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89 lines
3.2 KiB
89 lines
3.2 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 pytest
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import numpy as np
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import mindspore
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore import Tensor
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from mindspore.ops.composite import GradOperation
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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_mirror_pad():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test1_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]
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test_1_paddings = ((0, 0), (0, 0), (1, 1), (2, 2))
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test1_arr_exp = [[[[6, 5, 4, 5, 6, 5, 4], [3, 2, 1, 2, 3, 2, 1], [6, 5, 4, 5, 6, 5, 4],
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[9, 8, 7, 8, 9, 8, 7], [6, 5, 4, 5, 6, 5, 4]]]]
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test2_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]
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test_2_paddings = ((0, 0), (0, 0), (1, 1), (2, 2))
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test2_arr_exp = [[[[2, 1, 1, 2, 3, 3, 2], [2, 1, 1, 2, 3, 3, 2], [5, 4, 4, 5, 6, 6, 5],
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[8, 7, 7, 8, 9, 9, 8], [8, 7, 7, 8, 9, 9, 8]]]]
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reflectOp = nn.Pad(mode='REFLECT', paddings=test_1_paddings)
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symmOp = nn.Pad(mode='SYMMETRIC', paddings=test_2_paddings)
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x_test_1 = Tensor(np.array(test1_arr_in), dtype=mindspore.float32)
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x_test_2 = Tensor(np.array(test2_arr_in), dtype=mindspore.float32)
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y_test_1 = reflectOp(x_test_1).asnumpy()
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y_test_2 = symmOp(x_test_2).asnumpy()
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print(np.array(test1_arr_in))
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print(y_test_1)
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np.testing.assert_equal(np.array(test1_arr_exp), y_test_1)
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np.testing.assert_equal(np.array(test2_arr_exp), y_test_2)
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class Grad(nn.Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = GradOperation(get_all=True, sens_param=True)
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self.network = network
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def construct(self, input_, output_grad):
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return self.grad(self.network)(input_, output_grad)
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.pad = nn.Pad(mode="REFLECT", paddings=((0, 0), (0, 0), (1, 0), (0, 2)))
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def construct(self, x):
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return self.pad(x)
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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_mirror_pad_backprop():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]] # size -> 3*3
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test_arr_in = Tensor(test_arr_in, dtype=mindspore.float32)
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dy = (np.ones((1, 1, 4, 5)) * 0.1).astype(np.float32)
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expected_dx = np.array([[[[0.2, 0.2, 0.1],
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[0.4, 0.4, 0.2],
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[0.2, 0.2, 0.1]]]])
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net = Grad(Net())
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dx = net(test_arr_in, Tensor(dy))
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dx = dx[0].asnumpy()
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np.testing.assert_array_almost_equal(dx, expected_dx)
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