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108 lines
2.8 KiB
108 lines
2.8 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test Activations """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.api import _executor
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from ..ut_filter import non_graph_engine
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class SoftmaxNet(nn.Cell):
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def __init__(self, dim):
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super(SoftmaxNet, self).__init__()
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self.softmax = nn.Softmax(dim)
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def construct(self, x):
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return self.softmax(x)
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@non_graph_engine
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def test_compile():
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net = SoftmaxNet(0)
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input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
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net(input_tensor)
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@non_graph_engine
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def test_compile_axis():
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net = SoftmaxNet(-1)
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prob = 355
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input_data = np.random.randn(4, 16, 1, 1).astype(np.float32) * prob
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input_tensor = Tensor(input_data)
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net(input_tensor)
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class LogSoftmaxNet(nn.Cell):
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def __init__(self, dim):
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super(LogSoftmaxNet, self).__init__()
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self.logsoftmax = nn.LogSoftmax(dim)
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def construct(self, x):
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return self.logsoftmax(x)
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@non_graph_engine
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def test_compile_logsoftmax():
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net = LogSoftmaxNet(0)
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input_tensor = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
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net(input_tensor)
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class Net1(nn.Cell):
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def __init__(self):
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super(Net1, self).__init__()
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self.relu = nn.ReLU()
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def construct(self, x):
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return self.relu(x)
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def test_compile_relu():
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net = Net1()
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input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
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_executor.compile(net, input_data)
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class Net_gelu(nn.Cell):
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def __init__(self):
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super(Net_gelu, self).__init__()
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self.gelu = nn.GELU()
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def construct(self, x):
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return self.gelu(x)
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def test_compile_gelu():
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net = Net_gelu()
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input_data = Tensor(np.array([[1.2, 2.1], [2.2, 3.2]], dtype=np.float32))
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_executor.compile(net, input_data)
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class NetLeakyReLU(nn.Cell):
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def __init__(self, alpha):
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super(NetLeakyReLU, self).__init__()
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self.leaky_relu = nn.LeakyReLU(alpha)
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def construct(self, x):
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return self.leaky_relu(x)
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def test_compile_leaky_relu():
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net = NetLeakyReLU(alpha=0.1)
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input_data = Tensor(np.array([[1.6, 0, 0.6], [6, 0, -6]], dtype=np.float32))
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_executor.compile(net, input_data)
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