wehub-resource-sync
382 lines
12 KiB
Python
382 lines
12 KiB
Python
# Copyright (c) 2021 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
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import paddle
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from paddle.base import core
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np.set_printoptions(threshold=np.inf)
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def np_eigvals(a):
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res = np.linalg.eigvals(a)
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if a.dtype == np.float32 or a.dtype == np.complex64:
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res = res.astype(np.complex64)
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else:
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res = res.astype(np.complex128)
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return res
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class TestEigvalsOp(OpTest):
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def setUp(self):
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np.random.seed(1)
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paddle.enable_static()
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self.python_api = paddle.linalg.eigvals
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self.op_type = "eigvals"
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self.set_dtype()
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self.set_input_dims()
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self.set_input_data()
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np_output = np_eigvals(self.input_data)
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self.inputs = {'X': self.input_data}
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self.outputs = {'Out': np_output}
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def set_dtype(self):
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self.dtype = np.float32
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def set_input_dims(self):
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self.input_dims = (5, 5)
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def set_input_data(self):
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if self.dtype == np.float32 or self.dtype == np.float64:
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self.input_data = np.random.random(self.input_dims).astype(
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self.dtype
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)
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else:
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self.input_data = (
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np.random.random(self.input_dims)
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+ np.random.random(self.input_dims) * 1j
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).astype(self.dtype)
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def test_check_output(self):
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self.__class__.no_need_check_grad = True
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self.check_output_with_place_customized(
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checker=self.verify_output, place=core.CPUPlace(), check_pir=True
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)
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def verify_output(self, outs):
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actual_outs = np.sort(np.array(outs[0]))
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expect_outs = np.sort(np.array(self.outputs['Out']))
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self.assertTrue(
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actual_outs.shape == expect_outs.shape,
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"Output shape has diff.\n"
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"Expect shape "
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+ str(expect_outs.shape)
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+ "\n"
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+ "But Got"
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+ str(actual_outs.shape)
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+ " in class "
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+ self.__class__.__name__,
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)
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n_dim = actual_outs.shape[-1]
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for actual_row, expect_row in zip(
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actual_outs.reshape((-1, n_dim)), expect_outs.reshape((-1, n_dim))
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):
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is_mapped_index = np.zeros((n_dim,))
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for i in range(n_dim):
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is_mapped = False
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for j in range(n_dim):
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if is_mapped_index[j] == 0 and np.isclose(
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np.array(actual_row[i]),
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np.array(expect_row[j]),
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atol=1e-5,
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):
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is_mapped_index[j] = True
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is_mapped = True
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break
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self.assertTrue(
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is_mapped,
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"Output has diff in class "
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+ self.__class__.__name__
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+ "\nExpect "
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+ str(expect_outs)
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+ "\n"
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+ "But Got"
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+ str(actual_outs)
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+ "\nThe data "
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+ str(actual_row[i])
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+ " in "
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+ str(actual_row)
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+ " mismatch.",
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)
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class TestEigvalsOpFloat64(TestEigvalsOp):
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def set_dtype(self):
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self.dtype = np.float64
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class TestEigvalsOpComplex64(TestEigvalsOp):
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def set_dtype(self):
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self.dtype = np.complex64
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class TestEigvalsOpComplex128(TestEigvalsOp):
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def set_dtype(self):
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self.dtype = np.complex128
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class TestEigvalsOpLargeScare(TestEigvalsOp):
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def set_input_dims(self):
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self.input_dims = (128, 128)
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class TestEigvalsOpLargeScareFloat64(TestEigvalsOpLargeScare):
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def set_dtype(self):
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self.dtype = np.float64
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class TestEigvalsOpLargeScareComplex64(TestEigvalsOpLargeScare):
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def set_dtype(self):
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self.dtype = np.complex64
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class TestEigvalsOpLargeScareComplex128(TestEigvalsOpLargeScare):
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def set_dtype(self):
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self.dtype = np.complex128
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class TestEigvalsOpBatch1(TestEigvalsOp):
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def set_input_dims(self):
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self.input_dims = (1, 2, 3, 4, 4)
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class TestEigvalsOpBatch2(TestEigvalsOp):
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def set_input_dims(self):
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self.input_dims = (3, 1, 4, 5, 5)
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class TestEigvalsOpBatch3(TestEigvalsOp):
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def set_input_dims(self):
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self.input_dims = (6, 2, 9, 6, 6)
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class TestEigvalsOp_ZeroSize(TestEigvalsOp):
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def set_input_dims(self):
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self.input_dims = (6, 0, 2, 2)
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class TestEigvalsOp_ZeroSize2(TestEigvalsOp):
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def set_input_dims(self):
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self.input_dims = (6, 2, 0, 0)
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def verify_output(self, outs):
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actual_outs = np.sort(np.array(outs[0]))
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expect_outs = np.sort(np.array(self.outputs['Out']))
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self.assertTrue(
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actual_outs.shape == expect_outs.shape,
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"Output shape has diff.\n"
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"Expect shape "
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+ str(expect_outs.shape)
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+ "\n"
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+ "But Got"
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+ str(actual_outs.shape)
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+ " in class "
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+ self.__class__.__name__,
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)
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class TestEigvalsAPI(unittest.TestCase):
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def setUp(self):
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np.random.seed(0)
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self.small_dims = [6, 6]
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self.large_dims = [128, 128]
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self.batch_dims = [6, 9, 2, 2]
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self.set_dtype()
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self.input_dims = self.small_dims
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self.set_input_data()
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self.small_input = np.copy(self.input_data)
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self.input_dims = self.large_dims
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self.set_input_data()
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self.large_input = np.copy(self.input_data)
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self.input_dims = self.batch_dims
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self.set_input_data()
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self.batch_input = np.copy(self.input_data)
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def set_dtype(self):
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self.dtype = np.float32
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def set_input_data(self):
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if self.dtype == np.float32 or self.dtype == np.float64:
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self.input_data = np.random.random(self.input_dims).astype(
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self.dtype
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)
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else:
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self.input_data = (
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np.random.random(self.input_dims)
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+ np.random.random(self.input_dims) * 1j
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).astype(self.dtype)
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def verify_output(self, actual_outs, expect_outs):
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actual_outs = np.array(actual_outs)
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expect_outs = np.array(expect_outs)
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self.assertTrue(
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actual_outs.shape == expect_outs.shape,
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"Output shape has diff."
