|
- # 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.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.common.initializer import initializer
- from mindspore.common.parameter import Parameter
- from mindspore.communication.management import init, get_rank, get_group_size
- from mindspore.ops import operations as P
-
- context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
-
- init()
- rank = get_rank()
- size = get_group_size()
- x = np.ones([3, 1, 3, 3]).astype(np.float32) * 0.01 * (rank + 1)
-
-
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.x1 = Parameter(initializer(Tensor(x), x.shape), name='x1')
- self.x2 = Parameter(initializer(Tensor(x), x.shape), name='x2')
- self.x3 = Parameter(initializer(Tensor(x), x.shape), name='x3')
-
- self.broadcast1 = P.Broadcast(0)
- self.broadcast2 = P.Broadcast(1)
- self.broadcast3 = P.Broadcast(2)
-
- def construct(self):
- return (self.broadcast1((self.x1,)),
- self.broadcast2((self.x2,)),
- self.broadcast3((self.x3,)))
-
-
- def test_Broadcast():
- broadcast = Net()
- output = broadcast()
-
- expect0 = np.ones([3, 1, 3, 3]).astype(np.float32) * 1
- expect1 = np.ones([3, 1, 3, 3]).astype(np.float32) * 2
- expect2 = np.ones([3, 1, 3, 3]).astype(np.float32) * 3
-
- diff0 = output[0][0].asnumpy() - expect0
- error0 = np.ones(shape=expect0.shape) * 1.0e-5
- assert np.all(diff0 < error0)
- assert output[0][0].shape == expect0.shape
-
- diff1 = output[1][0].asnumpy() - expect1
- error1 = np.ones(shape=expect1.shape) * 1.0e-5
- assert np.all(diff1 < error1)
- assert output[1][0].shape == expect1.shape
-
- diff2 = output[2][0].asnumpy() - expect2
- error2 = np.ones(shape=expect2.shape) * 1.0e-5
- assert np.all(diff2 < error2)
- assert output[2][0].shape == expect2.shape
|
