wehub-resource-sync
331 lines
10 KiB
Python
331 lines
10 KiB
Python
# 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.
|
|
|
|
import unittest
|
|
|
|
import numpy as np
|
|
from op_test import get_device, get_places
|
|
|
|
import paddle
|
|
|
|
|
|
class TensorFillDiagonal_Test(unittest.TestCase):
|
|
def test_dim2_normal(self):
|
|
expected_np = np.array([[1, 2, 2], [2, 1, 2], [2, 2, 1]]).astype(
|
|
'float32'
|
|
)
|
|
expected_grad = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]).astype(
|
|
'float32'
|
|
)
|
|
|
|
typelist = ['float32', 'float64', 'int32', 'int64']
|
|
places = get_places()
|
|
|
|
for idx, p in enumerate(places):
|
|
if idx == 0:
|
|
paddle.set_device('cpu')
|
|
else:
|
|
paddle.set_device(get_device())
|
|
for dtype in typelist:
|
|
x = paddle.ones((3, 3), dtype=dtype)
|
|
x.stop_gradient = False
|
|
y = x * 2
|
|
y.retain_grads()
|
|
y.fill_diagonal_(1, offset=0, wrap=True)
|
|
loss = y.sum()
|
|
loss.backward()
|
|
|
|
self.assertEqual(
|
|
(y.numpy().astype('float32') == expected_np).all(), True
|
|
)
|
|
self.assertEqual(
|
|
(y.grad.numpy().astype('float32') == expected_grad).all(),
|
|
True,
|
|
)
|
|
|
|
def test_offset(self):
|
|
expected_np = np.array([[2, 2, 1], [2, 2, 2], [2, 2, 2]]).astype(
|
|
'float32'
|
|
)
|
|
expected_grad = np.array([[1, 1, 0], [1, 1, 1], [1, 1, 1]]).astype(
|
|
'float32'
|
|
)
|
|
|
|
typelist = ['float32', 'float64', 'int32', 'int64']
|
|
places = get_places()
|
|
|
|
for idx, p in enumerate(places):
|
|
if idx == 0:
|
|
paddle.set_device('cpu')
|
|
else:
|
|
paddle.set_device(get_device())
|
|
for dtype in typelist:
|
|
x = paddle.ones((3, 3), dtype=dtype)
|
|
x.stop_gradient = False
|
|
y = x * 2
|
|
y.retain_grads()
|
|
y.fill_diagonal_(1, offset=2, wrap=True)
|
|
loss = y.sum()
|
|
loss.backward()
|
|
|
|
self.assertEqual(
|
|
(y.numpy().astype('float32') == expected_np).all(), True
|
|
)
|
|
self.assertEqual(
|
|
(y.grad.numpy().astype('float32') == expected_grad).all(),
|
|
True,
|
|
)
|
|
|
|
def test_bool(self):
|
|
expected_np = np.array(
|
|
[[False, True, True], [True, False, True], [True, True, False]]
|
|
)
|
|
|
|
typelist = ['bool']
|
|
places = get_places()
|
|
|
|
for idx, p in enumerate(places):
|
|
if idx == 0:
|
|
paddle.set_device('cpu')
|
|
else:
|
|
paddle.set_device(get_device())
|
|
for dtype in typelist:
|
|
x = paddle.ones((3, 3), dtype=dtype)
|
|
x.stop_gradient = True
|
|
x.fill_diagonal_(0, offset=0, wrap=True)
|
|
|
|
self.assertEqual((x.numpy() == expected_np).all(), True)
|
|
|
|
def test_dim2_unnormal_wrap(self):
|
|
expected_np = np.array(
|
|
[
|
|
[1, 2, 2],
|
|
[2, 1, 2],
|
|
[2, 2, 1],
|
|
[2, 2, 2],
|
|
[1, 2, 2],
|
|
