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体验新版 GitCode,发现更多精彩内容 >>
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提交
dea41da7
编写于
8月 12, 2020
作者:
J
Jack Zhou
提交者:
GitHub
8月 12, 2020
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差异文件
add nll loss API for the paddlepaddle api2.0
* add nll loss API, update demo code of the comment
上级
1d730ffb
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
276 addition
and
89 deletion
+276
-89
paddle/fluid/pybind/op_function_generator.cc
paddle/fluid/pybind/op_function_generator.cc
+1
-0
python/paddle/fluid/tests/unittests/test_nll_loss.py
python/paddle/fluid/tests/unittests/test_nll_loss.py
+100
-1
python/paddle/nn/functional/__init__.py
python/paddle/nn/functional/__init__.py
+1
-0
python/paddle/nn/functional/loss.py
python/paddle/nn/functional/loss.py
+114
-0
python/paddle/nn/layer/loss.py
python/paddle/nn/layer/loss.py
+60
-88
未找到文件。
paddle/fluid/pybind/op_function_generator.cc
浏览文件 @
dea41da7
...
...
@@ -40,6 +40,7 @@ std::map<std::string, std::set<std::string>> op_ins_map = {
{
"assign"
,
{
"X"
}},
{
"fake_quantize_dequantize_moving_average_abs_max"
,
{
"X"
,
"InScale"
,
"InAccum"
,
"InState"
}},
{
"nll_loss"
,
{
"X"
,
"Label"
,
"Weight"
}},
};
// NOTE(zhiqiu): Like op_ins_map.
...
...
python/paddle/fluid/tests/unittests/test_nll_loss.py
浏览文件 @
dea41da7
...
...
@@ -445,7 +445,6 @@ class TestNLLLoss(unittest.TestCase):
startup_prog
=
fluid
.
Program
()
place
=
fluid
.
CUDAPlace
(
0
)
if
fluid
.
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
#place = fluid.CPUPlace()
with
fluid
.
program_guard
(
prog
,
startup_prog
):
input
=
fluid
.
data
(
name
=
'input'
,
shape
=
[
5
,
3
,
5
,
5
],
dtype
=
'float64'
)
...
...
@@ -879,5 +878,105 @@ class TestNLLLossOp2DNoReduce(OpTest):
self
.
label_shape
=
[
5
,
5
,
5
]
class
TestNLLLossName
(
unittest
.
TestCase
):
def
test_name
(
self
):
prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
place
=
paddle
.
CPUPlace
()
with
paddle
.
static
.
program_guard
(
prog
,
startup_prog
):
x
=
paddle
.
data
(
name
=
'x'
,
shape
=
[
10
,
10
],
dtype
=
'float64'
)
label
=
paddle
.
data
(
name
=
'label'
,
shape
=
[
10
],
dtype
=
'int64'
)
nll_loss
=
paddle
.
nn
.
loss
.
NLLLoss
(
name
=
'nll_loss'
)
res
=
nll_loss
(
x
,
label
)
self
.
assertTrue
(
res
.
name
.
startswith
(
'nll_loss'
))
class
TestNLLLossInvalidArgs
(
unittest
.
TestCase
):
def
test_x_dim_value_error
(
self
):
def
test_x_dim_lt_2
():
prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
place
=
paddle
.
CPUPlace
()
with
paddle
.
static
.
program_guard
(
prog
,
startup_prog
):
x
=
paddle
.
data
(
name
=
'x'
,
shape
=
[
10
,
],
dtype
=
'float64'
)
label
=
paddle
.
data
(
name
=
'label'
,
shape
=
[
10
,
],
dtype
=
'float64'
)
nll_loss
=
paddle
.
nn
.
loss
.
NLLLoss
()
res
=
nll_loss
(
x
,
label
)
self
.
assertRaises
(
ValueError
,
test_x_dim_lt_2
)
def
test_x_dim_imperative_lt_2
():
with
fluid
.
dygraph
.
guard
():
x_np
=
np
.
array
(
[
0.88103855
,
0.9908683
,
0.6226845
,
0.53331435
,
0.07999352
]).
astype
(
np
.
float32
)
label_np
=
np
.
array
([
0
,
2
,
1
,
1
,
0
]).
astype
(
np
.
int64
)
x
=
paddle
.
to_variable
(
x_np
)
label
=
paddle
.
to_variable
(
label_np
)
nll_loss
=
paddle
.
nn
.
loss
.
