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66 lines
2.6 KiB
66 lines
2.6 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 mindspore.common.dtype as mstype
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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.common.api import ms_function
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context.set_context(device_target="Ascend")
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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.image_gradients = nn.ImageGradients()
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@ms_function
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def construct(self, x):
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return self.image_gradients(x)
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def test_image_gradients():
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image = Tensor(np.array([[[[1, 2], [3, 4]]]]), dtype=mstype.int32)
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expected_dy = np.array([[[[2, 2], [0, 0]]]]).astype(np.int32)
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expected_dx = np.array([[[[1, 0], [1, 0]]]]).astype(np.int32)
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net = Net()
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dy, dx = net(image)
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assert not np.any(dx.asnumpy() - expected_dx)
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assert not np.any(dy.asnumpy() - expected_dy)
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def test_image_gradients_multi_channel_depth():
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# 4 x 2 x 2 x 2
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dtype = mstype.int32
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image = Tensor(np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]],
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[[[3, 5], [7, 9]], [[11, 13], [15, 17]]],
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[[[5, 10], [15, 20]], [[25, 30], [35, 40]]],
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[[[10, 20], [30, 40]], [[50, 60], [70, 80]]]]), dtype=dtype)
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expected_dy = Tensor(np.array([[[[2, 2], [0, 0]], [[2, 2], [0, 0]]],
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[[[4, 4], [0, 0]], [[4, 4], [0, 0]]],
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[[[10, 10], [0, 0]], [[10, 10], [0, 0]]],
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[[[20, 20], [0, 0]], [[20, 20], [0, 0]]]]), dtype=dtype)
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expected_dx = Tensor(np.array([[[[1, 0], [1, 0]], [[1, 0], [1, 0]]],
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[[[2, 0], [2, 0]], [[2, 0], [2, 0]]],
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[[[5, 0], [5, 0]], [[5, 0], [5, 0]]],
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[[[10, 0], [10, 0]], [[10, 0], [10, 0]]]]), dtype=dtype)
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net = Net()
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dy, dx = net(image)
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assert not np.any(dx.asnumpy() - expected_dx.asnumpy())
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assert not np.any(dy.asnumpy() - expected_dy.asnumpy())
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