wehub-resource-sync
401 lines
13 KiB
Python
401 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 OpTest, get_places
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import paddle
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from paddle import base
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class TestInverseOp(OpTest):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float64"
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self.python_api = paddle.inverse
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def setUp(self):
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self.op_type = "inverse"
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self.config()
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np.random.seed(123)
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mat = np.random.random(self.matrix_shape).astype(self.dtype)
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if self.dtype == 'complex64' or self.dtype == 'complex128':
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mat = (
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np.random.random(self.matrix_shape)
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+ 1j * np.random.random(self.matrix_shape)
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).astype(self.dtype)
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inverse = np.linalg.inv(mat)
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self.inputs = {'Input': mat}
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self.outputs = {'Output': inverse}
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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_grad(self):
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self.check_grad(['Input'], 'Output', check_pir=True)
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class TestInverseOpBatched(TestInverseOp):
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def config(self):
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self.matrix_shape = [8, 4, 4]
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self.dtype = "float64"
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self.python_api = paddle.inverse
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class TestInverseOpZeroSize(TestInverseOp):
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def config(self):
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self.matrix_shape = [0, 0]
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self.dtype = "float64"
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self.python_api = paddle.inverse
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class TestInverseOpBatchedZeroSize(TestInverseOp):
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def config(self):
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self.matrix_shape = [7, 0, 0]
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self.dtype = "float64"
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self.python_api = paddle.inverse
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class TestInverseOpLarge(TestInverseOp):
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def config(self):
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self.matrix_shape = [32, 32]
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self.dtype = "float64"
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self.python_api = paddle.inverse
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def test_grad(self):
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self.check_grad(
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['Input'], 'Output', max_relative_error=1e-6, check_pir=True
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)
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class TestInverseOpFP32(TestInverseOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "float32"
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self.python_api = paddle.inverse
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def test_grad(self):
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self.check_grad(
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['Input'], 'Output', max_relative_error=1e-2, check_pir=True
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)
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class TestInverseOpBatchedFP32(TestInverseOpFP32):
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def config(self):
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self.matrix_shape = [8, 4, 4]
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self.dtype = "float32"
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self.python_api = paddle.inverse
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class TestInverseOpLargeFP32(TestInverseOpFP32):
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def config(self):
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self.matrix_shape = [32, 32]
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self.dtype = "float32"
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self.python_api = paddle.inverse
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class TestInverseOpComplex64(TestInverseOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "complex64"
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self.python_api = paddle.inverse
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def test_grad(self):
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self.check_grad(['Input'], 'Output', check_pir=True)
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class TestInverseOpComplex128(TestInverseOp):
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def config(self):
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self.matrix_shape = [10, 10]
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self.dtype = "complex128"
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self.python_api = paddle.inverse
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def test_grad(self):
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self.check_grad(['Input'], 'Output', check_pir=True)
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class TestInverseOpBatchedComplex(TestInverseOp):
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def config(self):
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self.matrix_shape = [2, 3, 5, 5]
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self.dtype = "complex64"
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self.python_api = paddle.inverse
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def test_grad(self):
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self.check_grad(['Input'], 'Output', check_pir=True)
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class TestInverseAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def check_static_result(self, place):
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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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input = paddle.static.data(
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name="input", shape=[4, 4], dtype="float64"
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)
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result = paddle.inverse(x=input)
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input_np = np.random.random([4, 4]).astype("float64")
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result_np = np.linalg.inv(input_np)
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exe = base.Executor(place)
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fetches = exe.run(
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paddle.static.default_main_program(),
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feed={"input": input_np},
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fetch_list=[result],
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)
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np.testing.assert_allclose(
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fetches[0], np.linalg.inv(input_np), rtol=1e-05
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)
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_np = np.random.random([4, 4]).astype("float64")
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input = paddle.to_tensor(input_np)
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result = paddle.inverse(input)
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np.testing.assert_allclose(
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result.numpy(), np.linalg.inv(input_np), rtol=1e-05
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)
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def test_dygraph_with_name(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_np = np.random.random([4, 4]).astype("float64")
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input = paddle.to_tensor(input_np)
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result = paddle.inverse(input, name='test_inverse')
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np.testing.assert_allclose(
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result.numpy(), np.linalg.inv(input_np), rtol=1e-05
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)
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def test_static_with_name(self):
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for place in self.places:
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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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input = paddle.static.data(
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name="input", shape=[4, 4], dtype="float64"
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)
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result = paddle.inverse(x=input, name='test_inverse_static')
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input_np = np.random.random([4, 4]).astype("float64")
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exe = base.Executor(place)
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fetches = exe.run(
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paddle.static.default_main_program(),
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feed={"input": input_np},
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fetch_list=[result],
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)
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np.testing.assert_allclose(
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fetches[0], np.linalg.inv(input_np), rtol=1e-05
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)
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class TestInverseAPIError(unittest.TestCase):
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def test_errors(self):
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input_np = np.random.random([4, 4]).astype("float64")
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# input must be Variable.
