未验证 提交 dea41da7 编写于 作者: J Jack Zhou 提交者: GitHub

add nll loss API for the paddlepaddle api2.0

* add nll loss API, update demo code of the comment
上级 1d730ffb
......@@ -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.
......
......@@ -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()
......@@ -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
......
......@@ -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
......@@ -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 hs to be a Tensor of size either (batch_size, C) or
each classes. It has 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.
Return type: Variable.
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]`.
Examples:
.. code-block:: python
# declarative mode
import paddle.fluid as fluid
import numpy as np
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)
nll_loss = paddle.nn.layer.NLLLoss()
log_softmax = paddle.nn.LogSoftmax(axis=1)
exe = fluid.Executor(place)
static_result = exe.run(
prog,
feed={"input": input_np,
"label": label_np},
fetch_list=[res])
print(static_result)
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]
# 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())
"""
def __init__(self, weight=None, reduction='mean', ignore_index=-100):
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." % reduction)
super(NLLLoss, self).__init__()
self.weight = weight
self.reduction = reduction
self.ignore_index = ignore_index
self._weight = weight
self._ignore_index = ignore_index
self._reduction = reduction
self._name = name
def forward(self, input, label):
dtype = self._helper.input_dtype(input)
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']:
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)
return out
return F.nll_loss(
input,
label,
weight=self._weight,
ignore_index=self._ignore_index,
reduction=self._reduction,
name=self._name)
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