wehub-resource-sync
125 lines
3.4 KiB
Python
125 lines
3.4 KiB
Python
# Copyright (c) 2020 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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# 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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enable_to_static_guard,
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)
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import paddle
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from paddle import nn
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class SimpleReturnLayer(nn.Layer):
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def forward(self, x):
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return x
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class AddAttrLayer(nn.Layer):
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def __init__(self):
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super().__init__()
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self.attr = None
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def forward(self, x):
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out = x + self.attr
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return out
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class IsInstanceLayer(nn.Layer):
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def __init__(self, layer):
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super().__init__()
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self.layer = layer
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def forward(self, x):
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if isinstance(self.layer, (AddAttrLayer,)):
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self.layer.attr = x
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res = self.layer(x)
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return res
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class SequentialLayer(nn.Layer):
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def __init__(self, layers):
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super().__init__()
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self.layers = nn.LayerList(layers)
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def forward(self, x):
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res = x
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for layer in self.layers:
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if isinstance(layer, AddAttrLayer):
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layer.attr = x
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res = layer(res)
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return res
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def train(model, to_static):
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with enable_to_static_guard(to_static):
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x = paddle.ones(shape=[2, 3], dtype='int32')
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out = model(x)
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return out.numpy()
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class TestIsinstance(Dy2StTestBase):
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def test_isinstance_simple_return_layer(self):
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model_creator = lambda: paddle.jit.to_static(
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IsInstanceLayer(SimpleReturnLayer())
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)
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self._test_model(model_creator)
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def test_isinstance_add_attr_layer(self):
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model_creator = lambda: paddle.jit.to_static(
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IsInstanceLayer(AddAttrLayer())
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)
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self._test_model(model_creator)
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def test_sequential_layer(self):
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def model_creator():
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layers = []
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for i in range(5):
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layers.append(SimpleReturnLayer())
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layers.append(AddAttrLayer())
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return paddle.jit.to_static(SequentialLayer(layers))
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self._test_model(model_creator)
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def _test_model(self, model_creator):
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st_model = model_creator()
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st_out = train(st_model, to_static=True)
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dy_model = model_creator()
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dy_out = train(dy_model, to_static=False)
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np.testing.assert_allclose(
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dy_out,
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st_out,
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rtol=1e-05,
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err_msg=f'dy_out:\n {dy_out}\n st_out:\n{st_out}',
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)
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if __name__ == "__main__":
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unittest.main()
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