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体验新版 GitCode,发现更多精彩内容 >>
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fd8ec69d
编写于
11月 23, 2022
作者:
C
ccrrong
提交者:
GitHub
11月 23, 2022
浏览文件
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电子邮件补丁
差异文件
remove eye (#48127)
上级
25ffe9c2
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
0 addition
and
251 deletion
+0
-251
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+0
-108
python/paddle/fluid/tests/unittests/npu/test_eye_op_npu.py
python/paddle/fluid/tests/unittests/npu/test_eye_op_npu.py
+0
-33
python/paddle/fluid/tests/unittests/test_eye_op.py
python/paddle/fluid/tests/unittests/test_eye_op.py
+0
-46
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+0
-64
未找到文件。
python/paddle/fluid/layers/tensor.py
浏览文件 @
fd8ec69d
...
...
@@ -68,7 +68,6 @@ __all__ = [
'zeros_like'
,
'ones_like'
,
'diag'
,
'eye'
,
'triu'
,
]
...
...
@@ -1787,113 +1786,6 @@ def diag(diagonal):
return
out
def
eye
(
num_rows
,
num_columns
=
None
,
batch_shape
=
None
,
dtype
=
'float32'
,
name
=
None
):
"""
This function constructs a or a batch of 2-D tensor with ones on the diagonal and zeros elsewhere.
Args:
num_rows(int): the number of rows in each batch tensor.
num_columns(int, optional): the number of columns in each batch tensor.
If None, default: num_rows.
batch_shape(list, optional): If provided, the returned tensor will have a leading
batch size of this shape, the data type of ``batch_shape`` is int. Default is None.
dtype(np.dtype|str, optional): The data type of the returned tensor.
It should be int32, int64, float16, float32, float64, default is 'float32'.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
Returns:
Tensor: An identity Tensor or LoDTensor of shape batch_shape + [num_rows, num_columns].
Examples:
.. code-block:: python
import paddle.fluid as fluid
data = fluid.layers.eye(3, dtype='int32')
# [[1, 0, 0]
# [0, 1, 0]
# [0, 0, 1]]
data = fluid.layers.eye(2, 3, dtype='int32')
# [[1, 0, 0]
# [0, 1, 0]]
data = fluid.layers.eye(2, batch_shape=[3])
# Construct a batch of 3 identity tensors, each 2 x 2.
# data[i, :, :] is a 2 x 2 identity tensor, i = 0, 1, 2.
"""
def
_check_attr
(
attr
,
message
):
if
isinstance
(
attr
,
((
Variable
,
core
.
VarBase
,
core
.
eager
.
Tensor
))):
assert
len
(
attr
.
shape
)
==
1
and
attr
.
shape
[
0
]
in
[
1
,
-
1
]
elif
not
isinstance
(
attr
,
int
)
or
attr
<
0
:
raise
TypeError
(
"{} should be a non-negative int."
.
format
(
message
))
_check_attr
(
num_rows
,
"num_rows"
)
if
not
isinstance
(
dtype
,
core
.
VarDesc
.
VarType
):
dtype
=
convert_np_dtype_to_dtype_
(
dtype
)
if
num_columns
is
not
None
:
_check_attr
(
num_columns
,
"num_columns"
)
else
:
num_columns
=
num_rows
if
in_dygraph_mode
():
out
=
_C_ops
.
eye
(
num_rows
,
num_columns
,
dtype
,
_current_expected_place
()
)
elif
_in_legacy_dygraph
():
out
=
_legacy_C_ops
.
eye
(
'dtype'
,
dtype
,
'num_rows'
,
num_rows
,
'num_columns'
,
num_columns
)
else
:
helper
=
LayerHelper
(
"eye"
,
**
locals
())
check_dtype
(
dtype
,
'dtype'
,
[
'float16'
,
'float32'
,
'float64'
,
'int32'
,
'int64'
],
'eye'
,
)
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
dtype
)
helper
.
append_op
(
type
=
'eye'
,
inputs
=
{},
outputs
=
{
'Out'
:
[
out
]},
attrs
=
{
'num_rows'
:
num_rows
,
'num_columns'
:
num_columns
,
'dtype'
:
dtype
,
},
stop_gradient
=
True
,
)
if
batch_shape
is
not
None
:
re_shape
=
[
1
]
*
len
(
batch_shape
)
re_shape
=
re_shape
+
[
num_rows
,
num_columns
]
expand_times
=
batch_shape
+
[
1
,
1
]
if
_non_static_mode
():
out
,
_
=
_legacy_C_ops
.
