forked from mindspore-Ecosystem/mindspore
73 lines
2.3 KiB
Python
73 lines
2.3 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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from mindspore.ops import composite as C
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from mindspore import Tensor
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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class Roll(nn.Cell):
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def __init__(self, shift, axis):
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super(Roll, self).__init__()
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self.shift = shift
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self.axis = axis
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self.roll = nn.Roll(self.shift, self.axis)
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def construct(self, x):
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return self.roll(x)
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class RollGrad(nn.Cell):
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def __init__(self, network):
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super(RollGrad, self).__init__()
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self.grad = C.GradOperation(get_all=True, sens_param=True)
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self.network = network
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def construct(self, input_data, grad_np):
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gout = self.grad(self.network)(input_data, grad_np)
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return gout
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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_roll_1d():
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"""
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Feature: RightShift gpu TEST.
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Description: 1d test case for RightShift
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Expectation: the result match to numpy
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"""
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x_np = np.array([-1, -5, -3, -14, 64]).astype(np.int8)
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x_grad_np = np.array([-1, -5, -3, -14, 64]).astype(np.int8)
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except_output = np.array([-5, -3, -14, 64, -1]).astype(np.int8)
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shift = 4
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axis = 0
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x_ms = Tensor(x_np)
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net = Roll(shift, axis)
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grad_net = RollGrad(net)
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output_grad_ms = grad_net(Tensor(x_np), Tensor(x_grad_np))
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except_grad_output = np.array([64, -1, -5, -3, -14]).astype(np.int8)
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output_ms = net(x_ms)
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assert np.allclose(except_output, output_ms.asnumpy())
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assert np.allclose(except_grad_output, output_grad_ms[0].asnumpy())
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