mindspore/tests/ut/python/nn/test_activation.py

# 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)