[2, 1, 2],
|
|
[2, 2, 1],
|
|
]
|
|
).astype('float32')
|
|
expected_grad = np.array(
|
|
[
|
|
[0, 1, 1],
|
|
[1, 0, 1],
|
|
[1, 1, 0],
|
|
[1, 1, 1],
|
|
[0, 1, 1],
|
|
[1, 0, 1],
|
|
[1, 1, 0],
|
|
]
|
|
).astype('float32')
|
|
|
|
typelist = ['float32', 'float64', 'int32', 'int64']
|
|
places = get_places()
|
|
|
|
for idx, p in enumerate(places):
|
|
if idx == 0:
|
|
paddle.set_device('cpu')
|
|
else:
|
|
paddle.set_device(get_device())
|
|
for dtype in typelist:
|
|
x = paddle.ones((7, 3), dtype=dtype)
|
|
x.stop_gradient = False
|
|
y = x * 2
|
|
y.retain_grads()
|
|
y.fill_diagonal_(1, offset=0, wrap=True)
|
|
loss = y.sum()
|
|
loss.backward()
|
|
|
|
self.assertEqual(
|
|
(y.numpy().astype('float32') == expected_np).all(), True
|
|
)
|
|
self.assertEqual(
|
|
(y.grad.numpy().astype('float32') == expected_grad).all(),
|
|
True,
|
|
)
|
|
|
|
def test_dim2_unnormal_unwrap(self):
|
|
expected_np = np.array(
|
|
[
|
|
[1, 2, 2],
|
|
[2, 1, 2],
|
|
[2, 2, 1],
|
|
[2, 2, 2],
|
|
[2, 2, 2],
|
|
[2, 2, 2],
|
|
[2, 2, 2],
|
|
]
|
|
).astype('float32')
|
|
expected_grad = np.array(
|
|
[
|
|
[0, 1, 1],
|
|
[1, 0, 1],
|
|
[1, 1, 0],
|
|
[1, 1, 1],
|
|
[1, 1, 1],
|
|
[1, 1, 1],
|
|
[1, 1, 1],
|
|
]
|
|
).astype('float32')
|
|
|
|
typelist = ['float32', 'float64', 'int32', 'int64']
|
|
places = get_places()
|
|
|
|
for idx, p in enumerate(places):
|
|
if idx == 0:
|
|
paddle.set_device('cpu')
|
|
else:
|
|
paddle.set_device(get_device())
|
|
for dtype in typelist:
|
|
x = paddle.ones((7, 3), dtype=dtype)
|
|
x.stop_gradient = False
|
|
y = x * 2
|
|
y.retain_grads()
|
|
y.fill_diagonal_(1, offset=0, wrap=False)
|
|
loss = y.sum()
|
|
loss.backward()
|
|
|
|
self.assertEqual(
|
|
(y.numpy().astype('float32') == expected_np).all(), True
|
|
)
|
|
self.assertEqual(
|
|
(y.grad.numpy().astype('float32') == expected_grad).all(),
|
|
True,
|
|
)
|
|
|
|
def test_dim_larger2_normal(self):
|
|
expected_np = np.array(
|
|
[
|
|
[[1, 2, 2], [2, 2, 2], [2, 2, 2]],
|
|
[[2, 2, 2], [2, 1, 2], [2, 2, 2]],
|
|
[[2, 2, 2], [2, 2, 2], [2, 2, 1]],
|
|
]
|
|
).astype('float32')
|
|
expected_grad = np.array(
|
|
[
|
|
[[0, 1, 1], [1, 1, 1], [1, 1, 1]],
|
|
[[1, 1, 1], [1, 0, 1], [1, 1, 1]],
|
|
[[1, 1, 1], [1, 1, 1], [1, 1, 0]],
|
|
]
|
|
).astype('float32')
|
|
|
|
typelist = ['float32', 'float64', 'int32', 'int64']
|
|
places = get_places()
|
|
|
|
for idx, p in enumerate(places):
|
|
if idx == 0:
|
|
paddle.set_device('cpu')
|
|
else:
|
|
paddle.set_device(get_device())
|
|
for dtype in typelist:
|
|
x = paddle.ones((3, 3, 3), dtype=dtype)
|
|
x.stop_gradient = False
|
|
y = x * 2
|
|
y.retain_grads()
|
|
y.fill_diagonal_(1, offset=0, wrap=True)
|
|
