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147 lines
4.0 KiB
147 lines
4.0 KiB
# 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 as np
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from op_test import OpTest
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from paddle.fluid import compiler, Program, program_guard
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class TestInf(OpTest):
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def setUp(self):
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self.op_type = "isinf"
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self.dtype = np.float32
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self.init_dtype()
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x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
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x[0] = np.inf
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x[-1] = np.inf
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self.inputs = {'X': x}
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self.outputs = {'Out': np.array(True).astype(self.dtype)}
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def init_dtype(self):
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pass
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def test_output(self):
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self.check_output()
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class TestRaiseError(unittest.TestCase):
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def test_errors(self):
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def test_type():
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fluid.layers.isfinite([10])
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self.assertRaises(TypeError, test_type)
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def test_dtype():
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data = fluid.data(shape=[10], dtype="float16", name="input")
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fluid.layers.isfinite(data)
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self.assertRaises(TypeError, test_dtype)
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestFP16Inf(TestInf):
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def init_dtype(self):
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self.dtype = np.float16
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class TestNAN(OpTest):
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def setUp(self):
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self.op_type = "isnan"
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self.dtype = np.float32
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self.init_dtype()
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x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
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x[0] = np.nan
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x[-1] = np.nan
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self.inputs = {'X': x}
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self.outputs = {'Out': np.array(True).astype(self.dtype)}
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def init_dtype(self):
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pass
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def test_output(self):
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self.check_output()
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestFP16NAN(TestNAN):
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def init_dtype(self):
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self.dtype = np.float16
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class TestIsfinite(OpTest):
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def setUp(self):
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self.op_type = "isfinite"
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self.dtype = np.float32
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self.init_dtype()
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x = np.random.uniform(0.1, 1, [11, 17]).astype(self.dtype)
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x[0] = np.inf
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x[-1] = np.nan
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out = np.isinf(x) | np.isnan(x)
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self.inputs = {'X': x}
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self.outputs = {'Out': np.array(False).astype(self.dtype)}
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def init_dtype(self):
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pass
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def test_output(self):
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self.check_output()
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestFP16Isfinite(TestIsfinite):
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def init_dtype(self):
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self.dtype = np.float16
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class BadInputTest(unittest.TestCase):
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def test_error(self):
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with fluid.program_guard(fluid.Program()):
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def test_has_inf_bad_x():
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data = [1, 2, 3]
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result = fluid.layers.has_inf(data)
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self.assertRaises(TypeError, test_has_inf_bad_x)
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def test_has_nan_bad_x():
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data = [1, 2, 3]
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result = fluid.layers.has_nan(data)
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self.assertRaises(TypeError, test_has_nan_bad_x)
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with fluid.dygraph.guard():
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data = paddle.zeros([2, 3])
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result = paddle.fluid.layers.has_inf(data)
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expect_value = np.array([False])
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self.assertEqual((result.numpy() == expect_value).all(), True)
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result = paddle.fluid.layers.has_nan(data)
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self.assertEqual((result.numpy() == expect_value).all(), True)
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if __name__ == '__main__':
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unittest.main()
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