeeze(input_tensor, (2,))
|
|
f = mnp.squeeze(input_tensor, (0, 2))
|
|
return a, b, c, d, e, f
|
|
|
|
|
|
def onp_squeeze(input_array):
|
|
a = onp.squeeze(input_array)
|
|
b = onp.squeeze(input_array, 0)
|
|
c = onp.squeeze(input_array, axis=None)
|
|
d = onp.squeeze(input_array, axis=-3)
|
|
e = onp.squeeze(input_array, (2,))
|
|
f = onp.squeeze(input_array, (0, 2))
|
|
return a, b, c, d, e, f
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_squeeze():
|
|
onp_array = onp.random.random((1, 3, 1, 4, 2)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_squeezed = onp_squeeze(onp_array)
|
|
m_squeezed = mnp_squeeze(mnp_array)
|
|
check_all_results(o_squeezed, m_squeezed)
|
|
|
|
onp_array = onp.random.random((1, 1, 1, 1, 1)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_squeezed = onp_squeeze(onp_array)
|
|
m_squeezed = mnp_squeeze(mnp_array)
|
|
check_all_results(o_squeezed, m_squeezed)
|
|
|
|
|
|
# Test np.rollaxis
|
|
def mnp_rollaxis(input_tensor):
|
|
a = mnp.rollaxis(input_tensor, 0, 1)
|
|
b = mnp.rollaxis(input_tensor, 0, 2)
|
|
c = mnp.rollaxis(input_tensor, 2, 1)
|
|
d = mnp.rollaxis(input_tensor, 2, 2)
|
|
e = mnp.rollaxis(input_tensor, 0)
|
|
f = mnp.rollaxis(input_tensor, 1)
|
|
return a, b, c, d, e, f
|
|
|
|
|
|
def onp_rollaxis(input_array):
|
|
a = onp.rollaxis(input_array, 0, 1)
|
|
b = onp.rollaxis(input_array, 0, 2)
|
|
c = onp.rollaxis(input_array, 2, 1)
|
|
d = onp.rollaxis(input_array, 2, 2)
|
|
e = onp.rollaxis(input_array, 0)
|
|
f = onp.rollaxis(input_array, 1)
|
|
return a, b, c, d, e, f
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_rollaxis():
|
|
onp_array = onp.random.random((3, 4, 5)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_rolled = onp_rollaxis(onp_array)
|
|
m_rolled = mnp_rollaxis(mnp_array)
|
|
check_all_results(o_rolled, m_rolled)
|
|
|
|
|
|
# Test np.swapaxes
|
|
def mnp_swapaxes(input_tensor):
|
|
a = mnp.swapaxes(input_tensor, 0, 1)
|
|
b = mnp.swapaxes(input_tensor, 1, 0)
|
|
c = mnp.swapaxes(input_tensor, 1, 1)
|
|
d = mnp.swapaxes(input_tensor, 2, 1)
|
|
e = mnp.swapaxes(input_tensor, 1, 2)
|
|
f = mnp.swapaxes(input_tensor, 2, 2)
|
|
return a, b, c, d, e, f
|
|
|
|
|
|
def onp_swapaxes(input_array):
|
|
a = onp.swapaxes(input_array, 0, 1)
|
|
b = onp.swapaxes(input_array, 1, 0)
|
|
c = onp.swapaxes(input_array, 1, 1)
|
|
d = onp.swapaxes(input_array, 2, 1)
|
|
e = onp.swapaxes(input_array, 1, 2)
|
|
f = onp.swapaxes(input_array, 2, 2)
|
|
return a, b, c, d, e, f
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_swapaxes():
|
|
onp_array = onp.random.random((3, 4, 5)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_swaped = onp_swapaxes(onp_array)
|
|
m_swaped = mnp_swapaxes(mnp_array)
|
|
check_all_results(o_swaped, m_swaped)
|
|
|
|
|
|
# Test np.reshape
|
|
def mnp_reshape(input_tensor):
|
|
a = mnp.reshape(input_tensor, (3, 8))
|
|
b = mnp.reshape(input_tensor, [3, -1])
|
|
c = mnp.reshape(input_tensor, (-1, 12))
|
|
d = mnp.reshape(input_tensor, (-1,))
|
|
e = mnp.reshape(input_tensor, 24)
|
|
f = mnp.reshape(input_tensor, [2, 4, -1])
|
|
g = input_tensor.reshape(3, 8)
|
|
h = input_tensor.reshape(3, -1)
|
|
i = input_tensor.reshape([-1, 3])
|
|
j = input_tensor.reshape(-1)
|
|
return a, b, c, d, e, f, g, h, i, j
|
|
|
|
|
|
def onp_reshape(input_array):
|
|
a = onp.reshape(input_array, (3, 8))
|
|
b = onp.reshape(input_array, [3, -1])
|
|
c = onp.reshape(input_array, (-1, 12))
|
|
d = onp.reshape(input_array, (-1,))
|
|
e = onp.reshape(input_array, 24)
|
|
f = onp.reshape(input_array, [2, 4, -1])
|
|
g = input_array.reshape(3, 8)
|
|
h = input_array.reshape(3, -1)
|
|
i = input_array.reshape([-1, 3])
|
|
j = input_array.reshape(-1)
|
|
return a, b, c, d, e, f, g, h, i, j
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_reshape():
|
|