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self.assertRaises(TypeError, paddle.inverse, input_np)
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# The data type of input must be float32 or float64.
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for dtype in ["bool", "int32", "int64", "float16"]:
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input = paddle.static.data(
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name='input_' + dtype, shape=[4, 4], dtype=dtype
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)
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self.assertRaises(TypeError, paddle.inverse, input)
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# The number of dimensions of input must be >= 2.
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input = paddle.static.data(name='input_2', shape=[4], dtype="float32")
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self.assertRaises(ValueError, paddle.inverse, input)
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class TestInverseSingularAPI(unittest.TestCase):
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def setUp(self):
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self.places = get_places()
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def check_static_result(self, place):
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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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input = paddle.static.data(
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name="input", shape=[4, 4], dtype="float64"
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)
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result = paddle.inverse(x=input)
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input_np = np.zeros([4, 4]).astype("float64")
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exe = base.Executor(place)
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try:
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fetches = exe.run(
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paddle.static.default_main_program(),
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feed={"input": input_np},
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fetch_list=[result],
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)
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except RuntimeError as ex:
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print("The mat is singular")
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except ValueError as ex:
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print("The mat is singular")
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def test_static(self):
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for place in self.places:
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self.check_static_result(place=place)
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_np = np.ones([4, 4]).astype("float64")
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input = paddle.to_tensor(input_np)
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try:
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result = paddle.inverse(input)
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except RuntimeError as ex:
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print("The mat is singular")
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except ValueError as ex:
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print("The mat is singular")
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class TestInverseAPI_ZeroSize(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.places = get_places()
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def test_dygraph(self):
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for place in self.places:
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with base.dygraph.guard(place):
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input_np = np.random.random([4, 0]).astype("float64")
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input = paddle.to_tensor(input_np)
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input.stop_gradient = False
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result = paddle.linalg.inv(input)
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np_out = np.random.random([4, 0]).astype("float64")
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np.testing.assert_allclose(result.numpy(), np_out, rtol=1e-05)
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loss = paddle.sum(result)
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loss.backward()
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np.testing.assert_allclose(input.grad.shape, input.shape)
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class TestInverseAPICompatibility(unittest.TestCase):
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def setUp(self):
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np.random.seed(123)
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self.shape = [6, 6]
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self.dtype = 'float64'
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self.init_data()
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def init_data(self):
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self.np_input = np.random.random(self.shape).astype(self.dtype)
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# Ensure invertible
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while np.linalg.det(self.np_input) == 0:
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self.np_input = np.random.random(self.shape).astype(self.dtype)
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self.ref_output = np.linalg.inv(self.np_input)
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self.out_shape = self.np_input.shape
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def test_dygraph_compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_input)
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paddle_dygraph_out = []
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out1 = paddle.inverse(x)
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paddle_dygraph_out.append(out1)
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out2 = paddle.inverse(x=x)
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paddle_dygraph_out.append(out2)
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out3 = paddle.inverse(input=x)
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paddle_dygraph_out.append(out3)
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out4 = paddle.empty(self.out_shape)
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paddle.inverse(x, out=out4)
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paddle_dygraph_out.append(out4)
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out5 = x.inverse()
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paddle_dygraph_out.append(out5)
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ref_out = np.linalg.inv(self.np_input)
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for out in paddle_dygraph_out:
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np.testing.assert_allclose(ref_out, out.numpy(), rtol=1e-5)
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paddle.enable_static()
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def test_edge_cases(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_input)
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out = paddle.inverse(x)
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expected = np.linalg.inv(self.np_input)
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np.testing.assert_allclose(out.numpy(), expected, rtol=1e-5)
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paddle.enable_static()
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def test_static_compatibility(self):
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paddle.enable_static()
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with base.program_guard(main, startup):
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x = paddle.static.data(name="x", shape=self.shape, dtype=self.dtype)
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out1 = paddle.inverse(x)
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out2 = paddle.inverse(x=x)
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out3 = paddle.inverse(input=x)
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exe = base.Executor(paddle.CPUPlace())
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fetches = exe.run(
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main,
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feed={"x": self.np_input},
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fetch_list=[out1, out2, out3],
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)
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ref_out = np.linalg.inv(self.np_input)
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for out in fetches:
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np.testing.assert_allclose(out, ref_out, rtol=1e-5)
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def test_tensor_method_compatibility(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_input)
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out1 = x.inverse()
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out2 = x.inverse()
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np.testing.assert_allclose(out1.numpy(), out2.numpy(), rtol=1e-5)
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paddle.enable_static()
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def test_parameter_aliases(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.np_input)
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output_default = paddle.inverse(x)
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output_torch = paddle.inverse(input=x)
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np.testing.assert_allclose(
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output_default.numpy(), output_torch.numpy(), rtol=1e-5
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)
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def test_dimension_validation(self):
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paddle.disable_static()
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# 0D Tensor should raise ValueError
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scalar_input = paddle.to_tensor(1.0)
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with self.assertRaises(ValueError):
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paddle.inverse(scalar_input)
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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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