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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- import numpy as np
- import mindspore.nn as nn
- import mindspore.context as context
- import mindspore.common.dtype as mstype
- from mindspore import Tensor
- from mindspore.common.api import ms_function
-
- context.set_context(device_target="Ascend")
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.image_gradients = nn.ImageGradients()
-
- @ms_function
- def construct(self, x):
- return self.image_gradients(x)
-
-
- def test_image_gradients():
- image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32)
- expected_dy = np.array([[[[2,2],[0,0]]]]).astype(np.int32)
- expected_dx = np.array([[[[1,0],[1,0]]]]).astype(np.int32)
- net = Net()
- dy, dx = net(image)
- assert np.any(dx.asnumpy()-expected_dx) == False
- assert np.any(dy.asnumpy()-expected_dy) == False
-
-
- def test_image_gradients_multi_channel_depth():
- # 4 x 2 x 2 x 2
- dtype = mstype.int32
- image = Tensor(np.array([[[[1,2],[3,4]], [[5,6],[7,8]]],
- [[[3,5],[7,9]], [[11,13],[15,17]]],
- [[[5,10],[15,20]], [[25,30],[35,40]]],
- [[[10,20],[30,40]], [[50,60],[70,80]]]]), dtype=dtype)
- expected_dy = Tensor(np.array([[[[2,2],[0,0]], [[2,2],[0,0]]],
- [[[4,4],[0,0]], [[4,4],[0,0]]],
- [[[10,10],[0,0]], [[10,10],[0,0]]],
- [[[20,20],[0,0]], [[20,20],[0,0]]]]), dtype=dtype)
- expected_dx = Tensor(np.array([[[[1,0],[1,0]], [[1,0],[1,0]]],
- [[[2,0],[2,0]], [[2,0],[2,0]]],
- [[[5,0],[5,0]], [[5,0],[5,0]]],
- [[[10,0],[10,0]], [[10,0],[10,0]]]]), dtype=dtype)
- net = Net()
- dy, dx = net(image)
-
- assert np.any(dx.asnumpy()-expected_dx.asnumpy()) == False
- assert np.any(dy.asnumpy()-expected_dy.asnumpy()) == False
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