onp_array = onp.random.random((2, 3, 4)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_reshaped = onp_reshape(onp_array)
|
|
m_reshaped = mnp_reshape(mnp_array)
|
|
check_all_results(o_reshaped, m_reshaped)
|
|
|
|
|
|
# Test np.ravel
|
|
def mnp_ravel(input_tensor):
|
|
a = mnp.ravel(input_tensor)
|
|
return a
|
|
|
|
|
|
def onp_ravel(input_array):
|
|
a = onp.ravel(input_array)
|
|
return a
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_ravel():
|
|
onp_array = onp.random.random((2, 3, 4)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_ravel = onp_ravel(onp_array)
|
|
m_ravel = mnp_ravel(mnp_array).asnumpy()
|
|
match_array(o_ravel, m_ravel)
|
|
|
|
|
|
# Test np.concatenate
|
|
def mnp_concatenate(input_tensor):
|
|
a = mnp.concatenate(input_tensor, None)
|
|
b = mnp.concatenate(input_tensor, 0)
|
|
c = mnp.concatenate(input_tensor, 1)
|
|
d = mnp.concatenate(input_tensor, 2)
|
|
return a, b, c, d
|
|
|
|
|
|
def onp_concatenate(input_array):
|
|
a = onp.concatenate(input_array, None)
|
|
b = onp.concatenate(input_array, 0)
|
|
c = onp.concatenate(input_array, 1)
|
|
d = onp.concatenate(input_array, 2)
|
|
return a, b, c, d
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_concatenate():
|
|
onp_array = onp.random.random((5, 4, 3, 2)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_concatenate = onp_concatenate(onp_array)
|
|
m_concatenate = mnp_concatenate(mnp_array)
|
|
check_all_results(o_concatenate, m_concatenate)
|
|
|
|
|
|
def mnp_append(arr1, arr2):
|
|
a = mnp.append(arr1, arr2)
|
|
b = mnp.append(arr1, arr2, axis=0)
|
|
c = mnp.append(arr1, arr2, axis=-1)
|
|
return a, b, c
|
|
|
|
def onp_append(arr1, arr2):
|
|
a = onp.append(arr1, arr2)
|
|
b = onp.append(arr1, arr2, axis=0)
|
|
c = onp.append(arr1, arr2, axis=-1)
|
|
return a, b, c
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_append():
|
|
onp_array = onp.random.random((4, 3, 2)).astype('float32')
|
|
onp_value = onp.random.random((4, 3, 2)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
mnp_value = to_tensor(onp_value)
|
|
onp_res = onp_append(onp_array, onp_value)
|
|
mnp_res = mnp_append(mnp_array, mnp_value)
|
|
check_all_results(onp_res, mnp_res)
|
|
|
|
|
|
def construct_arrays(n=1, ndim=1, axis=None, low=1, high=5):
|
|
onp_array_lst = []
|
|
mnp_array_lst = []
|
|
shape = onp.random.randint(low=low, high=high, size=ndim)
|
|
new_shape = [sh for sh in shape]
|
|
while n > 0:
|
|
n -= 1
|
|
onp_array1 = onp.random.randint(
|
|
low=low, high=high, size=shape).astype(onp.float32)
|
|
onp_array_lst.append(onp_array1)
|
|
mnp_array_lst.append(to_tensor(onp_array1))
|
|
if axis is not None and axis < ndim:
|
|
new_shape[axis] += onp.random.randint(2)
|
|
onp_array2 = onp.random.randint(
|
|
low=low, high=high, size=new_shape).astype(onp.float32)
|
|
onp_array_lst.append(onp_array2)
|
|
mnp_array_lst.append(to_tensor(onp_array2))
|
|
return onp_array_lst, mnp_array_lst
|
|
|
|
# Test np.xstack
|
|
|
|
|
|
def prepare_array_sequences(n_lst, ndim_lst, axis=None, low=1, high=5):
|
|
onp_seq_lst = []
|
|
mnp_seq_lst = []
|
|
for n in n_lst:
|
|
for ndim in ndim_lst:
|
|
onp_array_lst, mnp_array_lst = construct_arrays(
|
|
n=n, ndim=ndim, axis=axis, low=low, high=high)
|
|
onp_seq_lst.append(onp_array_lst)
|
|
mnp_seq_lst.append(mnp_array_lst)
|
|
return onp_seq_lst, mnp_seq_lst
|
|
|
|
|
|
def mnp_column_stack(input_tensor):
|
|
return mnp.column_stack(input_tensor)
|
|
|
|
|
|
def onp_column_stack(input_array):
|
|
return onp.column_stack(input_array)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_column_stack():
|
|
onp_seq_lst, mnp_seq_lst = prepare_array_sequences(
|
|
n_lst=[1, 5], ndim_lst=[1, 2, 3, 4], axis=1)
|
|
for i, onp_seq in enumerate(onp_seq_lst):
|
|
onp_seq = onp_seq_lst[i]
|
|
mnp_seq = mnp_seq_lst[i]
|
|