NLLLoss
()
res
=
nll_loss
(
x
,
label
)
self
.
assertRaises
(
ValueError
,
test_x_dim_imperative_lt_2
)
def
test_reduction_value_error
(
self
):
def
test_NLLLoss_reduction_not_sum_mean_none
():
prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
place
=
paddle
.
CPUPlace
()
with
paddle
.
static
.
program_guard
(
prog
,
startup_prog
):
x
=
paddle
.
data
(
name
=
'x'
,
shape
=
[
10
,
10
],
dtype
=
'float64'
)
label
=
paddle
.
data
(
name
=
'label'
,
shape
=
[
10
],
dtype
=
'int64'
)
nll_loss
=
paddle
.
nn
.
loss
.
NLLLoss
(
reduction
=
''
)
res
=
nll_loss
(
x
,
label
)
self
.
assertRaises
(
ValueError
,
test_NLLLoss_reduction_not_sum_mean_none
)
def
test_NLLLoss_reduction_imperative_not_sum_mean_none
():
with
fluid
.
dygraph
.
guard
():
x_np
=
np
.
array
(
[[
0.88103855
,
0.9908683
,
0.6226845
],
[
0.53331435
,
0.07999352
,
0.8549948
],
[
0.25879037
,
0.39530203
,
0.698465
],
[
0.73427284
,
0.63575995
,
0.18827209
],
[
0.05689114
,
0.0862954
,
0.6325046
]]).
astype
(
np
.
float32
)
label_np
=
np
.
array
([
0
,
2
,
1
,
1
,
0
]).
astype
(
np
.
int64
)
x
=
paddle
.
to_variable
(
x_np
)
label
=
paddle
.
to_variable
(
label_np
)
nll_loss
=
paddle
.
nn
.
loss
.
NLLLoss
(
reduction
=
''
)
res
=
nll_loss
(
x
,
label
)
self
.
assertRaises
(
ValueError
,
test_NLLLoss_reduction_imperative_not_sum_mean_none
)
def
test_nll_loss_function_reduction_not_sum_mean_none
():
prog
=
paddle
.
static
.
Program
()
startup_prog
=
paddle
.
static
.
Program
()
place
=
paddle
.
CPUPlace
()
with
paddle
.
static
.
program_guard
(
prog
,
startup_prog
):
x
=
paddle
.
data
(
name
=
'x'
,
shape
=
[
10
,
10
],
dtype
=
'float64'
)
label
=
paddle
.
data
(
name
=
'label'
,
shape
=
[
10
],
dtype
=
'int64'
)
res
=
paddle
.
nn
.
functional
.
nll_loss
(
x
,
label
,
reduction
=
''
)
self
.
assertRaises
(
ValueError
,
test_nll_loss_function_reduction_not_sum_mean_none
)
def
test_nll_loss_function_reduction_imperative_not_sum_mean_none
():
with
fluid
.
dygraph
.
guard
():
x_np
=
np
.
array
(
[[
0.88103855
,
0.9908683
,
0.6226845
],
[
0.53331435
,
0.07999352
,
0.8549948
],
[
0.25879037
,
0.39530203
,
0.698465
],
[
0.73427284
,
0.63575995
,
0.18827209
],
[
0.05689114
,
0.0862954
,
0.6325046
]]).
astype
(
np
.
float32
)
label_np
=
np
.
array
([
0
,
2
,
1
,
1
,
0
]).
astype
(
np
.
int64
)
x
=
paddle
.
to_variable
(
x_np
)
label
=
paddle
.
to_variable
(
label_np
)
res
=
paddle
.
nn
.
functional
.
nll_loss
(
x
,
label
,
reduction
=
''
)
self
.
assertRaises
(
ValueError
,
test_nll_loss_function_reduction_imperative_not_sum_mean_none
)
if
__name__
==
"__main__"
:
unittest
.
main
()
python/paddle/nn/functional/__init__.py
浏览文件 @
dea41da7
...
