98 lines
2.7 KiB
Python
98 lines
2.7 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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"""
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@File : test_compile.py
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@Author:
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@Date : 2019-03-20
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@Desc : test mindspore compile method
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"""
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import logging
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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, Model, context
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from mindspore.nn.optim import Momentum
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from mindspore.ops.composite import add_flags
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from ...ut_filter import non_graph_engine
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log = logging.getLogger("test")
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log.setLevel(level=logging.ERROR)
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.conv = nn.Conv2d(3, 64, 3, has_bias=False, weight_init='normal')
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self.relu = nn.ReLU()
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self.flatten = nn.Flatten()
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def construct(self, x):
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x = self.conv(x)
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x = self.relu(x)
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out = self.flatten(x)
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return out
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loss = nn.MSELoss()
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# Test case 1 : test the new compiler interface
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# _build_train_graph is deprecated
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def test_build():
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""" test_build """
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Tensor(np.random.randint(0, 255, [1, 3, 224, 224]))
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Tensor(np.random.randint(0, 10, [1, 10]))
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net = Net()
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opt = Momentum(net.get_parameters(), learning_rate=0.1, momentum=0.9)
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Model(net, loss_fn=loss, optimizer=opt, metrics=None)
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# Test case 2 : test the use different args to run graph
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class Net2(nn.Cell):
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""" Net2 definition """
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def __init__(self):
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super(Net2, self).__init__()
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self.relu = nn.ReLU()
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def construct(self, x):
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x = self.relu(x)
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return x
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@non_graph_engine
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def test_different_args_run():
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""" test_different_args_run """
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np1 = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me1 = Tensor(np1)
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np2 = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me2 = Tensor(np2)
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net = Net2()
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net = add_flags(net, predit=True)
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context.set_context(mode=context.GRAPH_MODE)
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model = Model(net)
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me1 = model.predict(input_me1)
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me2 = model.predict(input_me2)
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out_me1 = me1.asnumpy()
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out_me2 = me2.asnumpy()
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print(np1)
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print(np2)
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print(out_me1)
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print(out_me2)
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assert not np.allclose(out_me1, out_me2, 0.01, 0.01)
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