reshape2
(
out
,
None
,
'shape'
,
re_shape
)
return
_legacy_C_ops
.
expand
(
out
,
None
,
'expand_times'
,
expand_times
)
if
not
isinstance
(
batch_shape
,
list
):
raise
TypeError
(
"batch_shape should be a list"
)
for
batch_val
in
batch_shape
:
if
batch_val
<=
0
:
raise
TypeError
(
"batch_shape should be a positive int list"
)
from
.nn
import
expand
from
paddle
import
reshape
out
=
reshape
(
x
=
out
,
shape
=
re_shape
)
out
=
expand
(
x
=
out
,
expand_times
=
expand_times
)
out
.
stop_gradient
=
True
return
out
def
ones_like
(
x
,
out
=
None
):
"""
**ones_like**
...
...
python/paddle/fluid/tests/unittests/npu/test_eye_op_npu.py
浏览文件 @
fd8ec69d
...
...
@@ -139,39 +139,6 @@ class API_TestTensorEye(unittest.TestCase):
paddle
.
enable_static
()
self
.
assertEqual
((
out
.
numpy
()
==
expected_result
).
all
(),
True
)
paddle
.
disable_static
(
paddle
.
NPUPlace
(
0
))
batch_shape
=
[
2
]
out
=
fluid
.
layers
.
eye
(
10
,
10
,
dtype
=
"int32"
,
batch_shape
=
batch_shape
)
result
=
np
.
eye
(
10
,
dtype
=
"int32"
)
expected_result
=
[]
for
index
in
reversed
(
batch_shape
):
tmp_result
=
[]
for
i
in
range
(
index
):
tmp_result
.
append
(
result
)
result
=
tmp_result
expected_result
=
np
.
stack
(
result
,
axis
=
0
)
paddle
.
enable_static
()
self
.
assertEqual
(
out
.
numpy
().
shape
==
np
.
array
(
expected_result
).
shape
,
True
)
self
.
assertEqual
((
out
.
numpy
()
==
expected_result
).
all
(),
True
)
paddle
.
disable_static
(
paddle
.
NPUPlace
(
0
))
batch_shape
=
[
3
,
2
]
out
=
fluid
.
layers
.
eye
(
10
,
10
,
dtype
=
"int32"
,
batch_shape
=
batch_shape
)
result
=
np
.
eye
(
10
,
dtype
=
"int32"
)
expected_result
=
[]
for
index
in
reversed
(
batch_shape
):
tmp_result
=
[]
for
i
in
range
(
index
):
tmp_result
.
append
(
result
)
result
=
tmp_result
expected_result
=
np
.
stack
(
result
,
axis
=
0
)
paddle
.
enable_static
()
self
.
assertEqual
(
out
.
numpy
().
shape
==
np
.
array
(
expected_result
).
shape
,
True
)
self
.
assertEqual
((
out
.
numpy
()
==
expected_result
).
all
(),
True
)
def
test_errors
(
self
):
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
()):
...
...
python/paddle/fluid/tests/unittests/test_eye_op.py
浏览文件 @
fd8ec69d
...
...
@@ -109,40 +109,6 @@ class API_TestTensorEye(unittest.TestCase):
paddle
.
enable_static
()
self
.
assertEqual
((
out
.
numpy
()
==
expected_result
).
all
(),
True
)
paddle
.
disable_static
()
batch_shape
=
[
2
]
out
=
fluid
.
layers
.
eye
(
10
,
10
,
dtype
=
"int64"
,
batch_shape
=
batch_shape
)
result
=
np
.
eye
(
10
,
dtype
=
"int64"
)
expected_result
=
[]
for
index
in
reversed
(
batch_shape
):
tmp_result
=
[]
for
i
in
range
(
index
):
tmp_result
.
append
(
result
)
result
=
tmp_result
expected_result
=
np
.
stack
(
result
,
axis
=
0
)
paddle
.
enable_static
()
self
.
assertEqual
(
out
.
numpy
().
shape
==
np
.
array
(
expected_result
).
shape
,
True
)
self
.
assertEqual
((
out
.
numpy
()
==
expected_result
).
all
(),
True
)
paddle
.
disable_static
()
batch_shape
=
[
3
,
2
]
out
=
fluid
.
layers
.
eye
(
10
,
10
,
dtype
=
"int64"
,
batch_shape
=
batch_shape
)
result
=
np
.
eye
(
10
,
dtype
=
"int64"
)
expected_result
=
[]
for
index
in
reversed
(
batch_shape
):
tmp_result
=
[]
for
i
in
range
(
index
):
tmp_result
.
append
(
result
)
result
=
tmp_result
expected_result
=
np
.
stack
(
result
,
axis
=
0
)
paddle
.
enable_static
()
self
.
assertEqual
(
out
.
numpy
().
shape
==
np
.
array
(
expected_result
).
shape
,
True
)
self
.
assertEqual
((
out
.
numpy
()
==
expected_result
).
all
(),
True
)
def
test_errors
(
self
):
with
paddle
.
static
.
program_guard
(
paddle
.
static
.
