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.
216 lines
6.6 KiB
216 lines
6.6 KiB
# Copyright 2020 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.
|
|
# ============================================================================
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.common.api import ms_function
|
|
from mindspore.ops import operations as P
|
|
from mindspore.ops.operations import _inner_ops as inner
|
|
|
|
|
|
x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
|
|
axis0 = 3
|
|
keep_dims0 = True
|
|
|
|
x1 = np.random.rand(2, 3, 4, 4).astype(np.float32)
|
|
axis1 = 3
|
|
keep_dims1 = False
|
|
|
|
x2 = np.random.rand(2, 3, 1, 4).astype(np.float32)
|
|
axis2 = 2
|
|
keep_dims2 = True
|
|
|
|
x3 = np.random.rand(2, 3, 1, 4).astype(np.float32)
|
|
axis3 = 2
|
|
keep_dims3 = False
|
|
|
|
x4 = np.random.rand(2, 3, 4, 4).astype(np.float32)
|
|
axis4 = ()
|
|
np_axis4 = None
|
|
keep_dims4 = True
|
|
|
|
x5 = np.random.rand(2, 3, 4, 4).astype(np.float32)
|
|
axis5 = ()
|
|
np_axis5 = None
|
|
keep_dims5 = False
|
|
|
|
x6 = np.random.rand(2, 3, 4, 4).astype(np.float32)
|
|
axis6 = -2
|
|
keep_dims6 = False
|
|
|
|
x7 = np.random.rand(2, 3, 4, 4).astype(np.float32)
|
|
axis7 = (-2, -1)
|
|
keep_dims7 = True
|
|
|
|
x8 = np.random.rand(1, 1, 1, 1).astype(np.float32)
|
|
axis8 = ()
|
|
np_axis8 = None
|
|
keep_dims8 = True
|
|
|
|
context.set_context(device_target='GPU')
|
|
|
|
|
|
class ReduceMin(nn.Cell):
|
|
def __init__(self):
|
|
super(ReduceMin, self).__init__()
|
|
|
|
self.x0 = Tensor(x0)
|
|
self.axis0 = axis0
|
|
self.keep_dims0 = keep_dims0
|
|
|
|
self.x1 = Tensor(x1)
|
|
self.axis1 = axis1
|
|
self.keep_dims1 = keep_dims1
|
|
|
|
self.x2 = Tensor(x2)
|
|
self.axis2 = axis2
|
|
self.keep_dims2 = keep_dims2
|
|
|
|
self.x3 = Tensor(x3)
|
|
self.axis3 = axis3
|
|
self.keep_dims3 = keep_dims3
|
|
|
|
self.x4 = Tensor(x4)
|
|
self.axis4 = axis4
|
|
self.keep_dims4 = keep_dims4
|
|
|
|
self.x5 = Tensor(x5)
|
|
self.axis5 = axis5
|
|
self.keep_dims5 = keep_dims5
|
|
|
|
self.x6 = Tensor(x6)
|
|
self.axis6 = axis6
|
|
self.keep_dims6 = keep_dims6
|
|
|
|
self.x7 = Tensor(x7)
|
|
self.axis7 = axis7
|
|
self.keep_dims7 = keep_dims7
|
|
|
|
self.x8 = Tensor(x8)
|
|
self.axis8 = axis8
|
|
self.keep_dims8 = keep_dims8
|
|
|
|
@ms_function
|
|
def construct(self):
|
|
return (P.ReduceMin(self.keep_dims0)(self.x0, self.axis0),
|
|
P.ReduceMin(self.keep_dims1)(self.x1, self.axis1),
|
|
P.ReduceMin(self.keep_dims2)(self.x2, self.axis2),
|
|
P.ReduceMin(self.keep_dims3)(self.x3, self.axis3),
|
|
P.ReduceMin(self.keep_dims4)(self.x4, self.axis4),
|
|
P.ReduceMin(self.keep_dims5)(self.x5, self.axis5),
|
|
P.ReduceMin(self.keep_dims6)(self.x6, self.axis6),
|
|
P.ReduceMin(self.keep_dims7)(self.x7, self.axis7),
|
|
P.ReduceMin(self.keep_dims8)(self.x8, self.axis8))
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_ReduceMin():
|
|
reduce_min = ReduceMin()
|
|
output = reduce_min()
|
|
|
|