o_column_stack = onp_column_stack(onp_seq)
|
|
m_column_stack = mnp_column_stack(mnp_seq)
|
|
check_all_results(o_column_stack, m_column_stack)
|
|
|
|
|
|
def mnp_hstack(input_tensor):
|
|
return mnp.hstack(input_tensor)
|
|
|
|
|
|
def onp_hstack(input_array):
|
|
return onp.hstack(input_array)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_hstack():
|
|
onp_seq_lst0, mnp_seq_lst0 = prepare_array_sequences(
|
|
n_lst=[1, 5], ndim_lst=[2, 3, 4], axis=1)
|
|
onp_seq_lst1, mnp_seq_lst1 = prepare_array_sequences(
|
|
n_lst=[1, 5], ndim_lst=[1], axis=0)
|
|
onp_seq_lst = onp_seq_lst0 + onp_seq_lst1
|
|
mnp_seq_lst = mnp_seq_lst0 + mnp_seq_lst1
|
|
for i, onp_seq in enumerate(onp_seq_lst):
|
|
mnp_seq = mnp_seq_lst[i]
|
|
o_hstack = onp_hstack(onp_seq)
|
|
m_hstack = mnp_hstack(mnp_seq)
|
|
check_all_results(o_hstack, m_hstack)
|
|
|
|
|
|
def mnp_dstack(input_tensor):
|
|
return mnp.dstack(input_tensor)
|
|
|
|
|
|
def onp_dstack(input_array):
|
|
return onp.dstack(input_array)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_dstack():
|
|
onp_seq_lst, mnp_seq_lst = prepare_array_sequences(
|
|
n_lst=[1, 5], ndim_lst=[1, 2, 3, 4], axis=2)
|
|
for i, onp_seq in enumerate(onp_seq_lst):
|
|
mnp_seq = mnp_seq_lst[i]
|
|
o_dstack = onp_dstack(onp_seq)
|
|
m_dstack = mnp_dstack(mnp_seq)
|
|
check_all_results(o_dstack, m_dstack)
|
|
|
|
|
|
def mnp_vstack(input_tensor):
|
|
return mnp.vstack(input_tensor)
|
|
|
|
|
|
def onp_vstack(input_array):
|
|
return onp.vstack(input_array)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_vstack():
|
|
onp_seq_lst0, mnp_seq_lst0 = prepare_array_sequences(
|
|
n_lst=[1, 5], ndim_lst=[2, 3, 4], axis=0)
|
|
onp_seq_lst1, mnp_seq_lst1 = prepare_array_sequences(
|
|
n_lst=[1, 5], ndim_lst=[1])
|
|
onp_seq_lst = onp_seq_lst0 + onp_seq_lst1
|
|
mnp_seq_lst = mnp_seq_lst0 + mnp_seq_lst1
|
|
for i, onp_seq in enumerate(onp_seq_lst):
|
|
mnp_seq = mnp_seq_lst[i]
|
|
o_vstack = onp_vstack(onp_seq)
|
|
m_vstack = mnp_vstack(mnp_seq)
|
|
check_all_results(o_vstack, m_vstack)
|
|
# Test np.atleastxd
|
|
|
|
|
|
def mnp_atleast1d(*arys):
|
|
return mnp.atleast_1d(*arys)
|
|
|
|
|
|
def onp_atleast1d(*arys):
|
|
return onp.atleast_1d(*arys)
|
|
|
|
|
|
def mnp_atleast2d(*arys):
|
|
return mnp.atleast_2d(*arys)
|
|
|
|
|
|
def onp_atleast2d(*arys):
|
|
return onp.atleast_2d(*arys)
|
|
|
|
|
|
def mnp_atleast3d(*arys):
|
|
return mnp.atleast_3d(*arys)
|
|
|
|
|
|
def onp_atleast3d(*arys):
|
|
return onp.atleast_3d(*arys)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_atleast1d():
|
|
run_non_kw_test(mnp_atleast1d, onp_atleast1d, Cases())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_atleast2d():
|
|
run_non_kw_test(mnp_atleast2d, onp_atleast2d, Cases())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_atleast3d():
|
|
run_non_kw_test(mnp_atleast3d, onp_atleast3d, Cases())
|
|
|
|
|
|
# Test np.where
|
|
def mnp_where(condition, x, y):
|
|
return mnp.where(condition, x, y)
|
|
|
|
|
|
def onp_where(condition, x, y):
|
|
return onp.where(condition, x, y)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_where():
|
|
test_case = Cases()
|
|
for condition1 in test_case.bool_broadcastables[:2]:
|
|
for x in test_case.broadcastables[:2]:
|
|
for y in test_case.broadcastables[:2]:
|
|
for condition2 in test_case.broadcastables[:2]:
|
|
match_res(mnp_where, onp_where, condition1, x, y)
|
|
match_res(mnp_where, onp_where, condition2, x, y)
|
|
|
|
|
|
# Test ndarray.flatten
|
|
def mnp_ndarray_flatten(input_tensor):
|
|
a = input_tensor.flatten()
|
|
b = input_tensor.flatten(order='F')
|
|
c = input_tensor.flatten(order='C')
|
|
return a, b, c
|
|
|
|
|
|
def onp_ndarray_flatten(input_array):
|
|
a = input_array.flatten()
|
|
b = input_array.flatten(order='F')
|
|
c = input_array.flatten(order='C')
|
|
