forked from mindspore-Ecosystem/mindspore
122 lines
3.9 KiB
Python
122 lines
3.9 KiB
Python
# 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.
|
|
# ============================================================================
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.context as context
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.ops import operations as P
|
|
|
|
class NetDiv(nn.Cell):
|
|
def __init__(self):
|
|
super(NetDiv, self).__init__()
|
|
self.div = P.Div()
|
|
|
|
def construct(self, x, y):
|
|
return self.div(x, y)
|
|
|
|
def div(nptype):
|
|
x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(nptype)
|
|
y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(nptype)
|
|
x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(nptype)
|
|
y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(nptype)
|
|
x2_np = np.random.randint(1, 5, (2, 1, 1, 4)).astype(nptype)
|
|
y2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(nptype)
|
|
x3_np = np.random.randint(1, 5, 1).astype(nptype)
|
|
y3_np = np.random.randint(1, 5, 1).astype(nptype)
|
|
x4_np = np.array(78).astype(nptype)
|
|
y4_np = np.array(37.5).astype(nptype)
|
|
|
|
x0 = Tensor(x0_np)
|
|
y0 = Tensor(y0_np)
|
|
x1 = Tensor(x1_np)
|
|
y1 = Tensor(y1_np)
|
|
x2 = Tensor(x2_np)
|
|
y2 = Tensor(y2_np)
|
|
x3 = Tensor(x3_np)
|
|
y3 = Tensor(y3_np)
|
|
x4 = Tensor(x4_np)
|
|
y4 = Tensor(y4_np)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
|
div_net = NetDiv()
|
|
output0 = div_net(x0, y0)
|
|
expect0 = np.divide(x0_np, y0_np)
|
|
diff0 = output0.asnumpy() - expect0
|
|
error0 = np.ones(shape=expect0.shape) * 1.0e-5
|
|
assert np.all(diff0 < error0)
|
|
assert output0.shape == expect0.shape
|
|
|
|
output1 = div_net(x1, y1)
|
|
expect1 = np.divide(x1_np, y1_np)
|
|
diff1 = output1.asnumpy() - expect1
|
|
error1 = np.ones(shape=expect1.shape) * 1.0e-5
|
|
assert np.all(diff1 < error1)
|
|
assert output1.shape == expect1.shape
|
|
|
|
output2 = div_net(x2, y2)
|
|
expect2 = np.divide(x2_np, y2_np)
|
|
diff2 = output2.asnumpy() - expect2
|
|
error2 = np.ones(shape=expect2.shape) * 1.0e-5
|
|
assert np.all(diff2 < error2)
|
|
assert output2.shape == expect2.shape
|
|
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
|
|
output3 = div_net(x3, y3)
|
|
expect3 = np.divide(x3_np, y3_np)
|
|
diff3 = output3.asnumpy() - expect3
|
|
error3 = np.ones(shape=expect3.shape) * 1.0e-5
|
|
assert np.all(diff3 < error3)
|
|
assert output3.shape == expect3.shape
|
|
|
|
output4 = div_net(x4, y4)
|
|
expect4 = np.divide(x4_np, y4_np)
|
|
diff4 = output4.asnumpy() - expect4
|
|
error4 = np.ones(shape=expect4.shape) * 1.0e-5
|
|
assert np.all(diff4 < error4)
|
|
assert output4.shape == expect4.shape
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_div_float64():
|
|
div(np.float64)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_div_float32():
|
|
div(np.float32)
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_div_float16():
|
|
div(np.float16)
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_div_int64():
|
|
div(np.int64)
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_div_int32():
|
|
div(np.int32)
|