wehub-resource-sync
260 lines
9.9 KiB
Python
260 lines
9.9 KiB
Python
# Copyright (c) 2020 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.base import core
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class ApiSubtractTest(unittest.TestCase):
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def setUp(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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self.place = get_device_place()
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else:
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self.place = core.CPUPlace()
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self.input_x = np.random.rand(10, 15).astype("float32")
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self.input_y = np.random.rand(10, 15).astype("float32")
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self.input_z = np.random.rand(15).astype("float32")
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self.input_a = np.array([0, np.nan, np.nan]).astype('int64')
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self.input_b = np.array([2, np.inf, -np.inf]).astype('int64')
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self.input_c = np.array([4, 1, 3]).astype('int64')
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self.np_expected1 = np.subtract(self.input_x, self.input_y)
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self.np_expected2 = np.subtract(self.input_x, self.input_z)
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self.np_expected3 = np.subtract(self.input_a, self.input_c)
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self.np_expected4 = np.subtract(self.input_b, self.input_c)
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self.np_expected5 = np.subtract(self.input_x, self.input_y * 2)
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self.np_expected6 = np.subtract(self.input_x, self.input_z * 2)
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self.np_expected7 = np.subtract(self.input_a, self.input_c * 2)
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self.np_expected8 = np.subtract(self.input_b, self.input_c * 2)
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def test_static_api(self):
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paddle.enable_static()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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data_x = paddle.static.data(
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"x", shape=self.input_x.shape, dtype="float32"
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)
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data_y = paddle.static.data(
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"y", shape=self.input_y.shape, dtype="float32"
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)
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result_max = paddle.subtract(data_x, data_y)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"x": self.input_x, "y": self.input_y},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(res, self.np_expected1, rtol=1e-05)
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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data_x = paddle.static.data(
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"x", shape=self.input_x.shape, dtype="float32"
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)
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data_z = paddle.static.data(
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"z", shape=self.input_z.shape, dtype="float32"
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)
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result_max = paddle.subtract(data_x, data_z)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"x": self.input_x, "z": self.input_z},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(res, self.np_expected2, rtol=1e-05)
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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data_a = paddle.static.data(
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"a", shape=self.input_a.shape, dtype="int64"
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)
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data_c = paddle.static.data(
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"c", shape=self.input_b.shape, dtype="int64"
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)
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result_max = paddle.subtract(data_a, data_c)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"a": self.input_a, "c": self.input_c},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(res, self.np_expected3, rtol=1e-05)
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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data_b = paddle.static.data(
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"b", shape=self.input_b.shape, dtype="int64"
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)
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data_c = paddle.static.data(
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"c", shape=self.input_c.shape, dtype="int64"
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)
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result_max = paddle.subtract(data_b, data_c)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"b": self.input_b, "c": self.input_c},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05)
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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data_x = paddle.static.data(
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"x", shape=self.input_x.shape, dtype="float32"
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)
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data_y = paddle.static.data(
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"y", shape=self.input_y.shape, dtype="float32"
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)
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result_max = paddle.sub(data_x, data_y, alpha=2)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"x": self.input_x, "y": self.input_y},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(res, self.np_expected5, rtol=1e-05)
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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data_x = paddle.static.data(
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"x", shape=self.input_x.shape, dtype="float32"
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)
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data_z = paddle.static.data(
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"z", shape=self.input_z.shape, dtype="float32"
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)
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result_max = paddle.sub(data_x, data_z, alpha=2)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"x": self.input_x, "z": self.input_z},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(res, self.np_expected6, rtol=1e-05)
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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data_a = paddle.static.data(
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"a", shape=self.input_a.shape, dtype="int64"
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)
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data_c = paddle.static.data(
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"c", shape=self.input_b.shape, dtype="int64"
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)
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result_max = paddle.sub(data_a, data_c, alpha=2)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"a": self.input_a, "c": self.input_c},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(res, self.np_expected7, rtol=1e-05)
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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data_b = paddle.static.data(
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"b", shape=self.input_b.shape, dtype="int64"
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)
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data_c = paddle.static.data(
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"c", shape=self.input_c.shape, dtype="int64"
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)
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result_max = paddle.sub(data_b, data_c, alpha=2)
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exe = paddle.static.Executor(self.place)
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(res,) = exe.run(
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feed={"b": self.input_b, "c": self.input_c},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(res, self.np_expected8, rtol=1e-05)
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def test_dynamic_api(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.input_x)
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y = paddle.to_tensor(self.input_y)
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z = paddle.to_tensor(self.input_z)
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a = paddle.to_tensor(self.input_a)
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b = paddle.to_tensor(self.input_b)
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c = paddle.to_tensor(self.input_c)
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res = paddle.subtract(x, y)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected1, rtol=1e-05)
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# test broadcast
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res = paddle.subtract(x, z)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected2, rtol=1e-05)
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res = paddle.subtract(a, c)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected3, rtol=1e-05)
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res = paddle.subtract(b, c)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected4, rtol=1e-05)
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res = paddle.sub(x, y, alpha=2)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected5, rtol=1e-05)
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res = paddle.sub(x, z, alpha=2)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected6, rtol=1e-05)
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res = paddle.sub(a, c, alpha=2)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected7, rtol=1e-05)
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res = paddle.sub(b, c, alpha=2)
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res = res.numpy()
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np.testing.assert_allclose(res, self.np_expected8, rtol=1e-05)
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x.sub_(y, alpha=2)
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np.testing.assert_allclose(x, self.np_expected5, rtol=1e-05)
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class ApiSubtractTestZeroSize(ApiSubtractTest):
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def setUp(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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self.place = get_device_place()
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else:
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self.place = core.CPUPlace()
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self.input_x = np.random.rand(0, 15).astype("float32")
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self.input_y = np.random.rand(1, 15).astype("float32")
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self.input_z = np.random.rand(15).astype("float32")
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self.input_a = np.random.rand(3).astype('int64')
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self.input_b = np.random.rand(3).astype('int64')
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self.input_c = np.random.rand(3).astype('int64')
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self.np_expected1 = np.subtract(self.input_x, self.input_y)
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self.np_expected2 = np.subtract(self.input_x, self.input_z)
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self.np_expected3 = np.subtract(self.input_a, self.input_c)
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self.np_expected4 = np.subtract(self.input_b, self.input_c)
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self.np_expected5 = np.subtract(self.input_x, self.input_y * 2)
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self.np_expected6 = np.subtract(self.input_x, self.input_z * 2)
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self.np_expected7 = np.subtract(self.input_a, self.input_c * 2)
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self.np_expected8 = np.subtract(self.input_b, self.input_c * 2)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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