return a, b, c
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_ndarray_flatten():
|
|
onp_array = onp.random.random((3, 4, 5)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_flatten = onp_ndarray_flatten(onp_array)
|
|
m_flatten = mnp_ndarray_flatten(mnp_array)
|
|
check_all_results(o_flatten, m_flatten)
|
|
|
|
|
|
# Test ndarray.transpose
|
|
def mnp_ndarray_transpose(input_tensor):
|
|
a = input_tensor.T
|
|
b = input_tensor.transpose()
|
|
c = input_tensor.transpose((0, 2, 1))
|
|
d = input_tensor.transpose([0, 2, 1])
|
|
return a, b, c, d
|
|
|
|
|
|
def onp_ndarray_transpose(input_array):
|
|
a = input_array.T
|
|
b = input_array.transpose()
|
|
c = input_array.transpose((0, 2, 1))
|
|
d = input_array.transpose([0, 2, 1])
|
|
return a, b, c, d
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_ndarray_transpose():
|
|
onp_array = onp.random.random((3, 4, 5)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_transposed = onp_ndarray_transpose(onp_array)
|
|
m_transposed = mnp_ndarray_transpose(mnp_array)
|
|
check_all_results(o_transposed, m_transposed)
|
|
|
|
|
|
# Test ndarray.astype
|
|
def mnp_ndarray_astype(input_tensor):
|
|
a = input_tensor.astype("float16")
|
|
b = input_tensor.astype(onp.float64)
|
|
c = input_tensor.astype(mnp.bool_)
|
|
return a, b, c
|
|
|
|
|
|
def onp_ndarray_astype(input_array):
|
|
a = input_array.astype("float16")
|
|
b = input_array.astype(onp.float64)
|
|
c = input_array.astype(onp.bool_)
|
|
return a, b, c
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_ndarray_astype():
|
|
onp_array = onp.random.random((3, 4, 5)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_astype = onp_ndarray_astype(onp_array)
|
|
m_astype = mnp_ndarray_astype(mnp_array)
|
|
for arr1, arr2 in zip(o_astype, m_astype):
|
|
assert arr1.dtype == arr2.asnumpy().dtype
|
|
|
|
|
|
def onp_concatenate_type_promotion(onp_array1, onp_array2, onp_array3, onp_array4):
|
|
o_concatenate = onp.concatenate((onp_array1,
|
|
onp_array2,
|
|
onp_array3,
|
|
onp_array4), -1)
|
|
return o_concatenate
|
|
|
|
|
|
def mnp_concatenate_type_promotion(mnp_array1, mnp_array2, mnp_array3, mnp_array4):
|
|
m_concatenate = mnp.concatenate([mnp_array1,
|
|
mnp_array2,
|
|
mnp_array3,
|
|
mnp_array4], -1)
|
|
return m_concatenate
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_concatenate_type_promotion():
|
|
onp_array = onp.random.random((5, 1)).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
onp_array1 = onp_array.astype(onp.float16)
|
|
onp_array2 = onp_array.astype(onp.bool_)
|
|
onp_array3 = onp_array.astype(onp.float32)
|
|
onp_array4 = onp_array.astype(onp.int32)
|
|
|
|
mnp_array1 = mnp_array.astype(onp.float16)
|
|
mnp_array2 = mnp_array.astype(onp.bool_)
|
|
mnp_array3 = mnp_array.astype(onp.float32)
|
|
mnp_array4 = mnp_array.astype(onp.int32)
|
|
o_concatenate = onp_concatenate_type_promotion(
|
|
onp_array1, onp_array2, onp_array3, onp_array4).astype('float32')
|
|
m_concatenate = mnp_concatenate_type_promotion(
|
|
mnp_array1, mnp_array2, mnp_array3, mnp_array4)
|
|
check_all_results(o_concatenate, m_concatenate, error=1e-7)
|
|
|
|
|
|
def mnp_stack(*arrs):
|
|
a = mnp.stack(arrs, axis=-4)
|
|
b = mnp.stack(arrs, axis=-3)
|
|
c = mnp.stack(arrs, axis=0)
|
|
d = mnp.stack(arrs, axis=3)
|
|
e = mnp.stack(arrs, axis=2)
|
|
return a, b, c, d, e
|
|
|
|
|
|
def onp_stack(*arrs):
|
|
a = onp.stack(arrs, axis=-4)
|
|
b = onp.stack(arrs, axis=-3)
|
|
c = onp.stack(arrs, axis=0)
|
|
d = onp.stack(arrs, axis=3)
|
|
e = onp.stack(arrs, axis=2)
|
|
return a, b, c, d, e
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_stack():
|
|
arr = rand_int(3, 4, 5, 6)
|
|
match_res(mnp.stack, onp.stack, arr)
|
|
for i in range(-4, 4):
|
|
match_res(mnp.stack, onp.stack, arr, axis=i)
|
|
|
|
arrs = [rand_int(3, 4, 5) for i in range(10)]
