wehub-resource-sync
209 lines
7.1 KiB
Python
209 lines
7.1 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 as np
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from op_test import get_device_place, is_custom_device
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import paddle
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from paddle import base
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paddle.enable_static()
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class TestAllcloseLayer(unittest.TestCase):
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def allclose_check(self, use_cuda, dtype='float32'):
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a = paddle.static.data(name="a", shape=[2], dtype=dtype)
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b = paddle.static.data(name="b", shape=[2], dtype=dtype)
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result = paddle.allclose(
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a, b, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan"
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)
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result_nan = paddle.allclose(
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a, b, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan"
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)
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result_corner = paddle.allclose(
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a, b, rtol=0.01, atol=0.0, name="corner_case"
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)
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place = get_device_place() if use_cuda else 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 = np.array([10000.0, 1e-07]).astype(dtype)
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y = np.array([10000.1, 1e-08]).astype(dtype)
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result_v, result_nan_v, result_c = exe.run(
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feed={'a': x, 'b': y},
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fetch_list=[result, result_nan, result_corner],
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)
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self.assertEqual(result_v, False)
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self.assertEqual(result_nan_v, False)
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x = np.array([10000.0, 1e-08]).astype(dtype)
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y = np.array([10000.1, 1e-09]).astype(dtype)
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result_v, result_nan_v, result_c = exe.run(
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feed={'a': x, 'b': y},
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fetch_list=[result, result_nan, result_corner],
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)
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self.assertEqual(result_v, True)
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self.assertEqual(result_nan_v, True)
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x = np.array([1.0, float('nan')]).astype(dtype)
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y = np.array([1.0, float('nan')]).astype(dtype)
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result_v, result_nan_v, result_c = exe.run(
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feed={'a': x, 'b': y},
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fetch_list=[result, result_nan, result_corner],
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)
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self.assertEqual(result_v, False)
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self.assertEqual(result_nan_v, True)
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# for corner case
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x = np.array([10.1, 10.1]).astype(dtype)
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y = np.array([10, 10]).astype(dtype)
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result_v, result_nan_v, result_c = exe.run(
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feed={'a': x, 'b': y},
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fetch_list=[result, result_nan, result_corner],
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)
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corner_res = dtype == 'float64'
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self.assertEqual(result_c, corner_res)
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def test_allclose_cpu_fp32(self):
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main = base.Program()
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startup = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, startup),
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):
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self.allclose_check(use_cuda=False, dtype='float32')
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def test_allclose_cpu_fp64(self):
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main = base.Program()
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startup = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, startup),
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):
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self.allclose_check(use_cuda=False, dtype='float64')
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def test_allclose_gpu_fp32(self):
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if base.core.is_compiled_with_cuda() or is_custom_device():
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main = base.Program()
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startup = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, startup),
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):
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self.allclose_check(use_cuda=True, dtype='float32')
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def test_allclose_gpu_fp64(self):
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if base.core.is_compiled_with_cuda() or is_custom_device():
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main = base.Program()
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startup = base.Program()
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with (
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base.unique_name.guard(),
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base.program_guard(main, startup),
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):
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self.allclose_check(use_cuda=True, dtype='float64')
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def test_dygraph_mode(self):
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x_1 = np.array([10000.0, 1e-07]).astype("float32")
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y_1 = np.array([10000.1, 1e-08]).astype("float32")
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x_2 = np.array([10000.0, 1e-08]).astype("float32")
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y_2 = np.array([10000.1, 1e-09]).astype("float32")
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x_3 = np.array([1.0, float('nan')]).astype("float32")
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y_3 = np.array([1.0, float('nan')]).astype("float32")
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x_4 = np.array([10.1]).astype("float32")
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y_4 = np.array([10]).astype("float32")
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x_5 = np.array([10.1]).astype("float64")
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y_5 = np.array([10]).astype("float64")
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with base.dygraph.guard():
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x_v_1 = paddle.to_tensor(x_1)
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y_v_1 = paddle.to_tensor(y_1)
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ret_1 = paddle.allclose(
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x_v_1,
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y_v_1,
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rtol=1e-05,
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atol=1e-08,
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equal_nan=False,
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name='test_1',
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)
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self.assertEqual(ret_1.numpy(), False)
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ret_1 = paddle.allclose(
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x_v_1,
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y_v_1,
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rtol=1e-05,
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atol=1e-08,
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equal_nan=True,
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name='test_2',
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)
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self.assertEqual(ret_1.numpy(), False)
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x_v_2 = paddle.to_tensor(x_2)
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y_v_2 = paddle.to_tensor(y_2)
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ret_2 = paddle.allclose(
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x_v_2,
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y_v_2,
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rtol=1e-05,
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atol=1e-08,
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equal_nan=False,
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name='test_3',
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)
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self.assertEqual(ret_2.numpy(), True)
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ret_2 = paddle.allclose(
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x_v_2,
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y_v_2,
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rtol=1e-05,
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atol=1e-08,
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equal_nan=True,
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name='test_4',
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)
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self.assertEqual(ret_2.numpy(), True)
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x_v_3 = paddle.to_tensor(x_3)
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y_v_3 = paddle.to_tensor(y_3)
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ret_3 = paddle.allclose(
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x_v_3,
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y_v_3,
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rtol=1e-05,
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atol=1e-08,
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equal_nan=False,
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name='test_5',
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)
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self.assertEqual(ret_3.numpy(), False)
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ret_3 = paddle.allclose(
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x_v_3,
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y_v_3,
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rtol=1e-05,
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atol=1e-08,
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equal_nan=True,
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name='test_6',
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)
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self.assertEqual(ret_3.numpy(), True)
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# for corner case
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x_v_4 = paddle.to_tensor(x_4)
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y_v_4 = paddle.to_tensor(y_4)
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ret_4 = paddle.allclose(
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x_v_4, y_v_4, rtol=0.01, atol=0.0, name='test_7'
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)
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self.assertEqual(ret_4.numpy(), False)
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x_v_5 = paddle.to_tensor(x_5)
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y_v_5 = paddle.to_tensor(y_5)
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ret_5 = paddle.allclose(
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x_v_5, y_v_5, rtol=0.015, atol=0.0, name='test_8'
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)
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self.assertEqual(ret_5.numpy(), True)
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if __name__ == "__main__":
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unittest.main()
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