forked from mindspore-Ecosystem/mindspore
576 lines
20 KiB
Python
576 lines
20 KiB
Python
# 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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import mindspore
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from mindspore import Tensor
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class SubNet(nn.Cell):
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def __init__(self):
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super(SubNet, self).__init__()
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self.sub = P.Sub()
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def construct(self, x, y):
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return self.sub(x, y)
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class DivNet(nn.Cell):
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def __init__(self):
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super(DivNet, 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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class FloorDivNet(nn.Cell):
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def __init__(self):
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super(FloorDivNet, self).__init__()
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self.floor_div = P.FloorDiv()
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def construct(self, x, y):
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return self.floor_div(x, y)
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class ModNet(nn.Cell):
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def __init__(self):
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super(ModNet, self).__init__()
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self.mod = P.Mod()
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def construct(self, x, y):
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return self.mod(x, y)
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class FloorModNet(nn.Cell):
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def __init__(self):
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super(FloorModNet, self).__init__()
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self.floor_mod = P.FloorMod()
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def construct(self, x, y):
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return self.floor_mod(x, y)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_sub():
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"""
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Feature: ALL To ALL
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Description: test cases for Sub.
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Expectation: the result match to numpy
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"""
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x = np.random.rand(2, 3, 4, 4).astype(np.float32)
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y = np.random.rand(4, 1).astype(np.float32)
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net = SubNet()
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output = net(Tensor(x), Tensor(y, mindspore.float32))
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expect_output = x - y
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assert np.all(output.asnumpy() == expect_output)
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# float64
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x = np.random.rand(2, 3, 4, 4).astype(np.float64)
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y = np.random.rand(4, 1).astype(np.float64)
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net = SubNet()
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output = net(Tensor(x), Tensor(y, mindspore.float64))
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expect_output = x - y
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assert np.all(output.asnumpy() == expect_output)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_div():
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"""
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Feature: ALL To ALL
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Description: test cases for Div.
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Expectation: the result match to numpy
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"""
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prop = 1 if np.random.random() < 0.5 else -1
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x0_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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y0_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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x1_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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y1_np = np.random.randint(1, 100, (2, 1, 4, 4)).astype(np.float32) * prop
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x2_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.float16) * prop
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y2_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float16) * prop
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x3_np = np.random.randint(1, 100, 1).astype(np.float32) * prop
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y3_np = np.random.randint(1, 100, 1).astype(np.float32) * prop
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x4_np = np.array(768).astype(np.float32) * prop
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y4_np = np.array(3072.5).astype(np.float32) * prop
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x5_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.int32) * prop
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y5_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int32) * prop
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x6_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int32) * prop
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y6_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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x7_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.int64) * prop
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y7_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int64) * prop
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x8_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float64) * prop
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y8_np = np.random.randint(1, 100, (2, 1, 4, 4)).astype(np.float64) * prop
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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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x7 = Tensor(x7_np)
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y7 = Tensor(y7_np)
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x8 = Tensor(x8_np)
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y8 = Tensor(y8_np)
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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div = DivNet()
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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).astype(np.float16)
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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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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 = x5_np // y5_np
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assert np.all(output5.asnumpy() == expect5)
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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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output7 = div(x7, y7)
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expect7 = np.divide(x7_np, y7_np).astype(np.int64)
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assert np.all(output7.asnumpy() == expect7)
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assert output7.shape == expect7.shape
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output8 = div(x8, y8)
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expect8 = np.divide(x8_np, y8_np)
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diff8 = output8.asnumpy() - expect8
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error8 = np.ones(shape=expect8.shape) * 1.0e-7
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assert np.all(diff8 < error8)
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assert output8.shape == expect8.shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_floor_div():
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"""
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Feature: ALL To ALL
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Description: test cases for FloorDiv.
