# 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. # ============================================================================ """ test loss """ 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 _executor 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_compile(): # input shape 1 x 1 x 2 x 2 image = Tensor(np.array([[[[1,2],[3,4]]]]), dtype=mstype.int32) net = Net() _executor.compile(net, image) def test_compile_multi_channel(): # input shape 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) net = Net() _executor.compile(net, image)