wehub-resource-sync
78 lines
2.0 KiB
Python
78 lines
2.0 KiB
Python
# Copyright (c) 2019 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 unittest
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import numpy as np
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from dygraph_to_static_utils import (
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Dy2StTestBase,
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)
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import paddle
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from paddle import nn
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np.random.seed(1)
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def apply_to_static(support_to_static, model, image_shape=None):
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if support_to_static:
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specs = None
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model = paddle.jit.to_static(model, input_spec=specs)
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return model
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class Layer0(nn.Layer):
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def __init__(self, level):
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super().__init__()
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self._linear1 = nn.Linear(10, 5)
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self._linear2 = nn.Linear(10, 5)
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self.layer1 = Layer1(level)
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self.layer1 = apply_to_static(True, self.layer1)
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def forward(self, x):
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out1 = self._linear1(x)
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out2 = self._linear2(x)
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# out2.stop_gradient = True not raise error
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a = [out1, out2]
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b = self.layer1(a)
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# self.layer1(out1, out2) will raise error
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return b
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class Layer1(nn.Layer):
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def __init__(self, level):
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super().__init__()
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self.level = level
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self._linear = nn.Linear(5, 2)
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def forward(self, x):
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inp = x[self.level]
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val = self._linear(inp)
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return val
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class TestDuplicateOutput(Dy2StTestBase):
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def test_case(self):
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# create network
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layer = Layer0(0)
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a = paddle.rand(shape=[10, 10])
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out = layer(a)
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loss = out.mean()
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loss.backward()
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if __name__ == '__main__':
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unittest.main()
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