forked from mindspore-Ecosystem/mindspore
96 lines
2.9 KiB
Python
96 lines
2.9 KiB
Python
# Copyright 2022 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 as ms
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import mindspore.ops.operations.math_ops as P
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from mindspore import Tensor
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from mindspore.common.api import jit
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class TraceNet(nn.Cell):
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def __init__(self):
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super(TraceNet, self).__init__()
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self.trace = P.Trace()
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@jit
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def construct(self, x):
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return self.trace(x)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu
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@pytest.mark.env_onecard
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def test_trace_dyn():
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"""
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Feature: test Trace ops in gpu.
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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='GPU')
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net = TraceNet()
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x_dyn = Tensor(shape=[None, None], dtype=ms.float32)
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net.set_inputs(x_dyn)
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x = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=ms.float32)
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out = net(x)
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expect_shape = ()
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assert out.asnumpy().shape == expect_shape
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_trace_2d_int32():
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"""
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Feature: Returns the sum along diagonals of the int32 array
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Description: 2D x, int32
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Expectation: success
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"""
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for mode in [context.PYNATIVE_MODE, context.GRAPH_MODE]:
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context.set_context(mode=mode, device_target="GPU")
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x = Tensor(np.array([[1, 20, 5], [4, 5, 9]]).astype(np.int32))
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input_x = x.asnumpy()
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net = TraceNet()
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y = net(x)
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trace_expect = np.trace(input_x).astype(np.int32)
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assert (y.asnumpy() == trace_expect).all()
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_trace_2d_double():
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"""
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Feature: Returns the sum along diagonals of the double array
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Description: 2D x, double
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Expectation: success
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"""
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for mode in [context.PYNATIVE_MODE, context.GRAPH_MODE]:
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context.set_context(mode=mode, device_target="GPU")
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x = Tensor(
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np.array([[3.8, 4.5, 5.], [4.2, 4.5, 6.9]]).astype(np.double))
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input_x = x.asnumpy()
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net = TraceNet()
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y = net(x)
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trace_expect = np.trace(input_x).astype(np.double)
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assert (y.asnumpy() == trace_expect).all()
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