|
|
match_res(mnp.stack, onp.stack, arrs)
|
|
match_res(mnp.stack, onp.stack, tuple(arrs))
|
|
match_res(mnp_stack, onp_stack, *arrs)
|
|
for i in range(-4, 4):
|
|
match_res(mnp.stack, onp.stack, arrs, axis=i)
|
|
|
|
|
|
def mnp_roll(input_tensor):
|
|
a = mnp.roll(input_tensor, -3)
|
|
b = mnp.roll(input_tensor, [-2, -3], 1)
|
|
c = mnp.roll(input_tensor, (3, 0, -5), (-1, -2, 0))
|
|
d = mnp.roll(input_tensor, (4,), [0, 0, 1])
|
|
return a, b, c, d
|
|
|
|
|
|
def onp_roll(input_array):
|
|
a = onp.roll(input_array, -3)
|
|
b = onp.roll(input_array, [-2, -3], 1)
|
|
c = onp.roll(input_array, (3, 0, -5), (-1, -2, 0))
|
|
d = onp.roll(input_array, (4,), [0, 0, 1])
|
|
return a, b, c, d
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_roll():
|
|
arr = rand_int(3, 4, 5)
|
|
match_res(mnp_roll, onp_roll, arr)
|
|
arr = rand_int(1, 4, 6).astype("int64")
|
|
match_res(mnp_roll, onp_roll, arr)
|
|
|
|
|
|
def mnp_moveaxis(a):
|
|
a = mnp.moveaxis(a, 3, 3)
|
|
b = mnp.moveaxis(a, -1, 4)
|
|
c = mnp.moveaxis(a, (2, 1, 4), (0, 3, 2))
|
|
d = mnp.moveaxis(a, [-2, -5], [2, -4])
|
|
return a, b, c, d
|
|
|
|
|
|
def onp_moveaxis(a):
|
|
a = onp.moveaxis(a, 3, 3)
|
|
b = onp.moveaxis(a, -1, 4)
|
|
c = onp.moveaxis(a, (2, 1, 4), (0, 3, 2))
|
|
d = onp.moveaxis(a, [-2, -5], [2, -4])
|
|
return a, b, c, d
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_moveaxis():
|
|
a = rand_int(2, 4, 5, 9, 6)
|
|
match_res(mnp_moveaxis, onp_moveaxis, a)
|
|
|
|
|
|
def mnp_tile(x):
|
|
a = mnp.tile(x, 1)
|
|
b = mnp.tile(x, 3)
|
|
c = mnp.tile(x, [5, 1])
|
|
d = mnp.tile(x, [5, 1, 2, 3, 7])
|
|
return a, b, c, d
|
|
|
|
|
|
def onp_tile(x):
|
|
a = onp.tile(x, 1)
|
|
b = onp.tile(x, 3)
|
|
c = onp.tile(x, [5, 1])
|
|
d = onp.tile(x, [5, 1, 2, 3, 7])
|
|
return a, b, c, d
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_tile():
|
|
a = rand_int(2, 3, 4)
|
|
match_res(mnp_tile, onp_tile, a)
|
|
|
|
|
|
def mnp_broadcast_to(x):
|
|
a = mnp.broadcast_to(x, (2, 3))
|
|
b = mnp.broadcast_to(x, (8, 1, 3))
|
|
return a, b
|
|
|
|
|
|
def onp_broadcast_to(x):
|
|
a = onp.broadcast_to(x, (2, 3))
|
|
b = onp.broadcast_to(x, (8, 1, 3))
|
|
return a, b
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_broadcast_to():
|
|
x = rand_int()
|
|
match_res(mnp_broadcast_to, onp_broadcast_to, x)
|
|
x = rand_int(3)
|
|
match_res(mnp_broadcast_to, onp_broadcast_to, x)
|
|
x = rand_int(1, 3)
|
|
match_res(mnp_broadcast_to, onp_broadcast_to, x)
|
|
|
|
|
|
def mnp_broadcast_arrays(*args):
|
|
return mnp.broadcast_arrays(*args)
|
|
|
|
|
|
def onp_broadcast_arrays(*args):
|
|
return onp.broadcast_arrays(*args)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_broadcast_arrays():
|
|
test_case = Cases()
|
|
broadcastables = test_case.broadcastables
|
|
for i in range(len(broadcastables)):
|
|
arrs = broadcastables[i:]
|
|
match_res(mnp_broadcast_arrays, onp_broadcast_arrays, *arrs)
|
|
|
|
|
|
def mnp_flip(x):
|
|
a = mnp.flip(x)
|
|
b = mnp.flip(x, 0)
|
|
c = mnp.flip(x, 1)
|
|
d = mnp.flip(x, (-3, -1))
|
|
return a, b, c, d
|
|
|
|
|
|
def onp_flip(x):
|
|
a = onp.flip(x)
|
|
b = onp.flip(x, 0)
|
|
c = onp.flip(x, 1)
|
|
d = onp.flip(x, (-3, -1))
|
|
return a, b, c, d
|
|
|
|
|
|
@pytest.mark.level2
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_flip():
|
|
x = rand_int(2, 3, 4)
|
|
run_multi_test(mnp_flip, onp_flip, (x,))
|
|
|
|
|
|
def mnp_flipud(x):
|
|
return mnp.flipud(x)
|
|
|
|
|
|
def onp_flipud(x):
|
|
return onp.flipud(x)
|
|
|
|
|
|
@pytest.mark.level2
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_flipud():
|
|
x = rand_int(2, 3, 4)
|
|
run_multi_test(mnp_flipud, onp_flipud, (x,))
|
|
|
|
|
|
def mnp_fliplr(x):
|
|
return mnp.fliplr(x)
|
|
|
|
|
|
def onp_fliplr(x):
|
|
return onp.fliplr(x)
|
|
|
|
|
|
@pytest.mark.level2
