wehub-resource-sync
400 lines
13 KiB
Python
400 lines
13 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 (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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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 TestCrossOp(OpTest):
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def setUp(self):
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self.op_type = "cross"
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self.python_api = paddle.cross
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self.initTestCase()
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self.inputs = {
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'X': np.random.random(self.shape).astype(self.dtype),
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'Y': np.random.random(self.shape).astype(self.dtype),
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}
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if self.dtype is np.complex64 or self.dtype is np.complex128:
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self.inputs = {
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'X': (
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np.random.random(self.shape)
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+ 1j * np.random.random(self.shape)
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).astype(self.dtype),
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'Y': (
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np.random.random(self.shape)
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+ 1j * np.random.random(self.shape)
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).astype(self.dtype),
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}
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self.init_output()
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def initTestCase(self):
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self.attrs = {'dim': -2}
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self.dtype = np.float64
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self.shape = (1024, 3, 1)
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def init_output(self):
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x = np.squeeze(self.inputs['X'], 2)
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y = np.squeeze(self.inputs['Y'], 2)
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z_list = []
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for i in range(1024):
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z_list.append(np.cross(x[i], y[i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', check_pir=True)
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class TestCrossOpCase1(TestCrossOp):
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def initTestCase(self):
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self.shape = (2048, 3)
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self.dtype = np.float32
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def init_output(self):
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z_list = []
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for i in range(2048):
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z_list.append(np.cross(self.inputs['X'][i], self.inputs['Y'][i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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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 TestCrossFP16Op(TestCrossOp):
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def initTestCase(self):
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self.shape = (2048, 3)
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self.dtype = np.float16
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def init_output(self):
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z_list = []
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for i in range(2048):
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z_list.append(np.cross(self.inputs['X'][i], self.inputs['Y'][i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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class TestCrossComplex64Op(TestCrossOp):
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def initTestCase(self):
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self.shape = (2048, 3)
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self.dtype = np.complex64
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def init_output(self):
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z_list = []
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for i in range(2048):
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z_list.append(np.cross(self.inputs['X'][i], self.inputs['Y'][i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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class TestCrossComplex128Op(TestCrossOp):
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def initTestCase(self):
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self.shape = (2048, 3)
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self.dtype = np.complex128
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def init_output(self):
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z_list = []
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for i in range(2048):
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z_list.append(np.cross(self.inputs['X'][i], self.inputs['Y'][i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestCrossBF16Op(OpTest):
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def setUp(self):
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self.op_type = "cross"
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self.python_api = paddle.cross
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self.initTestCase()
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self.x = np.random.random(self.shape).astype(np.float32)
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self.y = np.random.random(self.shape).astype(np.float32)
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self.inputs = {
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'X': convert_float_to_uint16(self.x),
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'Y': convert_float_to_uint16(self.y),
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}
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self.init_output()
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def initTestCase(self):
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self.attrs = {'dim': -2}
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self.dtype = np.uint16
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self.shape = (1024, 3, 1)
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def init_output(self):
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x = np.squeeze(self.x, 2)
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y = np.squeeze(self.y, 2)
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z_list = []
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for i in range(1024):
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z_list.append(np.cross(x[i], y[i]))
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out = np.array(z_list).astype(np.float32).reshape(self.shape)
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self.outputs = {'Out': convert_float_to_uint16(out)}
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def test_check_output(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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if core.is_bfloat16_supported(place):
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self.check_output_with_place(place, check_pir=True)
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def test_check_grad_normal(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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if core.is_bfloat16_supported(place):
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self.check_grad_with_place(
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place, ['X', 'Y'], 'Out', check_pir=True
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)
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class TestCrossAPI(unittest.TestCase):
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def input_data(self):
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self.data_x = np.array(
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[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0], [3.0, 3.0, 3.0]]
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).astype('float32')
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self.data_y = np.array(
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[[1.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]
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).astype('float32')
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self.data_x_zero = np.array([]).reshape(0, 3).astype('float32')
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self.data_y_zero = np.array([]).reshape(0, 3).astype('float32')
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def test_cross_api(self):
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self.input_data()
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main = paddle.static.Program()
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startup = paddle.static.Program()
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# case 1:
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with paddle.static.program_guard(main, startup):
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x = paddle.static.data(name='x', shape=[-1, 3], dtype="float32")
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y = paddle.static.data(name='y', shape=[-1, 3], dtype="float32")
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z = paddle.cross(x, y, axis=1)
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exe = base.Executor(base.CPUPlace())
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(res,) = exe.run(
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main,
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feed={'x': self.data_x, 'y': self.data_y},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.array(
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[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
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)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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main = paddle.static.Program()
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startup = paddle.static.Program()
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# case 2:
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with paddle.static.program_guard(main, startup):
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x = paddle.static.data(name='x', shape=[-1, 3], dtype="float32")
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y = paddle.static.data(name='y', shape=[-1, 3], dtype="float32")
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z = paddle.cross(x, y)
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exe = base.Executor(base.CPUPlace())
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(res,) = exe.run(
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main,
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feed={'x': self.data_x, 'y': self.data_y},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.array(
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[[-1.0, -1.0, -1.0], [2.0, 2.0, 2.0], [-1.0, -1.0, -1.0]]
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)
