# 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