wehub-resource-sync
94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
# Copyright (c) 2018 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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from op_test import is_custom_device
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import paddle
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from paddle import base
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from paddle.base import core
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class TestException(unittest.TestCase):
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def test_exception(self):
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exception = None
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try:
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core.__unittest_throw_exception__()
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except RuntimeError as ex:
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self.assertIn("This is a test of exception", str(ex))
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exception = ex
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self.assertIsNotNone(exception)
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def test_gpu_success(self):
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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try:
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core._test_enforce_gpu_success()
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except Exception as e:
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self.assertTrue(isinstance(e, OSError))
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self.assertIn(
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"CUDA error(35), CUDA driver version is insufficient for CUDA runtime version.",
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str(e),
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)
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self.assertIn(
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"[Hint: 'cudaErrorInsufficientDriver'. This indicates that the installed NVIDIA CUDA driver is older than the CUDA runtime library. This is not a supported configuration.Users should install an updated NVIDIA display driver to allow the application to run.]",
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str(e),
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)
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class TestExceptionNoCStack(unittest.TestCase):
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def setUp(self):
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paddle.enable_static()
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# test no C++ stack format
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base.set_flags({'FLAGS_call_stack_level': 1})
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def test_exception_in_static_mode(self):
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x = paddle.static.data(name='X', shape=[-1, 13], dtype='float32')
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y = paddle.static.data(name='Y', shape=[-1, 1], dtype='float32')
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predict = paddle.static.nn.fc(x, size=1)
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loss = paddle.nn.functional.square_error_cost(input=predict, label=y)
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avg_loss = paddle.mean(loss)
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paddle.optimizer.SGD(learning_rate=0.01).minimize(avg_loss)
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place = base.CPUPlace()
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exe = base.Executor(place)
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exe.run(base.default_startup_program())
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x = numpy.random.random(size=(8, 12)).astype('float32')
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y = numpy.random.random(size=(8, 1)).astype('float32')
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with self.assertRaises(ValueError):
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exe.run(
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base.default_main_program(),
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feed={'X': x, 'Y': y},
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fetch_list=[avg_loss.name],
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)
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def test_exception_in_dynamic_mode(self):
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place = base.CPUPlace()
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with base.dygraph.guard(place):
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x = numpy.random.random(size=(10, 2)).astype('float32')
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linear = paddle.nn.Linear(1, 10)
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data = paddle.to_tensor(x)
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with self.assertRaises(ValueError):
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res = linear(data)
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if __name__ == "__main__":
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unittest.main()
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