Program
()):
...
...
@@ -212,18 +178,6 @@ class TestEyeRowsCol(UnittestBase):
paddle
.
eye
(
-
1
)
class
TestEyeRowsCol2
(
TestEyeRowsCol
):
def
call_func
(
self
,
x
):
rows
=
paddle
.
assign
(
3
)
cols
=
paddle
.
assign
(
10
)
out
=
paddle
.
fluid
.
layers
.
eye
(
rows
,
cols
)
return
out
def
test_error
(
self
):
with
self
.
assertRaises
(
TypeError
):
paddle
.
fluid
.
layers
.
eye
(
-
1
)
if
__name__
==
"__main__"
:
paddle
.
enable_static
()
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
fd8ec69d
...
...
@@ -2407,70 +2407,6 @@ class TestLayer(LayerTest):
conv3d1
.
bias
.
numpy
(),
conv3d2
.
bias
.
numpy
()
)
def
test_eye_op
(
self
):
np_eye
=
np
.
eye
(
3
,
2
)
array_rlt1
=
[
np_eye
for
_
in
range
(
3
)]
stack_rlt1
=
np
.
stack
(
array_rlt1
,
axis
=
0
)
array_rlt2
=
[
stack_rlt1
for
_
in
range
(
4
)]
stack_rlt2
=
np
.
stack
(
array_rlt2
,
axis
=
0
)
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
eager_eye_tensor
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
)
eager_eye_tensor_rlt1
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
,
batch_shape
=
[
3
]
)
eager_eye_tensor_rlt2
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
,
batch_shape
=
[
4
,
3
]
)
eager_diag_tensor
=
layers
.
eye
(
20
)
eager_eye_tensor_value
=
eager_eye_tensor
.
numpy
()
eager_eye_tensor_rlt1_value
=
eager_eye_tensor_rlt1
.
numpy
()
eager_eye_tensor_rlt2_value
=
eager_eye_tensor_rlt2
.
numpy
()
eager_diag_tensor_value
=
eager_diag_tensor
.
numpy
()
eye_tensor
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
)
eye_tensor_rlt1
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
,
batch_shape
=
[
3
]
)
eye_tensor_rlt2
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
,
batch_shape
=
[
4
,
3
]
)
diag_tensor
=
layers
.
eye
(
20
)
eye_tensor_value
=
eye_tensor
.
numpy
()
eye_tensor_rlt1_value
=
eye_tensor_rlt1
.
numpy
()
eye_tensor_rlt2_value
=
eye_tensor_rlt2
.
numpy
()
diag_tensor_value
=
diag_tensor
.
numpy
()
np
.
testing
.
assert_allclose
(
eager_eye_tensor_value
,
np_eye
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
eager_eye_tensor_rlt1_value
,
stack_rlt1
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
eager_eye_tensor_rlt2_value
,
stack_rlt2
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
eager_diag_tensor_value
,
np
.
eye
(
20
),
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
eye_tensor_value
,
np_eye
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
eye_tensor_rlt1_value
,
stack_rlt1
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
eye_tensor_rlt2_value
,
stack_rlt2
,
rtol
=
1e-05
)
np
.
testing
.
assert_allclose
(
diag_tensor_value
,
np
.
eye
(
20
),
rtol
=
1e-05
)
with
self
.
assertRaises
(
TypeError
):
layers
.
eye
(
num_rows
=
3.1
)
with
self
.
assertRaises
(
TypeError
):
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2.2
)
with
self
.
assertRaises
(
TypeError
):
layers
.
eye
(
num_rows
=
3
,
batch_shape
=
2
)
with
self
.
assertRaises
(
TypeError
):
layers
.
eye
(
num_rows
=
3
,
batch_shape
=
[
-
1
])
def
func_while_loop
(
self
):
with
self
.
static_graph
():
i
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int64'
,
value
=
0
)
...
...
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