forked from mindspore-Ecosystem/mindspore
108 lines
2.8 KiB
Python
Executable File
108 lines
2.8 KiB
Python
Executable File
# 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 Activations """
|
|
import numpy as np
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore.common.api import _executor
|
|
from ..ut_filter import non_graph_engine
|
|
|
|
|
|
class SoftmaxNet(nn.Cell):
|
|
def __init__(self, dim):
|
|
super(SoftmaxNet, self).__init__()
|
|
self.softmax = nn.Softmax(dim)
|
|
|
|
def construct(self, x):
|
|
return self.softmax(x)
|
|
|
|
|
|
@non_graph_engine
|
|
def test_compile():
|
|
net = SoftmaxNet(0)
|
|
input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
|
|
net(input_tensor)
|
|
|
|
|
|
@non_graph_engine
|
|
def test_compile_axis():
|
|
net = SoftmaxNet(-1)
|
|
prob = 355
|
|
input_data = np.random.randn(4, 16, 1, 1).astype(np.float32) * prob
|
|
input_tensor = Tensor(input_data)
|
|
net(input_tensor)
|
|
|
|
|
|
class LogSoftmaxNet(nn.Cell):
|
|
def __init__(self, dim):
|
|
super(LogSoftmaxNet, self).__init__()
|
|
self.logsoftmax = nn.LogSoftmax(dim)
|
|
|
|
def construct(self, x):
|
|
return self.logsoftmax(x)
|
|
|
|
|
|
@non_graph_engine
|
|
def test_compile_logsoftmax():
|
|
net = LogSoftmaxNet(0)
|
|
input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]]))
|
|
net(input_tensor)
|
|
|
|
|
|
class Net1(nn.Cell):
|
|
def __init__(self):
|
|
super(Net1, self).__init__()
|
|
self.relu = nn.ReLU()
|
|
|
|
def construct(self, x):
|
|
return self.relu(x)
|
|
|
|
|
|
def test_compile_relu():
|
|
net = Net1()
|
|
input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
|
|
_executor.compile(net, input_data)
|
|
|
|
|
|
class Net_gelu(nn.Cell):
|
|
def __init__(self):
|
|
super(Net_gelu, self).__init__()
|
|
self.gelu = nn.GELU()
|
|
|
|
def construct(self, x):
|
|
return self.gelu(x)
|
|
|
|
|
|
def test_compile_gelu():
|
|
net = Net_gelu()
|
|
input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
|
|
_executor.compile(net, input_data)
|
|
|
|
|
|
class NetLeakyReLU(nn.Cell):
|
|
def __init__(self, alpha):
|
|
super(NetLeakyReLU, self).__init__()
|
|
self.leaky_relu = nn.LeakyReLU(alpha)
|
|
|
|
def construct(self, x):
|
|
return self.leaky_relu(x)
|
|
|
|
|
|
def test_compile_leaky_relu():
|
|
net = NetLeakyReLU(alpha=0.1)
|
|
input_data = Tensor(np.array([[1.6, 0, 0.6], [6, 0, -6]], dtype=np.float32))
|
|
_executor.compile(net, input_data)
|