You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
220 lines
6.9 KiB
220 lines
6.9 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import print_function
|
|
|
|
import unittest
|
|
import numpy as np
|
|
from op_test import OpTest
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
from paddle.fluid import core
|
|
from paddle.fluid import Program, program_guard
|
|
|
|
|
|
class TestDiagV2Op(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "diag_v2"
|
|
self.x = np.random.rand(10, 10)
|
|
self.offset = 0
|
|
self.padding_value = 0.0
|
|
self.out = np.diag(self.x, self.offset)
|
|
|
|
self.init_config()
|
|
self.inputs = {'X': self.x}
|
|
self.attrs = {
|
|
'offset': self.offset,
|
|
'padding_value': self.padding_value
|
|
}
|
|
self.outputs = {'Out': self.out}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def init_config(self):
|
|
pass
|
|
|
|
|
|
class TestDiagV2OpCase1(TestDiagV2Op):
|
|
def init_config(self):
|
|
self.offset = 1
|
|
self.out = np.diag(self.x, self.offset)
|
|
|
|
|
|
class TestDiagV2OpCase2(TestDiagV2Op):
|
|
def init_config(self):
|
|
self.offset = -1
|
|
self.out = np.diag(self.x, self.offset)
|
|
|
|
|
|
class TestDiagV2OpCase3(TestDiagV2Op):
|
|
def init_config(self):
|
|
self.x = np.random.randint(-10, 10, size=(10, 10))
|
|
self.out = np.diag(self.x, self.offset)
|
|
|
|
|
|
class TestDiagV2OpCase4(TestDiagV2Op):
|
|
def init_config(self):
|
|
self.x = np.random.rand(100)
|
|
self.padding_value = 8
|
|
n = self.x.size
|
|
self.out = self.padding_value * np.ones((n, n)) + np.diag(
|
|
self.x, self.offset) - np.diag(self.padding_value * np.ones(n))
|
|
|
|
|
|
class TestDiagV2Error(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
|
|
def test_diag_v2_type():
|
|
x = [1, 2, 3]
|
|
output = paddle.diag(x)
|
|
|
|
self.assertRaises(TypeError, test_diag_v2_type)
|
|
|
|
|
|
class TestDiagV2API(unittest.TestCase):
|
|
def setUp(self):
|
|
self.input_np = np.random.random(size=(10, 10)).astype(np.float32)
|
|
self.expected0 = np.diag(self.input_np)
|
|
self.expected1 = np.diag(self.input_np, k=1)
|
|
self.expected2 = np.diag(self.input_np, k=-1)
|
|
|
|
self.input_np2 = np.random.rand(100)
|
|
self.offset = 0
|
|
self.padding_value = 8
|
|
n = self.input_np2.size
|
|
self.expected3 = self.padding_value * np.ones(
|
|
(n, n)) + np.diag(self.input_np2, self.offset) - np.diag(
|
|
self.padding_value * np.ones(n))
|
|
|
|
self.input_np3 = np.random.randint(-10, 10, size=(100)).astype(np.int64)
|
|
self.padding_value = 8.0
|
|
n = self.input_np3.size
|
|
self.expected4 = self.padding_value * np.ones(
|
|
(n, n)) + np.diag(self.input_np3, self.offset) - np.diag(
|
|
self.padding_value * np.ones(n))
|
|
|
|
self.padding_value = -8
|
|
self.expected5 = self.padding_value * np.ones(
|
|
(n, n)) + np.diag(self.input_np3, self.offset) - np.diag(
|
|
self.padding_value * np.ones(n))
|
|
|
|
def run_imperative(self):
|
|
x = paddle.to_tensor(self.input_np)
|
|
y = paddle.diag(x)
|
|
self.assertTrue(np.allclose(y.numpy(), self.expected0))
|
|
|
|
y = paddle.diag(x, offset=1)
|
|