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Expectation: the result match to numpy
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"""
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prop = 1 if np.random.random() < 0.5 else -1
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x0_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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y0_np = np.random.randint(1, 100, (2, 1, 4, 4)).astype(np.float32) * prop
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x1_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.float16) * prop
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y1_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float16) * prop
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x2_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.int32) * prop
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y2_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int32) * prop
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x3_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int32) * prop
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y3_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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x4_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.int64) * prop
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y4_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int64) * prop
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x5_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float64) * prop
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y5_np = np.random.randint(1, 100, (2, 1, 4, 4)).astype(np.float64) * prop
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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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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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floor_div = FloorDivNet()
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output0 = floor_div(x0, y0)
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expect0 = np.floor_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 = floor_div(x1, y1)
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expect1 = np.floor_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 = floor_div(x2, y2)
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expect2 = np.floor_divide(x2_np, y2_np).astype(np.float16)
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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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output3 = floor_div(x3, y3)
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expect3 = np.floor_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 = floor_div(x4, y4)
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expect4 = np.floor_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 = floor_div(x5, y5)
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expect5 = np.floor_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-7
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assert np.all(diff5 < error5)
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assert output5.shape == expect5.shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_floor_div_cpu_dynamic_shape():
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"""
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Feature: test FloorDiv op in cpu.
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Description: test the ops in dynamic shape.
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Expectation: expect correct shape result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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net = FloorDivNet()
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x_dyn = Tensor(shape=[None, 32], dtype=mindspore.float32)
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y_dyn = Tensor(shape=[16, None], dtype=mindspore.float32)
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net.set_inputs(x_dyn, y_dyn)
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x = np.random.randn(16, 32)
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y = np.random.randn(16, 32)
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output = net(Tensor(x, mindspore.float32), Tensor(y, mindspore.float32))
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expect_shape = (16, 32)
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assert output.asnumpy().shape == expect_shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_mod():
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"""
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Feature: ALL To ALL
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Description: test cases for Mod.
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Expectation: the result match to numpy
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"""
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prop = 1 if np.random.random() < 0.5 else -1
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x0_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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y0_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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x1_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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y1_np = np.random.randint(1, 100, (2, 1, 4, 4)).astype(np.float32) * prop
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x2_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.float16) * prop
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y2_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float16) * prop
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x3_np = np.random.randint(1, 100, 1).astype(np.float32) * prop
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y3_np = np.random.randint(1, 100, 1).astype(np.float32) * prop
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x4_np = np.array(768).astype(np.float32) * prop
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y4_np = np.array(3072.5).astype(np.float32) * prop
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x5_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.int32) * prop
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y5_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int32) * prop
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x6_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int32) * prop
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y6_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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x7_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.int64) * prop
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y7_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int64) * prop
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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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x7 = Tensor(x7_np)
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y7 = Tensor(y7_np)
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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mod = ModNet()
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output0 = mod(x0, y0)
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expect0 = np.mod(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 = mod(x1, y1)
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expect1 = np.mod(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 = mod(x2, y2)
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expect2 = np.mod(x2_np, y2_np).astype(np.float16)
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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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output3 = mod(x3, y3)
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expect3 = np.mod(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 = mod(x4, y4)
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expect4 = np.mod(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 = mod(x5, y5)
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expect5 = np.mod(x5_np, y5_np)
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assert np.all(output5.asnumpy() == expect5)
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assert output5.shape == expect5.shape
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output6 = mod(x6, y6)
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expect6 = np.mod(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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output7 = mod(x7, y7)
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expect7 = np.mod(x7_np, y7_np).astype(np.int64)
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assert np.all(output7.asnumpy() == expect7)
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assert output6.shape == expect6.shape
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_mod_cpu_dynamic_shape():
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"""
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Feature: test Mod op in cpu.
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Description: test the ops in dynamic shape.
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Expectation: expect correct shape result.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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net = ModNet()
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x_dyn = Tensor(shape=[None, 32], dtype=mindspore.float32)
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y_dyn = Tensor(shape=[16, None], dtype=mindspore.float32)
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net.set_inputs(x_dyn, y_dyn)
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x = np.random.randn(16, 32)
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y = np.random.randn(16, 32)
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output = net(Tensor(x, mindspore.float32), Tensor(y, mindspore.float32))
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expect_shape = (16, 32)
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assert output.asnumpy().shape == expect_shape
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|
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_floor_mod():
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"""
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Feature: ALL To ALL
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Description: test cases for FloorMod.
