ZPaC
5 years ago
parent
f21ee64fd0
commit
1dcc34e785
@ -0,0 +1,116 @@
|
||||
# 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.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)
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_div():
|
||||
x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
|
||||
y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
|
||||
x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
|
||||
y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float32)
|
||||
x2_np = np.random.randint(1, 5, (2, 1, 1, 4)).astype(np.float32)
|
||||
y2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
|
||||
x3_np = np.random.randint(1, 5, 1).astype(np.float32)
|
||||
y3_np = np.random.randint(1, 5, 1).astype(np.float32)
|
||||
x4_np = np.array(768).astype(np.float32)
|
||||
y4_np = np.array(3072.5).astype(np.float32)
|
||||
x5_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
|
||||
y5_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
|
||||
x6_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int32)
|
||||
y6_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.int32)
|
||||
|
||||
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)
|
||||
x5 = Tensor(x5_np)
|
||||
y5 = Tensor(y5_np)
|
||||
x6 = Tensor(x6_np)
|
||||
y6 = Tensor(y6_np)
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
|
||||
div = NetDiv()
|
||||
output0 = div(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(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(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(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(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
|
||||
|
||||
output5 = div(x5, y5)
|
||||
expect5 = np.divide(x5_np, y5_np)
|
||||
diff5 = output5.asnumpy() - expect5
|
||||
error5 = np.ones(shape=expect5.shape) * 1.0e-5
|
||||
assert np.all(diff5 < error5)
|
||||
assert output5.shape == expect5.shape
|
||||
|
||||
output6 = div(x6, y6)
|
||||
expect6 = np.divide(x6_np, y6_np)
|
||||
diff6 = output6.asnumpy() - expect6
|
||||
error6 = np.ones(shape=expect6.shape) * 1.0e-5
|
||||
assert np.all(diff6 < error6)
|
||||
assert output6.shape == expect6.shape
|
||||
Loading…
Reference in new issue