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_fliplr():
|
|
x = rand_int(2, 3, 4)
|
|
run_multi_test(mnp_fliplr, onp_fliplr, (x,))
|
|
|
|
|
|
def mnp_split(input_tensor):
|
|
a = mnp.split(input_tensor, indices_or_sections=1)
|
|
b = mnp.split(input_tensor, indices_or_sections=3)
|
|
return a, b
|
|
|
|
|
|
def onp_split(input_array):
|
|
a = onp.split(input_array, indices_or_sections=1)
|
|
b = onp.split(input_array, indices_or_sections=3)
|
|
return a, b
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_split():
|
|
onp_arrs = [
|
|
onp.random.randint(1, 5, size=(9, 4, 5)).astype('float32')
|
|
]
|
|
mnp_arrs = [to_tensor(arr) for arr in onp_arrs]
|
|
for onp_arr, mnp_arr in zip(onp_arrs, mnp_arrs):
|
|
o_split = onp_split(onp_arr)
|
|
m_split = mnp_split(mnp_arr)
|
|
for expect_lst, actual_lst in zip(o_split, m_split):
|
|
for expect, actual in zip(expect_lst, actual_lst):
|
|
match_array(expect, actual.asnumpy())
|
|
|
|
|
|
def mnp_array_split(input_tensor):
|
|
a = mnp.array_split(input_tensor, indices_or_sections=4, axis=2)
|
|
b = mnp.array_split(input_tensor, indices_or_sections=3, axis=1)
|
|
c = mnp.array_split(input_tensor, indices_or_sections=6)
|
|
return a, b, c
|
|
|
|
|
|
def onp_array_split(input_array):
|
|
a = onp.array_split(input_array, indices_or_sections=4, axis=2)
|
|
b = onp.array_split(input_array, indices_or_sections=3, axis=1)
|
|
c = onp.array_split(input_array, indices_or_sections=6)
|
|
return a, b, c
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_array_split():
|
|
onp_arr = onp.random.randint(1, 5, size=(9, 7, 13)).astype('float32')
|
|
mnp_arr = to_tensor(onp_arr)
|
|
o_split = onp_split(onp_arr)
|
|
m_split = mnp_split(mnp_arr)
|
|
for expect_lst, actual_lst in zip(o_split, m_split):
|
|
for expect, actual in zip(expect_lst, actual_lst):
|
|
match_array(expect, actual.asnumpy())
|
|
|
|
|
|
def mnp_vsplit(input_tensor):
|
|
a = mnp.vsplit(input_tensor, indices_or_sections=3)
|
|
return a
|
|
|
|
|
|
def onp_vsplit(input_array):
|
|
a = onp.vsplit(input_array, indices_or_sections=3)
|
|
return a
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_vsplit():
|
|
onp_arrs = [
|
|
onp.random.randint(1, 5, size=(9, 4, 5)).astype('float32')
|
|
]
|
|
mnp_arrs = [to_tensor(arr) for arr in onp_arrs]
|
|
for onp_arr, mnp_arr in zip(onp_arrs, mnp_arrs):
|
|
o_vsplit = onp_vsplit(onp_arr)
|
|
m_vsplit = mnp_vsplit(mnp_arr)
|
|
for expect_lst, actual_lst in zip(o_vsplit, m_vsplit):
|
|
for expect, actual in zip(expect_lst, actual_lst):
|
|
match_array(expect, actual.asnumpy())
|
|
|
|
|
|
def mnp_hsplit(input_tensor):
|
|
a = mnp.hsplit(input_tensor, indices_or_sections=3)
|
|
return a
|
|
|
|
|
|
def onp_hsplit(input_array):
|
|
a = onp.hsplit(input_array, indices_or_sections=3)
|
|
return a
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_hsplit():
|
|
onp_arrs = [
|
|
onp.random.randint(1, 5, size=(4, 9, 5)).astype('float32')
|
|
]
|
|
mnp_arrs = [to_tensor(arr) for arr in onp_arrs]
|
|
for onp_arr, mnp_arr in zip(onp_arrs, mnp_arrs):
|
|
o_hsplit = onp_hsplit(onp_arr)
|
|
m_hsplit = mnp_hsplit(mnp_arr)
|
|
for expect_lst, actual_lst in zip(o_hsplit, m_hsplit):
|
|
for expect, actual in zip(expect_lst, actual_lst):
|
|
match_array(expect, actual.asnumpy())
|
|
|
|
|
|
def mnp_dsplit(input_tensor):
|
|
a = mnp.dsplit(input_tensor, indices_or_sections=3)
|
|
return a
|
|
|
|
def onp_dsplit(input_array):
|
|
a = onp.dsplit(input_array, indices_or_sections=3)
|
|
return a
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_dsplit():
|
|
onp_arrs = [
|
|
onp.random.randint(1, 5, size=(5, 4, 9)).astype('float32')
|
|
]
|
|
mnp_arrs = [to_tensor(arr) for arr in onp_arrs]
|
|
for onp_arr, mnp_arr in zip(onp_arrs, mnp_arrs):
|
|
o_dsplit = onp_dsplit(onp_arr)
|
|
m_dsplit = mnp_dsplit(mnp_arr)