loss = y.sum()
|
|
loss.backward()
|
|
|
|
self.assertEqual(
|
|
(y.numpy().astype('float32') == expected_np).all(), True
|
|
)
|
|
self.assertEqual(
|
|
(y.grad.numpy().astype('float32') == expected_grad).all(),
|
|
True,
|
|
)
|
|
|
|
|
|
class TensorFillDiagonal_ZeroSize(unittest.TestCase):
|
|
def _test_normal(self, shape):
|
|
expected_np = np.random.random(shape)
|
|
expected_grad = np.random.random(shape)
|
|
|
|
places = get_places()
|
|
|
|
for idx, p in enumerate(places):
|
|
if idx == 0:
|
|
paddle.set_device('cpu')
|
|
else:
|
|
paddle.set_device(get_device())
|
|
|
|
x = paddle.ones(shape)
|
|
x.stop_gradient = False
|
|
y = x * 2
|
|
y.retain_grads()
|
|
y.fill_diagonal_(1, offset=0, wrap=True)
|
|
loss = y.sum()
|
|
loss.backward()
|
|
|
|
self.assertEqual(
|
|
(y.numpy().astype('float32') == expected_np).all(), True
|
|
)
|
|
self.assertEqual(
|
|
(y.grad.numpy().astype('float32') == expected_grad).all(),
|
|
True,
|
|
)
|
|
|
|
def test_normal(self):
|
|
self._test_normal([0, 3])
|
|
self._test_normal([0, 0])
|
|
|
|
|
|
class TestFillDiagonalAlias(unittest.TestCase):
|
|
def test_alias_fill_value_success(self):
|
|
"""
|
|
Test case: Verify that 'fill_value' can be used as an alias for 'value'.
|
|
"""
|
|
# 1. Initialize data
|
|
x = paddle.zeros([4, 4], dtype='float32')
|
|
# 2. Use the new alias parameter 'fill_value'
|
|
# This aligns with PyTorch's API
|
|
x.fill_diagonal_(fill_value=5.0)
|
|
# 3. Verify results
|
|
x_np = x.numpy()
|
|
# Check if diagonal elements are updated correctly
|
|
for i in range(4):
|
|
self.assertEqual(x_np[i, i], 5.0)
|
|
# Check if off-diagonal elements remain 0
|
|
# (Manually reset diagonal to 0 and check if the whole matrix is 0)
|
|
np.fill_diagonal(x_np, 0)
|
|
self.assertTrue(np.all(x_np == 0))
|
|
|
|
def test_alias_conflict(self):
|
|
"""
|
|
Test case: Verify that providing both 'value' and 'fill_value' raises an error.
|
|
To avoid ambiguity, specifying both parameters is prohibited.
|
|
"""
|
|
x = paddle.zeros([3, 3], dtype='float32')
|
|
# Expect TypeError or ValueError when both arguments are provided
|
|
with self.assertRaises(ValueError):
|
|
x.fill_diagonal_(value=1.0, fill_value=2.0)
|
|
|
|
def test_positional_args(self):
|
|
x = paddle.zeros([4, 4], dtype='float32')
|
|
x.fill_diagonal_(5.0, False)
|
|
x_np = x.numpy()
|
|
for i in range(4):
|
|
self.assertEqual(x_np[i, i], 5.0)
|
|
np.fill_diagonal(x_np, 0)
|
|
self.assertTrue(np.all(x_np == 0))
|
|
|
|
def test_too_many_positional_args(self):
|
|
x = paddle.zeros([3, 3], dtype='float32')
|
|
with self.assertRaises(TypeError):
|
|
x.fill_diagonal_(1.0, False, 0)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|