self.assertTrue(np.allclose(y.numpy(), self.expected1))
|
|
|
|
y = paddle.diag(x, offset=-1)
|
|
self.assertTrue(np.allclose(y.numpy(), self.expected2))
|
|
|
|
x = paddle.to_tensor(self.input_np2)
|
|
y = paddle.diag(x, padding_value=8)
|
|
self.assertTrue(np.allclose(y.numpy(), self.expected3))
|
|
|
|
x = paddle.to_tensor(self.input_np3)
|
|
y = paddle.diag(x, padding_value=8.0)
|
|
self.assertTrue(np.allclose(y.numpy(), self.expected4))
|
|
|
|
y = paddle.diag(x, padding_value=-8)
|
|
self.assertTrue(np.allclose(y.numpy(), self.expected5))
|
|
|
|
def run_static(self, use_gpu=False):
|
|
x = paddle.data(name='input', shape=[10, 10], dtype='float32')
|
|
x2 = paddle.data(name='input2', shape=[100], dtype='float64')
|
|
x3 = paddle.data(name='input3', shape=[100], dtype='int64')
|
|
result0 = paddle.diag(x)
|
|
result1 = paddle.diag(x, offset=1)
|
|
result2 = paddle.diag(x, offset=-1)
|
|
result3 = paddle.diag(x, name='aaa')
|
|
result4 = paddle.diag(x2, padding_value=8)
|
|
result5 = paddle.diag(x3, padding_value=8.0)
|
|
result6 = paddle.diag(x3, padding_value=-8)
|
|
|
|
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
|
|
exe = fluid.Executor(place)
|
|
exe.run(fluid.default_startup_program())
|
|
res0, res1, res2, res4, res5, res6 = exe.run(
|
|
feed={
|
|
"input": self.input_np,
|
|
"input2": self.input_np2,
|
|
'input3': self.input_np3
|
|
},
|
|
fetch_list=[result0, result1, result2, result4, result5, result6])
|
|
|
|
self.assertTrue(np.allclose(res0, self.expected0))
|
|
self.assertTrue(np.allclose(res1, self.expected1))
|
|
self.assertTrue(np.allclose(res2, self.expected2))
|
|
self.assertTrue('aaa' in result3.name)
|
|
self.assertTrue(np.allclose(res4, self.expected3))
|
|
self.assertTrue(np.allclose(res5, self.expected4))
|
|
self.assertTrue(np.allclose(res6, self.expected5))
|
|
|
|
def test_cpu(self):
|
|
paddle.disable_static(place=paddle.fluid.CPUPlace())
|
|
self.run_imperative()
|
|
paddle.enable_static()
|
|
|
|
with fluid.program_guard(fluid.Program()):
|
|
self.run_static()
|
|
|
|
def test_gpu(self):
|
|
if not fluid.core.is_compiled_with_cuda():
|
|
return
|
|
|
|
paddle.disable_static(place=paddle.fluid.CUDAPlace(0))
|
|
self.run_imperative()
|
|
paddle.enable_static()
|
|
|
|
with fluid.program_guard(fluid.Program()):
|
|
self.run_static(use_gpu=True)
|
|
|
|
|
|
class TestDiagOp(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "diag"
|
|
self.init_config()
|
|
self.inputs = {'Diagonal': self.case}
|
|
|
|
self.outputs = {'Out': np.diag(self.inputs['Diagonal'])}
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def init_config(self):
|
|
self.case = np.arange(3, 6)
|
|
|
|
|
|
class TestDiagOpCase1(TestDiagOp):
|
|
def init_config(self):
|
|
self.case = np.array([3], dtype='int32')
|
|
|
|
|
|
class TestDiagError(unittest.TestCase):
|
|
def test_errors(self):
|
|
with program_guard(Program(), Program()):
|
|
|
|
def test_diag_type():
|
|
x = [1, 2, 3]
|
|
output = fluid.layers.diag(diag=x)
|
|
|
|
self.assertRaises(TypeError, test_diag_type)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|