forked from mindspore-Ecosystem/mindspore
100 lines
2.8 KiB
Python
100 lines
2.8 KiB
Python
# 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_initializer_fuzz """
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import pytest
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import mindspore.nn as nn
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from mindspore import Model
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self, in_str):
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a, b, c, d, e, f, g, h = in_str.strip().split()
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a = int(a)
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b = int(b)
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c = int(b)
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d = int(b)
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e = int(b)
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f = int(b)
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g = int(b)
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h = int(b)
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super(Net, self).__init__()
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self.conv = nn.Conv2d(a, b, c, pad_mode="valid")
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self.bn = nn.BatchNorm2d(d)
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self.relu = nn.ReLU()
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self.flatten = nn.Flatten()
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self.fc = nn.Dense(e * f * g, h)
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def construct(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.relu(x)
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x = self.flatten(x)
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out = self.fc(x)
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return out
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class LeNet5(nn.Cell):
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""" LeNet5 definition """
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def __init__(self, in_str):
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super(LeNet5, self).__init__()
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a1, a2, a3, a4, a5, a6, a7, a8, a9, a10, a11, a12, a13, a14, a15 = in_str.strip().split()
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a1 = int(a1)
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a2 = int(a2)
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a3 = int(a3)
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a4 = int(a4)
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a5 = int(a5)
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a6 = int(a6)
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a7 = int(a7)
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a8 = int(a8)
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a9 = int(a9)
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a10 = int(a10)
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a11 = int(a11)
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a12 = int(a12)
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a13 = int(a13)
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a14 = int(a14)
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a15 = int(a15)
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self.conv1 = nn.Conv2d(a1, a2, a3, pad_mode="valid")
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self.conv2 = nn.Conv2d(a4, a5, a6, pad_mode="valid")
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self.fc1 = nn.Dense(a7 * a8 * a9, a10)
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self.fc2 = nn.Dense(a11, a12)
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self.fc3 = nn.Dense(a13, a14)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=a15)
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self.flatten = nn.Flatten()
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def construct(self, x):
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x = self.max_pool2d(self.relu(self.conv1(x)))
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x = self.max_pool2d(self.relu(self.conv2(x)))
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x = self.flatten(x)
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x = self.relu(self.fc1(x))
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x = self.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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def test_shape_error():
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""" for fuzz test"""
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in_str = "3 6 5 6 -6 5 16 5 5 120 120 84 84 3 2"
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with pytest.raises(ValueError):
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net = LeNet5(in_str) # neural network
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Model(net)
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