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未验证 提交 c8d32c8c 编写于 作者: L LutaoChu 提交者： GitHub
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Fix diag OP bug on Windows Python3.8 

Fix diag OP bug on Windows Python3.8 ，remove the std::min
上级 55098b97
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Showing with 280 addition and 224 deletion
paddle/fluid/operators/diag_v2_op.cc
浏览文件 @ c8d32c8c
......@@ -32,16 +32,28 @@ class DiagV2Op : public framework::OperatorWithKernel {
auto offset = ctx->Attrs().Get<int>("offset");
if (x_dims.size() == 1UL) {
int64_t size = x_dims[0] + std::abs(offset);
ctx->SetOutputDim("Out", {size, size});
int64_t size_ = x_dims[0] + std::abs(offset);
ctx->SetOutputDim("Out", {size_, size_});
} else if (x_dims.size() == 2UL) {
int64_t size;
int64_t size_ = 0;
if (offset >= 0) {
size = std::min(x_dims[0], x_dims[1] - offset);
// Note(LutaoChu): Do not use std::min here, otherwise the calculation
// of `size_` will have unexpected result on Windows Python3.8
if (x_dims[0] < x_dims[1] - offset) {
size_ = x_dims[0];
} else {
size = std::min(x_dims[0] + offset, x_dims[1]);
size_ = x_dims[1] - offset;
}
ctx->SetOutputDim("Out", {size});
} else {
// Note(LutaoChu): Do not use std::min here, otherwise the calculation
// of `size_` will have unexpected result on Windows Python3.8
if (x_dims[0] + offset < x_dims[1]) {
size_ = x_dims[0] + offset;
} else {
size_ = x_dims[1];
}
}
ctx->SetOutputDim("Out", {size_});
} else {
PADDLE_THROW(platform::errors::InvalidArgument(
"The input tensor X's dimensions of DiagV2Op should be either 1 or "
......
python/paddle/fluid/tests/unittests/test_diag.py
浏览文件 @ c8d32c8c
......@@ -23,224 +23,6 @@ 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)
x = paddle.static.data('data', [3, 3])
self.assertRaises(TypeError, paddle.diag, x, offset=2.5)
self.assertRaises(TypeError, paddle.diag, x, padding_value=[9])
x = paddle.static.data('data2', [3, 3, 3])
self.assertRaises(ValueError, paddle.diag, x)
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))
self.input_np4 = np.random.random(size=(2000, 2000)).astype(np.float32)
self.expected6 = np.diag(self.input_np4)
self.expected7 = np.diag(self.input_np4, k=1)
self.expected8 = np.diag(self.input_np4, k=-1)
self.input_np5 = np.random.random(size=(2000)).astype(np.float32)
self.expected9 = np.diag(self.input_np5)
self.expected10 = np.diag(self.input_np5, k=1)
self.expected11 = np.diag(self.input_np5, k=-1)
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))
x = paddle.to_tensor(self.input_np4)
y = paddle.diag(x)
self.assertTrue(np.allclose(y.numpy(), self.expected6))
y = paddle.diag(x, offset=1)
self.assertTrue(np.allclose(y.numpy(), self.expected7))
y = paddle.diag(x, offset=-1)
self.assertTrue(np.allclose(y.numpy(), self.expected8))
x = paddle.to_tensor(self.input_np5)
y = paddle.diag(x)
self.assertTrue(np.allclose(y.numpy(), self.expected9))
y = paddle.diag(x, offset=1)
self.assertTrue(np.allclose(y.numpy(), self.expected10))
y = paddle.diag(x, offset=-1)
self.assertTrue(np.allclose(y.numpy(), self.expected11))
def run_static(self, use_gpu=False):
x = paddle.fluid.data(name='input', shape=[10, 10], dtype='float32')
x2 = paddle.fluid.data(name='input2', shape=[100], dtype='float64')
x3 = paddle.fluid.data(name='input3', shape=[100], dtype='int64')
x4 = paddle.fluid.data(name='input4', shape=[2000, 2000], dtype='float32')
x5 = paddle.fluid.data(name='input5', shape=[2000], dtype='float32')
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)
result7 = paddle.diag(x4)
result8 = paddle.diag(x4, offset=1)
result9 = paddle.diag(x4, offset=-1)
result10 = paddle.diag(x5)
result11 = paddle.diag(x5, offset=1)
result12 = paddle.diag(x5, offset=-1)
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, res7, res8, res9, res10, res11, res12 = exe.run(
feed={
"input": self.input_np,
"input2": self.input_np2,
'input3': self.input_np3,
'input4': self.input_np4,
'input5': self.input_np5
},
fetch_list=[
result0, result1, result2, result4, result5, result6, result7,
result8, result9, result10, result11, result12
])
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))
self.assertTrue(np.allclose(res7, self.expected6))
self.assertTrue(np.allclose(res8, self.expected7))
self.assertTrue(np.allclose(res9, self.expected8))
self.assertTrue(np.allclose(res10, self.expected9))
self.assertTrue(np.allclose(res11, self.expected10))
self.assertTrue(np.allclose(res12, self.expected11))
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"
......@@ -250,6 +32,7 @@ class TestDiagOp(OpTest):
self.outputs = {'Out': np.diag(self.inputs['Diagonal'])}
def test_check_output(self):
paddle.enable_static()
self.check_output()
def init_config(self):
......@@ -263,6 +46,7 @@ class TestDiagOpCase1(TestDiagOp):
class TestDiagError(unittest.TestCase):
def test_errors(self):
paddle.enable_static()
with program_guard(Program(), Program()):
def test_diag_type():
......