...
@@ -131,6 +131,7 @@ from .loss import l1_loss #DEFINE_ALIAS
from
.loss
import
log_loss
#DEFINE_ALIAS
from
.loss
import
margin_rank_loss
#DEFINE_ALIAS
from
.loss
import
mse_loss
#DEFINE_ALIAS
from
.loss
import
nll_loss
#DEFINE_ALIAS
# from .loss import nce #DEFINE_ALIAS
from
.loss
import
npair_loss
#DEFINE_ALIAS
from
.loss
import
rank_loss
#DEFINE_ALIAS
...
...
python/paddle/nn/functional/loss.py
浏览文件 @
dea41da7
...
...
@@ -27,6 +27,7 @@ from ...fluid.layers import log_loss #DEFINE_ALIAS
from
...fluid.layers
import
mse_loss
#DEFINE_ALIAS
from
...fluid.layers
import
npair_loss
#DEFINE_ALIAS
from
...fluid.layers
import
rank_loss
#DEFINE_ALIAS
from
...fluid.layers
import
reshape
from
...fluid.layers
import
sigmoid_cross_entropy_with_logits
#DEFINE_ALIAS
from
...fluid.layers
import
sigmoid_focal_loss
#DEFINE_ALIAS
from
...fluid.layers
import
smooth_l1
#DEFINE_ALIAS
...
...
@@ -39,6 +40,9 @@ from ...fluid.layers import edit_distance #DEFINE_ALIAS
from
...fluid.layers
import
huber_loss
#DEFINE_ALIAS
from
...fluid.layers
import
margin_rank_loss
#DEFINE_ALIAS
from
...fluid.layers
import
sampled_softmax_with_cross_entropy
#DEFINE_ALIAS
from
...fluid.layer_helper
import
LayerHelper
from
...fluid.framework
import
in_dygraph_mode
from
...fluid.framework
import
Variable
__all__
=
[
'bpr_loss'
,
...
...
@@ -54,6 +58,7 @@ __all__ = [
'margin_rank_loss'
,
'mse_loss'
,
# 'nce',
'nll_loss'
,
'npair_loss'
,
'rank_loss'
,
'sampled_softmax_with_cross_entropy'
,
...
...
@@ -154,3 +159,112 @@ def l1_loss(x, label, reduction='mean', name=None):
return
paddle
.
mean
(
unreduced
,
name
=
name
)
else
:
return
paddle
.
elementwise_sub
(
x
,
label
,
act
=
'abs'
,
name
=
name
)
def
nll_loss
(
input
,
label
,
weight
=
None
,
ignore_index
=-
100
,
reduction
=
'mean'
,
name
=
None
):
"""
This api returns negative log likelihood.
See more detail in :ref:`api_nn_loss_NLLLoss` .
Parameters:
input (Tensor): Input tensor, the shape is :math:`[N, C]`, `C` is the number of classes.
But in K-dimension situation, the shape is :math:`[N, C, d_1, d_2, ..., d_K]`.
The data type is float32, float64.
label (Tensor): Label tensor, the shape is :math:`[N,]` or :math:`[N, d_1, d_2, ..., d_K]`.
The data type is int64.
weight (Tensor, optional): Weight tensor, a manual rescaling weight given
to each class. If given, it has to be a 1D Tensor whose size is `[C, ]`. Otherwise,
it treated as if having all ones. the data type is
float32, float64, Default is ``'None'``.
ignore_index (int64, optional): Specifies a target value that is ignored
and does not contribute to the input gradient.
reduction (str, optional): Indicate how to average the loss,
the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
If `reduction` is ``'mean'``, the reduced mean loss is returned;
if `reduction` is ``'sum'``, the reduced sum loss is returned;
if `reduction` is ``'none'``, no reduction will be apllied.
Default is ``'mean'``.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
`Tensor`, the value of negative log likelihood loss.
