wehub-resource-sync
135 lines
3.6 KiB
Python
135 lines
3.6 KiB
Python
# 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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from __future__ import annotations
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import os
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import tempfile
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import unittest
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import numpy as np
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from dygraph_to_static_utils import Dy2StTestBase
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import paddle
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class BaseLayer(paddle.nn.Layer):
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def __init__(self, in_size, out_size):
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super().__init__()
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self._linear = paddle.nn.Linear(in_size, out_size)
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def build(self, x):
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out1 = self._linear(x)
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out2 = paddle.mean(out1)
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return out1, out2
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class LinearNetWithTuple(BaseLayer):
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def __init__(self, in_size, out_size):
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super().__init__(in_size, out_size)
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def forward(self, x) -> tuple[paddle.Tensor, str]:
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out1, out2 = self.build(x)
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return (out2, 'str')
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class LinearNetWithTuple2(BaseLayer):
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def __init__(self, in_size, out_size):
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super().__init__(in_size, out_size)
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def forward(self, x) -> tuple[paddle.Tensor, np.array]:
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out1, out2 = self.build(x)
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return (out2, np.ones([4, 16]))
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class LinearNetWithList(BaseLayer):
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def __init__(self, in_size, out_size):
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super().__init__(in_size, out_size)
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def forward(self, x) -> list[paddle.Tensor]:
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out1, out2 = self.build(x)
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return [out2]
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class LinearNetWithDict(BaseLayer):
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def __init__(self, in_size, out_size):
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super().__init__(in_size, out_size)
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def forward(self, x) -> dict[str, paddle.Tensor]:
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out1, out2 = self.build(x)
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return {'out': out2}
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class TestTyping(Dy2StTestBase):
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def setUp(self):
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self.in_num = 16
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self.out_num = 16
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self.x = paddle.randn([4, 16])
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self.spec = [paddle.static.InputSpec(shape=[None, 16], dtype='float32')]
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self.temp_dir = tempfile.TemporaryDirectory()
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def tearDown(self):
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self.temp_dir.cleanup()
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def build_net(self):
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return LinearNetWithTuple(self.in_num, self.out_num)
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def save_and_load(self, suffix=''):
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path = os.path.join(self.temp_dir.name, 'layer_typing_' + suffix)
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paddle.jit.save(self.net, path, input_spec=self.spec)
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return paddle.jit.load(path)
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def run_dy(self):
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out, _ = self.net(self.x)
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return out
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def test_type(self):
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self.net = self.build_net()
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out = self.run_dy()
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load_net = self.save_and_load('tuple')
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load_out = load_net(self.x)
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np.testing.assert_allclose(out, load_out, rtol=1e-05)
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class TestTypingTuple(TestTyping):
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def build_net(self):
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return LinearNetWithTuple2(self.in_num, self.out_num)
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def run_dy(self):
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out, np_data = self.net(self.x)
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np.testing.assert_allclose(np_data, np.ones_like(np_data))
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return out
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class TestTypingList(TestTyping):
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def build_net(self):
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return LinearNetWithList(self.in_num, self.out_num)
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def run_dy(self):
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out = self.net(self.x)[0]
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return out
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class TestTypingDict(TestTyping):
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def build_net(self):
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return LinearNetWithDict(self.in_num, self.out_num)
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def run_dy(self):
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out = self.net(self.x)['out']
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return out
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if __name__ == '__main__':
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unittest.main()
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