expect0 = np.min(x0, axis=axis0, keepdims=keep_dims0)
|
|
diff0 = abs(output[0].asnumpy() - expect0)
|
|
error0 = np.ones(shape=expect0.shape) * 1.0e-5
|
|
assert np.all(diff0 < error0)
|
|
assert output[0].shape == expect0.shape
|
|
|
|
expect1 = np.min(x1, axis=axis1, keepdims=keep_dims1)
|
|
diff1 = abs(output[1].asnumpy() - expect1)
|
|
error1 = np.ones(shape=expect1.shape) * 1.0e-5
|
|
assert np.all(diff1 < error1)
|
|
assert output[1].shape == expect1.shape
|
|
|
|
expect2 = np.min(x2, axis=axis2, keepdims=keep_dims2)
|
|
diff2 = abs(output[2].asnumpy() - expect2)
|
|
error2 = np.ones(shape=expect2.shape) * 1.0e-5
|
|
assert np.all(diff2 < error2)
|
|
assert output[2].shape == expect2.shape
|
|
|
|
expect3 = np.min(x3, axis=axis3, keepdims=keep_dims3)
|
|
diff3 = abs(output[3].asnumpy() - expect3)
|
|
error3 = np.ones(shape=expect3.shape) * 1.0e-5
|
|
assert np.all(diff3 < error3)
|
|
assert output[3].shape == expect3.shape
|
|
|
|
expect4 = np.min(x4, axis=np_axis4, keepdims=keep_dims4)
|
|
diff4 = abs(output[4].asnumpy() - expect4)
|
|
error4 = np.ones(shape=expect4.shape) * 1.0e-5
|
|
assert np.all(diff4 < error4)
|
|
assert output[4].shape == expect4.shape
|
|
|
|
expect5 = np.min(x5, axis=np_axis5, keepdims=keep_dims5)
|
|
diff5 = abs(output[5].asnumpy() - expect5)
|
|
error5 = np.ones(shape=expect5.shape) * 1.0e-5
|
|
assert np.all(diff5 < error5)
|
|
assert output[5].shape == expect5.shape
|
|
|
|
expect6 = np.min(x6, axis=axis6, keepdims=keep_dims6)
|
|
diff6 = abs(output[6].asnumpy() - expect6)
|
|
error6 = np.ones(shape=expect6.shape) * 1.0e-5
|
|
assert np.all(diff6 < error6)
|
|
assert output[6].shape == expect6.shape
|
|
|
|
expect7 = np.min(x7, axis=axis7, keepdims=keep_dims7)
|
|
diff7 = abs(output[7].asnumpy() - expect7)
|
|
error7 = np.ones(shape=expect7.shape) * 1.0e-5
|
|
assert np.all(diff7 < error7)
|
|
|
|
expect8 = np.min(x8, axis=np_axis8, keepdims=keep_dims8)
|
|
diff8 = abs(output[8].asnumpy() - expect8)
|
|
error8 = np.ones(shape=expect8.shape) * 1.0e-5
|
|
assert np.all(diff8 < error8)
|
|
|
|
|
|
x_1 = x8
|
|
axis_1 = 0
|
|
x_2 = x1
|
|
axis_2 = 0
|
|
|
|
|
|
class ReduceMinDynamic(nn.Cell):
|
|
def __init__(self, x, axis):
|
|
super(ReduceMinDynamic, self).__init__()
|
|
self.reducemin = P.ReduceMin(False)
|
|
self.test_dynamic = inner.GpuConvertToDynamicShape()
|
|
self.x = x
|
|
self.axis = axis
|
|
|
|
def construct(self):
|
|
dynamic_x = self.test_dynamic(self.x)
|
|
return self.reducemin(dynamic_x, self.axis)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_reduce_min_dynamic():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
net1 = ReduceMinDynamic(Tensor(x_1), axis_1)
|
|
net2 = ReduceMinDynamic(Tensor(x_2), axis_2)
|
|
|
|
expect_1 = np.min(x_1, axis=0, keepdims=False)
|
|
expect_2 = np.min(x_2, axis=0, keepdims=False)
|
|
|
|
output1 = net1()
|
|
output2 = net2()
|
|
|
|
np.testing.assert_almost_equal(output1.asnumpy(), expect_1)
|
|
np.testing.assert_almost_equal(output2.asnumpy(), expect_2)
|