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117 lines
4.1 KiB
117 lines
4.1 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class NetDiv(nn.Cell):
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def __init__(self):
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super(NetDiv, self).__init__()
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self.div = P.Div()
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def construct(self, x, y):
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return self.div(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_div():
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x0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
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y0_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
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x1_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
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y1_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.float32)
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x2_np = np.random.randint(1, 5, (2, 1, 1, 4)).astype(np.float32)
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y2_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float32)
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x3_np = np.random.randint(1, 5, 1).astype(np.float32)
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y3_np = np.random.randint(1, 5, 1).astype(np.float32)
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x4_np = np.array(768).astype(np.float32)
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y4_np = np.array(3072.5).astype(np.float32)
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x5_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
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y5_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.float16)
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x6_np = np.random.randint(1, 5, (2, 3, 4, 4)).astype(np.int32)
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y6_np = np.random.randint(1, 5, (2, 1, 4, 4)).astype(np.int32)
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x0 = Tensor(x0_np)
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y0 = Tensor(y0_np)
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x1 = Tensor(x1_np)
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y1 = Tensor(y1_np)
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x2 = Tensor(x2_np)
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y2 = Tensor(y2_np)
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x3 = Tensor(x3_np)
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y3 = Tensor(y3_np)
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x4 = Tensor(x4_np)
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y4 = Tensor(y4_np)
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x5 = Tensor(x5_np)
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y5 = Tensor(y5_np)
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x6 = Tensor(x6_np)
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y6 = Tensor(y6_np)
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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div = NetDiv()
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output0 = div(x0, y0)
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expect0 = np.divide(x0_np, y0_np)
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diff0 = output0.asnumpy() - expect0
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error0 = np.ones(shape=expect0.shape) * 1.0e-5
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assert np.all(diff0 < error0)
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assert output0.shape == expect0.shape
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output1 = div(x1, y1)
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expect1 = np.divide(x1_np, y1_np)
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diff1 = output1.asnumpy() - expect1
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error1 = np.ones(shape=expect1.shape) * 1.0e-5
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assert np.all(diff1 < error1)
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assert output1.shape == expect1.shape
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output2 = div(x2, y2)
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expect2 = np.divide(x2_np, y2_np)
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diff2 = output2.asnumpy() - expect2
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error2 = np.ones(shape=expect2.shape) * 1.0e-5
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assert np.all(diff2 < error2)
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assert output2.shape == expect2.shape
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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output3 = div(x3, y3)
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expect3 = np.divide(x3_np, y3_np)
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diff3 = output3.asnumpy() - expect3
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error3 = np.ones(shape=expect3.shape) * 1.0e-5
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assert np.all(diff3 < error3)
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assert output3.shape == expect3.shape
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output4 = div(x4, y4)
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expect4 = np.divide(x4_np, y4_np)
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diff4 = output4.asnumpy() - expect4
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error4 = np.ones(shape=expect4.shape) * 1.0e-5
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assert np.all(diff4 < error4)
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assert output4.shape == expect4.shape
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output5 = div(x5, y5)
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expect5 = np.divide(x5_np, y5_np)
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diff5 = output5.asnumpy() - expect5
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error5 = np.ones(shape=expect5.shape) * 1.0e-5
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assert np.all(diff5 < error5)
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assert output5.shape == expect5.shape
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output6 = div(x6, y6)
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expect6 = np.divide(x6_np, y6_np)
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diff6 = output6.asnumpy() - expect6
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error6 = np.ones(shape=expect6.shape) * 1.0e-5
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assert np.all(diff6 < error6)
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assert output6.shape == expect6.shape
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