wehub-resource-sync
420 lines
16 KiB
Python
420 lines
16 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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from utils import dygraph_guard, static_guard
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import paddle
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from paddle.base import core
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class ApiMinimumTest(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.input_nan_a = np.array([0, np.nan, np.nan]).astype('float32')
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self.input_nan_b = np.array([0, 1, 2]).astype('float32')
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self.np_expected1 = np.minimum(self.input_x, self.input_y)
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self.np_expected2 = np.minimum(self.input_x, self.input_z)
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self.np_expected3 = np.minimum(self.input_a, self.input_c)
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self.np_expected4 = np.minimum(self.input_b, self.input_c)
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self.np_expected_nan_aa = np.minimum(
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self.input_nan_a, self.input_nan_a
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) # minimum(Nan, Nan)
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self.np_expected_nan_ab = np.minimum(
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self.input_nan_a, self.input_nan_b
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) # minimum(Nan, Num)
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self.np_expected_nan_ba = np.minimum(
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self.input_nan_b, self.input_nan_a
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) # minimum(Num, Nan)
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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("x", shape=[10, 15], dtype="float32")
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data_y = paddle.static.data("y", shape=[10, 15], dtype="float32")
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result_max = paddle.minimum(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("x", shape=[10, 15], dtype="float32")
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data_z = paddle.static.data("z", shape=[15], dtype="float32")
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result_max = paddle.minimum(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("a", shape=[3], dtype="int64")
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data_c = paddle.static.data("c", shape=[3], dtype="int64")
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result_max = paddle.minimum(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("b", shape=[3], dtype="int64")
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data_c = paddle.static.data("c", shape=[3], dtype="int64")
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result_max = paddle.minimum(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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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.minimum(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.minimum(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.minimum(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.minimum(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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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"XPU need fix the bug",
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)
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def test_equal_tensors(self):
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numpy_tensor = np.ones([10000]).astype("float32")
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paddle_x = paddle.to_tensor(numpy_tensor)
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paddle_x.stop_gradient = False
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numpy_tensor = np.ones([10000]).astype("float32")
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paddle_x2 = paddle.to_tensor(numpy_tensor)
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paddle_x2.stop_gradient = False
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numpy_tensor = np.ones([10000]).astype("float32")
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paddle_outgrad = paddle.to_tensor(numpy_tensor)
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paddle_out = paddle.minimum(paddle_x, paddle_x2)
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paddle_x_grad, paddle_x2_grad = paddle.grad(
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[paddle_out],
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[paddle_x, paddle_x2],
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grad_outputs=[paddle_outgrad],
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allow_unused=True,
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)
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np.testing.assert_allclose(
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paddle_out.numpy(),
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numpy_tensor,
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1e-2,
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1e-2,
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)
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np.testing.assert_allclose(
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paddle_x_grad.numpy(),
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numpy_tensor * 0.5,
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1e-2,
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1e-2,
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)
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np.testing.assert_allclose(
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paddle_x2_grad.numpy(),
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numpy_tensor * 0.5,
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1e-2,
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1e-2,
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)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"XPU need fix the bug",
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)
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def test_dynamic_nan(self):
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with dygraph_guard():
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nan_a = paddle.to_tensor(self.input_nan_a)
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nan_b = paddle.to_tensor(self.input_nan_b)
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res = paddle.minimum(nan_a, nan_a)
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res = res.numpy()
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np.testing.assert_allclose(
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res, self.np_expected_nan_aa, rtol=1e-05, equal_nan=True
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)
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res = paddle.minimum(nan_a, nan_b)
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res = res.numpy()
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np.testing.assert_allclose(
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res, self.np_expected_nan_ab, rtol=1e-05, equal_nan=True
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)
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res = paddle.minimum(nan_b, nan_a)
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res = res.numpy()
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np.testing.assert_allclose(
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res, self.np_expected_nan_ba, rtol=1e-05, equal_nan=True
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)
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@unittest.skipIf(
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core.is_compiled_with_xpu(),
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"XPU need fix the bug",
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)
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def test_static_nan(self):
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with static_guard():
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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("a", shape=[3], dtype="float32")
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data_b = paddle.static.data("b", shape=[3], dtype="float32")
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result_max = paddle.minimum(data_a, data_b)
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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_nan_a, "b": self.input_nan_a},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(
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res, self.np_expected_nan_aa, rtol=1e-05, equal_nan=True
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)
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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("a", shape=[3], dtype="float32")
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data_b = paddle.static.data("b", shape=[3], dtype="float32")
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result_max = paddle.minimum(data_a, data_b)
