You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
52 lines
1.7 KiB
52 lines
1.7 KiB
# 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 pytest
|
|
from mindspore import Tensor, Parameter
|
|
from mindspore.ops import operations as P
|
|
import mindspore.nn as nn
|
|
import numpy as np
|
|
import mindspore.context as context
|
|
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self, value):
|
|
super(Net, self).__init__()
|
|
self.var = Parameter(value, name="var")
|
|
self.assign = P.Assign()
|
|
|
|
def construct(self, value):
|
|
return self.assign(self.var, value)
|
|
|
|
x = np.array([[1.2, 1], [1, 0]]).astype(np.float32)
|
|
value = np.array([[1, 2], [3, 4.0]]).astype(np.float32)
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_assign():
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
var = Tensor(x)
|
|
assign = Net(var)
|
|
output = assign(Tensor(value))
|
|
|
|
error = np.ones(shape=[2, 2]) * 1.0e-6
|
|
diff1 = output.asnumpy() - value
|
|
diff2 = assign.var.default_input.asnumpy() - value
|
|
assert np.all(diff1 < error)
|
|
assert np.all(-diff1 < error)
|
|
assert np.all(diff2 < error)
|
|
assert np.all(-diff2 < error)
|