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65 lines
1.8 KiB
65 lines
1.8 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import math
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import paddle
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from paddle import nn
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from paddle.jit import to_static
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from paddle.static import InputSpec
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def test_applicative_evaluation():
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def m_sqrt2(x):
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return paddle.scale(x, math.sqrt(2))
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subgraph = to_static(m_sqrt2, input_spec=[InputSpec([-1])])
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paddle.jit.save(subgraph, './temp_test_to_static')
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fn = paddle.jit.load('./temp_test_to_static')
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x = paddle.arange(10, dtype=paddle.float32)
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y = fn(x)
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print(x)
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print(y)
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def test_nested_sequential():
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class Net(nn.Layer):
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def __init__(self):
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super().__init__()
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group1 = nn.Sequential(
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nn.Linear(2, 3),
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nn.Sigmoid(), )
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group2 = nn.Sequential(
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nn.Sequential(nn.Linear(3, 3)),
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nn.Linear(3, 4),
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nn.ReLU(), )
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self.layers = nn.Sequential(group1, group2)
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def forward(self, x):
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return self.layers(x)
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net = Net()
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x = paddle.randn([4, 2])
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y = net(x)
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print(y)
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subgraph = to_static(net, input_spec=[InputSpec([-1, 2])])
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paddle.jit.save(subgraph, './temp_test_to_static')
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fn = paddle.jit.load('./temp_test_to_static')
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y = fn(x)
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print(y)
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