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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_nan_a, "b": self.input_nan_b},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(
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res, self.np_expected_nan_ab, rtol=1e-05, equal_nan=True
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)
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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("a", shape=[3], dtype="float32")
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data_b = paddle.static.data("b", shape=[3], dtype="float32")
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result_max = paddle.minimum(data_a, data_b)
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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_nan_b, "b": self.input_nan_a},
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fetch_list=[result_max],
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)
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np.testing.assert_allclose(
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res, self.np_expected_nan_ba, rtol=1e-05, equal_nan=True
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)
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def test_0size_input(self):
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numpy_tensor = np.ones([0, 1, 2]).astype("float32")
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paddle_x = paddle.to_tensor(numpy_tensor)
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paddle_x.stop_gradient = False
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numpy_tensor = np.ones([1, 3598, 2]).astype("float32")
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paddle_x2 = paddle.to_tensor(numpy_tensor)
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paddle_x2.stop_gradient = False
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numpy_tensor = np.ones([0, 3598, 2]).astype("float32")
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paddle_outgrad = paddle.to_tensor(numpy_tensor)
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paddle_out = paddle.minimum(paddle_x, paddle_x2)
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paddle_x_grad, paddle_x2_grad = paddle.grad(
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[paddle_out],
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[paddle_x, paddle_x2],
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grad_outputs=[paddle_outgrad],
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allow_unused=True,
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)
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np.testing.assert_allclose(
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paddle_out.numpy(),
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numpy_tensor,
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1e-2,
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1e-2,
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)
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numpy_tensor = np.ones([0, 1, 2]).astype("float32")
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np.testing.assert_allclose(
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paddle_x_grad.numpy(),
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numpy_tensor,
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1e-2,
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1e-2,
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)
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numpy_tensor = np.zeros([1, 3598, 2]).astype("float32")
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np.testing.assert_allclose(
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paddle_x2_grad.numpy(),
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numpy_tensor,
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1e-2,
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1e-2,
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)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestElementwiseMinimumOp_Stride(unittest.TestCase):
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def setUp(self):
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self.python_api = paddle.minimum
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self.public_python_api = paddle.minimum
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self.place = get_device_place()
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def init_dtype(self):
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self.dtype = np.float64
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.minimum(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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def test_dynamic_api(self):
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self.init_dtype()
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self.init_input_output()
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paddle.disable_static()
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self.y_trans = paddle.to_tensor(self.y_trans, place=self.place)
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self.x = paddle.to_tensor(self.x, place=self.place)
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self.y = paddle.to_tensor(self.y, place=self.place)
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if self.strided_input_type == "transpose":
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y_trans_tmp = paddle.transpose(self.y_trans, self.perm)
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elif self.strided_input_type == "as_stride":
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y_trans_tmp = paddle.as_strided(
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self.y_trans, self.shape_param, self.stride_param
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)
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else:
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raise TypeError(f"Unsupported test type {self.strided_input_type}.")
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res = paddle.minimum(self.x, y_trans_tmp)
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res = res.numpy()
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np.testing.assert_allclose(res, self.out, rtol=1e-05)
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class TestElementwiseMinimumOp_Stride1(TestElementwiseMinimumOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.out = np.minimum(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseMinimumOp_Stride2(TestElementwiseMinimumOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.out = np.minimum(self.x, self.y)
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self.perm = [0, 2, 1, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseMinimumOp_Stride3(TestElementwiseMinimumOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
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self.out = np.minimum(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseMinimumOp_Stride4(TestElementwiseMinimumOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [20, 2, 13, 1]).astype(self.dtype)
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self.out = np.minimum(self.x, self.y)
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self.perm = [1, 0, 2, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseMinimumOp_Stride5(TestElementwiseMinimumOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "as_stride"
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self.x = np.random.uniform(0.1, 1, [23, 10, 1, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(self.dtype)
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self.y_trans = self.y
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self.y = self.y[:, 0:1, :, 0:1]
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self.out = np.minimum(self.x, self.y)
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self.shape_param = [23, 1, 13, 1]
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self.stride_param = [520, 260, 20, 1]
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class TestElementwiseMinimumOp_Stride_ZeroDim1(TestElementwiseMinimumOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, []).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.minimum(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseMinimumOp_Stride_ZeroSize1(
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TestElementwiseMinimumOp_Stride
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):
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def init_data(self):
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self.strided_input_type = "transpose"
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self.x = np.random.rand(1, 0, 2).astype('float32')
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self.y = np.random.rand(3, 0, 1).astype('float32')
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self.out = np.minimum(self.x, self.y)
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self.perm = [2, 1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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if __name__ == '__main__':
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unittest.main()
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