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"\nExpect shape "
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+ str(expect_outs.shape)
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+ "\n"
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+ "But Got"
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+ str(actual_outs.shape)
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+ " in class "
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+ self.__class__.__name__,
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)
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n_dim = actual_outs.shape[-1]
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for actual_row, expect_row in zip(
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actual_outs.reshape((-1, n_dim)), expect_outs.reshape((-1, n_dim))
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):
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is_mapped_index = np.zeros((n_dim,))
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for i in range(n_dim):
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is_mapped = False
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for j in range(n_dim):
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if is_mapped_index[j] == 0 and np.isclose(
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np.array(actual_row[i]),
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np.array(expect_row[j]),
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atol=1e-5,
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):
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is_mapped_index[j] = True
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is_mapped = True
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break
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self.assertTrue(
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is_mapped,
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"Output has diff in class "
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+ self.__class__.__name__
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+ "\nExpect "
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+ str(expect_outs)
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+ "\n"
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+ "But Got"
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+ str(actual_outs)
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+ "\nThe data "
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+ str(actual_row[i])
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+ " in "
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+ str(actual_row)
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+ " mismatch.",
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)
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def run_dygraph(self, place):
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paddle.disable_static()
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paddle.set_device("cpu")
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small_input_tensor = paddle.to_tensor(self.small_input, place=place)
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large_input_tensor = paddle.to_tensor(self.large_input, place=place)
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batch_input_tensor = paddle.to_tensor(self.batch_input, place=place)
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paddle_outs = paddle.linalg.eigvals(small_input_tensor, name='small_x')
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np_outs = np_eigvals(self.small_input)
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self.verify_output(paddle_outs, np_outs)
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paddle_outs = paddle.linalg.eigvals(large_input_tensor, name='large_x')
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np_outs = np_eigvals(self.large_input)
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self.verify_output(paddle_outs, np_outs)
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paddle_outs = paddle.linalg.eigvals(batch_input_tensor, name='small_x')
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np_outs = np_eigvals(self.batch_input)
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self.verify_output(paddle_outs, np_outs)
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def run_static(self, place):
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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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small_input_tensor = paddle.static.data(
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name='small_x', shape=self.small_dims, dtype=self.dtype
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)
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large_input_tensor = paddle.static.data(
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name='large_x', shape=self.large_dims, dtype=self.dtype
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)
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batch_input_tensor = paddle.static.data(
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name='batch_x', shape=self.batch_dims, dtype=self.dtype
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)
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small_outs = paddle.linalg.eigvals(
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small_input_tensor, name='small_x'
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)
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large_outs = paddle.linalg.eigvals(
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large_input_tensor, name='large_x'
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)
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batch_outs = paddle.linalg.eigvals(
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batch_input_tensor, name='batch_x'
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)
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exe = paddle.static.Executor(place)
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paddle_outs = exe.run(
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feed={
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"small_x": self.small_input,
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"large_x": self.large_input,
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"batch_x": self.batch_input,
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},
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fetch_list=[small_outs, large_outs, batch_outs],
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)
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np_outs = np_eigvals(self.small_input)
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self.verify_output(paddle_outs[0], np_outs)
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np_outs = np_eigvals(self.large_input)
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self.verify_output(paddle_outs[1], np_outs)
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np_outs = np_eigvals(self.batch_input)
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self.verify_output(paddle_outs[2], np_outs)
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def test_cases(self):
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places = [core.CPUPlace()]
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# if (core.is_compiled_with_cuda() or is_custom_device()):
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# places.append(get_device_place())
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for place in places:
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self.run_dygraph(place)
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self.run_static(place)
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def test_error(self):
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paddle.disable_static()
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x = paddle.to_tensor([1])
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with self.assertRaises(ValueError):
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paddle.linalg.eigvals(x)
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self.input_dims = [1, 2, 3, 4]
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self.set_input_data()
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x = paddle.to_tensor(self.input_data)
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with self.assertRaises(ValueError):
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paddle.linalg.eigvals(x)
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class TestEigvalsAPIFloat64(TestEigvalsAPI):
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def set_dtype(self):
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self.dtype = np.float64
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class TestEigvalsAPIComplex64(TestEigvalsAPI):
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def set_dtype(self):
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self.dtype = np.complex64
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class TestEigvalsAPIComplex128(TestEigvalsAPI):
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def set_dtype(self):
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self.dtype = np.complex128
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if __name__ == "__main__":
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unittest.main()
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