wehub-resource-sync
237 lines
6.5 KiB
Python
237 lines
6.5 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 unittest
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import numpy
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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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from paddle.base import core
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def case0(x):
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a = paddle.to_tensor([1.0, 2.0, 3.0], dtype="int64")
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return a
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def case1(x):
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paddle.set_default_dtype("float64")
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a = paddle.to_tensor([1, 2, 3], stop_gradient=False, dtype='float32')
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return a
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def case2(x):
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if core.is_compiled_with_cuda():
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place = paddle.CUDAPlace(0)
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elif core.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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else:
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place = paddle.CPUPlace()
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a = paddle.to_tensor(
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[1.0, 2.0, 3.0], place=place, dtype="int64", stop_gradient=False
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)
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return a
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def case3(x):
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paddle.set_default_dtype("float64")
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if core.is_compiled_with_cuda():
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place = paddle.CUDAPlace(0)
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elif core.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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else:
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place = paddle.CPUPlace()
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a = paddle.to_tensor([1.0, 2.0, 3.0], place=place)
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return a
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def case4(x):
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paddle.set_default_dtype("float64")
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if core.is_compiled_with_cuda():
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place = paddle.CUDAPlace(0)
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elif core.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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else:
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place = paddle.CPUPlace()
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a = paddle.to_tensor([1], place=place)
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b = paddle.to_tensor([2.1], place=place, stop_gradient=False, dtype="int64")
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c = paddle.to_tensor([a, b, [1]], dtype="float32")
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return c
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def case5(x):
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paddle.set_default_dtype("float64")
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a = paddle.to_tensor([1, 2])
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return a
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def case6(x):
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na = numpy.array([1, 2], dtype='int32')
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a = paddle.to_tensor(na)
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return a
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def case7(x):
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a = paddle.to_tensor(10.0)
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return a
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def case8(x):
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a = paddle.to_tensor({1: 1})
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return a
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def case_to_tensor_default_dtype():
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return paddle.to_tensor(1)
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class TestToTensorReturnVal(Dy2StTestBase):
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def test_to_tensor_badreturn(self):
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paddle.disable_static()
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x = paddle.to_tensor([3])
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a = paddle.jit.to_static(case0)(x)
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b = case0(x)
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self.assertTrue(a.dtype == b.dtype)
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self.assertTrue(a.stop_gradient == b.stop_gradient)
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self.assertTrue(a.place._equals(b.place))
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a = paddle.jit.to_static(case1)(x)
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b = case1(x)
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self.assertTrue(a.dtype == b.dtype)
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self.assertTrue(a.stop_gradient == b.stop_gradient)
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self.assertTrue(a.place._equals(b.place))
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a = paddle.jit.to_static(case2)(x)
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b = case2(x)
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self.assertTrue(a.dtype == b.dtype)
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self.assertTrue(a.stop_gradient == b.stop_gradient)
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self.assertTrue(a.place._equals(b.place))
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a = paddle.jit.to_static(case3)(x)
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b = case3(x)
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self.assertTrue(a.dtype == b.dtype)
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self.assertTrue(a.stop_gradient == b.stop_gradient)
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self.assertTrue(a.place._equals(b.place))
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a = paddle.jit.to_static(case4)(x)
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b = case4(x)
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self.assertTrue(a.dtype == b.dtype)
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self.assertTrue(a.stop_gradient == b.stop_gradient)
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self.assertTrue(a.place._equals(b.place))
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a = paddle.jit.to_static(case5)(x)
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b = case5(x)
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self.assertTrue(a.dtype == b.dtype)
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self.assertTrue(a.stop_gradient == b.stop_gradient)
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self.assertTrue(a.place._equals(b.place))
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a = paddle.jit.to_static(case6)(x)
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b = case6(x)
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self.assertTrue(a.dtype == b.dtype)
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self.assertTrue(a.stop_gradient == b.stop_gradient)
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self.assertTrue(a.place._equals(b.place))
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a = paddle.jit.to_static(case7)(x)
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b = case7(x)
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self.assertTrue(a.dtype == b.dtype)
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self.assertTrue(a.stop_gradient == b.stop_gradient)
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self.assertTrue(a.place._equals(b.place))
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def test_to_tensor_default_dtype(self):
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a = paddle.jit.to_static(case_to_tensor_default_dtype)()
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b = case_to_tensor_default_dtype()
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self.assertTrue(a.dtype == b.dtype)
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self.assertTrue(a.stop_gradient == b.stop_gradient)
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self.assertTrue(a.place._equals(b.place))
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def test_to_tensor_err_log(self):
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paddle.disable_static()
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x = paddle.to_tensor([3])
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try:
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a = paddle.jit.to_static(case8)(x)
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except Exception as e:
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self.assertTrue(
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"Do not support transform type `<class 'dict'>` to tensor"
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in str(e)
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)
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class TestStatic(Dy2StTestBase):
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def test_static(self):
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paddle.enable_static()
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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if core.is_compiled_with_cuda():
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place = paddle.CUDAPlace(0)
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elif core.is_compiled_with_xpu():
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place = paddle.XPUPlace(0)
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else:
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place = paddle.CPUPlace()
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paddle.set_default_dtype("float64")
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x = paddle.to_tensor(
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paddle.randn([5, 2]),
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dtype='float64',
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stop_gradient=False,
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place=place,
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)
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fc_net = paddle.nn.Linear(2, 1)
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out = fc_net(x)
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sgd = paddle.optimizer.SGD()
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sgd.minimize(paddle.mean(out))
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exe = paddle.static.Executor()
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exe.run(startup_prog)
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res = exe.run(fetch_list=[x, out])
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class TestInt16(Dy2StTestBase):
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def test_static(self):
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paddle.enable_static()
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data = np.array([1, 2], dtype="int16")
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x = paddle.to_tensor(data)
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self.assertEqual(x.dtype, paddle.int16)
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y = paddle.to_tensor([1, 2], dtype="int16")
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self.assertEqual(y.dtype, paddle.int16)
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class TestNestedListWithTensor(Dy2StTestBase):
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def test_nested_list_with_tensor(self):
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paddle.enable_static()
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x = paddle.to_tensor(1)
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y = paddle.to_tensor([[x]])
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self.assertEqual(y.shape, [1, 1])
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self.assertEqual(y.dtype, paddle.int64)
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if __name__ == '__main__':
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unittest.main()
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