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50 lines
1.7 KiB
50 lines
1.7 KiB
5 years ago
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# 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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""" test loss """
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import numpy as np
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import mindspore.nn as nn
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import mindspore.context as context
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import mindspore.common.dtype as mstype
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from mindspore import Tensor
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from mindspore.common.api import _executor
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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_compile():
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# input shape 1 x 1 x 2 x 2
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image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
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net = Net()
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_executor.compile(net, image)
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def test_compile_multi_channel():
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# input shape 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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net = Net()
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_executor.compile(net, image)
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