Examples:
.. code-block:: python
import paddle
import numpy as np
from paddle.nn.functional import nll_loss
log_softmax = paddle.nn.LogSoftmax(axis=1)
input_np = np.array([[0.88103855, 0.9908683 , 0.6226845 ],
[0.53331435, 0.07999352, 0.8549948 ],
[0.25879037, 0.39530203, 0.698465 ],
[0.73427284, 0.63575995, 0.18827209],
[0.05689114, 0.0862954 , 0.6325046 ]]).astype(np.float32)
label_np = np.array([0, 2, 1, 1, 0]).astype(np.int64)
place = paddle.CPUPlace()
paddle.disable_static(place)
input = paddle.to_variable(input_np)
log_out = log_softmax(input)
label = paddle.to_variable(label_np)
result = nll_loss(log_out, label)
print(result.numpy()) # [1.0720209]
"""
if
reduction
not
in
[
'sum'
,
'mean'
,
'none'
]:
raise
ValueError
(
"The value of 'reduction' in nll_loss should be 'sum', 'mean' or "
"'none', but received %s, which is not allowed."
%
reduction
)
input_shape
=
list
(
input
.
shape
)
input_dims
=
len
(
input_shape
)
if
input_dims
<
2
:
raise
ValueError
(
'Expected 2 or more dimensions (got {})'
.
format
(
input_dims
))
n
=
input_shape
[
0
]
c
=
input_shape
[
1
]
if
in_dygraph_mode
():
if
input_dims
!=
2
and
input_dims
!=
4
:
input
,
_
=
core
.
ops
.
reshape2
(
input
,
'shape'
,
[
n
,
c
,
1
,
-
1
])
label
,
_
=
core
.
ops
.
reshape2
(
label
,
'shape'
,
[
n
,
1
,
-
1
])
out_shape
=
[
n
]
+
input_shape
[
2
:]
out
,
total_weight
=
core
.
ops
.
nll_loss
(
input
,
label
,
weight
,
'ignore_index'
,
ignore_index
,
'reduction'
,
reduction
)
if
input_dims
!=
2
and
input_dims
!=
4
and
reduction
==
'none'
:
out
,
_
=
core
.
ops
.
reshape2
(
out
,
'shape'
,
out_shape
)
return
out
helper
=
LayerHelper
(
'nll_loss'
,
**
locals
())
if
input_dims
!=
2
and
input_dims
!=
4
:
input
=
reshape
(
input
,
shape
=
[
n
,
c
,
1
,
-
1
])
label
=
reshape
(
label
,
shape
=
[
n
,
1
,
-
1
])
out_shape
=
[
n
]
+
input_shape
[
2
:]
fluid
.
data_feeder
.
check_variable_and_dtype
(
input
,
'input'
,
[
'float32'
,
'float64'
],
'nll_loss'
)
fluid
.
data_feeder
.
check_variable_and_dtype
(
label
,
'label'
,
[
'int64'
],
'nll_loss'
)
inputs
=
{
'X'
:
input
,
'Label'
:
label
}
attrs
=
{
'reduction'
:
reduction
,
'ignore_index'
:
ignore_index
}
if
weight
is
not
None
:
if
isinstance
(
weight
,
Variable
):
inputs
[
'Weight'
]
=
weight
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
input
.
dtype
)
total_weight
=
helper
.
create_variable_for_type_inference
(
dtype
=
input
.
dtype
)
outputs
=
{
'Out'
:
out
,
'Total_weight'
:
total_weight
}
helper
.
append_op
(
type
=
'nll_loss'
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
attrs
)
if
input_dims
!=
2
and
input_dims
!=
4
and
reduction
==
'none'
:
out
=
reshape
(
out
,
shape
=
out_shape
)
return
out
python/paddle/nn/layer/loss.py
浏览文件 @
dea41da7
...
...
@@ -15,6 +15,7 @@
# TODO: define loss functions of neural network
import
paddle.fluid
as
fluid
import
paddle
from
..
import
functional
as
F
__all__
=
[
# 'NCELoss',
...
...
