forked from mindspore-Ecosystem/mindspore
55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
# 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 BiasAdd """
|
|
import numpy as np
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor, Parameter
|
|
from mindspore.common.initializer import initializer
|
|
from mindspore.ops import operations as P
|
|
from ..ut_filter import non_graph_engine
|
|
|
|
|
|
class Net(nn.Cell):
|
|
"""Net definition"""
|
|
|
|
def __init__(self,
|
|
output_channels,
|
|
bias_init='zeros',
|
|
):
|
|
super(Net, self).__init__()
|
|
self.biasAdd = P.BiasAdd()
|
|
|
|
if isinstance(bias_init, Tensor):
|
|
if bias_init.dim() != 1 or bias_init.shape[0] != output_channels:
|
|
raise ValueError("bias_init shape error")
|
|
|
|
self.bias = Parameter(initializer(
|
|
bias_init, [output_channels]), name="bias")
|
|
|
|
def construct(self, input_x):
|
|
return self.biasAdd(input_x, self.bias)
|
|
|
|
|
|
@non_graph_engine
|
|
def test_compile():
|
|
bias_init = Tensor(np.ones([3]).astype(np.float32))
|
|
net = Net(3, bias_init=bias_init)
|
|
input_data = Tensor(np.ones([1, 3, 3, 3], np.float32))
|
|
# since simulator currently not support matMul
|
|
# enable it when staging function is ready
|
|
output = net(input_data)
|
|
print(output.asnumpy())
|