wehub-resource-sync
94 lines
2.4 KiB
Python
94 lines
2.4 KiB
Python
# Copyright (c) 2023 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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class FakeNet:
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def __init__(self):
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self.var = paddle.to_tensor([2.0])
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f = FakeNet()
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g = paddle.to_tensor([1.0])
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class Net(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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# unsupported g as store.
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t = g * 2 + x
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t = f.var * t
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return t
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class TestFallback(Dy2StTestBase):
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def setUp(self):
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self.x = paddle.to_tensor(1.0).astype('int')
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def test_name_load(self):
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net_dy = Net()
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net_st = Net()
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output_dy = net_dy(self.x)
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output_st = paddle.jit.to_static(net_st)(self.x)
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np.testing.assert_allclose(output_dy.numpy(), output_st.numpy())
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class TestLoad2(Dy2StTestBase):
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def test_name_load_nograd(self):
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@paddle.no_grad()
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def func(x):
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x = paddle.shape(x)
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return x
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x = paddle.to_tensor([[3, 3], [1, 1]])
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output_st = paddle.jit.to_static(func)(x)
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output_dy = func(x)
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np.testing.assert_allclose(output_dy.numpy(), output_st.numpy())
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class LoadInCallKwargsNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.extra_inputs = []
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def forward(self, x):
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for i in range(len(self.extra_inputs)):
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x = paddle.nn.functional.linear(weight=self.extra_inputs[i].T, x=x)
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return x
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class TestLoadInCallKwargs(Dy2StTestBase):
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def test_name_load_nograd(self):
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net = LoadInCallKwargsNet()
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x = paddle.rand([10, 10])
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net.extra_inputs.append(paddle.rand([10, 10]))
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output_st = paddle.jit.to_static(net)(x)
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output_dy = net(x)
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np.testing.assert_allclose(output_dy.numpy(), output_st.numpy())
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if __name__ == "__main__":
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unittest.main()
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