wehub-resource-sync
137 lines
3.6 KiB
Python
137 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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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 (
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Dy2StTestBase,
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)
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import paddle
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class BufferLayers(paddle.nn.Layer):
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def __init__(self, out_channel):
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super().__init__()
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self.out_channel = out_channel
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def forward(self, x):
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mean = paddle.mean(x)
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if mean < 0.0:
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x = x * self._mask()
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out = x - mean
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return out
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def _mask(self):
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return paddle.to_tensor(np.zeros([self.out_channel], 'float32'))
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class SequentialNet(paddle.nn.Layer):
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def __init__(self, sub_layer, in_channel, out_channel):
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super().__init__()
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self.layer = paddle.nn.Sequential(
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('l1', paddle.nn.Linear(in_channel, in_channel)),
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('l2', paddle.nn.Linear(in_channel, out_channel)),
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('l3', sub_layer(out_channel)),
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)
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def forward(self, x):
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out = self.layer(x)
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return out
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class NestSequentialNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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group1 = paddle.nn.Sequential(
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paddle.nn.Linear(10, 10),
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paddle.nn.Sigmoid(),
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)
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group2 = paddle.nn.Sequential(
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paddle.nn.Linear(10, 3),
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paddle.nn.ReLU(),
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)
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self.layers = paddle.nn.Sequential(group1, group2)
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def forward(self, x):
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return self.layers(x)
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class TestSequential(Dy2StTestBase):
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def setUp(self):
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self.seed = 2021
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self.temp_dir = tempfile.TemporaryDirectory()
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self._init_config()
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def _init_config(self):
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self.net = SequentialNet(BufferLayers, 10, 3)
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self.model_path = os.path.join(self.temp_dir.name, 'sequential_net')
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def tearDown(self):
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self.temp_dir.cleanup()
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def _init_seed(self):
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paddle.seed(self.seed)
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np.random.seed(self.seed)
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def _run(self, to_static):
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self._init_seed()
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net = self.net
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if to_static:
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net = paddle.jit.to_static(net)
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x = paddle.rand([16, 10], 'float32')
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out = net(x)
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if to_static:
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load_out = self._test_load(net, x)
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np.testing.assert_allclose(
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load_out,
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out,
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rtol=1e-05,
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err_msg=f'load_out is {load_out}\\st_out is {out}',
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)
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return out
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def test_train(self):
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dy_out = self._run(to_static=False)
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st_out = self._run(to_static=True)
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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'dygraph_res is {dy_out}\nstatic_res is {st_out}',
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)
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def _test_load(self, net, x):
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paddle.jit.save(net, self.model_path)
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load_net = paddle.jit.load(self.model_path)
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out = load_net(x)
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return out
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class TestNestSequential(TestSequential):
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def _init_config(self):
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self.net = NestSequentialNet()
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self.model_path = os.path.join(
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self.temp_dir.name, 'nested_sequential_net'
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)
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if __name__ == '__main__':
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unittest.main()
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