wehub-resource-sync
105 lines
2.8 KiB
Python
105 lines
2.8 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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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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SEED = 2020
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class Pool2D(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.pool2d = paddle.nn.AvgPool2D(kernel_size=2, stride=1)
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def forward(self, x):
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# Add func `get_result` for testing arg_name_to_idx in ast transformation.
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def get_result(x):
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return self.pool2d(x)
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pre = get_result(x)
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return pre
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class Linear(paddle.nn.Layer):
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def __init__(self, input_dim=10, output_dim=5):
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super().__init__()
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self.fc = paddle.nn.Linear(
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input_dim,
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output_dim,
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weight_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.99)
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),
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bias_attr=paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.5)
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),
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)
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self.act = paddle.nn.ReLU()
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def forward(self, x):
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pre = self.fc(x)
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pre = self.act(pre)
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loss = paddle.mean(pre)
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return pre, loss
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class TestPool2D(Dy2StTestBase):
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def setUp(self):
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self.dygraph_class = Pool2D
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self.data = np.random.random((1, 2, 4, 4)).astype('float32')
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def train(self):
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dy_layer = paddle.jit.to_static(self.dygraph_class())
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x = paddle.to_tensor(self.data)
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prediction = dy_layer(x)
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if isinstance(prediction, (list, tuple)):
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prediction = prediction[0]
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return prediction.numpy()
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def train_static(self):
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with enable_to_static_guard(True):
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return self.train()
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def train_dygraph(self):
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with enable_to_static_guard(False):
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return self.train()
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def test_to_static(self):
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dygraph_res = self.train_dygraph()
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static_res = self.train_static()
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np.testing.assert_allclose(
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dygraph_res,
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static_res,
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rtol=1e-05,
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)
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class TestLinear(TestPool2D):
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def setUp(self):
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self.dygraph_class = Linear
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self.data = np.random.random((4, 10)).astype('float32')
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if __name__ == '__main__':
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unittest.main()
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