forked from mindspore-Ecosystem/mindspore
73 lines
2.2 KiB
Python
73 lines
2.2 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_checkparam """
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import numpy as np
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import pytest
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import mindspore
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import mindspore.nn as nn
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from mindspore import Model, context
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from mindspore.common.tensor import Tensor
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class LeNet5(nn.Cell):
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""" LeNet5 definition """
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def __init__(self):
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super(LeNet5, self).__init__()
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self.conv1 = nn.Conv2d(3, 6, 5, pad_mode="valid")
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self.conv2 = nn.Conv2d(6, 16, 5, pad_mode="valid")
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self.fc1 = nn.Dense(16 * 5 * 5, 120)
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self.fc2 = nn.Dense(120, 84)
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self.fc3 = nn.Dense(84, 3)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2)
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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 predict_checke_param(in_str):
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""" predict_checke_param """
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net = LeNet5() # neural network
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context.set_context(mode=context.GRAPH_MODE)
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model = Model(net)
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a1, a2, b1, b2, b3, b4 = in_str.strip().split()
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a1 = int(a1)
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a2 = int(a2)
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b1 = int(b1)
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b2 = int(b2)
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b3 = int(b3)
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b4 = int(b4)
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nd_data = np.random.randint(a1, a2, [b1, b2, b3, b4])
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input_data = Tensor(nd_data, mindspore.float32)
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model.predict(input_data)
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def test_predict_checke_param_failed():
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""" test_predict_checke_param_failed """
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in_str = "0 255 0 3 32 32"
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with pytest.raises(ValueError):
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predict_checke_param(in_str)
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