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Expectation: the result match to numpy
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|
"""
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prop = 1 if np.random.random() < 0.5 else -1
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x0_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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y0_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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x1_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
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y1_np = np.random.randint(1, 100, (2, 1, 4, 4)).astype(np.float32) * prop
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x2_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.float16) * prop
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y2_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float16) * prop
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x3_np = np.random.randint(1, 100, 1).astype(np.float32) * prop
|
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y3_np = np.random.randint(1, 100, 1).astype(np.float32) * prop
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x4_np = np.array(768).astype(np.float32) * prop
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|
y4_np = np.array(3072.5).astype(np.float32) * prop
|
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x5_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.int32) * prop
|
|
y5_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int32) * prop
|
|
x6_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int32) * prop
|
|
y6_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.float32) * prop
|
|
x7_np = np.random.randint(1, 100, (2, 1, 1, 4)).astype(np.int64) * prop
|
|
y7_np = np.random.randint(1, 100, (2, 3, 4, 4)).astype(np.int64) * prop
|
|
|
|
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)
|
|
x7 = Tensor(x7_np)
|
|
y7 = Tensor(y7_np)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
floor_mod = FloorModNet()
|
|
output0 = floor_mod(x0, y0)
|
|
expect0 = np.mod(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 = floor_mod(x1, y1)
|
|
expect1 = np.mod(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 = floor_mod(x2, y2)
|
|
expect2 = np.mod(x2_np, y2_np).astype(np.float16)
|
|
diff2 = output2.asnumpy() - expect2
|
|
error2 = np.ones(shape=expect2.shape) * 1.0e-5
|
|
assert np.all(diff2 < error2)
|
|
assert output2.shape == expect2.shape
|
|
|
|
output3 = floor_mod(x3, y3)
|
|
expect3 = np.mod(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 = floor_mod(x4, y4)
|
|
expect4 = np.mod(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 = floor_mod(x5, y5)
|
|
expect5 = np.mod(x5_np, y5_np)
|
|
assert np.all(output5.asnumpy() == expect5)
|
|
assert output5.shape == expect5.shape
|
|
|
|
output6 = floor_mod(x6, y6)
|
|
expect6 = np.mod(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
|
|
|
|
output7 = floor_mod(x7, y7)
|
|
expect7 = np.mod(x7_np, y7_np).astype(np.int64)
|
|
assert np.all(output7.asnumpy() == expect7)
|
|
assert output6.shape == expect6.shape
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
def test_floor_mod_cpu_dynamic_shape():
|
|
"""
|
|
Feature: test FloorMod op in cpu.
|
|
Description: test the ops in dynamic shape.
|
|
Expectation: expect correct shape result.
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
|
|
net = FloorModNet()
|
|
x_dyn = Tensor(shape=[None, 32], dtype=mindspore.float32)
|
|
y_dyn = Tensor(shape=[16, None], dtype=mindspore.float32)
|
|
net.set_inputs(x_dyn, y_dyn)
|
|
x = np.random.randn(16, 32)
|
|
y = np.random.randn(16, 32)
|
|
output = net(Tensor(x, mindspore.float32), Tensor(y, mindspore.float32))
|
|
expect_shape = (16, 32)
|
|
assert output.asnumpy().shape == expect_shape
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.parametrize('dtype', [np.float32, np.float64])
|
|
def test_dynamic_sub(dtype):
|
|
"""
|
|
Feature: ALL To ALL
|
|
Description: test cases for Sub dynamic shape.
|
|
Expectation: the result match to numpy
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
x = np.random.rand(2, 3, 4, 4).astype(dtype)
|
|
y = np.random.rand(4, 1).astype(dtype)
|
|
benchmark_output = x - y
|
|
loss = 1e-5
|
|
sub_net = SubNet()
|
|
real_x = Tensor(x)
|
|
real_y = Tensor(y)
|
|
dy_x_shape = [None for _ in x.shape]
|
|
dy_y_shape = [None for _ in y.shape]
|
|
input_dyn_x = Tensor(shape=dy_x_shape, dtype=real_x.dtype)
|
|
input_dyn_y = Tensor(shape=dy_y_shape, dtype=real_y.dtype)
|
|
sub_net.set_inputs(input_dyn_x, input_dyn_y)
|
|
ms_result = sub_net(real_x, real_y)
|
|
np.testing.assert_allclose(benchmark_output, ms_result.asnumpy(), rtol=loss, atol=loss)
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
ms_result = sub_net(real_x, real_y)
|
|
np.testing.assert_allclose(benchmark_output, ms_result.asnumpy(), rtol=loss, atol=loss)
|