python/paddle/fluid/tests/unittests/test_diag_v2.py 0 → 100644
浏览文件 @ c8d32c8c
# 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):
paddle.enable_static()
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):
paddle.enable_static()
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)
x = paddle.static.data('data', [3, 3])
self.assertRaises(TypeError, paddle.diag, x, offset=2.5)
self.assertRaises(TypeError, paddle.diag, x, padding_value=[9])
x = paddle.static.data('data2', [3, 3, 3])
self.assertRaises(ValueError, paddle.diag, x)
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))
self.input_np4 = np.random.random(size=(2000, 2000)).astype(np.float32)
self.expected6 = np.diag(self.input_np4)
self.expected7 = np.diag(self.input_np4, k=1)
self.expected8 = np.diag(self.input_np4, k=-1)
self.input_np5 = np.random.random(size=(2000)).astype(np.float32)
self.expected9 = np.diag(self.input_np5)
self.expected10 = np.diag(self.input_np5, k=1)
self.expected11 = np.diag(self.input_np5, k=-1)
self.input_np6 = np.random.random(size=(2000, 1500)).astype(np.float32)
self.expected12 = np.diag(self.input_np6, k=-1)
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))
x = paddle.to_tensor(self.input_np4)
y = paddle.diag(x)
self.assertTrue(np.allclose(y.numpy(), self.expected6))
y = paddle.diag(x, offset=1)
self.assertTrue(np.allclose(y.numpy(), self.expected7))
y = paddle.diag(x, offset=-1)
self.assertTrue(np.allclose(y.numpy(), self.expected8))
x = paddle.to_tensor(self.input_np5)
y = paddle.diag(x)
self.assertTrue(np.allclose(y.numpy(), self.expected9))
y = paddle.diag(x, offset=1)
self.assertTrue(np.allclose(y.numpy(), self.expected10))
y = paddle.diag(x, offset=-1)
self.assertTrue(np.allclose(y.numpy(), self.expected11))
x = paddle.to_tensor(self.input_np6)
y = paddle.diag(x, offset=-1)
self.assertTrue(np.allclose(y.numpy(), self.expected12))
def run_static(self, use_gpu=False):
x = paddle.static.data(name='input', shape=[10, 10], dtype='float32')
x2 = paddle.static.data(name='input2', shape=[100], dtype='float64')
x3 = paddle.static.data(name='input3', shape=[100], dtype='int64')
x4 = paddle.static.data(
name='input4', shape=[2000, 2000], dtype='float32')
x5 = paddle.static.data(name='input5', shape=[2000], dtype='float32')
x6 = paddle.static.data(
name='input6', shape=[2000, 1500], dtype='float32')
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)
result7 = paddle.diag(x4)
result8 = paddle.diag(x4, offset=1)
result9 = paddle.diag(x4, offset=-1)
result10 = paddle.diag(x5)
result11 = paddle.diag(x5, offset=1)
result12 = paddle.diag(x5, offset=-1)
result13 = paddle.diag(x6, offset=-1)
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, res7, res8, res9, res10, res11, res12, res13 = exe.run(
feed={
"input": self.input_np,
"input2": self.input_np2,
'input3': self.input_np3,
'input4': self.input_np4,
'input5': self.input_np5,
'input6': self.input_np6
},
fetch_list=[
result0, result1, result2, result4, result5, result6, result7,
result8, result9, result10, result11, result12, result13
])
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))
self.assertTrue(np.allclose(res7, self.expected6))
self.assertTrue(np.allclose(res8, self.expected7))
self.assertTrue(np.allclose(res9, self.expected8))
self.assertTrue(np.allclose(res10, self.expected9))
self.assertTrue(np.allclose(res11, self.expected10))
self.assertTrue(np.allclose(res12, self.expected11))
self.assertTrue(np.allclose(res13, self.expected12))
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)
if __name__ == "__main__":
unittest.main()
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