@@ -460,11 +461,11 @@ class NLLLoss(fluid.dygraph.Layer):
:alias_main: paddle.nn.NLLLoss
:alias: paddle.nn.NLLLoss,paddle.nn.layer.NLLLoss,paddle.nn.layer.loss.NLLLoss
This
op accepts input and target label and returns negative log likelihood
This
class accepts input and target label and returns negative log likelihood
cross error. It is useful to train a classification problem with C classes.
The input for the loss is epected to contain log-probabilities of
each classes. It h
s to be a Tensor of size either (batch_size, C) or
each classes. It h
as to be a Tensor of size either (batch_size, C) or
(batch_size, C, d1, d2, ..., dK) with K >= 1 for the K-dimensional case.
The label for the loss should be a class index in the range [0, C-1]
where C is the number of classes. If ignore_index is specified, the
...
...
@@ -494,106 +495,77 @@ class NLLLoss(fluid.dygraph.Layer):
\\
end{cases}
Parameters:
input (Variable): Input tensor, the data type is float32, float64.
label (Variable): Label tensor, the data type is int64_t.
weight (Variable, optional): Weight tensor, a manual rescaling weight given
to each class. If given, it has to be a Tensor of size `C`. Otherwise,
weight (Tensor, optional): Weight tensor, a manual rescaling weight given
to each class. If given, it has to be a 1D Tensor whose size is `[C, ]`. Otherwise,
it treated as if having all ones. the data type is
float32, float64, Default is ``'None'``.
ignore_index (int64, optional): Specifies a target value that is ignored
and does not contribute to the input gradient.
reduction (str, optional): Indicate how to average the loss,
the candicates are ``'none'`` | ``'mean'`` | ``'sum'``.
If :attr:`reduction` is ``'mean'``, the reduced mean loss is returned;
If `reduction` is ``'mean'``, the reduced mean loss is returned;
if `reduction` is ``'sum'``, the reduced sum loss is returned;
if `reduction` is ``'none'``, no reduction will be apllied.
Default is ``'mean'``.
ignore_index (int64, optional): Specifies a target value that is ignored
and does not contribute to the input gradient
.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`
.
Returns:
The tensor variable storing the nll_loss.
Shape:
input (Tensor): Input tensor, the shape is :math:`[N, C]`, `C` is the number of classes.
But in K-dimension situation, the shape is :math:`[N, C, d_1, d_2, ..., d_K]`.
The data type is float32, float64.
label (Tensor): Label tensor, the shape is :math:`[N,]` or :math:`[N, d_1, d_2, ..., d_K]`.
The data type is int64.
output (Tensor): the `negative log likelihood loss` between input `x` and `label`.
If `reduction` is `'none'`, the shape is `[N, *]`.
If `reduction` is `'sum'` or `'mean'`, the shape is `[1]`.
Return type: Variable.
Examples:
.. code-block:: python
# declarative mode
import paddle.fluid as fluid
import numpy as np
import paddle
import paddle
import numpy as np
input_np = np.random.random(size=(10, 10)).astype(np.float32)
label_np = np.random.randint(0, 10, size=(10,)).astype(np.int64)
prog = fluid.Program()
startup_prog = fluid.Program()
place = fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(name='input', shape=[10, 10], dtype='float32')
label = fluid.data(name='label', shape=[10], dtype='int64')
nll_loss = paddle.nn.loss.NLLLoss()
res = nll_loss(input, label)
exe = fluid.Executor(place)
static_result = exe.run(
prog,
feed={"input": input_np,
"label": label_np},
fetch_list=[res])
print(static_result)
# imperative mode
import paddle.fluid.dygraph as dg
with dg.guard(place) as g:
input = dg.to_variable(input_np)
label = dg.to_variable(label_np)
output = nll_loss(input, label)
print(output.numpy())
"""
nll_loss = paddle.nn.layer.NLLLoss()
log_softmax = paddle.nn.LogSoftmax(axis=1)
def
__init__
(
self
,
weight
=
None
,
reduction
=
'mean'
,
ignore_index
=-
100
):
super
(
NLLLoss
,
self
).