|
|
for expect_lst, actual_lst in zip(o_dsplit, m_dsplit):
|
|
for expect, actual in zip(expect_lst, actual_lst):
|
|
match_array(expect, actual.asnumpy())
|
|
|
|
|
|
def mnp_take_along_axis(*arrs):
|
|
x = arrs[0]
|
|
a = mnp.take_along_axis(x, arrs[1], axis=None)
|
|
b = mnp.take_along_axis(x, arrs[2], axis=1)
|
|
c = mnp.take_along_axis(x, arrs[3], axis=-1)
|
|
d = mnp.take_along_axis(x, arrs[4], axis=0)
|
|
return a, b, c, d
|
|
|
|
|
|
def onp_take_along_axis(*arrs):
|
|
x = arrs[0]
|
|
a = onp.take_along_axis(x, arrs[1], axis=None)
|
|
b = onp.take_along_axis(x, arrs[2], axis=1)
|
|
c = onp.take_along_axis(x, arrs[3], axis=-1)
|
|
d = onp.take_along_axis(x, arrs[4], axis=0)
|
|
return a, b, c, d
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_take_along_axis():
|
|
x = rand_int(6, 7, 8, 9)
|
|
indices1 = rand_int(2).astype(onp.int32)
|
|
indices2 = rand_int(6, 3, 8, 1).astype(onp.int32)
|
|
indices3 = rand_int(6, 1, 8, 5).astype(onp.int32)
|
|
indices4 = rand_int(4, 1, 1, 1).astype(onp.int32)
|
|
run_multi_test(mnp_take_along_axis, onp_take_along_axis,
|
|
(x, indices1, indices2, indices3, indices4))
|
|
|
|
|
|
def mnp_take(x, indices):
|
|
a = mnp.take(x, indices)
|
|
b = mnp.take(x, indices, axis=-1)
|
|
c = mnp.take(x, indices, axis=0, mode='wrap')
|
|
d = mnp.take(x, indices, axis=1, mode='clip')
|
|
return a, b, c, d
|
|
|
|
|
|
def onp_take(x, indices):
|
|
a = onp.take(x, indices)
|
|
b = onp.take(x, indices, axis=-1)
|
|
c = onp.take(x, indices, axis=0, mode='wrap')
|
|
d = onp.take(x, indices, axis=1, mode='clip')
|
|
return a, b, c, d
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_take():
|
|
x = rand_int(2, 3, 4, 5)
|
|
indices = rand_int(2, 3).astype(onp.int32)
|
|
run_multi_test(mnp_take, onp_take, (x, indices))
|
|
|
|
|
|
def mnp_repeat(x):
|
|
a = mnp.repeat(x, 2)
|
|
b = mnp.repeat(x, 3, axis=0)
|
|
c = mnp.repeat(x, (4, 1, 5), axis=1)
|
|
d = mnp.repeat(x, (3, 2, 1, 0, 4), axis=-1)
|
|
e = mnp.repeat(x, 0)
|
|
return a, b, c, d, e
|
|
|
|
|
|
def onp_repeat(x):
|
|
a = onp.repeat(x, 2)
|
|
b = onp.repeat(x, 3, axis=0)
|
|
c = onp.repeat(x, (4, 1, 5), axis=1)
|
|
d = onp.repeat(x, (3, 2, 1, 0, 4), axis=-1)
|
|
e = onp.repeat(x, 0)
|
|
return a, b, c, d, e
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_repeat():
|
|
x = rand_int(2, 3, 4, 5)
|
|
run_multi_test(mnp_repeat, onp_repeat, (x,))
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_select():
|
|
choicelist = rand_int(2, 3, 4, 5)
|
|
condlist = choicelist > 2
|
|
match_res(mnp.select, onp.select, condlist, choicelist)
|
|
match_res(mnp.select, onp.select, condlist, choicelist, default=10)
|
|
|
|
condlist = rand_bool(5, 4, 1, 3)
|
|
choicelist = rand_int(5, 3)
|
|
match_res(mnp.select, onp.select, condlist, choicelist)
|
|
match_res(mnp.select, onp.select, condlist, choicelist, default=10)
|
|
|
|
condlist = rand_bool(3, 1, 7)
|
|
choicelist = rand_int(3, 5, 2, 1)
|
|
match_res(mnp.select, onp.select, condlist, choicelist)
|
|
match_res(mnp.select, onp.select, condlist, choicelist, default=10)
|
|
|
|
|
|
class ReshapeExpandSqueeze(Cell):
|
|
def __init__(self):
|
|
super(ReshapeExpandSqueeze, self).__init__()
|
|
|
|
def construct(self, x):
|
|
x = mnp.expand_dims(x, 2)
|
|
x = mnp.reshape(x, (1, 2, 3, 4, 1, 1))
|
|
x = mnp.squeeze(x)
|
|
return x
|
|
|
|
|
|
class TransposeConcatRavel(Cell):
|
|
def __init__(self):
|
|
super(TransposeConcatRavel, self).__init__()
|
|
|
|
def construct(self, x1, x2, x3):
|
|
x1 = mnp.transpose(x1, [0, 2, 1])
|
|
x2 = x2.transpose(0, 2, 1)
|
|
x = mnp.concatenate((x1, x2, x3), -1)
|
|
x = mnp.ravel(x)
|
|
return x
|
|
|
|
|
|
class RollSwap(Cell):
|
|
def __init__(self):
|
|
super(RollSwap, self).__init__()
|
|
|
|
def construct(self, x):
|
|
x = mnp.rollaxis(x, 2)
|
|
x = mnp.swapaxes(x, 0, 1)