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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main = paddle.static.Program()
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startup = paddle.static.Program()
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# case 3:
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with paddle.static.program_guard(main, startup):
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x = paddle.static.data(name='x', shape=[0, 3], dtype="float32")
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y = paddle.static.data(name='y', shape=[0, 3], dtype="float32")
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z = paddle.cross(x, y, axis=1)
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exe = base.Executor(base.CPUPlace())
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(res,) = exe.run(
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main,
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feed={'x': self.data_x_zero, 'y': self.data_y_zero},
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fetch_list=[z],
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return_numpy=False,
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)
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expect_out = np.empty((0, 3))
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np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05)
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main = paddle.static.Program()
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startup = paddle.static.Program()
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def test_dygraph_api(self):
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self.input_data()
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# case 1:
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# with base.dygraph.guard():
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# x = paddle.to_tensor(self.data_x)
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# y = paddle.to_tensor(self.data_y)
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# z = paddle.cross(x, y)
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# np_z = z.numpy()
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# expect_out = np.array([[-1.0, -1.0, -1.0], [2.0, 2.0, 2.0],
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# [-1.0, -1.0, -1.0]])
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# np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case 2:
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x)
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y = paddle.to_tensor(self.data_y)
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z = paddle.cross(x, y, axis=1)
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np_z = z.numpy()
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expect_out = np.array(
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[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
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)
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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# case 3:
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with base.dygraph.guard():
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x = paddle.to_tensor(self.data_x_zero)
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y = paddle.to_tensor(self.data_y_zero)
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z = paddle.cross(x, y, axis=1)
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np_z = z.numpy()
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expect_out = np.empty((0, 3))
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np.testing.assert_allclose(expect_out, np_z, rtol=1e-05)
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class TestCrossOpZeroSizeTest(TestCrossOp):
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def initTestCase(self):
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self.shape = (0, 3, 3)
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self.dtype = np.float64
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self.attr = {'dim': -1}
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def init_output(self):
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z_list = []
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for i in range(0):
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z_list.append(np.cross(self.inputs['X'][i], self.inputs['Y'][i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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class TestCrossOpZeroSizeTest1(TestCrossOp):
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def initTestCase(self):
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self.shape = (3, 0, 3)
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self.dtype = np.float64
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self.attr = {'dim': -1}
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def init_output(self):
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z_list = []
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for i in range(3):
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z_list.append(np.cross(self.inputs['X'][i], self.inputs['Y'][i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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class TestCrossOpZeroSizeTest2(TestCrossOp):
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def initTestCase(self):
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self.shape = (0, 0, 3)
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self.dtype = np.float64
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self.attr = {'dim': -1}
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def init_output(self):
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z_list = []
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for i in range(0):
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z_list.append(np.cross(self.inputs['X'][i], self.inputs['Y'][i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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class TestCrossOpZeroSizeCPUTest(TestCrossOp):
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def initTestCase(self):
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self.shape = (0, 0, 3)
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self.dtype = np.float64
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self.attr = {'dim': -1}
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def init_output(self):
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z_list = []
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for i in range(0):
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z_list.append(np.cross(self.inputs['X'][i], self.inputs['Y'][i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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def test_check_output(self):
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place = paddle.CPUPlace()
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self.check_output_with_place(place, check_pir=True)
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def test_check_grad_normal(self):
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place = paddle.CPUPlace()
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self.check_grad_with_place(place, ['X', 'Y'], 'Out', check_pir=True)
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class TestCrossOpZeroSizeCPUTest1(TestCrossOpZeroSizeCPUTest):
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def initTestCase(self):
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self.shape = (3, 0, 3)
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self.dtype = np.float64
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self.attr = {'dim': -1}
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def init_output(self):
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z_list = []
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for i in range(3):
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z_list.append(np.cross(self.inputs['X'][i], self.inputs['Y'][i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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class TestCrossOpZeroSizeCPUTest2(TestCrossOpZeroSizeCPUTest):
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def initTestCase(self):
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self.shape = (0, 0, 3)
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self.dtype = np.float64
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self.attr = {'dim': -1}
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def init_output(self):
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z_list = []
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for i in range(0):
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z_list.append(np.cross(self.inputs['X'][i], self.inputs['Y'][i]))
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self.outputs = {'Out': np.array(z_list).reshape(self.shape)}
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class TestLinalgCrossDefaultDim(unittest.TestCase):
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def test_linalg_cross_default_dim(self):
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# Test that paddle.linalg.cross defaults to dim=-1, not axis=9 auto
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# Using shape [3, 2, 3] where auto-axis picks dim 0, but dim=-1 picks dim 2
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paddle.disable_static()
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np_x = np.random.randn(3, 2, 3).astype('float32')
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np_y = np.random.randn(3, 2, 3).astype('float32')
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x = paddle.to_tensor(np_x)
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y = paddle.to_tensor(np_y)
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# linalg.cross with default (should use dim=-1)
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out_default = paddle.linalg.cross(x, y)
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# linalg.cross with explicit dim=-1
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out_neg1 = paddle.linalg.cross(x, y, dim=-1)
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# linalg.cross with explicit dim=2
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out_dim2 = paddle.linalg.cross(x, y, dim=2)
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# linalg.cross with explicit dim=0
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out_dim0 = paddle.linalg.cross(x, y, dim=0)
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np.testing.assert_allclose(
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out_default.numpy(), out_neg1.numpy(), rtol=1e-5
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)
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np.testing.assert_allclose(
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out_default.numpy(), out_dim2.numpy(), rtol=1e-5
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)
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# dim=0 should give different result when shape is [3, 2, 3]
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with self.assertRaises(AssertionError):
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np.testing.assert_allclose(
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out_default.numpy(), out_dim0.numpy(), rtol=1e-5
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)
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paddle.enable_static()
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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