__init__
()
self
.
weight
=
weight
self
.
reduction
=
reduction
self
.
ignore_index
=
ignore_index
input_np = np.array([[0.88103855, 0.9908683 , 0.6226845 ],
[0.53331435, 0.07999352, 0.8549948 ],
[0.25879037, 0.39530203, 0.698465 ],
[0.73427284, 0.63575995, 0.18827209],
[0.05689114, 0.0862954 , 0.6325046 ]]).astype(np.float32)
label_np = np.array([0, 2, 1, 1, 0]).astype(np.int64)
def
forward
(
self
,
input
,
label
):
dtype
=
self
.
_helper
.
input_dtype
(
input
)
place = paddle.CPUPlace()
paddle.disable_static(place)
input = paddle.to_variable(input_np)
log_out = log_softmax(input)
label = paddle.to_variable(label_np)
result = nll_loss(log_out, label)
print(result.numpy()) # [1.0720209]
fluid
.
data_feeder
.
check_variable_and_dtype
(
input
,
'input'
,
[
'float32'
,
'float64'
],
'nll_loss'
)
fluid
.
data_feeder
.
check_variable_and_dtype
(
label
,
'label'
,
[
'int64'
],
'nll_loss'
)
"""
if
self
.
reduction
not
in
[
'sum'
,
'mean'
,
'none'
]:
def
__init__
(
self
,
weight
=
None
,
ignore_index
=-
100
,
reduction
=
'mean'
,
name
=
None
):
if
reduction
not
in
[
'sum'
,
'mean'
,
'none'
]:
raise
ValueError
(
"The value of 'reduction' in nll_loss should be 'sum', 'mean' or 'none', but "
"received %s, which is not allowed."
%
self
.
reduction
)
x_shape
=
list
(
input
.
shape
)
n
=
x_shape
[
0
]
c
=
x_shape
[
1
]
x_dims
=
len
(
x_shape
)
if
x_dims
<
2
:
raise
ValueError
(
'Expected 2 or more dimensions (got {})'
.
format
(
x_dims
))
if
x_dims
!=
2
and
x_dims
!=
4
:
input
=
fluid
.
layers
.
reshape
(
input
,
shape
=
[
n
,
c
,
1
,
-
1
])
label
=
fluid
.
layers
.
reshape
(
label
,
shape
=
[
n
,
1
,
-
1
])
out_shape
=
[
n
]
+
x_shape
[
2
:]
inputs
=
{
'X'
:
input
,
'Label'
:
label
}
attrs
=
{
'reduction'
:
self
.
reduction
,
'ignore_index'
:
self
.
ignore_index
}
if
self
.
weight
is
not
None
:
if
isinstance
(
self
.
weight
,
fluid
.
framework
.
Variable
):
inputs
[
'Weight'
]
=
self
.
weight
out
=
self
.
_helper
.
create_variable_for_type_inference
(
dtype
=
input
.
dtype
)
total_weight
=
self
.
_helper
.
create_variable_for_type_inference
(
dtype
=
input
.
dtype
)
outputs
=
{
'Out'
:
out
,
'Total_weight'
:
total_weight
}
self
.
_helper
.
append_op
(
type
=
'nll_loss'
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
attrs
)
if
x_dims
!=
2
and
x_dims
!=
4
and
self
.
reduction
==
'none'
:
out
=
fluid
.
layers
.
reshape
(
out
,
shape
=
out_shape
)
"The value of 'reduction' in nll_loss should be 'sum', 'mean' or "
"'none', but received %s, which is not allowed."
%
reduction
)
super
(
NLLLoss
,
self
).
__init__
()
self
.
_weight
=
weight
self
.
_ignore_index
=
ignore_index
self
.
_reduction
=
reduction
self
.
_name
=
name
return
out
def
forward
(
self
,
input
,
label
):
return
F
.
nll_loss
(
input
,
label
,
weight
=
self
.
_weight
,
ignore_index
=
self
.
_ignore_index
,
reduction
=
self
.
_reduction
,
name
=
self
.
_name
)
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