|
|
return x
|
|
|
|
|
|
test_case_array_ops = [
|
|
('ReshapeExpandSqueeze', {
|
|
'block': ReshapeExpandSqueeze(),
|
|
'desc_inputs': [mnp.ones((2, 3, 4))]}),
|
|
|
|
('TransposeConcatRavel', {
|
|
'block': TransposeConcatRavel(),
|
|
'desc_inputs': [mnp.ones((2, 3, 4)),
|
|
mnp.ones((2, 3, 4)),
|
|
mnp.ones((2, 4, 1))]}),
|
|
|
|
('RollSwap', {
|
|
'block': RollSwap(),
|
|
'desc_inputs': [mnp.ones((2, 3, 4))]})
|
|
]
|
|
|
|
test_case_lists = [test_case_array_ops]
|
|
test_exec_case = functools.reduce(lambda x, y: x + y, test_case_lists)
|
|
# use -k to select certain testcast
|
|
# pytest tests/python/ops/test_ops.py::test_backward -k LayerNorm
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_expand_dims_exception():
|
|
with pytest.raises(TypeError):
|
|
mnp.expand_dims(mnp.ones((3, 3)), 1.2)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_swapaxes_exception():
|
|
with pytest.raises(ValueError):
|
|
mnp.swapaxes(mnp.ones((3, 3)), 1, 10)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_flatten():
|
|
lst = [[1.0, 2.0], [3.0, 4.0]]
|
|
tensor_list = to_tensor(lst)
|
|
assert tensor_list.flatten().asnumpy().tolist() == [1.0, 2.0, 3.0, 4.0]
|
|
assert tensor_list.flatten(order='F').asnumpy().tolist() == [
|
|
1.0, 3.0, 2.0, 4.0]
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_reshape():
|
|
lst = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
|
|
tensor_list = to_tensor(lst)
|
|
with pytest.raises(TypeError):
|
|
tensor_list = tensor_list.reshape({0, 1, 2})
|
|
with pytest.raises(ValueError):
|
|
tensor_list = tensor_list.reshape(1, 2, 3)
|
|
assert tensor_list.reshape([-1, 4]).shape == (2, 4)
|
|
assert tensor_list.reshape(1, -1, 4).shape == (1, 2, 4)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_squeeze():
|
|
lst = [[[1.0], [2.0], [3.0]]]
|
|
tensor_list = to_tensor(lst)
|
|
with pytest.raises(TypeError):
|
|
tensor_list = tensor_list.squeeze(1.2)
|
|
with pytest.raises(ValueError):
|
|
tensor_list = tensor_list.squeeze(4)
|
|
assert tensor_list.squeeze().shape == (3,)
|
|
assert tensor_list.squeeze(axis=2).shape == (1, 3)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_ravel():
|
|
lst = [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]]
|
|
tensor_list = to_tensor(lst)
|
|
assert tensor_list.ravel().shape == (8,)
|
|
assert tensor_list.ravel().asnumpy().tolist() == [
|
|
1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_swapaxes():
|
|
lst = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]
|
|
tensor_list = to_tensor(lst)
|
|
with pytest.raises(TypeError):
|
|
tensor_list = tensor_list.swapaxes(0, (1,))
|
|
with pytest.raises(ValueError):
|
|
tensor_list = tensor_list.swapaxes(0, 3)
|
|
assert tensor_list.swapaxes(0, 1).shape == (3, 2)
|
|
|
|
|
|
def mnp_rot90(input_tensor):
|
|
a = mnp.rot90(input_tensor)
|
|
b = mnp.rot90(input_tensor, 2)
|
|
c = mnp.rot90(input_tensor, 3)
|
|
d = mnp.rot90(input_tensor, 4)
|
|
e = mnp.rot90(input_tensor, 5, (0, -1))
|
|
f = mnp.rot90(input_tensor, 1, (2, 0))
|
|
g = mnp.rot90(input_tensor, -3, (-1, -2))
|
|
h = mnp.rot90(input_tensor, 3, (2, 1))
|
|
return a, b, c, d, e, f, g, h
|
|
|
|
|
|
def onp_rot90(input_array):
|
|
a = onp.rot90(input_array)
|
|
b = onp.rot90(input_array, 2)
|
|
c = onp.rot90(input_array, 3)
|
|
d = onp.rot90(input_array, 4)
|
|
e = onp.rot90(input_array, 5, (0, -1))
|
|
f = onp.rot90(input_array, 1, (2, 0))
|
|
g = onp.rot90(input_array, -3, (-1, -2))
|
|
h = onp.rot90(input_array, 3, (2, 1))
|
|
return a, b, c, d, e, f, g, h
|
|
|
|
|
|
@pytest.mark.level2
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_rot90():
|
|
onp_array = rand_int(3, 4, 5).astype('float32')
|
|
mnp_array = to_tensor(onp_array)
|
|
o_rot = onp_rot90(onp_array)
|
|
m_rot = mnp_rot90(mnp_array)
|
|
check_all_results(o_rot, m_rot)
|